U.S. patent application number 10/676424 was filed with the patent office on 2004-07-01 for method for identification of biologically active agents.
This patent application is currently assigned to Baylor College of Medicine. Invention is credited to Botas, Juan, Boulin, Christian, Cummings, Christopher J., Falt, Edvard, Gonzalez, Cayetano, Serrano, Luis, Zoghbi, Huda.
Application Number | 20040126319 10/676424 |
Document ID | / |
Family ID | 30116013 |
Filed Date | 2004-07-01 |
United States Patent
Application |
20040126319 |
Kind Code |
A1 |
Falt, Edvard ; et
al. |
July 1, 2004 |
Method for identification of biologically active agents
Abstract
The present invention provides a method for screening for the
effect of a test agent on a population of biological specimens,
preferably insects, comprising the steps of providing a population
of specimens, administering at least one test agent to the
population, creating a digitized movie showing the movements of
members of the population, measuring at least one trait of members
of the population, and correlating the traits of the population
with the effect of the test agent. The invention also provides a
method for preparing a medicament useful for the treatment of a
mammalian disease.
Inventors: |
Falt, Edvard; (Allston,
MA) ; Serrano, Luis; (Heidelberg, DE) ;
Gonzalez, Cayetano; (Madrid, ES) ; Boulin,
Christian; (Wiesloch, DE) ; Cummings, Christopher
J.; (Brookline, MA) ; Botas, Juan; (Houston,
TX) ; Zoghbi, Huda; (Houston, TX) |
Correspondence
Address: |
PALMER & DODGE, LLP
KATHLEEN M. WILLIAMS
111 HUNTINGTON AVENUE
BOSTON
MA
02199
US
|
Assignee: |
Baylor College of Medicine
European Molecular Biology Laboratory (EMBL)
EnVivo Pharmaceuticals
|
Family ID: |
30116013 |
Appl. No.: |
10/676424 |
Filed: |
October 1, 2003 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
10676424 |
Oct 1, 2003 |
|
|
|
10618913 |
Jul 14, 2003 |
|
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60396339 |
Jul 15, 2002 |
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Current U.S.
Class: |
424/9.2 ;
435/6.16 |
Current CPC
Class: |
G06T 2207/30004
20130101; G06T 7/20 20130101; G06T 7/0012 20130101 |
Class at
Publication: |
424/009.2 ;
435/006 |
International
Class: |
C12Q 001/68; A61K
049/00 |
Claims
1. A method for screening for the effect of a test agent on a
population of fly larvae comprising providing a population of fly
larvae; administering at least one test agent to said population;
creating a digital image showing at least one trait of specimens in
the population; and correlating the traits of the population with
the effect of the test agent(s) administered to the population.
2. A method for screening for the effects of a test agent on a
population of fly larvae comprising providing a plurality of
populations of fly larvae; administering at least one test agent to
each of said populations; creating a digital image showing at least
two traits of specimens in each population; for each population,
correlating the traits of the population with the effect of the
test agent(s) administered to the population.
3. The method of claim 1 further comprising the step of determining
at least one trait of said population.
4. The method of claim 2 further comprising the step of determining
at least two traits of each population.
5. The method of claim 1 or 2 wherein said trait is selected from
the group consisting of total distance traveled over a defined
period of time, distance traveled in X direction over a defined
period of time; distance traveled in Y direction over a defined
period of time; total distance moved per time unit; distance moved
in X direction per time unit; distance moved in Y direction per
time unit); the rate of change of velocity per time unit, turning,
stumbling, spatial position, and path shape.
6. The method of claim 1 or 2 wherein said step of determining
comprises measuring data selected from the group consisting of
X-pos, X-speed, speed, turning, stumbling, size, T-count, P-count,
T-length, Cross150, Cross250, and F-count.
7. The method of claim 1 or 2 wherein said trait is selected from
the group consisting of movement of one fly larva toward or away
from another fly larva, occurrence of no relative spatial
displacement of two fly larvae, occurrence of two fly larvae within
a defined distance from each other, and occurrence of two fly
larvae more than a defined distance away from each other.
8. The method of claim 1 or 2, wherein said trait is a
morphological trait.
9. A method for ranking test agents comprising providing a
plurality of populations of fly larvae; contacting each of the
populations with a different test agent; determining at least one
trait for each of said population to produce an agent phenoprofile;
and ranking said test agents according to the similarity or
difference of each agent phenoprofile with a reference phenoprofile
defined by said at least one trait as measured in a reference
population of fly larvae.
10. A method of screening for an agent with a desired biological
activity comprising: providing a plurality of populations of fly
larvae; contacting each of said populations with a different test
agent; determining an agent phenoprofile for each of said
populations, wherein the agent phenoprofile comprises a
quantitative description of one or more traits exhibited by fly
larvae in the population; comparing the agent phenoprofile to a
reference phenoprofile, wherein the reference phenoprofile
comprises a quantitative description of said one or more traits
exhibited by fly larvae in a reference population; and selecting
said agent based on the comparison of the agent phenoprofile and
the reference phenoprofile corresponding to each agent.
11. The method of claim 9 or 10 wherein said trait is selected from
the group consisting of total distance traveled over a defined
period of time, distance traveled in X direction over a defined
period of time; distance traveled in Y direction over a defined
period of time; total distance moved per time unit; distance moved
in X direction per time unit; distance moved in Y direction per
time unit); the rate of change of velocity per time unit, turning,
stumbling, spatial position, and path shape.
12. The method of claim 9 or 10 wherein said step of determining
comprises measuring data selected from the group consisting of
X-pos, X-speed, speed, turning, stumbling, size, T-count, P-count,
T-length, Cross150, Cross250, and F-count.
13. The method of claim 9 or 10 wherein said trait is selected from
the group consisting of movement of one fly larva toward or away
from another fly larva, occurrence of no relative spatial
displacement of two fly larvae, occurrence of two fly larvae within
a defined distance from each other, and occurrence of two fly
larvae more than a defined distance away from each other.
14. The method of claim 9 or 10 wherein said trait is a
morphological trait.
15. A method of screening for an agent with a desired biological
activity comprising: providing a population of fly larvae;
contacting said population with a test agent; determining an agent
phenoprofile for said population, wherein the agent phenoprofile
comprises a quantitative description of one or more traits
exhibited by fly larvae in said population; comparing the agent
phenoprofile to a reference phenoprofile, wherein the reference
phenoprofile comprises a quantitative description of said one or
more traits exhibited by fly larvae in a reference population; and
selecting said agent based on the comparison of the agent
phenoprofile and the reference phenoprofile corresponding to said
agent.
16. The method of claim 15 wherein said trait is selected from the
group consisting of total distance traveled over a defined period
of time, distance traveled in X direction over a defined period of
time; distance traveled in Y direction over a defined period of
time; total distance moved per time unit; distance moved in X
direction per time unit; distance moved in Y direction per time
unit); the rate of change of velocity per time unit, turning,
stumbling, spatial position, and path shape.
17. The method of claim 15 wherein said step of determining
comprises measuring data selected from the group consisting of
X-pos, X-speed, speed, turning, stumbling, size, T-count, P-count,
T-length, Cross150, Cross250, and F-count.
18. The method of claim 15 wherein said trait is selected from the
group consisting of movement of one fly larva toward or away from
another fly larva, occurrence of no relative spatial displacement
of two fly larvae, occurrence of two fly larvae within a defined
distance from each other, and occurrence of two fly larvae more
than a defined distance away from each other.
19. The method of claim 15, wherein said trait is a morphological
trait.
20. A method for determining parameters useful for a phenoprint
comprising: measuring a plurality of traits in a first population
of fly larvae, said first population having a first phenoprofile;
measuring said traits in a second population of fly larvae, said
second population having a second phenoprofile; comparing the
traits of the first population and the second population; and
identifying one or more traits that are different in said first and
second populations, said one or more different traits defining the
phenoprint.
21. The method of claim 1 or 9, wherein said step of determining
comprises determining more than one trait.
22. The method of claim 21, wherein said at least two traits define
a phenoprint.
23. A method for determining whether a test agent modifies, delays
or prevents onset of a phenotype in a transgenic fly larva
comprising: providing a population of transgenic fly larvae,
wherein the population develops a phenotype due to expression of a
transgene; contacting said population with a test agent; for the
population contacted with the test agent, determining an agent
phenoprofile for the population at a plurality of times during the
life of the fly larva; comparing the agent phenoprofile generated
at each of the plurality of times to a reference phenoprofile
generated at each of the plurality of times for a reference
population, wherein the reference population consists of fly larvae
not contacted with said test agent; and determining whether said
test agent modifies, delays or prevents onset of a trait in said
population contacted with a test agent compared to said reference
population.
24. A method of preparing a medicament for use in treatment of a
disease in mammals comprising providing a population of fly larvae
with a phenotype with characteristics of a mammalian disease;
contacting said population with a test agent; determining an agent
phenoprofile for said population, wherein the agent phenoprofile
comprises a quantitative description of one or more traits
exhibited by fly larvae in said population; comparing the agent
phenoprofile to a reference phenoprofile, wherein the reference
phenoprofile comprises a quantitative description of said one or
more traits exhibited by specimens in a reference population; and
selecting said agent based on the comparison of the agent
phenoprofile and the reference phenoprofile corresponding to said
agent; and formulating said agent for administration to a
mammal.
25. The method of claim 23 or 24 wherein said trait is selected
from the group consisting of total distance traveled over a defined
period of time, distance traveled in X direction over a defined
period of time; distance traveled in Y direction over a defined
period of time; total distance moved per time unit; distance moved
in X direction per time unit; distance moved in Y direction per
time unit); the rate of change of velocity per time unit, turning,
stumbling, spatial position, and path shape.
26. The method of claim 23 or 24 wherein said step of determining
comprises measuring data selected from the group consisting of
X-pos, X-speed, speed, turning, stumbling, size, T-count, P-count,
T-length, Cross150, Cross250, and F-count.
27. The method of claim 23 or 24 wherein said trait is selected
from the group consisting of movement of one fly larva toward or
away from another fly larva, occurrence of no relative spatial
displacement of two fly larvae, occurrence of two fly larvae within
a defined distance from each other, and occurrence of two fly
larvae more than a defined distance away from each other.
28. The method of claim 23 or 24, wherein said trait is a
morphological trait.
29. The method of claim 1, 2, 9, 10, 15, 20, or 24, wherein said
fly larva is transgenic.
30. The method of claim 29, wherein said fly larva is transgenic
for a gene encoding a polypeptide with an expanded polyglutamine
tract as compared to the wild-type polypeptide.
31. The method of claim 30, wherein the expression of the transgene
results neurodegeneration in said specimen.
32. The method of claim 1, 2, 9, 10, 15, 20, or 24 wherein said fly
larva comprises a genetic mutation resulting in a loss of function
or a gain of function.
33. The method of claim 9, 10, 15, 23, or 24, wherein said fly
larva is a transgenic fly larva, and said reference population is
selected from the group consisting of (i) transgenic fly larvae not
contacted with a test agent; (ii) transgenic fly larvae contacted
with an agent with a known activity on said fly larvae; (iii)
nontransgenic fly larvae with the genetic background of the
transgenic fly larvae; or (iv) transgenic fly larvae not expressing
a disease gene and not contacted with a test agent.
34. The method of claim 9, 10, 15, 23, or 24, wherein said
reference population is selected from the group consisting of (i)
fly larvae comprising a genetic mutation not contacted with a test
agent; (ii) fly larvae comprising a genetic mutation contacted with
an agent with a known activity on said fly larvae; or (iii) fly
larvae without the genetic mutation.
Description
PRIORITY
[0001] This application claims priority as a Divisional under 35
U.S.C. .sctn. 121 of U.S. application Ser. No. 10/618,913, filed
Jul. 14, 2003, which claims priority to U.S. Provisional
Application Serial No. 60/396,339, filed Jul. 15, 2002, the
contents of which are incorporated herein in their entirety.
BACKGROUND OF THE INVENTION
[0002] Neurodegenerative diseases are among some of the most
devastating diseases afflicting humans. Examples of
neurodegenerative diseases include Alzheimer's Disease, Parkinson's
Disease, Huntington's Disease and spinocerebellar ataxia (SCA).
However, the discovery and development of therapeutics for
disorders of the central nervous system (CNS), particularly for
neurodegenerative diseases, has historically been very
difficult.
[0003] The investigation of pathogenic mechanisms in
neurodegenerative disease has been facilitated by the recent
development of disease models in Drosophila. By introducing human
disease genes with dominant gain-of-function mutations into
Drosophila, models for a number of neurodegenerative diseases have
been generated, including models for Huntington's disease and
spinocerebellar ataxia (see, for example, Chan et al. (2000) Cell
Death Differ. 7:1075-1080; Feany et al. (2000) Nature 404:394-398;
Femandez-Funez et al. (2000) Nature 408:101-106; Fortini et al.
(2000) Trends Genet. 16:161-167; Jackson et al. (1998) Neuron
21:633-642; Kazemi-Esfarjani et al. (2000) Science 287:1837-1840;
Warrick et al. (1998) Cell 93:939-949. Specific cell or tissue
expression can be achieved by placing the human gene under control
of the GAL4/UAS transcriptional activation system from yeast (Brand
et al. (1993) Development 118:401-415). Due to the ease with which
genetic studies can be pursued in Drosophila, these models have
been especially useful in identifying genes that modify the
disease.
[0004] In some cases, expression of the transgene recapitulates one
or more neuropathological features of the human disease. For
example, in a Drosophila model for Parkinson's disease produced by
neuronal expression of human mutated alpha-synuclein,
age-dependent, progressive degeneration of dopamine-containing
cells is seen accompanied by the presence of Lewy bodies that
resemble those seen in the human disease, both by their
immunoreactivity for alpha-synuclein and by their appearance in the
electron microscope (Feany et al. (2000)). In the SCA1.sup.82Q
flies, expression of the mutated human ataxin-1 (associated with
SCA) is accompanied by adult-onset degeneration of neurons, with
nuclear inclusions that are immunologically positive for the
mutated protein, ubiquitin, Hsp70 and proteosome components
(Fernandez-Funez et al. (2000)). In the case of Huntington's
disease, expression of exon-1 of huntingtin, containing an expanded
polyglutamine repeat, causes a progressive degeneration, whose time
of onset and severity are linked to the length of the repeat, as is
seen in the human disease (Marsh et al. (2000) Hum. Mol. Genet.
9:13-25).
[0005] Although great advances have been made in understanding the
biological basis of neurological disorders, this scientific
progress has generally not yet been translated into effective new
treatments for these devastating disorders. There remains a
tremendous need for new methods of drug discovery for CNS
disorders, particularly for neurodegenerative diseases.
SUMMARY OF THE INVENTION
[0006] In one aspect, the invention provides methods for screening
for the effects of a test agent on a population of animals which
entail providing a population of animals, administering at least
one test agent to the population, creating a digitized movie
showing movement of animals in the population, determining at least
one trait, optionally at least two traits of the population, and
correlating the traits of the population with the effect of the
test agent(s) administered to the population. In one embodiment,
the invention provides methods for screening for the effects of a
test agent on a population of animals which entail providing a
plurality of populations of animals, administering at least one
test agent to each of the populations, creating a digitized movie
showing movement of animals in each population, determining at
least two traits of each population and, for each population,
correlating the traits of the population with the effect of the
test agent(s) administered to the population.
[0007] Another aspect of the invention provides methods for ranking
test agents which entail providing a plurality of populations of
animals, contacting each of the populations with a different test
agent, measuring at least one trait, optionally at least two
traits, for each of these population to produce an agent
phenoprofile and ranking the test agents according to the
similarity or difference of each agent phenoprofile with a
reference phenoprofile defined by the at least one or at least two
traits as exhibited in a reference population of animals.
[0008] In another aspect, the invention provides methods of
screening for an agent with a desired biological activity which
entail: providing a plurality of populations of animals; contacting
each of the populations with a different test agent; determining an
agent phenoprofile for each of these populations, wherein the agent
phenoprofile comprises a quantitative description of one or more
traits exhibited by animals in the population; comparing the agent
phenoprofile to a reference phenoprofile, wherein the reference
phenoprofile comprises a quantitative description of the one or
more traits exhibited by animals in a reference population; and
selecting the agent based on the comparison of the agent
phenoprofile corresponding to each agent and the reference
phenoprofile.
[0009] Another aspect of the invention provides methods for
determining parameters useful for a phenoprint which entail:
measuring a plurality of traits in a first population of flies, the
first population having a first phenoprofile; measuring the traits
in a second population of flies, the second population having a
second phenoprofile; comparing the traits of the first population
and the second population; and identifying one or more traits that
are different in the first and second populations, the one or more
different traits defining the phenoprint. In some further
embodiments, at least 2 traits define the phenoprint.
[0010] In another aspect, the invention provides methods for
determining whether a test agent modifies, delays or prevents onset
of a phenotype in a transgenic fly which entail: providing a
population of transgenic flies, wherein the population develops a
phenotype due to expression of a transgene; contacting the
population with a test agent; for the population contacted with the
test agent, determining an agent phenoprofile for the population at
a plurality of times during the life of the fly; comparing the
agent phenoprofile generated at each of the plurality of times to a
reference phenoprofile generated at each of the plurality of times
for a reference population, wherein the reference population
consists of the transgenic flies not contacted with the test agent;
and determining whether the test agent modifies, delays or prevents
onset of a trait in a population contacted with a test agent
compared to the reference population.
[0011] In another aspect, the invention provides methods of
preparing a medicament for use in treatment of a disease in mammals
which entail providing a population of flies with a phenotype with
characteristics of a mammalian disease, using a method of screening
for an agent with a desired biological activity of the invention to
identify an agent with an agent phenoprofile that is similar to a
phenoprofile of a population of flies with a healthy phenotype, and
formulating the agent for administration to a mammal. In some
further embodiments, population of flies have a phenotype
characteristic of a mammalian neurodegenerative disease.
[0012] In a further aspect, the invention provides systems for use
in accordance with the methods of the invention. Accordingly,
another aspect of the invention provides systems to monitor the
activity of flies in a plurality of containers. The systems may
include: a container platform having an array of containers; a
camera configured to capture a movie of the movement of flies
within a container; a robot configured to remove a container from
the platform, position the container in front of the camera, and
return the container to the platform; and a processor configured to
process the movie captured by the camera.
[0013] In some embodiments of the methods of the invention, the
animals are flies. In some embodiments of the methods of the
invention, the flies are transgenic. In further embodiments, the
flies are transgenic for a gene encoding a polypeptide with an
expanded polyglutamine tract as compared to the wild-type
polypeptide and, in still further embodiments, expression of the
transgene results neurodegeneration in the flies. In some
embodiments of the methods of the invention, the flies contain a
genetic mutation resulting in a loss of function or a gain of
function.
[0014] In some embodiments of the methods of the invention, the
reference population is (i) the transgenic flies not contacted with
a test agent; (ii) the transgenic flies contacted with an agent
with a know activity on the flies or in mammals; or (iii)
nontransgenic flies with the genetic background of the transgenic
flies, (iv) transgenic flies not expressing a disease gene and not
contacted with a test agent.
[0015] In some embodiments of the methods of the invention, the
reference population is (i) the flies containing the genetic
mutation not contacted with a test agent; (ii) the flies containing
the genetic mutation contacted with an agent with a known activity
on the flies or in mammals; or (iii) flies without the genetic
mutation.
[0016] In some embodiments of the methods of the invention, the
traits are movement traits. In further embodiments, the movement
traits are selected from the group consisting of total distance, X
only distance, Y only distance, average speed, average X-only
speed, average Y-only speed, and acceleration, and which are
determined based on the measurement of one or more of X-pos,
X-speed, speed, turning, stumbling, size, T-count, P-count,
T-length, Cross150, Cross250, and F-count.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] The present invention can be best understood by reference to
the following description taken in conjunction with the
accompanying drawing figures, in which like parts may be referred
to by like numerals:
[0018] FIG. 1 is a side view of an exemplary motion tracking
system;
[0019] FIG. 2 is an exemplary process for processing and analyzing
a digitized movie;
[0020] FIG. 3 is an exemplary process for processing frames of a
digitized movie;
[0021] FIG. 4 depicts an exemplary frame of a digitized movie;
[0022] FIG. 5 depicts an exemplary background approximation of an
exemplary frame of a digitized movie;
[0023] FIG. 6 depicts an exemplary binary image of an exemplary
frame of a digitized movie;
[0024] FIG. 7 depicts a normalized sum of an exemplary binary image
of an exemplary frame of a digitized movie;
[0025] FIG. 8 depicts an exemplary image block;
[0026] FIG. 9 is an exemplary process for tracking motion of
specimens captured by a digitized movie;
[0027] FIG. 10 depicts an exemplary trajectory;
[0028] FIGS. 11A and 11B depict assigning an exemplary trajectory
to an exemplary image block;
[0029] FIG. 12 depicts assigning two exemplary trajectories to an
exemplary image block;
[0030] FIGS. 13A to 13E depict exemplary frames of a digitized
movie;
[0031] FIGS. 14A to 14E depict exemplary binary images of the
exemplary frames depicted in FIGS. 13A to 13E;
[0032] FIGS. 15A to 15D depict exemplary binary images;
[0033] FIG. 16 depicts exemplary trajectories;
[0034] FIG. 17 depicts an exemplary amount of turning;
[0035] FIGS. 18A and 18B depict an exemplary amount of
stumbling;
[0036] FIG. 19 is a bar graph from Example 2 showing the results of
an assay of treated and control flies;
[0037] FIG. 20 is a line graph from Example 3 showing motor
performance, assessed by the Cross150 score (y-axis) plotted
against time (x-axis);
[0038] FIGS. 21A-21J from Example 3 are ten plots showing the
average p-values for different populations for each combination of
a certain number of video repeats and replica vials; and
[0039] FIG. 22 from Example 4 is a line graph showing motor
performance on the y-axis (Cross150) plotted against time on the
x-axis (Trials).
DETAILED DESCRIPTION OF THE INVENTION
[0040] Introduction
[0041] The present invention provides new methodology for screening
for agents with a desired biological activity. The invention is
particularly useful for high throughput screening for agents with
anti-neurodegenerative activity. The invention also provides new
and efficient methodology for the quantitative description and/or
characterization of one or more traits (e.g., behavior or locomotor
activity) associated with an animal disease model. The invention
also provides other methods and assays useful for identification of
agents with therapeutic activity.
[0042] Although the methods of the invention can be applied using a
variety of animals, as described below, they find particular
application when practiced using populations of flies, e.g.,
Drosphila melanogaster. For convenience, but not for limitation,
the description below will generally describe the invention as used
when the test animals are flies.
[0043] The invention is described in detail in the following
sections; however, a brief introductory description of one
illustrative embodiment will assist the reader in the understanding
of the invention. However, this introduction describing a
particular embodiment is not to be construed as limiting the
invention. In one embodiment, the invention provides methods for
screening for the effects of a test agent on a population of
animals which entail providing a population of animals,
administering at least one test agent to the population, creating a
digitized movie showing movement of animals in the population,
determining at least two traits of the population, and correlating
the traits of the population with the effect of the test agent(s)
administered to the population. In another embodiment, the
invention provides methods for screening for the effects of a test
agent on a population of animals which entail providing a plurality
of populations of animals, administering at least one test agent to
each of the populations, creating a digitized movie showing
movement of animals in each population, determining at least two
traits of each population and, for each population, correlating the
traits of the population with the effect of the test agent(s)
administered to the population. In this context, the plurality of
populations is at least 3 populations, and often more than 3, e.g.,
at least about 10 populations, at least about 20 populations, at
least about 100 populations, or at least about 200 populations. In
some embodiments of the invention, a large number of test
populations are efficiently analyzed, for example, at least about
10 populations, at least about 20 populations, at least about 100
populations, at least about 200 populations, at least about 300
populations, at least about 400 populations or more can be tested
in a single day.
[0044] Thus, for example methods of the invention are used to
screen for biologically active agents in the following manner: Two
stocks of Drosphila melanogaster are obtained; a parental stock and
a transgenic stock that differs from the parent by virtue of
comprising and expressing a transgene that causes a disease
phenotype in the flies. An exemplary transgenic fly is a fly that
exhibits neurodegeneration as a result of transgene expression.
[0045] In one aspect of the invention as encompassed in this
illustrative embodiment, a number of traits exhibited by the
parental stock and the transgenic stock are measured, and the
traits of the two stocks are compared to identify particular traits
that distinguish the two stocks. The measured traits usually
include movement traits, behavioral traits, and/or morphological
traits. In one aspect, the traits are measured by detecting and
serially analyzing the movement of a population of flies in
containers, e.g., vials. Movement of the flies is monitored by a
recording instrument, such as a CCD-video camera, the resultant
images are digitized, analyzed using processor-assisted algorithms,
and the analysis data is stored in a computer-accessible manner.
For example, in measuring traits related to fly movement, the
trajectory of each animal may be monitored by calculation of one or
more variables (e.g., speed, vertical only speed, vertical
distance, turning frequency, frequency of small movements, etc.)
for the animal. Values of such a variable are then averaged for
population of animals in the container and a global value is
obtained describing the trait for each population (e.g., parental
stock flies and transgenic flies). Global values for each trait are
compared and a subset of traits that differs significantly between
the populations is identified. The subset of traits and the values
of the traits for a particular population (e.g., the parental fly
stock) is referred to as a "phenoprint" of that population. The
phenoprint for a population is a useful tool in the identification
of therapeutic agents. For example, an agent that affects various
traits of the transgenic fly population with a neurodegenerative
phenotype in a fashion that makes them more similar to the
phenoprint of the parental population is likely to have biological
activity protective against the effects of neurodegeneration.
[0046] In another aspect of the invention as encompassed in this
illustrative embodiment, an automated system is used for
highthroughput screening of agents with biological activity. In one
embodiment, for use in such a system, populations of transgenic
flies, e.g., 2-50 flies, are contained in optically transparent
vials containing support medium. A different test agent is
administered to the flies in each vial, and the automated system is
used to determine the traits for each population. As above, the
traits can be measured by detecting and serially analyzing the
movement of a population of flies in containers, e.g., vials.
Movement of the flies is monitored by a recording instrument, such
as a CCD-video camera, the resultant images are digitized.
Movement, behavioral and morphological traits are determined by
analysis of the images using processor-assisted algorithms, and the
analysis data is stored in a computer-accessible manner. By
comparing the traits of populations treated with different test
agents with each other and/or with reference populations (such as
parental wild-type flies) the ability of large numbers of test
agents to affect neurodegeneration can be rapidly assessed. For
example, the ability of an agent to change at least some traits of
a transgenic population with a neurodegenerative phenotype to the
traits characteristic of the parental flies is indicative of a
desirable biological activity. The high throughput assay system of
the invention allows for large scale testing of and/or screening
for agents. The analysis of multiple traits, including specific
traits described herein, allows the effects of test agents to be
determined with much greater precision and sensitivity than other
methods.
[0047] A wide variety of other embodiments will now be
described.
[0048] Test and Reference Populations
[0049] This section describes test and reference populations, and
introduces a number of other terms used to describe the
invention.
[0050] A test population is a population of test animals that is
contacted with a test agent. In one aspect of the invention, the
effect of a test agent on a test population is determined. More
usually, the effect of a number of different test agents on a
number of different test populations is determined. In the latter
case, the test animals in each of the different test populations is
genetically similar or the same (e.g., all of a particular fly
strain, all comprising the same transgene, etc., and optionally all
male or all female). Thus, the fact that the test agent varies
between test populations while the test animals are constant allows
the effect of various test agents to be compared. The size of the
population can vary, but for flies is usually between about 2 and
50 flies (inclusive), for example, between about 5 and about 30
flies, or between about 10 and about 30 flies. Usually the test
population is confined in a container, such as a vial. Usually the
container is optically transparent so that the traits of the
population can be recorded.
[0051] The effect of the test agent on a population can be
determined by measuring one or more traits exhibited by the
population. Examples of traits that can be measured in the practice
of the invention are described in some detail below. Briefly,
however, exemplary traits include movement traits (e.g., path
length, stumbling, turning, and/or speed), behavioral traits (e.g.,
appetite, mating behavior, and/or life span), and morphological
traits (e.g., shape, size, or location in the animal of a cell,
organ or appendage, or size, shape or growth rate of the animal, or
the change of any such parameters over time). As is discussed
below, movement is of particular interest. In one example, using
the automated motion tracking system described herein, movement and
behavior traits (particularly behavior trait(s) involving locomotor
activity) of populations of flies are assessed over a short period
of time (e.g., 1-20 seconds, more often 4 to 10 seconds) after a
brief stimulus.
[0052] A description (e.g., a quantitative description) of one or
more of the measured traits together defines a "phenoprofile" of
the test population. A hypothetical example of a phenoprofile is
provided in Table 1, infra. The phenoprofile of a population
treated with a specific test agent is referred to as the "agent
phenoprofile."
[0053] Another type of phenoprofile is a "reference phenoprofile,"
which is a quantitative description of the traits exhibited by a
reference population. A reference population may be any of several
different populations of flies, and in some methods of the
invention, traits of a test population of flies are compared to
traits of a reference population of animals, or stated somewhat
differently, an agent phenoprofile is compared to a reference
phenoprofile. Animals used as the reference population in any given
assay will generally depend on the test population and/or on the
particular method and/or assay performed. For example, when a
method involves the use of transgenic flies which express a
particular transgene that results in specific behavior trait(s), a
reference population may be non-transgenic flies with the same
genetic background as the transgenic flies (except for the
particular transgene that results in the behavior phenotype). As
another example, when a method analyzes a population of flies
treated with a test agent, the reference population may be a
population of the same flies not treated with the test agent or the
reference population may be a population of flies treated with a
specified agent, for example an agent that has a known effect on
the animals. As another example, when a method involves the use of
flies with a genetic alteration which results in a change in level
of expression of an endogenous polypeptide (e.g., an alteration
which produces a gain of function or a loss of function result), a
reference population may be flies without the mutation. In some
instances, a reference population may consist of a population of
flies with a different transgene than that of the test population
so that a phenotype due to expression of a transgene in a test
population can be compared to a phenotype due to the expression of
a different transgene in the reference population.
[0054] In some embodiments, more than one reference population of
flies is used. For example, when analyzing the effect of a test
agent on a test population, the phenoprofile that results from
exposure to the agent (the agent phenoprofile) may be compared to a
reference phenoprofile of the same population of flies not treated
with a test agent and to a reference phenoprofile of wild-type
flies. It will be apparent that the test and reference populations
in any assay are the same species.
[0055] The particular traits exhibited by (and thus the particular
phenoprofile of) the test and/or reference population(s) is
influenced by the genotype of the animal, the properties of any
test agent to which the animal is exposed, the age of the animal
and other factors. In this context, the term "genotype" is defined
broadly and includes, for example, a variety of gene expression
events such as the expression of a mutated gene, the failure of
expression of a normally expressed gene and/or the expression of a
transgene.
[0056] Test Animals
[0057] Animals for use in the methods of the invention are
generally members of the class insecta, for example, but not
limited to, dipterans and lepidopterans, although in principle
other animals (e.g., an organism classified in the kingdom
Animalia), including other invertebrates, e.g., nematodes such as
C. elegans, and vertebrates, e.g., zebrafish and mice, may be used
in the methods. In a preferred embodiment, the methods of the
invention are adapted to screen insects. As used herein, "insect"
refers to an organism classified in the class insecta, and
preferably refers to an organism in the order diptera. Of
particular use in many embodiments is an insect which is a fly.
Examples of such flies include members of the family Drosophilidae,
including Drosophila melanogaster, and other flies such as Simulium
sp. (black fly), Musca domestica (house fly), Mediterranean
fruitfly, C. capitata (Medfly), black fly, blow fly, cluster fly,
drain fly, Hessian fly, and the like. In certain embodiments, the
flies are transgenic flies, e.g., transgenic Drosophila
melanogaster. A transgenic animal is an animal comprising
heterologous DNA (e.g., from a different species) incorporated into
its chromosomes. In other embodiments, the animals contain a
genetic alteration which results in a change in level of expression
of an endogenous polypeptide (e.g., an alteration which produces a
gain of function or a loss of function result). The term animal or
transgenic animal can refer to animals at any stage of development,
e.g. adult, fertilized eggs, embryos, larva, etc.
[0058] In particular embodiments, test animals used in methods of
the invention exhibit one or more traits that is indicative of
and/or characterizes a neurodegenerative condition in the animal
(e.g., including impaired motor skills, impaired cognition,
neuronal cell death, etc.). In some cases, test animals are flies
which exhibit phenotypes which characterize adult onset
neurodegenerative disorders, e.g., following the initial hours of
adult life, the flies exhibit a neurodegeneration phenotype,
including, but not limited to: progressive loss of neuromuscular
control, e.g. of the wings; progressive degeneration of general
coordination; progressive degenerative of locomotion; and
progressive degeneration of appetite. Some flies may also be
further characterized in that death occurs prematurely compared to
wild-type flies, for example, at 4 to 6 days of adult life. Useful
test animals include animal models for adult onset
neurodegenerative disorders, such as: Parkinson's Disease,
Alzheimer's Disease, Huntington's Disease, spinocerebellar ataxia
(SCA), and the like.
[0059] In some embodiments, animals for use in methods of the
invention are transgenic insects (or other transgenic animals) that
harbor a stably integrated transgene that is expressed in a manner
sufficient to result in a phenotype different from that of
wild-type animals, e.g., a neurodegenerative phenotype. The term
"transgene" is used herein to describe genetic material which has
been or is about to be artificially inserted into the genome of a
cell. In some instances, the transgene must be expressed in a
specific manner spatially and/or temporally in the animal to result
in the desired phenotype. For example, with regard to a
neurodegenerative phenotype, spatial expression of a particular
transgene may be limited to neuronal cells. In other instances,
specific spatial and/or temporal expression of a transgene is not
required to result in the desired phenotype, including a
neurodegenerative phenotype.
[0060] Examples of transgenes used in insects, such as flies,
include, but are not limited to, mammalian transgenes, human
transgenes, genes found to be associated with a human disease
(e.g., CNS or neurodegenerative disease) and genes that encode
proteins associated directly or indirectly with a human disease.
For example, introduction of human disease genes with dominant
gain-of-function mutations into Drosophila has generated fly models
for a number of neurodegenerative diseases. See, for example, Chan
et al. (2000); Feany et al. (2000); Femandez-Funez et al. (2000);
Fortini et al. (2000); Jackson et al. (1998); Kazemi-Esfarjani et
al. (2000); Warrick et al. (1998); Wittmann et al. (2001) Science
293:711-4.
[0061] Examples of genes associated with human neurodegenerative
diseases include those identified as having an expanded
trinucleotide sequence as compared to the wild-type gene and thus,
encode for a polypeptide with an expanded polyglutamine tract as
compared to the wild-type polypeptide. Examples of diseases
associated with polyglutamine repeats include Huntington's Disease,
spinocerebellar ataxia type 1 (SCA1), SCA2, SCA3, SCA6, SCA7,
SCA17, spinobulbar muscular atrophy and dentatorubropallidolusyan
atrophy (DRPLA) (Cummings et al. (2000) Human Mol. Genet.
9:909-916; Fischbeck (2001) Brain Res. Bull. 56:161-163.; Nakamura
et al. (2001) Hum. Mol. Genet. 10:1441-1448). For example,
expression of the mutated human ataxin-1 in transgenic flies (the
polypeptide encoded by the gene associated with SCA1) is
accompanied by adult-onset degeneration of neurons, with nuclear
inclusions that are immunologically positive for the mutated
protein, ubiquitin, Hsp70 and proteosome components
(Fernandez-Funez et al., 2000). In addition, in flies which express
the SCA1 or SCA3 disease genes, the disease is modified by
overexpression of chaperones (Fernandez-Funez et al., 2000; Warrick
et al., 1999). Transgenic flies that express exon-1 of huntingtin,
a polypeptide encoded by the gene associated with Huntington's
Disease and which contains an expanded polyglutamine repeat,
demonstrate a progressive neurodegeneration where the time of onset
and severity are linked to the length of the polyglutamine repeat
(Marsh et al., 2000).
[0062] Transgenic Drosophila with neuronal expression of human
mutated alpha-synuclein, a polypeptide encoded by a gene associated
with Parkinson's disease, demonstrate age-dependent, progressive
degeneration of dopamine-containing cells and the presence of Lewy
bodies (Feany et al., 2000). These transgenic flies expressing
mutated human alpha-synuclein have impaired motor performance
(Feany et al. (2002) and this disease in flies is modified by
overexpression of chaperones (Auluck et al. (2002) Science
295:865-868). Transgenic Drosophila expressing tau protein show
neurodegeneration (Wittmann et al. (2001) Science 293:711-4).
[0063] As noted, the transgenic flies used in the invention
generally exhibit at least one measurable behavior and/or
morphological phenotype associated with the expression of the
transgene. The phenotype of the transgenic fly may or may not be
similar to the behavior and/or morphological phenotype associated
with the expression of the transgene, or the gene from which the
transgene was derived, in another type of animal, such as a
vertebrate.
[0064] Transgenic animals for use in the invention can be prepared
using any convenient protocol that provides for stable integration
of the transgene into the animal genome in a manner sufficient to
provide for the requisite expression of the transgene. Methods for
preparing transgenic insects, including the use of mobile elements
such as PiggyBAC, MINOS, hermes, hobo and mariner, are described in
the art. See, for example, Horn et al. (2000) Dev. Genes Evol.
210:630-637; Handler et al. (1999) InsectMol. Biol. 8:449-457; Lobo
et al. (1999) Mol. Gen. Genet. 261:803-810; U.S. Pat. Nos.
6,051,430, 6,218,185, 6,225,121. Methods of random integration of
transgenes into the genome of a target Drosophila melanogaster
cell(s) are disclosed in U.S. Pat. No. 4,670,388, the disclosure of
which is herein incorporated by reference. Methods for preparing
transgenic flies, including the use of the P element, are described
in the art. See, for example, Brand et al. (1993); Phelps et al.
(1998) Methods 14:367-379; Spradling et al. (1982) Science
218:341-347; Spradling (1986) P ELEMENT MEDIATED TRANSFORMATION IN
DROSOPHILA: A PRACTICAL APPROACH (ed. D. D. Roberts, IRL Press,
Oxford) pp 175-179.
[0065] Generally, the transgene is stably integrated into the
genome of the animal under the control of a promoter that provides
for expression of the transgene. In some cases, the transgene is
stably integrated into the genome of the animal in a manner such
that its expression is controlled spatially to a desired cell type
and/or temporally to a particular developmental stage. In other
cases, although transgene expression is required, spatial and/or
temporal control of the expression is not necessary for the
generation of a phenotype associated with the transgene expression.
The transgene may be under the control of any convenient promoter
that provides for requisite spatial and temporal expression
pattern, if necessary, and the promoter may be endogenous or
exogenous. To obtain the desired targeted expression of the
randomly integrated transgene, integration of particular promoter
upstream of the transgene (e.g., an exogenous promoter), as a
single unit in the element or vector may be employed.
[0066] When an endogenous promoter is used, a suitable promoter is
located in the genome of the animal. The transgene may then be
integrated into the fly genome in a manner that provides for direct
or indirect expression activation by the promoter, i.e. in a manner
that provides for either cis or trans activation of gene expression
by the promoter. In other words, expression of the transgene may be
mediated directly by the promoter, or through one or more
transactivating agents. Where the transgene is under direct control
of the promoter, i.e. the promoter regulates expression of the
transgene in a cis fashion, the transgene is stably integrated into
the genome of the fly at a site sufficiently proximal to the
promoter and, if necessary, in frame with the promoter such that
cis regulation by the promoter occurs.
[0067] In other embodiments where expression of the transgene is
indirectly mediated by the endogenous promoter, the promoter
controls expression of the transgene through one or more
transactivating agents, usually one transactivating agent, i.e. an
agent whose expression is directly controlled by the promoter and
which binds to the region of the transgene in a manner sufficient
to turn on expression of the transgene. Any convenient
transactivator may be employed. For example, in a transgenic fly
which uses the GAL4 transactivator system, a GAL4 encoding sequence
is stably integrated into the genome of the animal in a manner such
that it is operatively linked to the endogenous promoter that
provides for expression in the cells of interest. With the GAL4
targeted expression system, the transgene which results in the
desired phenotype is generally stably integrated into a different
location of the genome, generally a random location in the genome,
where the transgene is operatively linked to an upstream activator
sequence, i.e. UAS sequence, to which GAL4 binds and turns on
expression of the transgene. Transgenic flies having a GAL4/UAS
transactivation system are known to those of skill in the art and
are described, for example, in Brand et al. (1993); Phelps et al.
(1998); and Femandez-Funez et al. (2000).
[0068] Non-transgenic flies
[0069] In some embodiments, animals for use in methods of the
invention are insects (or other animals) that have a mutation that
disrupts one or more of their endogenous genes thereby generating a
loss-of-function disease phenotype. In Drosophila, for example,
genes which are homologs of a human disease genes can be disrupted
to produce files with a loss-of function phenotype. See, for
example, Reiter et al. (2001) Genome Res. 11:1114-1125 and The et
al. (1997) Science 276:791-794.
[0070] A variety of loss-of-function mutations in endogenous fly
genes have been identified. Examples of such mutations in genes
that produce nervous system disorders include swiss cheese
(Kretzschmar et al. (1997) J. Neurosci. 17:7425-7432), spongecake,
eggroll (Min et al. (1997) Curr. Biol. 7:885-888), drop dead
(Buchanan et al. (1993) Neuron 10:839-850), pirouette (Eberl et al.
(1997) Proc. Natl. Acad. Sci. USA 94:14837-14842), and bubblegum
(Min et al. (1999) Science 284:1985-1988). The bubblegum mutant
provides an example of a direct connection between a fly
neurodegeneration mutant and a human disease. Both bubblegum flies
and patients with the metabolic disorder adrenoleukodystrophy (ALD)
accumulate abnormal amounts of very long chain fatty acids
(VLCFAs). The bubblegum mutant flies have a mutation in the VLCFA
acyl coenzyme A synthetase gene. This enzyme has reduced activity
in patients with ALD. Primary defects in glial cells have been
implicated as an important mechanism of neurodegeneration in
Drosophila. The drop dead and swiss cheese mutants show glial
abnormalities before neurons degenerate. Similarly, primary glial
cell defects underlie neurodegeneration in some forms of human
hereditary peripheral nerve degeneration, such as
Charcot-Marie-Tooth disease (Bennett et al. (2001) Curr. Opin.
Neurol. 14:621-627).
[0071] Examples of loss-of-function mutations in flies that produce
stereotypic paralysis and seizures include easily shocked (eas) and
slamdance (sda) (Pavlidis et al. (1994) Cell 79:23-33; Kuebler et
al. (2001) J Neurophysiol. 86:1211-1225). Drosophila is a faithful
system to identify factors that suppress seizure susceptibility.
For example, anti-epileptic drugs such as Gabapentin, Topiramate
and Phenyloin administered orally to flies reduce seizure and mean
recovery times following seizure (Reynolds et al. (2002) 43.sup.rd
Annual Drosophila Genetics Conference).
[0072] For use in the invention, animals can be prepared by any
protocol that disrupts the expression of a gene or genes. For
example, the disruption of genes in Drosophila may be accomplished
by using P-element transposons (Rubin et al. (1982) Science
218:348-353), chromosomal aberrations may be generated in
Drosophila by subjecting flies to irradiation (Sullivan et al.
(2000) Drosophila Protocols (2000) Cold Spring Harbor Laboratory
Press, New York, pp. 592-593). Additionally, single-base-pair
mutations can be can be generated in fly genes with chemical
mutagens such as ethylmethanesulfonate (EMS) or ethylnitrosourea
(ENU) (Sullivan et al. (2000)). The ability to identify chemically
generated point mutations using a set of single nucleotide
polymorphisms which span the Drosophila genome has broadened this
approach by facilitating chemical-mutagen suppressor screens of a
given loss of function phenotype. See, for example, Lukacsovich et
al. (2001) Genetics 157:727-742; Berger et al. (2001) Nat. Genet.
29:475-481.
[0073] In some embodiments, animals for use in methods of the
invention are wild-type insects (or other animals) that suffer from
age related motor dysfunction and age-related death. As in humans,
flies demonstrate poor motor performance in latter weeks of their
life (Fernandez et al. (1999) Experimental Gerontology 34:621-631;
Le Bourg (1987) Experimental Gerontology 4:359-369). Feeding
Drosophila with 4-phenylbutyrate (PBA) can significantly increase
lifespan, without diminution of locomotor vigor (Kang et al. (2002)
Proc. Natl. Acad. Sci. USA 99:838-843).
[0074] In some embodiments, animals for use in methods of the
invention are wild-type insects (or other animals) that are
subjected to environmental stimuli or treated with a substance that
produces a disease-like state. For example, rest behavior in
Drosophila is a sleep-like state where the animals choose a
preferred location, become immobile for periods a particular time
in the circadian day, and are relatively unresponsive to sensory
stimuli (Hendricks et al. (2000) Neuron 25:129-138). Rest is
affected by both homeostatic and circadian influences and when rest
is prevented, the flies increasingly tend to rest despite
stimulation and then exhibit a rest rebound. Drugs which act on a
mammalian adenosine receptor alter rest as they do sleep,
suggesting conserved neural mechanisms. In other examples,
wild-type Drosophila demonstrate behavioral traits that resemble
aggression when they are placed in a competitive situation, such as
courtship (Chen et al. (2002) Proc. Natl. Acad. Sci. USA
99:5664-5668) and Drosophila are sensitive to a depression-like or
stress-like environment [Le Bourg et al. (1999) Experimental
Gerontology 34:157-172; Le Bourg et al. (1995) Behavioural
Processes 34:175-184).
[0075] Animals treated with a substance for use in the invention,
for example, include wild-type animals exposed to an additive
substance. Upon exposure to ethanol or other addictive substances,
wild-type Drosophila display behaviors that are similar to
intoxication and addiction seen in rodents and humans (Bellen
(1998) Cell 93:909-912). One example of a fly mutant with enhanced
sensitivity to ethanol is cheapdate (Moore et al. (1998) Cell
93:997-1007). Other addictive substances for use in the animals may
include, for example, cocaine and nicotine (Bainton et al. (2000)
CurrBiol. 10:187-194; Torres et al. (1998) Synapse 29:148-161).
[0076] Chemical-induced models of human disease in animals include,
for example, those which target dopamine neurons such as
1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) or
6-hydroxydopamine (6-OHDA) (Beal (2001) Nat. Rev. Neurosci.
2:325-334). Other examples of chemicals for the generation of such
models include, but are not limited to, cholinergic agonists,
carbachol, muscarine, pilocarpine, and acetylcholine (Gorczyca et
al. (1991) J Neurobiol. 22:391-404). Additionally, olfactory
sensitivity, shock reactivity, and locomotor behavior in flies can
be manipulated with hydroxyurea (de Belle et al. (1994) Science
263:692-695).
[0077] Traits
[0078] A phenoprofile of a test or reference population is
determined by measuring traits of the population. The present
invention allows simultaneous measurement of multiple traits of a
population. Although a single trait may be measured, more often at
least 2, at least 3, at least 4, at least 5, at least 7 or at least
10 traits are assessed for a population. The traits measured can be
solely movement traits, solely behavioral traits solely
morphological traits or a mixture of traits in multiple categories.
In some embodiments at least one movement trait and at least one
non-movement trait is assessed.
[0079] The present invention provides for the analysis of the
movement of a plurality of biological specimens, and further
contemplates that the measurements made of a biological specimen
may additionally include other physical trait data. As used herein,
"physical trait data" refers to, but is not limited to, movement
trait data (e.g., animal behaviors related to locomotor activity of
the animal), and/or morphological trait data, and/or behavioral
trait data. Examples of such "movement traits" include, but are not
limited to:
[0080] a) total distance (average total distance traveled over a
defined period of time);
[0081] b) X only distance (average distance traveled in X direction
over a defined period of time;
[0082] c) Y only distance (average distance traveled in Y direction
over a defined period of time);
[0083] d) average speed (average total distance moved per time
unit);
[0084] e) average X-only speed (distance moved in X direction per
time unit);
[0085] f) average Y-only speed (distance moved in Y direction per
time unit);
[0086] g) acceleration (the rate of change of velocity with respect
to time);
[0087] h) turning;
[0088] i) stumbling;
[0089] j) spatial position of one animal to a particular defined
area or point (examples of spatial position traits include (1)
average time spent within a zone of interest (e.g., time spent in
bottom, center, or top of a container; number of visits to a
defined zone within container); (2) average distance between an
animal and a point of interest (e.g., the center of a zone); (3)
average length of the vector connecting two sample points (e.g.,
the line distance between two animals or between an animal and a
defined point or object); (4) average time the length of the vector
connecting the two sample points is less than, greater than, or
equal to a user define parameter; and the like);
[0090] m) path shape of the moving animal, i.e., a geometrical
shape of the path traveled by the animal (examples of path shape
traits include the following: (1) angular velocity (average speed
of change in direction of movement); (2) turning (angle between the
movement vectors of two consecutive sample intervals); (3)
frequency of turning (average amount of turning per unit of time);
(4) stumbling or meandering (change in direction of movement
relative to the distance); and the like. This is different from
stumbling as defined above. Turning parameters may include smooth
movements in turning (as defined by small degrees rotated) and/or
rough movements in turning (as defined by large degrees
rotated).
[0091] "Movement trait data" as used herein refers to the
measurements made of one or more movement traits. Examples of
"movement trait data" measurements include, but are not limited to
X-pos, X-speed, speed, turning, stumbling, size, T-count, P-count,
T-length, Cross150, Cross250, and F-count. Descriptions of these
particular measurements are provided below.
[0092] X-Pos: The X-Pos score is calculated by concatenating the
lists of x-positions for all trajectories and then computing the
average of all values in the concatenated list.
[0093] X-Speed: The X-Speed score is calculated by first computing
the lengths of the x-components of the speed vectors by taking the
absolute difference in x-positions for subsequent frames. The
resulting lists of x-speeds for all trajectories are then
concatenated and the average x-speed for the concatenated list is
computed.
[0094] Speed: The Speed score is calculated in the same way as the
X-Speed score, but instead of only using the length of the
x-component of the speed vector, the length of the whole vector is
used. That is, [length]=square root of
([x-length].sup.2+[y-length].sup.2).
[0095] Turning: The Turning score is calculated in the same way as
the Speed score, but instead of using the length of the speed
vector, the absolute angle between the current speed vector and the
previous one is used, giving a value between 0 and 90 degrees.
[0096] Stumbling: The Stumbling score is calculated in the same way
as the Speed score, but instead of using the length of the speed
vector, the absolute angle between the current speed vector and the
direction of body orientation is used, giving a value between 0 and
90 degrees.
[0097] Size: The Size score is calculated in the same way as the
Speed score, but instead of using the length of the speed vector,
the size of the detected fly is used.
[0098] T-Count: The T-Count score is the number of trajectories
detected in the movie.
[0099] P-Count: The P-Count score is the total number of points in
the movie (i.e., the number of points in each trajectory, summed
over all trajectories in the movie).
[0100] T-Length: The T-Length score is the sum of the lengths of
all speed vectors in the movie, giving the total length all flies
in the movie have walked.
[0101] Cross250: The Cross150 score is the number of trajectories
that either crossed the line at x=150 in the negative x-direction
(from bottom to top of the vial) during the movie, or that were
already above that line at the start of the movie. The latter
criteria was included to compensate for the fact that flies
sometimes don't fall to the bottom of the tube. In other words this
score measures the number of detected flies that either managed to
hold on to the tube or that managed to climb above the x=150 line
within the length of the movie.
[0102] Cross250: The Cross250 score is equivalent to the Cross150
score, but uses a line at x=250 instead.
[0103] F-Count: The F-Count score counts the number of detected
flies in each individual frame, and then takes the maximum of these
values over all frames. It thereby measures the maximum number of
flies that were simultaneously visible in any single frame during
the movie.
[0104] The assignment of directions in the X-Y coordinate system is
arbitrary. For purposes of this disclosure, "X" refers to the
vertical direction (typically along the long axis of the container
in which the flies are kept) and "Y" refers to movement in the
horizontal direction (e.g., along the surface of the vial).
[0105] For each of the various trait parameters described,
statistical measures can be determined. See, for example,
PRINCIPLES OF BIOSTATISTICS, second edition (2000) Mascello et al.,
Duxbury Press. Examples of statistics per trait parameter include
distribution, mean, variance, standard deviation, standard error,
maximum, minimum, frequency, latency to first occurrence, latency
to last occurrence, total duration (seconds or %), mean duration
(if relevant).
[0106] Certain other traits (which may involve animal movement) can
be termed "behavioral traits." Examples of behavioral traits
include, but are not limited to, appetite, mating behavior, sleep
behavior, grooming, egg-laying, life span, and social behavior
traits, for example, courtship and aggression. Social behavior
traits may include the relative movement and/or distances between
pairs of simultaneously tracked animals. Such social behavior trait
parameters can also be calculated for the relative movement of an
animal or between animal(s) and zones/points of interest.
Accordingly, "behavioral trait data" refers to the measurement of
one or more behavioral traits. Examples of such social behavior
trait traits include, for example, the following:
[0107] a) movement of one animal toward or away from another
animal;
[0108] b) occurrence of no relative spatial displacement of two
animals;
[0109] c) occurrence of two animals within a defined distance from
each other;
[0110] d) occurrence of two animals more than a defined distance
away from each other.
[0111] In addition to traits based on specimen movement and/or
behavior, other traits of the specimens may be determined and used
for comparison in the methods of the invention, such as
morphological traits. As used herein, "morphological traits" refer
to, but are not limited to gross morphology, histological
morphology (e.g., cellular morphology), and ultrastructural
morphology. Accordingly, "morphological trait data" refers to the
measurement of a morphological trait. Morphological traits include,
but are not limited to, those where a cell, an organ and/or an
appendage of the specimen is of a different shape and/or size
and/or in a different position and/or location in the specimen
compared to a wild-type specimen or compared to a specimen treated
with a drug as opposed to one not so treated. Examples of
morphological traits also include those where a cell, an organ
and/or an appendage of the specimen is of different color and/or
texture compared to that in a wild-type specimen. An example of a
morphological trait is the sex of an animal (i.e., morphological
differences due to sex of the animal). One morphological trait that
can be determined relates to eye morphology. For example,
neurodegeneration is readily observed in a Drosophila compound eye,
which can be scored without any preparation of the specimens
(Fernandez-Funez et al., 2000, Nature 408:101-106; Steffan et. al,
2001, Nature 413:739-743). This organism's eye is composed of a
regular trapezoidal arrangement of seven visible rhabdomeres
produced by the photoreceptor neurons of each Drosophila
ommatidium. Expression of mutant transgenes specifically in the
Drosophila eye leads to a progressive loss of rhabdomeres and
subsequently a rough-textured eye (Fernandez-Funez et al., 2000;
Steffan et. al, 2001). Administration of therapeutic compounds to
these organisms slows the photoreceptor degeneration and improves
the rough-eye phenotype (Steffan et. al, 2001). In one embodiment,
animal growth rate or size is measured. For example Drosophila
mutants that lack a highly conserved neurofibromatosis-1 (NF1)
homolog are reduced in size, which is a defect that can be rescued
by pharmacological manipulations that stimulate signalling through
the cAMP-PKA pathway (The et al., 1997, Science 276:791-794; Guo et
al., 1997, Science 276:795-798).
[0112] Effect of Sex and Environment On Traits
[0113] Traits exhibited by the populations may vary, for example,
with environmental conditions, age of the animals and/or sex of the
animals. For traits in which such variation occurs, assay and/or
apparatus design can be adjusted to control possible variations.
Apparatus for use in the invention can be adjusted or modified so
as to control environmental conditions (e.g., light, temperature,
humidity, etc.) during the assay. The ability to control and/or
determine the age of a fly population, for example, is well known
in the art. For those traits which has a sex-specific bias or
outcome, the system and software used to assess the trait can sort
the results based a detectable sex difference in of the animals.
For example, male and female flies differ detectably in body size.
Thus, analysis of sex-specific traits need not require separated
male and/or female populations. However, sex-specific populations
of animals can be generated by sorting using manual, robotic
(automated) and/or genetic methods as known in the art. For
example, a marked-Y chromosome carrying the wild-type allele of a
mutation that shows a rescuable maternal effect lethal phenotype
can be used. See, for example, Dibenedetto et al. (1987) Dev. Bio.
119:242-251.
[0114] Analysis of Traits
[0115] The present invention makes use of an automated system to
provide a quantitative description of traits and determine
phenoprofiles. An automated system is a system that includes one or
more of the following features or elements: a short cycle time,
operates continuously and/or requires little or no manual
intervention. For example, such a system would be a motion tracking
system and would include a machine apparatus coupled to a robotic
system for handling containers of animals (i.e., specimen
containers), a computer-vision system to measure animal traits and
a system to archive the output.
[0116] In one embodiment, a large number of test populations are
analyzed using the automated system, for example, at least about 10
populations, at least about 20 populations, at least about 100
populations, at least about 200 populations, at least about 300
populations, at least about 400 populations or more can be tested
in a single day.
[0117] In an aspect, the invention provides a system useful for the
practice of the screening and analysis methods described herein.
Generally the system includes a container platform having an array
of containers suitable for housing animals. For example, the
animals can be insects (e.g., flies) or other invertebrates.
Generally the system includes a nonvisual detection means (camera)
configured to capture a movie of the movement of animals in the
container, and a robot configured to move the containers into a
position such that the animals in the container can be viewed by
the camera, and a processor configured to process the movie
captured by the camera. In one embodiment, the robot is configured
to remove a container from the platform, position the container in
front of the camera, and return the container to the platform.
[0118] An exemplary automated system is described in FIG. 1, for
illustration and not limitation. It will be appreciated that a
variety of modifications can be made to the system described below,
and that other, different systems, may be used in the method of the
invention. As described below, motion tracking system 100 can
operate to monitor the activity of specimens in specimen containers
104. In the practice of the invention with flies, the specimen
containers (e.g., vials, tubes) contain nutrient medium, for
example, including agar support medium, food and/or yeast paste
(with or without test agent), and a population of about 2 to about
50, about 5 to about 30, about 10 to about 30, about 10 to about
40, or typically about 10 to about 20, flies. If desired, the files
can be reared, stored and assayed (one or more times) in the same
container.
[0119] A. Robotics
[0120] In FIG. 1, an exemplary motion tracking system 100 is
depicted. As described below in greater detail, motion-tracking
system 100 can operate to monitor the activity of specimens in
specimen containers 104. For the sake of example, motion tracking
system 100 is described below in connection with monitoring the
activity of flies within optically transparent tubes. It should be
noted, however, that motion-tracking system 100 can be used in
connection with monitoring the activities of various biological
specimens within various types of containers.
[0121] In one exemplary embodiment of motion tracking system 100, a
robot 114 removes a specimen container 104 from a specimen platform
102, which holds a plurality of specimen containers 104. Robot 114
positions specimen container 104 in front of camera 124. Specimen
container 104 is illuminated by a lamp 116 and a light screen 118.
Camera 124 then captures a movie of the activity of the biological
specimens within specimen container 104. After the movie has been
obtained, robot 114 places specimen container 104 back onto
specimen platform 102. Robot 114 can then remove another specimen
container 104 from specimen platform 102. A processor 126 can be
configured to coordinate and operate specimen platform 102, robot
104, and camera 124. As described below, motion tracking system 100
can be configured to receive, store, process, and analyze the
movies captured by camera 124.
[0122] In the present embodiment, specimen platform 102 includes a
base plate 106 into which a plurality of support posts 108 is
implanted. In one exemplary configuration, specimen platform 102
includes a total of 416 support posts 108 configured to form a
25.times.15 array to hold a total of 375 specimen containers 104.
As depicted in FIG. 1A, support posts 108 can be tapered to
facilitate the placement and removal of specimen containers 104. It
should be noted that specimen platform 102 can be configured to
hold any number of specimen containers 104 in any number of
configurations.
[0123] Motion tracking system 100 also includes a support beam 110
having a base plate 112 that can translate along support beam 110,
and a support beam 120 having a base plate 122 that can translate
along support beam 120. In FIG. 1A, support beam 110 and support
beam 120 are depicted extending along the Y axis and Z axis,
respectively. As such, base plate 112 and base plate 122 can
translate along the Z axis and Y axis, respectively. It should be
noted, however, that the labeling of the X, Y, and Z axes in FIG.
1A is arbitrary and provided for the sake of convenience and
clarity.
[0124] In the present embodiment, robot 114 and lamp 116 are
attached to base plate 112, and camera 124 is attached to base
plate 122. As such, robot 114 and lamp 116 can be translated along
the Z axis, and camera 124 can be translated along the Y axis.
Additionally, support beam 110 is attached to base plate 122, and
can thus translate along the Y axis. Support beam 120 can also be
configured to translate along the X axis. For example, support beam
120 can translate on two linear tracks, one on each end of support
beam 120, along the X axis. As such, robot 114 can be moved in the
X, Y, and Z directions. Additionally, robot 114 and camera 124 can
be moved to various X and Y positions over specimen platform 102.
Alternatively, specimen platform 102 can be configured to translate
in the X and/or Y directions.
[0125] Motion tracking system 100 can be placed within a suitable
environment to reduce the effect of external light conditions. For
example, motion tracking system 100 can be placed within a dark
container. Additionally, motion tracking system 100 can be placed
within a temperature and/or humidity controlled environment.
[0126] B. Capturing and Processing Images of Specimens
[0127] As noted above, motion-tracking system 100 can be used to
monitor the activity of specimens within specimen container 104. As
also noted above, in one exemplary application, the movement of
flies within specimen container 104 can be captured in a movie
taken by camera 124, then analyzed by processor 126. As used
herein, the term "movie" has its normal meaning in the art and
refers a series of images (e.g., digital images) called "frames"
captured over a period of time. A movie has two or more frames and
usually comprises at least 10 frames, often at least about 20
frames, often at least about 40 frames, and often more than 40
frames. The frames of a movie can be captured over any of a variety
of lengths of time such as, for example, at least one second, at
least about two, at least about 3, at least about 4, at least about
5, at least about 10, or at least about 15 seconds. The rate of
frame capture can also vary. Exemplary frame rates include at least
1 frame per second, at least 5 frames per second or at least 10
frames per second. Faster and slower rates are also
contemplated.
[0128] In the present exemplary application, to capture a movie of
the movement of flies within specimen container 104, robot 114
grabs a specimen container 104 and positions it in front of camera
124. However, before positioning specimen container 104 in front of
camera 124, robot 114 first raises specimen container 104 above a
distance, such as about 2 centimeters, above base plate 106, then
releases specimen container 104, which forces the flies within
specimen container 104 to fall down to the bottom of specimen
container 104. Robot 114 then grabs specimen container 104 again
and positions it to be filmed by camera 124. In one exemplary
embodiment, camera 124 captures about 40 consecutive frames at a
frame rate of about 10 frames per second. It should be noted,
however, that the number of frames captured and the frame rate used
can vary. Additionally, the step of dropping specimen container 104
prior to filming can be omitted.
[0129] As described above, motion tracking system 100 can be
configured to receive, store, process, and analyze the movie
captured by camera 124. In one exemplary embodiment, processor 126
includes a computer with a frame grabber card configured to
digitize the movie captured by camera 124. Alternatively, a digital
camera can be used to directly obtain digital images. Motion
tracking system 100 can also includes a storage medium 128, such as
a hard drive, compact disk, digital videodisc, and the like, to
store the digitized movie. It should be noted, however, that motion
tracking system 100 can include various hardware and/or software to
receive and store the movie captured by camera 124. Additionally,
processor 126 and/or storage medium 128 can be configured as a
single unit or multiple units.
[0130] With reference to FIG. 2, an exemplary process of processing
and analyzing the movie captured by camera 124 (FIG. 1) is
depicted. In one exemplary embodiment, the exemplary process
depicted in FIG. 2 can be implemented in a computer program.
[0131] In step 130, the frames of the movie are loaded into memory.
For example, processor 126 can be configured to obtain one or more
frames of the movie from storage medium 128 and load the frames
into memory. In step 132, the frames are processed, in part, to
identify the specimens within the movie. In step 134, the movements
of the specimens in the movie are tracked. In step 136, the
movements of the specimens are then analyzed. It should be noted
that one or more of these steps can be omitted and that one or more
additional steps can also be added. For example, the movements of
the specimens in the movie can be tracked (i.e., step 134) without
having to analyze the movements (i.e., step 136). As such, in some
applications, step 136 can be omitted.
[0132] With reference to FIG. 3, an exemplary process of processing
the frames of the movie (i.e., step 132 in FIG. 2) is depicted. In
one exemplary embodiment, the exemplary process depicted in FIG. 3
can be implemented in a computer program.
[0133] FIG. 4 depicts an exemplary frame of biological specimens
within a specimen container 104 (FIG. 1), which in this example are
flies within a transparent tube. As depicted in FIG. 4, the frame
includes images of flies in specimen container 104 (FIG. 1) as well
as unwanted images, such as dirt, blemishes, occlusions, and the
like. As such, with reference to FIG. 3, in step 138, a binary
image is created for each frame of the movie to better identify the
images that may correspond to flies in the frames.
[0134] In one exemplary embodiment, a background approximation for
the movie can be obtained by superimposing two or more frames of
the movie, then determining a characteristic pixel value for the
pixels in the frames. The characteristic pixel value can include an
average pixel value, a median pixel value, and the like.
Additionally, the background approximation can be obtained based on
a subset of frames or all of the frames of the movie. The
background approximation normalizes non-moving elements in the
frames of the movie. FIG. 5 depicts an exemplary background
approximation. In the exemplary background approximation, note that
the unwanted images in FIG. 4 have been removed, and the streaks
can indicate the movement of flies.
[0135] To generate a binary image, the background approximation is
subtracted from a frame of the movie. By subtracting the background
approximation from a frame, the binary image of the frame captures
the moving elements of the frame. Additionally, a gray-scale
threshold can be applied to the frames of the movie. For example,
if a pixel in a frame is darker than the threshold, it is
represented as being white in the binary image. If a pixel in the
frame is lighter than the threshold, it is represented as being
black in the binary image. More particularly, if the difference
between an image pixel value and the background pixel value is less
than the difference between a threshold value and the value of a
white pixel (i.e., [Image Pixel Value]-[Background Pixel
Value]<[Threshold Value]-[Pixel Value of White Pixel]), then the
binary image pixel is set as white. For example, if the pixel value
of a black pixel is assumed to be 0 and a white pixel is assumed to
be 255, an exemplary threshold value of 230 can be used.
[0136] With reference again to FIG. 3, in step 140, the image
blocks in the frames of the movie are screened by pixel size. More
particularly, image blocks in a frame having an area greater than a
maximum threshold or less than a minimum threshold are removed from
the binary image. For example, FIG. 6 depicts an exemplary binary
image, which was obtained by subtracting the background
approximation depicted in FIG. 5 from the exemplary frame depicted
in FIG. 4 and removing image blocks in the frames having areas
greater than 1600 pixels or less than 30 pixels. The image blocks
are also screened for eccentricity. As used herein, "eccentricity"
refers to the relationship between width and length of an image
block. For example, where a biological specimen of the invention is
a fly, the accepted eccentricity values range between 1 and 5 (that
is, the ratio of width to length is within a range of 1 to 5). The
eccentricity value of a given biological specimen can be determined
empirically by one of skill in the art based on the average width
and length measurements of the specimen. Once the eccentricity
value of a given biological specimen is determined, that value will
be permitted to increase by a doubling of the value or decrease by
half the value, and still be considered to be within the acceptable
range of eccentricity values for the particular biological
specimen. Image blocks which fall outside the accepted eccentricity
value for a given biological specimen (or sample of plural
biological specimens) will be excluded from the analysis (i.e.,
blocks that are too long and/or narrow to be a fly are
excluded).
[0137] As depicted in FIG. 6, the image blocks 144 that may
correspond to specimens, and more specifically flies in this
present exemplary application, can be more easily identified in the
binary image. FIG. 7 depicts a normalized sum of the binary images
of the frames of the movie, which can provide an indication of the
movements of the flies during the movie. In FIGS. 6 and 7, image
blocks 144 are depicted as being white, and the background depicted
as being black. It should be noted, however, that image blocks 144
can be black, and the background white.
[0138] With reference to FIG. 3, in step 142, data on image blocks
144 (FIG. 6) are collected and stored. In one exemplary embodiment,
the collected and stored data can include one or more
characteristics of image blocks 144 (FIG. 6), such as length,
width, location of the center, area, and orientation.
[0139] With reference to FIG. 8, a long axis 152 and a short axis
154 for image block 144 can be determined based on the shape and
geometry of image block 144. The length of long axis 152 and the
length of short axis 154 are stored as the length and width,
respectively, of image block 144.
[0140] A center 146 can be determined based on the center of
gravity of the pixels for image block 144. The center of gravity
can be determined using the image moment for an image block 144,
according to methods which are well established in the art. The
location of center 146 can then be determined based on a coordinate
system for the frame. With reference to FIG. 1, in the present
example, camera 124 is tilted such that the frames captured by
camera 124 are rotated 90 degrees. As such, as indicated by the
coordinate system used in FIG. 8, in the frames captured by camera
124, the top and bottom of specimen container 104 is located on the
left and right sides, respectively, of the frame. Furthermore, as
indicated by the coordinate system used in FIG. 8, for the purpose
of tracking the movement of image blocks 144, the X-axis
corresponds to the length of specimen container 104 (FIG. 1), where
the zero X position corresponds to a location near the top of
specimen container 104 (FIG. 1). The Y-axis corresponds to the
width of specimen container 104 (FIG. 1), where the zero Y position
corresponds to a location near the right edge of specimen container
104 (FIG. 1) as depicted in FIG. 1. Thus, when a fly moves from the
bottom of specimen container 104 (FIG. 1) toward the top, it moves
in a negative X direction. When the fly moves from left to right in
the specimen container 104 (FIG. 1), it moves in a negative Y
direction. In one exemplary embodiment, the zero X and Y position
is the upper left corner of a frame. It should be noted that the
labeling of the X and Y axes is arbitrary and provided for the sake
of convenience and clarity.
[0141] With reference to FIG. 8, an area 148 can be determined
based on the shape and geometry of image block 144. For example,
area 148 can be defined as the number of pixels that fall within
the bounds of image block 144. It should be noted that area 148 can
be determined in various manners and defined in various units.
[0142] An orientation 150 can be determined based on long axis 152
for image block 144. For example, as depicted in FIG. 8,
orientation 150 can be defined as an angle long axis 152 of image
block 144 and an axis of the coordinate system of the frame, such
as the Y axis as depicted in FIG. 8. It should be noted that
orientation 150 can be determined and defined in various
manners.
[0143] In one exemplary embodiment, data for image blocks 144 in
each frame of the movie are first collected and stored. As
described below, trajectories of the image blocks 144 are then
determined for the entire movie. Alternatively, data for image
blocks 144 and the trajectories of the image blocks 144 can be
determined frame-by-frame.
[0144] C Trajectory
[0145] With reference again to FIG. 2, in the present embodiment,
in step 134, the movements of the specimens in the movie are
tracked. More particularly, FIG. 9 depicts an exemplary process for
tracking the movements of the specimens in the movie. In one
exemplary embodiment, the exemplary process depicted in FIG. 9 can
be implemented in a computer program.
[0146] In step 156, for the first frame of the movie, trajectories
of image blocks 144 (FIG. 6) are initialized. More specifically, a
trajectory is initialized for each image block 144 identified in
the first frame. The trajectory includes various data, such as the
location of the center, area, and orientation of image block 144.
The trajectory also includes a velocity vector, which is initially
set to zero.
[0147] In step 158, a predicted position is determined. For
example, the predicted position of an image block 144 (FIG. 6)
and/or trajectory can be determined based on its previous position
and velocity vector. More specifically, in one configuration, the
predicted position can be determined as: [Predicted
Position]=[Previous Position]+[Prediction Factor].times.[Previous
Velocity Vector], where the prediction factor can vary between zero
and one.
[0148] For example, with reference to FIG. 10, assume that in one
frame a trajectory having a center position 182 and a velocity
vector 184 has been initialized based on image block 144. If the
prediction factor is zero, the predicted position in the next frame
would be the previous center position 182. If the prediction factor
is one, the prediction position in the next frame would be position
186. In one exemplary embodiment, a prediction factor of zero is
used, such that the predicted position is the same as the previous
position. However, the prediction factor used can be adjusted and
varied depending on the particular application.
[0149] Additionally, a predicted velocity can be determined based
on the previous velocity vector. For example, the predicted
velocity can be determined to be the same as the previous
velocity.
[0150] With reference to FIG. 9, in step 160, the next frame of the
movie is loaded and the trajectories are assigned to image blocks
144 (FIG. 6) in the new frame. More specifically, each trajectory
of a previous frame is compared to each image block 144 (FIG. 6) in
the new frame. If only one image block 144 (FIG. 6) is within a
search distance of a trajectory, and more specifically within the
predicted position of the trajectory, then that image block 144
(FIG. 6) is assigned to that trajectory. If none of the image
blocks 144 (FIG. 6) are within the search distance of a trajectory,
that trajectory is unassigned and will be hereafter referred to as
an "unassigned trajectory." However, if more than one image block
144 (FIG. 6) falls within the search distance of a trajectory, and
more specifically within the predicted position of the trajectory,
the image block 144 (FIG. 6) closest to the predicted position of
that trajectory is assigned to the trajectory.
[0151] For example, in one exemplary embodiment, if more than one
image block 144 (FIG. 6) falls within the search distance of a
trajectory, a distance between each of the image blocks 144 (FIG.
6) and the trajectory can be determined based on the position of
the image block 144 (FIG. 6), the prediction position of the
trajectory, a speed factor, the velocity of the image block 144
(FIG. 6), and the predicted velocity of the trajectory. More
particularly, the distance between each image block 144 (FIG. 6)
and the trajectory can be determined as the value of:
norm([Position of the image block]-[Predicted position of the image
block]+[Speed factor]*norm ([Velocity]-[Predicted Velocity])). A
norm function is the length of a two-dimensional vector, meaning
that only the magnitude of a vector is used. The speed factor can
be varied from zero to one, where zero corresponds to ignoring the
velocity of the image block and one corresponds to giving equal
weight to the velocity and the position of the image block. In the
present exemplary embodiment, the image block 144 (FIG. 6) having
the shortest distance is assigned to the trajectory. Additionally,
a speed factor of 0.5 is used.
[0152] With reference to FIG. 11A, assume that in one frame a
trajectory having a center position 188 and a velocity vector 190
has been initialized based on image block 144. With reference to
FIG. 11B, in the next frame, the trajectory, which is now depicted
as trajectory 196, is assigned to an image block 144. Assuming that
a prediction factor of zero is used, a search distance 198
associated with trajectory 196 is centered about the previous
center position 188 (FIG. 11A). Thus, in the example depicted in
FIG. 11B, image block 192 is assigned to trajectory 196, while
image block 194 is not. In one exemplary embodiment, a search
distance of [350 pixels per second]/[frame rate] is used, where the
frame rate is the frame rate of the movie. For example, if the
frame rate is 5 frames per second, then the search distance is 70
pixels/frame. It should be noted that various search distances can
be used depending on the application.
[0153] With reference to FIG. 9, in step 162, the trajectories of
the current frame are examined to determine if multiple
trajectories have been assigned to the same image block 144 (FIG.
6). For example, with reference to FIG. 12, assume that image block
144 lies within search distance 204 of trajectories 200 and 202. As
such, image block 144 is assigned to trajectories 200 and 202.
[0154] With reference to FIG. 9, in step 164, unassigned
trajectories are excluded from being merged. More particularly,
multiple trajectories assigned to an image block 144 (FIG. 6) are
examined to determined if any of the trajectories were unassigned
trajectories in the previous frame. The unassigned trajectories are
then excluded from being merged.
[0155] In step 166, trajectories assigned to an image block 144
outside of a merge distance are excluded from being merged. For
example, with reference to FIG. 12, assume that a merge distance
206 is associated with trajectories 200 and 202. If image block 144
does not lie within merge distance 206 of trajectories 200 and 202,
the two trajectories are excluded from being merged. If image block
144 does lie within merge distance 206 of trajectories 200 and 202,
the two trajectories are merged. In one exemplary embodiment, a
merge distance of [250 pixels per second]/[frame rate] is used. As
such, if the frame rate if 5 frames per second, then the merge
distance is 50 pixels/frame.
[0156] One of skill in the art will appreciate that a separation
distance, merge distance, and search distance used in the methods
of the invention may be modified depending on the particular
biological specimen to be analyzed, frame rate, image
magnification, and the like. In empirically determining a search,
merge, and separation distance for a given biological specimen, one
of skill in the art will appreciate that the value used is based on
an anticipated distance which a specimen will move between frames
of the movie, and will also vary with the size of the specimen, and
the speed at which the frames of the movie are acquired.
[0157] With reference to FIG. 9, in step 168, for trajectories that
were not excluded in steps 164 and 166, data for the trajectories
are saved. More particularly, an indication that the trajectories
are merged is stored. Additionally, one or more characteristics of
the image blocks 144 (FIG. 12) associated with the trajectories
before being merged is saved, such as area, orientation, and/or
velocity. As described below, this data can be later used to
separate the trajectories. In step 170, the multiple trajectories
are then merged, meaning that the merged trajectories are assigned
to the common image block 144 (FIG. 12).
[0158] For example, FIGS. 13A to 13C depict three frames of a movie
where two flies converge. Assume that FIGS. 14A to 14C depict
binary images of the frames depicted in FIGS. 13A to 13C,
respectively.
[0159] In FIG. 14A, two image blocks 208 and 212 are identified,
which correspond to the two flies depicted in FIG. 13A. Assume that
trajectories 210 and 214 were assigned to image blocks 208 and 212,
respectively, in a previous frame. As such, the data for trajectory
210 includes characteristics of image block 205, such as area,
orientation, and/or velocity. Similarly, the data for trajectory
214 includes characteristics of image block 212, such as area,
orientation, and/or velocity.
[0160] As depicted in FIG. 14B, assume that the two flies depicted
in FIG. 13B are in sufficient proximity that in the binary image of
the frame that a single image block 216 is identified. As also
depicted in FIG. 14B, image block 216 lies within search distance
218 of trajectories 210 and 214. As such, image block 216 is
assigned to trajectories 210 and 214. Additionally, assume that
image block 216 falls within the merge distance of trajectories 210
and 214. As such, in accordance with step 168 (FIG. 9), data for
trajectories 210 and 214 are saved. More specifically, one or more
characteristics of image blocks 208 and 212 (FIG. 14A) are stored
for trajectories 210 and 214, respectively. In accordance with step
170 (FIG. 9), trajectories 210 and 214 are merged, meaning that
they are associated with image block 216.
[0161] As depicted in FIG. 14C, assume that the two flies depicted
in FIG. 13C remain in sufficient proximity that in the binary image
of the frame that a single image block 220 is identified. As such,
trajectories 210 and 214 (FIG. 14B) remain merged. As also depicted
in FIG. 14C, image block 220 can have a different shape, area, and
orientation than image block 216 in FIG. 14B. Now assume that
velocity vector 222 is calculated based on the change in the
position of the center of image block 220 from the position of the
center of image block 216 (FIG. 14B). As such, the data of the
trajectory of image block 220 is appropriately updated.
[0162] Although in the above example two trajectories corresponding
to two flies are merged, it should be noted that any number of
trajectories corresponding to any number of flies (or any other
biological specimen) can be merged. For example, rather than two
flies crossing paths as depicted in FIGS. 13A to 13C, three or more
flies can converge.
[0163] As noted above, with reference again to FIG. 9, in step 166,
trajectories that are determined to have been unassigned
trajectories in the previous frame are excluded from being merged
with other trajectories. For example, with reference to FIG. 12, if
trajectory 202 is determined to have been an unassigned trajectory
in the previous frame, meaning that it had not been assigned to any
image block 144 (FIG. 6) in the previous frame, then trajectory 202
is not merged with trajectory 200. Instead, in one embodiment,
trajectory 200 is assigned to image block 144 (FIG. 6), while
trajectory 202 remains unassigned.
[0164] Now assume that FIGS. 15A to 15D depict the movement of a
fly over four frames of a movie. More specifically, assume that
during the four frames the fly begins to move, comes to a stop, and
then moves again.
[0165] Assume FIG. 15A depicts the first frame. As such, a
trajectory corresponding to image block 230 is initialized. As
depicted in FIG. 15B, assume that the fly has moved and that image
block 230 is the only image block that falls within the search
distance of the trajectory that was initialized based on image
block 230 in the earlier frame depicted in FIG. 15A. As such,
trajectory 232 is assigned to image block 230 and the data for
trajectory 232 is updated with the new location of the center,
area, and orientation of image block 230. Additionally, a velocity
vector is calculated based on the change in location of the center
of image block 230.
[0166] Now assume that the fly comes to a stop. As described above,
in one exemplary embodiment, a background approximation is
calculated and subtracted from each frame of the movie. As also
described above, flies that do not move throughout the movie are
averaged out with the background approximation. As such, when a fly
comes to a stop, the image block of that fly will decrease in area.
Indeed, if the fly remains stopped, the image block can decrease
until it disappears. Additionally, a fly can also physically leave
the frame.
[0167] As depicted in FIG. 15C, assume in the present example that
the fly has remained stopped sufficiently long enough that image
block 230 (FIG. 15B) has disappeared in the present frame. As such,
trajectory 232 becomes an unassigned trajectory.
[0168] Now assume that the fly begins to move again. As such, as
depicted in FIG. 15D, image block 230 is identified. Now assume
that the area of image block 230 is sufficiently large that image
block 230 lies within search distance 236 of trajectory 232. As
such, trajectory 232 now becomes assigned to image block 230.
[0169] With reference now to FIG. 9, in step 172, image blocks 144
(FIG. 6) in the current frame are examined to determine if any
remain unassigned. In step 174, the unassigned image blocks are
used to determine if any merged trajectories can be separated. More
specifically, if an unassigned image block falls within a
separation distance of a merged trajectory, one or more
characteristics of the unassigned image block is compared with one
or more characteristics that were stored for the trajectories prior
to the trajectories being merged to determine if any of the
trajectories can be separated from the merged trajectory.
[0170] For example, in one exemplary embodiment, the area of the
unassigned image block can be compared to the areas of the image
blocks associated with the trajectories before the trajectories
were merged. As described above, this data was stored before the
trajectories were merged. The trajectory with the stored area
closest to the area of the unassigned image block can be separated
from the merged trajectory and assigned to the unassigned image
block. Alternatively, if the stored area of a trajectory and that
of the unassigned image block are within a difference threshold,
then that trajectory can be separated from the merged trajectory
and assigned to the unassigned image block.
[0171] It should be noted that orientation or velocity can be used
to separate trajectories. Additionally, a combination of
characteristics can be used to separate trajectories. Furthermore,
if a combination of characteristics is used, then a weight can be
assigned to each characteristic. For example, if a combination of
area and orientation is used, the area can be assigned a greater
weight than the orientation.
[0172] As described above, FIGS. 13A to 13C depict three frames of
a movie where two flies converge, and FIGS. 14A to 14C depict
binary images of the frames depicted in FIGS. 13A to 13C.
Similarly, FIGS. 13D and 13E depict two frames of the movie where
the two flies diverge, and FIGS. 14D and 14E depict binary images
of the frames depicted in FIGS. 13D and 13E.
[0173] As described above, a merged trajectory was created based on
the merging of image blocks 208 and 212 (FIG. 14A) into image
blocks 216 (FIG. 14B) and 220 (FIG. 14C). Assume that in FIG. 14D,
the merged trajectories remain merged for image block 224. However,
in FIG. 14E, assume that the flies have separated sufficiently that
an image block 226 is identified apart from image block 228.
Additionally, assume that in the frame depicted in FIG. 14E image
block 226 is not assigned to a trajectory, but falls within the
separation distance of the merged trajectory. As such, in
accordance with step 174, one or more characteristics of image
block 226 is compared with the stored data of the merged
trajectories. More specifically, in accordance with the exemplary
embodiment described above, the area of image block 226 is compared
with the stored areas of image blocks 208 and 212 (FIG. 14A), which
correspond to the image blocks that were associated with
trajectories 210 and 214 (FIG. 14B), respectively, before the
trajectories were merged. In this example, the stored area image
block 212 (FIG. 14A), which corresponds to trajectory 214 (FIG.
14B) before it was merged with trajectory 210 (FIG. 14B), most
closely matches the area of image block 226. As such, trajectory
214 (FIG. 14B) is separated from the merged trajectory and assigned
to image block 226.
[0174] With reference again to FIG. 9, in step 178, if an
unassigned image block does not fall within the separation distance
of any merged trajectory, then a new trajectory is initialized for
the unassigned image blocks. In one embodiment, a separation
distance of 300/[frame rate], where the frame rate is the frame
rate of the movie, is used. It should be noted, however, that
various separation distances can be used.
[0175] In step 180, if the final frame has not been reached, then
the motion tracking process loops to step 158 and the next frame is
processed. If the final frame has been reached, then the motion
tracking process is ended.
[0176] In this manner, with reference to FIG. 1, the movements of
the biological specimens within specimen container 104 as captured
by camera 124 can be processed. For example, FIG. 16 depicts the
trajectories of the flies depicted in FIG. 4.
[0177] E. Analysis of Movement
[0178] Having thus tracked the movements of the specimens within
specimen container 104, the movements can then be analyzed for
various characteristics and/or traits. For example, in one
embodiment, various statistics on the movements of the specimens,
such as the x and y travel distance, path length, speed, turning,
and stumbling, can be calculated. These statistics can be
determined for each trajectory and/or averaged for a population,
such as for all the specimens in a specimen container 104).
[0179] The present invention provides for the analysis of the
movement of a plurality of biological specimens, and further
contemplates that the measurements made of a biological specimen
may additionally include other physical trait data. As used herein,
"physical trait data" refers to, but is not limited to, movement
trait data (e.g., animal behaviors related to locomotor activity of
the animal), and/or morphological trait data, and/or behavioral
trait data. Examples of such "movement traits" include, but are not
limited to:
[0180] a) total distance (average total distance traveled over a
defined period of time);
[0181] b) X only distance (average distance traveled in X direction
over a defined period of time;
[0182] c) Y only distance (average distance traveled in Y direction
over a defined period of time);
[0183] d) average speed (average total distance moved per time
unit);
[0184] e) average X-only speed (distance moved in X direction per
time unit);
[0185] f) average Y-only speed (distance moved in Y direction per
time unit);
[0186] g) acceleration (the rate of change of velocity with respect
to time);
[0187] h) turning;
[0188] i) stumbling;
[0189] j) spatial position of one animal to a particular defined
area or point (examples of spatial position traits include (1)
average time spent within a zone of interest (e.g., time spent in
bottom, center, or top of a container; number of visits to a
defined zone within container); (2) average distance between an
animal and a point of interest (e.g., the center of a zone); (3)
average length of the vector connecting two sample points (e.g.,
the line distance between two animals or between an animal and a
defined point or object); (4) average time the length of the vector
connecting the two sample points is less than, greater than, or
equal to a user define parameter; and the like);
[0190] m) path shape of the moving animal, i.e., a geometrical
shape of the path traveled by the animal (examples of path shape
traits include the following: (1) angular velocity (average speed
of change in direction of movement); (2) turning (angle between the
movement vectors of two consecutive sample intervals); (3)
frequency of turning (average amount of turning per unit of time);
(4) stumbling or meandering (change in direction of movement
relative to the distance); and the like. This is different from
stumbling as defined above. Turning parameters may include smooth
movements in turning (as defined by small degrees rotated) and/or
rough movements in turning (as defined by large degrees
rotated).
[0191] "Movement trait data" as used herein refers to the
measurements made of one or more movement traits. Examples of
"movement trait data" measurements include, but are not limited to
X-pos, X-speed, speed, turning, stumbling, size, T-count, P-count,
T-length, Cross150, Cross250, and F-count. Descriptions of these
particular measurements are provided below.
[0192] X-Pos: The X-Pos score is calculated by concatenating the
lists of x-positions for all trajectories and then computing the
average of all values in the concatenated list.
[0193] X-Speed: The X-Speed score is calculated by first computing
the lengths of the x-components of the speed vectors by taking the
absolute difference in x-positions for subsequent frames. The
resulting lists of x-speeds for all trajectories are then
concatenated and the average x-speed for the concatenated list is
computed.
[0194] Speed: The Speed score is calculated in the same way as the
X-Speed score, but instead of only using the length of the
x-component of the speed vector, the length of the whole vector is
used. That is, [length]=square root of
([x-length].sup.2+[y-length].sup.2).
[0195] Turning: The Turning score is calculated in the same way as
the Speed score, but instead of using the length of the speed
vector, the absolute angle between the current speed vector and the
previous one is used, giving a value between 0 and 90 degrees.
[0196] Stumbling: The Stumbling score is calculated in the same way
as the Speed score, but instead of using the length of the speed
vector, the absolute angle between the current speed vector and the
direction of body orientation is used, giving a value between 0 and
90 degrees.
[0197] Size: The Size score is calculated in the same way as the
Speed score, but instead of using the length of the speed vector,
the size of the detected fly is used.
[0198] T-Count: The T-Count score is the number of trajectories
detected in the movie.
[0199] P-Count: The P-Count score is the total number of points in
the movie (i.e., the number of points in each trajectory, summed
over all trajectories in the movie).
[0200] T-Length: The T-Length score is the sum of the lengths of
all speed vectors in the movie, giving the total length all flies
in the movie have walked.
[0201] Cross150: The Cross150 score is the number of trajectories
that either crossed the line at x=150 in the negative x-direction
(from bottom to top of the vial) during the movie, or that were
already above that line at the start of the movie. The latter
criteria was included to compensate for the fact that flies
sometimes don't fall to the bottom of the tube. In other words this
score measures the number of detected flies that either managed to
hold on to the tube or that managed to climb above the x=150 line
within the length of the movie.
[0202] Cross250: The Cross250 score is equivalent to the Cross150
score, but uses a line at x=250 instead.
[0203] F-Count: The F-Count score counts the number of detected
flies in each individual frame, and then takes the maximum of these
values over all frames. It thereby measures the maximum number of
flies that were simultaneously visible in any single frame during
the movie.
[0204] The assignment of directions in the X-Y coordinate system is
arbitrary. For purposes of this disclosure, "X" refers to the
vertical direction (typically along the long axis of the container
in which the flies are kept) and "Y" refers to movement in the
horizontal direction (e.g., along the surface of the vial).
[0205] For each of the various trait parameters described,
statistical measures can be determined. See, for example,
PRINCIPLES OF BIOSTATISTICS, second edition (2000) Mascello et al.,
Duxbury Press. Examples of statistics per trait parameter include
distribution, mean, variance, standard deviation, standard error,
maximum, minimum, frequency, latency to first occurrence, latency
to last occurrence, total duration (seconds or %), mean duration
(if relevant).
[0206] Certain other traits (which may involve animal movement) can
be termed "behavioral traits." Examples of behavioral traits
include, but are not limited to, appetite, mating behavior, sleep
behavior, grooming, egg-laying, life span, and social behavior
traits, for example, courtship and aggression. Social behavior
traits may include the relative movement and/or distances between
pairs of simultaneously tracked animals. Such social behavior trait
parameters can also be calculated for the relative movement of an
animal or between animal(s) and zones/points of interest.
Accordingly, "behavioral trait data" refers to the measurement of
one or more behavioral traits. Examples of such social behavior
trait traits include, for example, the following:
[0207] a) movement of one animal toward or away from another
animal;
[0208] b) occurrence of no relative spatial displacement of two
animals;
[0209] c) occurrence of two animals within a defined distance from
each other;
[0210] d) occurrence of two animals more than a defined distance
away from each other.
[0211] In addition to traits based on specimen movement and/or
behavior, other traits of the specimens may be determined and used
for comparison in the methods of the invention, such as
morphological traits. As used herein, "morphological traits" refer
to, but are not limited to gross morphology, histological
morphology (e.g., cellular morphology), and ultrastructural
morphology. Accordingly, "morphological trait data" refers to the
measurement of a morphological trait. Morphological traits include,
but are not limited to, those where a cell, an organ and/or an
appendage of the specimen is of a different shape and/or size
and/or in a different position and/or location in the specimen
compared to a wild-type specimen or compared to a specimen treated
with a drug as opposed to one not so treated. Examples of
morphological traits also include those where a cell, an organ
and/or an appendage of the specimen is of different color and/or
texture compared to that in a wild-type specimen. An example of a
morphological trait is the sex of an animal (i.e., morphological
differences due to sex of the animal). One morphological trait that
can be determined relates to eye morphology. For example,
neurodegeneration is readily observed in a Drosophila compound eye,
which can be scored without any preparation of the specimens
(Femandez-Funez et al., 2000, Nature 408:101-106; Steffan et. al,
2001, Nature 413:739-743). This organism's eye is composed of a
regular trapezoidal arrangement of seven visible rhabdomeres
produced by the photoreceptor neurons of each Drosophila
ommatidium. Expression of mutant transgenes specifically in the
Drosophila eye leads to a progressive loss of rhabdomeres and
subsequently a rough-textured eye (Femandez-Funez et al., 2000;
Steffan et. al, 2001). Administration of therapeutic compounds to
these organisms slows the photoreceptor degeneration and improves
the rough-eye phenotype (Steffan et. al, 2001). In one embodiment,
animal growth rate or size is measured. For example Drosophila
mutants that lack a highly conserved neurofibromatosis-1 (NF1)
homolog are reduced in size, which is a defect that can be rescued
by pharmacological manipulations that stimulate signalling through
the cAMP-PKA pathway (The et al., 1997, Science 276:791-794; Guo et
al., 1997, Science 276:795-798).
[0212] Traits exhibited by the populations may vary, for example,
with environmental conditions, age of a specimen and/or sex of a
specimen. For traits in which such variation occurs, assay and/or
apparatus design can be adjusted to control possible variations.
Apparatus for use in the invention can be adjusted or modified so
as to control environmental conditions (e.g., light, temperature,
humidity, etc.) during the assay. The ability to control and/or
determine the age of a fly population, for example, is well known
in the art. For those traits which have a sex-specific bias or
outcome, the system and software used to assess the trait can sort
the results based a detectable sex difference in of the specimens.
For example, male and female flies differ detectably in body size.
Thus, analysis of sex-specific traits need not require separated
male and/or female populations. However, sex-specific populations
of specimens can be generated by sorting using manual, robotic
(automated) and/or genetic methods as known in the art. For
example, a marked-Y chromosome carrying the wild-type allele of a
mutation that shows a rescuable maternal effect lethal phenotype
can be used. See, for example, Dibenedetto et al. (1987) Dev. Bio.
119:242-251.
[0213] In the present embodiment, x and y travel distances can be
determined based on the tracked positions of the centers of image
blocks 144 (FIG. 6) and/or the velocity vectors of the
trajectories. As noted above, the x and y travel distance for each
trajectory can be determined, which can indicate the x and y travel
distance of each specimen within specimen container 104.
Additionally or alternatively, an average x and y travel distance
for a population, such as all the specimens in a specimen container
104, can be determined.
[0214] Path length can also be determined based on the tracked
positions of the centers of image blocks 144 (FIG. 6) and/or the
velocity vectors of the trajectories. Again, a path length for each
trajectory can be determined, which can indicate the path length
for each specimen within specimen container 104. Additionally or
alternatively, an average path length for a population, such as all
the specimens in a specimen container 104, can be determined.
[0215] Speed can be determined based on the velocity vectors of the
trajectories. An average velocity for each trajectory can be
determined, which can indicate the average speed for each specimen
within specimen container 104. Additionally or alternatively, an
average speed for a population, such as all the specimens in a
specimen container 104, can be determined.
[0216] Turning can be determined as the angle between two velocity
vectors of the trajectories. As used herein, "turning" refers to a
change in the direction of the trajectory of a specimen such that a
second trajectory is different from a first trajectory. Turning may
be determined by detecting the existence of an angle 374 between
the velocity vector of a first frame and a second frame. More
specifically, "turning" may be determined herein as an angle 374 of
at least 1.sup.0, preferably greater than 2.sup.0, 5.sup.0,
10.sup.0, 20.sup.0, 30.sup.0, 40.sup.0, 50.sup.0, and up to or
greater than 90.sup.0. For example, with reference to FIG. 17,
assume that velocity vector 240 was determined based on the
movement of a specimen between frames 1 and 2; and velocity vector
242 was determined based on the movement of the specimen between
frames 2 and 3. As such, in this example, angle 244 defines the
amount of turning captured in frames 1, 2, and 3. In this manner,
the amount of turning for each trajectory can be determined, which
can indicate the amount of turning for each specimen within
specimen container 104. Additionally or alternatively, an average
amount of turning for a population, such as all the specimens in a
specimen container 104, can be determined.
[0217] Stumbling can be determined as the angle between the
orientation of a image block 144 (FIG. 6) and the velocity vector
of the image block 144 (FIG. 6) of the trajectories. Accordingly,
"stumbling" as used herein refers to a difference between the
direction of the orientation vector and the velocity vector of a
biological specimen. "Stumbling" may be determined according to the
invention, by the presence of an angle between the orientation
vector and velocity vector of a biological specimen of at least
1.sup.0, preferably greater than 2.sup.0, 5.sup.0, 10.sup.0,
20.sup.0, 40.sup.0, 60.sup.0, and up to or greater than 90.sup.0.
For example, with reference to FIG. 18A, assume that orientation
250 and velocity vector 252 of an image block 248 of a trajectory
are aligned (i.e., the angle between orientation 250 and velocity
vector 252 is zero degrees). As such, in this instance, the amount
of stumbling is zero, and thus at a minimum. With reference to FIG.
18B, now assume that orientation 250 and velocity vector 252 of
image block 248 of a trajectory are perpendicular (i.e., the angle
between orientation 250 and velocity vector 252 is 90 degrees). As
such, in this instance, amount of stumbling defined by angle 254 is
90 degrees, and thus at a maximum. In this manner, the amount of
stumbling for each trajectory can be determined, which can indicate
the amount of stumbling for each specimen within specimen container
104. Additionally or alternatively, an average amount of stumbling
for a population, such as all the specimens in a specimen container
104, can be determined.
[0218] Phenoprofiles
[0219] As discussed above, the term "phenoprofile" refers to a
trait or, more usually, a combination of traits exhibited by a
population of specimens (i.e., insects). Where the trait or
combination of traits is exhibited by a population following
exposure to a test agent it is referred to as an "agent
phenoprofile" and the phenoprofile exhibited by a reference
population is referred to as a "reference phenoprofile". The traits
are described by a quantitative or qualitative value. For
illustration, three hypothetical phenoprofiles with arbitrary units
are shown in Table 1.
1 TABLE 1 Phenoprofiles Test Test Reference Trait measured
Population 1 Population 2 Population x-only speed 5 1 6 stumbling
12 25 10 path length 100 25 100 turning 45 50 66
[0220] Usually, the phenoprofile is defined by measurements of at
least 2, at least 3, at least 4, at least 5, at least 7 or at least
10 traits. The traits can be solely movement traits, solely
behavioral traits, solely morphological traits or a mixture of
traits in multiple categories. In some embodiments the phenoprofile
is determined by measurement of at least 2, at least 3, at least 4,
often at least 5, and sometimes at least 7 movement traits.
[0221] In one embodiment, a trait and/or phenoprofile is determined
for an animal population as a whole. In such a case the result for
one population can be compared to the result for another
population. In another embodiment, a trait and/or phenoprofile is
determined for individual animals in a population. For example,
when a social behavior trait is evaluated, relationship between
individuals of the population is determined and used to generate a
phenoprofile.
[0222] An example of the measurement of a phenoprofile for a
population of flies is shown below in Example 3. In this example,
Drosophila expressing a mutant form of human Huntingtin are
compared to mutant flies which have been exposed to a potentially
therapeutic drug, and further compared against a reference
(wild-type) population of flies. The phenoprofile of the flies in
this example is quantitated based on the Cross150 score, which is
the number of trajectories (flies) which cross a position at x=150
in the negative x-direction. The phenoprofile of the flies
described in Example 3 is based further on measurements of speed,
T-length, turning and stumbling. Example 4 provides an example of
the phenoprofile measured in a different population of flies, that
is, flies which comprise a polyglutamine repeat present in the Sca1
locus (thus recapitulating spinocerebellar ataxia-1). The
phenoprofile of these flies is based on the quantitation of the
Cross150 score. Accordingly, one of skill in the art will
appreciate that for a given population of specimens to be analyzed,
any measurement or combination of measurements of movement,
behavior, or morphology as described herein may be used to
determine a phenoprofile for a given population, regardless of
whether the phenoprofile is based on the measurement of one trait
(e.g., Cross150) or a plurality of traits (e.g., Cross150, speed,
turning, stumbling, and T-length).
[0223] Comparisons of Phenoprofiles
[0224] Phenoprofiles can be determined for a large number of test
populations as well as for reference populations. In one aspect of
the invention, the phenoprofiles of test and/or reference
populations are compared with each other.
[0225] Since the traits that define phenoprofiles can be stored
electronically, comparison of phenoprofiles is conveniently
accomplished using computer implemented multivariate analysis. It
should be noted that the multivariate analysis can be implemented
using any commercially available multivariate analysis package,
such as Spotfire DecisionSite, which is available from Spotfire of
Somerville, Mass. (SPOTFIRE is a registered trademark).
Alternatively, a custom multivariate analysis algorithm can be
developed and applied to the recorded traits.
[0226] Comparison of phenoprofiles can be carried out to achieve
several different goals. In one embodiment, a plurality of agent
phenoprofiles are ranked according to their similarity to a
reference phenoprofile. Such ranking can be used to screen or rank
agent according to their biological effect on the animals. For
example, and not limitation, if the test populations comprise flies
exhibiting traits of a neurodegenerative condition, test agents can
be screened for the ability to ameliorate the symptoms of the
condition by (1) comparing the phenoprofiles of test populations
exposed to various test agents with a reference phenoprofile of a
healthy (e.g., wild-type) flies, with test agents that produce
phenoprofiles more similar to the reference phenoprofile being
ranked higher than test agents that produce phenoprofiles less
similar to the reference phenoprofile and/or (2) comparing the
phenoprofiles of the test populations with a reference phenoprofile
of a test animal (i.e., exhibiting traits of the neurodegenerative
condition), with test agents that produce phenoprofiles less
similar to the reference phenoprofile being ranked higher than test
agents that produce phenoprofiles more similar to the reference
phenoprofile. Thus, in some embodiments, comparison of an agent
phenoprofile to a reference phenoprofile is used to select an agent
that results in a desired activity, such as ability to produce an
agent phenoprofile that is similar to a phenoprofile of a healthy
(e.g., wild-type) animal.
[0227] In one embodiment, the test animals are transgenic flies
expressing a transgene whose expression results, indirectly or
directly, in the neurodegenerative condition in the animal.
Examples of such transgenes are genes encoding for a polypeptide
with an expanded polyglutamine tract as compared to the wild-type
polypeptide, such as genes whose expression results in or
contributes to Huntington's Disease, spinocerebellar ataxia type 1
(SCA1), SCA2, SCA3, SCA6, SCA7, SCA17, spinobulbar muscular
atrophy, dentatorubropallidolusyan atrophy (DRPLA), and other
diseases known in the art or to be discovered. In an embodiment,
the reference phenoprofile is of a wild-type fly or a fly treated
with an agent known to ameliorate the disease condition when
administered to mammals with the disease. In one embodiment the
reference phenoprofile is of a fly treated with a agent known to
reduce the manifestation of at least one trait associated with
expression of the transgene.
[0228] It will be appreciated that many other types of comparisons
are possible depending on the specific aims of the screen. For
example, the agent phenoprofiles can be compared with each other or
with a reference phenoprofile of an animal treated with an
specified agent whose biological activity is known or
suspected.
[0229] In some instances, methods of the invention are used to
determine whether an agent can delay onset of a phenotype of an
animal, for example, a phenotype associated with a particular gene
expression event, such as expression of a gene associated with a
neurodegenerative disease. In such methods, the agent phenoprofile
is determined at multiple times during development of the animal.
Comparison of the agent phenoprofile and the reference phenoprofile
at the various time points is used to determine whether contact
with the agent delays onset of the phenotype.
[0230] It will be appreciated that "comparison" of phenoprofiles
does not imply that the compared phenoprofiles were necessarily
produced at the same time. For example, a reference phenoprofile
can be generated and stored (in electronic form) at one time and
agent phenoprofiles generated at different times can be compared to
the reference phenoprofile. Conveniently, traits (e.g., fly
movement) can be recalled from the recorded movies. Thus, traits
(e.g., movement) of each population can be measured multiple times
and, if desired, can be conducted many times over the course of the
life span (e.g., adult life span) of the flies.
[0231] For example, in one aspect the invention provides a method
for determining whether a test agent delays onset of a phenotype in
a transgenic fly by providing population of transgenic flies,
wherein the population develops a phenotype due to expression of a
transgene (e.g., an adult onset disorder, contacting the flies with
test agents, and determining an agent phenoprofile for the
population in at a plurality of times during the life of the fly.
The agent phenoprofile generated at each of the times is compared
to a reference phenoprofile generated at corresponding times in a
reference population (e.g., transgenic flies not contacted with the
test agent), and it is determined whether the test agent delays
onset of a phenotype in a population contacted with a test agent
compared to the reference population.
[0232] Phenoprint
[0233] In a related aspect, the invention provides a method for
identifying a defined set of traits (called a "phenoprint") that
distinguish one population from a second population. This aspect of
the invention can best be described by reference to a particular
example, i.e., a set of traits that distinguishes a Drosophila
population consisting of fly models of neurodegenerative diseases
(i.e., flies transgenic for genes or gene fragments associated with
Parkinson's disease, Huntington's disease and SCA1, for example)
and a Drosophila population consisting of healthy flies (i.e., a
wild-type, non-transgenic fly). It is believed that for two such
populations (as well as for other combinations of populations)
there will be some traits (movement, morphological or behavioral)
for which the populations will differ significantly and some traits
for which they will not differ. A useful phenoprint consists of
traits that do differ, e.g., significantly (i.e., p<0.05). By
way of illustration, a phenoprofile for a Drosophila polyglutamine
transgenic fly could be, for example, "x-only speed of 5, stumbling
of 1000, path length of 98, and turning of 3." A phenoprint for a
particular pair of populations can be determined by comparing
traits of each population and identifying or selecting traits that
differ most (or significantly) between the two populations.
2TABLE 2 Reference Test Population Population Phenoprofile
Reference Phenoprofile (huntington disease Population Trait
measured (wild-type fly) transgenic fly) Phenoprint x-only speed 6
5 stumbling 10 1000 10 path length 100 98 turning 66 3 66 X only
distance 1000 998 average Y-only 20 500 20 speed average speed 20
18 acceleration 50 60
[0234] Identification of phenoprints that characterize a particular
disease model will be useful, for example, for identifying
sensitive and appropriate parameters of motor performance for
automated screening for agents that can alter the
disease-associated behavior phenotype, in particular, for agents
that correct a behavior phenotype toward a wild-type animal
behavior phenotype or for agents that delay development of a
phenotype associated with a particular disease gene expression
event. For example, with reference to Table 2, an exemplary assay
could use huntington disease transgenic flies as test animals and
screen test agents for the ability to modify the stumbling,
turning, and average Y-only speed in a test population to a value
close to (or closer to) the reference population phenoprint.
[0235] A phenoprint determined at a particular time can be compared
to a phenoprint determined at a different time and the rate of
change in a phenoprint over time, if any, can be determined.
Accordingly, the rate of change of a phenoprint for a particular
pair of populations can be determined by comparing phenoprints over
time of each population.
[0236] It will be apparent to the careful reader that a
"phenoprint" is a type of "phenoprofile," and that any comparison,
ranking, etc., that can be carried out using phenoprofiles (such as
described herein) can be carried out using phenoprints.
[0237] Test Agents
[0238] As noted above, the agent phenoprofile corresponding to a
particular test agent can be used to determine the biological
activity of the agent. Alternatively, when the biological activity
of an agent is known or suspected, the agent can be used to
determine the agent phenoprofile. It will be appreciated that,
although the term "test agent" is used to describe the agents, the
activity of the agent can be known or unknown.
[0239] Agents to be screened can be naturally occurring or
synthetic molecules. Agents can be obtained from natural sources,
such as, e.g., marine microorganisms, algae, plants, fungi, etc.
Agents can include, e.g., pharmaceuticals, therapeutics,
environmental, agricultural, or industrial agents, pollutants,
cosmeceuticals, drugs, organic compounds, lipids, fatty acids,
steroids, glucocorticoids, antibiotics, peptides, proteins, sugars,
carbohydrates, chimeric molecules, purines, pyrimidines,
derivatives, structural analogs or combinations thereof.
[0240] Usually, collections of compounds (known as libraries) are
used. Libraries of natural compounds in the form of bacterial,
fungal, plant and animal extracts are available or readily
produced. Alternatively, agents to be assayed can be from
combinatorial libraries of agents, including peptides or small
molecules, or from existing repertories of chemical compounds
synthesized in industry, e.g., by the chemical, pharmaceutical,
environmental, agricultural, marine, drug, and biotechnological
industries. Preparation of combinatorial chemical libraries is well
known to those of skill in the art. Compounds that can be
synthesized for combinatorial libraries include polypeptides,
proteins, nucleic acids, beta-turn mimetics, polysaccharides,
phospholipids, hormones, prostaglandins, steroids, aromatic
compounds, heterocyclic compounds, benzodiazepines, oligomeric
N-substituted glycines and oligocarbamates. Devices for the
preparation of combinatorial libraries are commercially available
(see, e.g., 357 MPS, 390 MPS, Advanced Chem Tech, Louisville, Ky.,
Symphony, Rainin, Woburn, Mass., 433A Applied Biosystems, Foster
City, Calif., 9050 Plus, Millipore, Bedford, Mass.). Compounds to
be screened can also be obtained from governmental or private
sources, including, for example, the National Cancer Institute's
(NCI) Natural Product Repository, Bethesda, Md.; the NCI Open
Synthetic Compound Collection, Bethesda, Md.; NCI's Developmental
Therapeutics Program; ComGenex, Princeton, N.J.; Tripos, Inc., St.
Louis, Mo.; 3D Pharmaceuticals, Exton, Pa.; and Martek Biosciences,
Columbia, Md.
[0241] For example, two companies sell libraries of known bioactive
or FDA-approved drugs which may be used in methods of the
invention. MicroSource Discovery Systems, Inc. (Gaylordsville,
Conn.) provides a Gen-Plus.TM. collection of 960 known bioactive
compounds, which contains significant overlap with the National
Institute for Neurological Disorders and Stroke (NINDS) compound
collection selected for the NINDS screening study. This set permits
the simultaneous evaluation of hundreds of marketed drugs and
biochemical standards. Prestwick Chemical (Washington, D.C.) sells
a library containing a collection of 640 high-purity chemical
compounds the majority of which are off-patent marketed drugs.
[0242] Additionally, natural or synthetically produced libraries
and compounds are readily modified through conventional chemical,
physical and biochemical means, and may be used to produce
combinatorial libraries.
[0243] Screening may also be directed to known pharmacologically
active compounds and analogs thereof. Known pharmacological agents
may be subjected to directed or random chemical modifications, such
as acylation, coalkylation, esterification, amidification, etc. to
produce structural analogs. New potential test agents may also be
created using methods such as rational drug design or computer
modeling.
[0244] As described above, compounds that may be assayed according
to the methods of the invention encompass numerous chemical
classes. For example, organic molecules, preferably small organic
compounds having a molecular weight of more than 50 and less than
about 2,500 daltons, are a type of compound for use in the methods
of the invention.
[0245] One exemplary library for use in methods of the invention
includes compounds based on 2,5-diketopiperazine (DKP) scaffold.
Generally, compounds of this library are biased toward particular
amines, exhibit stability to proteolysis, have a molecular weight
range of about 250 to about 450 daltons and have solubilities
greater than about 5 mM. Another exemplary library for use in
methods of the invention includes trimer pseudopeptides (or
peptoids). Generally, such libraries are composed of a large number
of compounds (e.g., over 10,000 compounds) distributed in pools of
individual peptoids and the peptoids exhibit proteolytic stability.
Trimer pseudopeptide libraries have been used in the identification
and development of lead compounds, such as G-protein coupled
receptor antagonists (see, for example, Blaker et al. (2000) Mol.
Pharmacol. 58:399-406; Gao et al. (1999) Curr. Med. Chem.
6:375-388).
[0246] The compounds identified through screening in one or more
assays, as described herein, can serve as conventional "lead
compounds" or can themselves be used as potential or actual
therapeutics.
[0247] Contacting Insects with Agents
[0248] In the methods of the subject invention, each compound
composition is brought into contact with the population of animals
in a manner such that the active agent of the compound composition
is capable of exerting activity on at least a substantial portion
of, if not all of, the individual animals of the population. By
substantial portion is meant at least 75%, usually at least 80% and
in many embodiments can be as high as 90 or 95% or higher.
Generally, the members of the population are contacted with each
compound test agent in a manner such that the active agent of the
composition is internalized by the animals. In some cases,
internalization will be by ingestion, i.e. orally, such that that
each compound composition will generally be contacted with the
plurality of animals by incorporating the compound composition in a
nutrient medium, e.g. water, yeast paste, aqueous solution of
additional nutrient agents, etc., of the animals. For example, the
candidate agent is generally orally administered to the fly by
mixing the agent into the fly nutrient medium, such as a yeast
paste, and placing the medium in the presence of the fly (either
the larva or adult fly) such that the fly feeds on the medium. In
some cases, members of a population are contacted with a compound
by exposing the population to the compound in the atmosphere,
including vaporization or aerosol delivery of the compound, or
spraying a liquid containing the compound onto the animals. In some
cases, members of the population (e.g., larval animals) are
injected with the compound.
[0249] The compound composition may be contacted with the
population of animals at any convenient stages during the life
cycle of the animal. Thus, depending on the particular animals
employed, the compound composition is contacted with the animals
during an immature life cycle stage, e.g. prelarval stage or larval
stage, or alternatively during an adult stage, or at multiple
times. Animal contact with the composition may occur once or many
times and administration of the compound may in an acute or a
chronic mode.
[0250] In some instances, a plurality of assay mixtures are run in
parallel with different agent concentrations to obtain a
differential response to the various concentrations of test agent.
Typically, one of these concentrations serves as a negative
control, i.e., no test agent.
[0251] Pharmaceutical Compositions
[0252] The invention further provides for (i) the use of agents
identified by the above-described screening assays for treatment of
disease in mammal, e.g., humans, (ii) pharmaceutical compositions
comprising an agent identified by the above-described screening
assay and (iii) methods for treating a mammal, e.g., human, with a
disease by administering an agent identified by the above-described
screening assays. In one embodiment, the invention provides a
method of preparing a medicament for use in treatment of a disease
in mammals by (a) providing a population of flies with
characteristics of a mammalian disease (b) using a method described
herein to identify an agent expected to ameliorate the disease
phenotype (e.g., an agent with an agent phenoprofile that is
similar to a phenoprofile of a population of flies with a healthy
phenotype) and (c) formulating the agent for administration to a
mammal. In some cases, the phenotype of the population of flies in
step (a) may be characteristic of a mammalian neurodegenerative
disease. The population of flies in step (a) may be transgenic
flies and, in some cases, the expression of the transgene may
result in neurodegeneration or a phenotype of a neurodegenerative
disease. Genes and transgenes associated with mammalian
neurodegenerative diseases and flies containing such transgenes are
described herein.
[0253] In one aspect, a method of preparing a medicament for use in
treating a disease is provided, comprising formulating the agent
for administration to a mammal, e.g., primate. For example,
suitable formulations may be sterile and/or substantially isotonic
and/or in full compliance with all Good Manufacturing Practice
(GMP) regulations of the U.S. Food and Drug Administration and/or
in a unit dosage form. See, Remington's Pharmaceutical Sciences
(17th ed.) Mack Publishing Co., Easton, Pa.; Avis et al (eds.)
(1993).
EXAMPLES
Example 1
High Throughput Screening of Compounds Using a Fly
Neurodegeneration Model
[0254] A library of compounds is screened for activity in an animal
model system for neurodegeneration. The test animals are transgenic
Drosophila melanogaster which express a human polypeptide
associated with SCA1, ataxin-1, in all neurons. These animals,
designated SCA1.sup.82Q, are generated using the GAL4/UAS system to
express the transgene which encodes full-length ataxin-1 82Q, an
isoform of ataxin-1 with an expanded glutamine repeat
(Fernandez-Funez et al. (2000)). SCA1.sup.82Q flies demonstrate
impaired motor performance in which they appear to lose balance,
e.g., fall on their backs and have difficulty righting themselves.
This impaired motor function is adult in onset and progresses over
time.
[0255] In the screening assay, a population of animals, about 10-20
flies, are in optically transparent vials. Test compounds are
administered to test populations by adding the test compound to a
yeast paste and the yeast paste is added to the vial. The library
of test compounds consists of compounds based on
2,5-diketopiperazine (DKP), is biased toward particular amines and
has molecular weights generally ranging from 250-400 g/mol, as
described in Szardenings et al. (1998) J. Med. Chem. 41:2194-2200.
Test compounds are administered at three concentrations
(approximately 0.1, 1.0 and 10 micrograms per vial) for 12 days of
treatment. Two reference populations of animals in the assay are
SCA182Q flies receiving no test compound ("negative reference
phenoprofile") and wild-type flies ("positive reference
phenoprofile").
[0256] Using an automated motion tracking system described herein,
movement of the files in the test populations and the reference
populations are imaged and analyzed. In the assay, after the flies
are gently tapped to the bottom, the motor activity of the flies in
each population is captured in 20-50 consecutive frames using a
CCD-video camera. In analysis of each frame, algorithms identify
each fly as an oval, define its center and record the polar vector
of the oval. Trajectories of the flies in a population are then
analyzed on the basis of defined parameters, including variables
such as, average speed, vertical-only speed, vertical distance,
frequency of turning, trajectory count, average object size, and
the variance about the mean trajectory (which identifies
"stumbling" behavior). Results of these parameters are stored and
assays of the populations are performed multiple times over the
course of the adult life span of the flies.
[0257] Multivariate analysis is used to compare parameter results
from the test populations of animals and from the reference
populations and the analysis is used to define a phenoprofile
associated with an test compound, i.e., agent phenoprofile and to
define the reference phenoprofiles. A comparison of the agent
phenoprofile to the reference phenoprofile is used to identify test
compounds with activity in the test animals. Agents producing agent
phenoprofiles similar to the positive reference phenoprofile and/or
dissimilar to the negative reference profile are candidates for
treatment of spinocerebellar ataxia in mammals.
Example 2
Motion Tracking With Wild-Type Flies.
[0258] Several sets of wild-type flies were assayed under various
conditions to test the motion tracking software. Lithium Chloride
(LiCl), a treatment for bipolar affective disorder in humans, is
also known to induce behavioral changes in Drosophila (Xia et al.,
1997). In this assay, flies fed 0.1M or 0.05M LiCl exhibited a
significant reduction in speed and an increased incidence of
turning and stumbling compared to controls. The results of this
assay are shown in the bar graph of FIG. 19.
Example 3
Motion Tracking With Drosophila Model of Huntington Disease.
[0259] Drosophila expressing a mutant form of human Huntington (HD)
have a functional deficit that is quantifiable, reproducible, and
is suitable for automated high-throughput screening. Drosophila (or
specimen) movements can be analyzed for various characteristics
and/or traits. For example, statistics on the movements of the
specimens, such as the x and y travel distance, path length, speed,
turning, and stumbling, can be calculated. These statistics can be
averaged for a population and plotted.
[0260] Differences between the HD model +/- drug (HDAC inhibitor,
TSA) and wild type (control) +/- drug (TSA) can clearly be detected
using the motion tracking software. Progressive motor dysfunction
and therapeutic treatment with drug can be measured by various
scoring parameters. Such results are shown in FIG. 20. In FIG. 20,
motor performance, assessed by the Cross150 score, is plotted on
the y-axis against time x-axis). The Cross150 score, or x travel
distance, is equal to the number of trajectories (specimens) that
cross a position at x=150 in the negative x-direction (from bottom
to top of the vial) during the movie. In other words, this score
measures the number of detected flies that climb above the x=150
line within the length of the movie. This graph demonstrates the
potential therapeutic effect of drug (TSA) on the HD model. Error
bars are +/- SEM). Control genotype is yw/elavGAL4. HD genotype is
HD/elavGAL4.
[0261] Movement characteristics of different models, or the effects
of certain drugs on those models, will be distinct. FIGS. 21A-21J
demonstrate (1) how well various scores define the differences
between disease model and wild-type control, (2) how well the
various scores detect improvements +/-drug treatment, and (3) how
many replica vials and repeat videos are needed for statistically
significant results. In FIGS. 21A-21J, the average p-values for
each combination of a certain number of video repeats and replica
vials for Test and Reference populations are shown. Lower -values
are indicated by darker coloring. The lower the p-value, the more
likely the score represents a significant difference between Test
and Reference populations. In FIGS. 21A, 21C, 21E, 21G, and 211,
the Reference population is wild-type control and the Test
population is the HD model. In FIGS. 211B, 21D, 21F, 21H, and 21J,
the Reference population is HD model without drug and the Test
population is the HD model with drug (TSA). Speed is shown in FIGS.
21A and 21B, turning is shown in FIGS. 21C and 21D, stumbling is
shown in FIGS. 21E and 21F, T-length is shown in FIGS. 21G and 21H,
and Cross 150 is shown in FIGS. 211 and 21J.
[0262] In FIGS. 21A, 21G and 211, Speed, T-Length, and Cross150
scores are very useful for identifying HD flies from wild-type
control flies--the p-value goes down when either number of replica
vials or number of repeat videos are increased, which is to be
expected. Turning and Stumbling scores do not appear do give
significant values not even for large number of replica vials or
videos repeats. In FIGS. 21B, 21D and 21F, the scores for Speed,
Turning, and Stumbling do not yield significant values. The scores
that best highlight the therapeutic effect of the drug in the HD
model are T-Length (FIGS. 21G and 21H) and Cross150 (FIGS. 21I and
21J). Note the striking differences between the Speed plots (FIGS.
21A and 21B). Speed is a useful score for telling apart HD flies
from wild type flies, however it does not appear to be effective
for telling apart HD untreated flies from HD with drug flies.
Although the drug seems to restore climbing ability for HD flies to
almost the same level as for wt flies, the same is not true for
speed.
Example 4
Motion Tracking With Drosophila Model of Spinocerebellar Ataxia
Type 1.
[0263] FIG. 22 shows the loss of motor performance in the SCA1
Drosophila model. SCA1 model and control trials were analyzed and
plotted by Phenoscreen software. Motor performance on the y-axis
(Cross150) is plotted against time on the x-axis (Trials). SCA1
model is indistinguishable from controls on first day of adult life
then they decline progressively in climbing ability. The error bars
are +/- SEM. Control fly genotype is yw/nirvanaGAL4. SCA1 fly
genotype is SCA1/nirvanaGAL4.
* * * * *