Sample Grant Application
Introduction
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Copyright
Please note that the application text is copyrighted. It may be used only for nonprofit
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organization, and NIAID are credited.
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Please note that the application text is copyrighted. It may be used only for nonprofit educational purposes
provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
See more online: http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx
PI: Wahlby, Carolina Ewa Asa

Title: Image analysis for high-throughput C. elegans infection and metabolism
assays

Received: 02/03/2010

FOA: PA10-067

Competition ID: ADOBE-FORMS-B

FOA Title: Research Project Grant (Parent R01)

1 R01 GM095672-01

Dual:

IPF: 10021177

Organization: BROAD INSTITUTE, INC.

Former Number:

Department:

IRG/SRG: MI

AIDS: N

Expedited: N

Subtotal Direct Costs(excludes consortium F&A)
Year 1:
250,000
Year 2:250,000
Year 3:250,000
Year 4:250,000
Year 5:250,000

Animals: N
Humans: N
Clinical Trial: N
Current HS Code: 10
HESC: NNew Investigator: Y
Early Stage Investigator: Y

Senior/Key Personnel:

Organization:

Role Category:

Carolina Wahlby Ph.D.

Broad Institute, Inc.

PD/PI

Anne Carpenter Ph.D

Broad Institute, Inc.

Other Professional-Platform Director

Frederick Ausubel Ph.D

Massachusetts General Hospital

Other (Specify)-Other Significant
Contributor

Polina Golland

Massachusetts Institute of Technology

Other (Specify)-Other Significant
Contributor

Gary Ruvkun Ph.D

Massachusetts General Hospital

Other (Specify)-Other Significant
Contributor

Tamar Riklin-Raviv Ph.D

Massachusetts Institute of Technology

Other (Specify)-Other Significant
Contributor

Appendices2010 wahlby isbi

Council: 10/2010

Accession Number: 3264072

APPLICATION FOR FEDERAL ASSISTANCE

3. DATE RECEIVED BY STATESF 424 (R&R)
1. * TYPE OF SUBMISSION
Pre-applicationOMB Number: 4040-0001
Expiration Date: 06/30/2011
State Application Identifier

4. a. Federal Identifier

Application

Changed/Corrected Applicationb. Agency Routing Identifier

Applicant Identifier

2. DATE SUBMITTED* Organizational DUNS: 

5. APPLICANT INFORMATION* Legal Name: %URDG,QVWLWXWH,QF
Department:

Division:* Street1: &DPEULGJH&HQWHUStreet2:&DPEULGJH* City:County / Parish:* State:0$0DVVDFKXVHWWVProvince:* Country:

86$81,7('67$7(6* ZIP / Postal Code: 

Person to be contacted on matters involving this application* First Name: 6WDFH\

Prefix:

Middle Name:* Last Name: 'RQQHOO\

Suffix:Fax Number: 

* Phone Number: 
Email: DZDUGV#EURDGLQVWLWXWHRUJ
6. * EMPLOYER IDENTIFICATION (EIN) or (TIN):
7. * TYPE OF APPLICANT:[[[[[[[

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Small Business Organization Type

Women Owned

If Revision, mark appropriate box(es).

8. * TYPE OF APPLICATION:
New

A. Increase Award

Resubmission

RenewalSocially and Economically Disadvantaged

Continuation

C. Increase Duration

D. Decrease Duration

E. Other (specify):

Revision* Is this application being submitted to other agencies?

B. Decrease Award

Yes

No

9. * NAME OF FEDERAL AGENCY:

What other Agencies?

10. CATALOG OF FEDERAL DOMESTIC ASSISTANCE NUMBER:
TITLE:1DWLRQDO,QVWLWXWHVRI+HDOWK
11. * DESCRIPTIVE TITLE OF APPLICANT'S PROJECT:,PDJHDQDO\VLVIRUKLJKWKURXJKSXW&HOHJDQVLQIHFWLRQDQGPHWDEROLVPDVVD\V
12. PROPOSED PROJECT:* Start Date
* Ending Date



* 13. CONGRESSIONAL DISTRICT OF APPLICANT
0$

14. PROJECT DIRECTOR/PRINCIPAL INVESTIGATOR CONTACT INFORMATION
Prefix:* First Name: &DUROLQD

Middle Name:* Last Name: :DKOE\
Position/Title:

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15. ESTIMATED PROJECT FUNDING

Page 2

16. * IS APPLICATION SUBJECT TO REVIEW BY STATE EXECUTIVE
ORDER 12372 PROCESS?

a. Total Federal Funds Requested

b. Total Non-Federal Funds

c. Total Federal & Non-Federal Funds



THIS PREAPPLICATION/APPLICATION WAS MADEAVAILABLE TO THE STATE EXECUTIVE ORDER 12372
PROCESS FOR REVIEW ON:

a. YES

DATE:
b. NO

PROGRAM IS NOT COVERED BY E.O. 12372; OR

d. Estimated Program Income

PROGRAM HAS NOT BEEN SELECTED BY STATE FORREVIEW
17. By signing this application, I certify (1) to the statements contained in the list of certifications* and (2) that the statements herein are
true, complete and accurate to the best of my knowledge. I also provide the required assurances * and agree to comply with any resulting
terms if I accept an award. I am aware that any false, fictitious. or fraudulent statements or claims may subject me to criminal, civil, or
administrative penalities. (U.S. Code, Title 18, Section 1001)* I agree* The list of certifications and assurances, or an Internet site where you may obtain this list, is contained in the announcement or agency specific instructions.

18. SFLLL or other Explanatory Documentation
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19. Authorized Representative* First Name: 6WDFH\

Prefix:

Middle Name:
Suffix:* Last Name: 'RQQHOO\* Position/Title: 'LUHFWRU6SRQVRUHG5HVHDUFKDQG3ODQQLQJ* Organization: %URDG,QVWLWXWH,QF
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6FRWW%UHLGLQJ
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View Attachment    !"#"Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

424 R&R and PHS-398 Specific
Table Of Contents

Page Numbers

SF 424 R&R Face Page------------------------------------------------------------------------------------------

1

Table of Contents---------------------------------------------------------------------------------------------

3

Performance Sites---------------------------------------------------------------------------------------------

4

Research & Related Other Project Information------------------------------------------------------------------

5

Project Summary/Abstract (Description)----------------------------------------

6

Public Health Relevance Statement (Narrative attachment)----------------------------------------

7

Facilities & Other Resources----------------------------------------

8

Equipment----------------------------------------

12

Research & Related Senior/Key Person--------------------------------------------------------------------------

13

Biographical Sketches for each listed Senior/Key Person----------------------------------------

16

PHS 398 Specific Cover Page Supplement------------------------------------------------------------------------

36

PHS 398 Specific Modular Budget-------------------------------------------------------------------------------

38

Personnel Justification----------------------------------------

41

Additional Narrative Justification----------------------------------------

42

PHS 398 Specific Research Plan--------------------------------------------------------------------------------

43

Specific Aims----------------------------------------

44

Research Strategy----------------------------------------

45

Bibliography & References Cited----------------------------------------

57

Letters of Support----------------------------------------

67

Resource Sharing Plan----------------------------------------

82

PHS 398 Checklist---------------------------------------------------------------------------------------------

83

Appendix
Number of Attachments in Appendix:

1

Table of Contents

Page 3

Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina
OMB Number: 4040-0010
Expiration Date: 08/31/2011

Project/Performance Site Location(s)Project/Performance Site Primary Location
Organization Name: Broad
DUNS Number:* Street1:

I am submitting an application as an individual, and not on behalf of a company, state,
local or tribal government, academia, or other type of organization.

Institute, Inc.

6235447850000

7 Cambridge Center

Street2:
County:* City:Cambridge* State:MA: Massachusetts

Province:* Country: USA:

UNITED STATES* Project/ Performance Site Congressional District: MA* ZIP / Postal Code: 02142-1401

Project/Performance Site Location 1008

I am submitting an application as an individual, and not on behalf of a company, state,
local or tribal government, academia, or other type of organization.

Organization Name:DUNS Number:* Street1:
Street2:* City:County:* State:Province:* Country: USA:

UNITED STATES* ZIP / Postal Code:* Project/ Performance Site Congressional District:

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Performance Sites

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RESEARCH & RELATED Other Project Information
1. * Are Human Subjects Involved?
1.a

No

Yes

If YES to Human Subjects
Is the Project Exempt from Federal regulations?

Yes

If yes, check appropriate exemption number.

1

If no, is the IRB review Pending?

Yes

No
2

3

4

5

6

No

IRB Approval Date:
Human Subject Assurance Number:
2. * Are Vertebrate Animals Used?
2.a.

Yes

No

Yes

No

If YES to Vertebrate Animals
Is the IACUC review Pending?
IACUC Approval Date:
Animal Welfare Assurance Number

3. * Is proprietary/privileged information included in the application?

Yes

4.a. * Does this project have an actual or potential impact on the environment?

No
Yes

No

4.b. If yes, please explain:
4.c. If this project has an actual or potential impact on the environment, has an exemption been authorized or an environmental assessment (EA) or
environmental impact statement (EIS) been performed?
YesNo
4.d. If yes, please explain:
5. * Is the research performance site designated, or eligible to be designated, as a historic place?

Yes

No

6. * Does this project involve activities outside of the United States or partnerships with international collaborators?

Yes

5.a. If yes, please explain:No

6.a. If yes, identify countries:
6.b. Optional Explanation:
7. * Project Summary/Abstract 1240-Abstract.pdf

Add Attachment
Add Attachment

8. * Project Narrative 1241-Narrative.pdf

9. Bibliography & References Cited 1242-BibliographyResubmitted.pdf
10. Facilities & Other Resources 1243-FacilitiesAndResources.pdf
11. Equipment 1244-Equipment.pdf
12. Other Attachments

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Abstract

Please note that the application text is copyrighted. It may be used only for nonprofit educational purposes
provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
See more online: http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx

High-throughput screening (HTS) is a technique for searching large libraries of chemical or genetic perturbants,
to ﬁnd new treatments for a disease or to better understand disease pathways. As automated image analysis
for cultured cells has improved, microscopy has emerged as one of the most powerful and informative ways toanalyze screening samples. However, many diseases and biological pathways can be better studied in wholeanimals—particularly diseases that involve organ systems and multicellular interactions, such as metabolism
and infection. The worm Caenorhabditis elegans is a well-established and effective model organism, used bythousands of researchers worldwide to study complex biological processes. Samples of C. elegans can be
robotically prepared and imaged by high-throughput microscopy, but existing image-analysis methods are insufﬁcient for most assays. In this project, image-analysis algorithms that are capable of scoring high-throughput
assays of C. elegans will be developed.
The algorithms will be tested and reﬁned in three high-throughput screens, which will uncover chemical andgenetic regulators of fat metabolism and infection: (1) A C. elegans viability assay to identify modulators of
infection. The proposed algorithms use a probabilistic shape model of C. elegans in order to identify and measure individual worms even when the animals touch or cross. These methods are the basis for quantifying manyother phenotypes, including body morphology and subtle variations in reporter signal levels. (2) A C. elegans
lipid assay to identify genes that regulate fat metabolism. The algorithms proposed for illumination correction,level-set–based foreground segmentation, well-edge detection, and artifact removal will result in improved robustness in high-throughput experiments. (3) A ﬂuorescence gene expression assay to identify regulators of the
response of the C. elegans host to Staphylococcus aureus infection. The proposed techniques for constructing
anatomical maps of C. elegans will make it possible to quantify a variety of changes in ﬂuorescent localization
patterns in a biologically relevant way.
In addition to discovering new metabolism- and infection-related drugs and genetic regulators through these
speciﬁc screens, this work will provide the C. elegans community with (a) a new framework for extracting morphological features from C. elegans for quantitative analysis of this organism, and (b) a versatile, modular,
open-source toolbox of algorithms enabling the discovery of genetic pathways, chemical probes, and drug candidates in whole organism high-throughput screens relevant to a variety of diseases.
This work is a close collaboration with C. elegans experts Fred Ausubel and Gary Ruvkun at Massachusetts
General Hospital/Harvard Medical School, with Polina Golland and Tammy Riklin-Raviv, experts in model-based
segmentation and statistical image analysis at MIT’s Computer Science and Artiﬁcial Intelligence Laboratory,
and with Anne Carpenter, developer of open-source image analysis software at the Broad Institute.

Project Description

Page 6Please notethat the applicationtext
is copyrighted.
may beCarolina
used only for nonprofit educational purposes
Principal
Investigator/Program
Director(Last,
first, middle):ItWahlby,
provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
See more online: http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx

OMB Number: 4040-0001
Expiration Date: 06/30/2011

RESEARCH & RELATED Senior/Key Person Profile (Expanded)
PROFILE - Project Director/Principal Investigator* First Name: &DUROLQD

Prefix:

Middle Name:* Last Name: :DKOE\

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Page 13    !"#"Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

RESEARCH & RELATED Senior/Key Person Profile (Expanded)
PROFILE - Senior/Key Person 2
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RESEARCH & RELATED Senior/Key Person Profile (Expanded)
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Page 15    !"#"Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Please note that the application text is copyrighted. It may be used only for nonprofit educational purposes
provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
See more online: http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx

BIOGRAPHICAL SKETCH
Provide the following information for the Senior/key personnel and other significant contributors.
Follow this format for each person. DO NOT EXCEED FOUR PAGES.
NAMEPOSITION TITLE

Wählby, Carolina

Computational Biologist
Broad Institute of Harvard and MIT

eRA COMMONS USER NAME (credential, e.g., agency login)EDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training andresidency training if applicable.)
DEGREEINSTITUTION AND LOCATION
MM/YY
FIELD OF STUDY(if applicable)

Uppsala University, Uppsala, Sweden(Thesis work at the Karolinska Institute,
Stockholm, Sweden)
Centre for Image Analysis, Uppsala University,
Uppsala, Sweden
Dept. Genetics and Pathology, Uppsala
University, Uppsala, Sweden

M.Sc.

06/98

Molecular Biotechnology

Ph.D.

10/03

Digital Image Analysis

postdoctoral

06/08

Applied image analysis
in molecular medicine

A. Personal Statement
The goal of the proposed research is to develop image analysis algorithms for high-throughput C. elegans
assays to identify regulators of infection and metabolism. My research is focused on the development ofalgorithms for image analysis applied to biological images acquired by microscopy; the questions addressed
have ranged from various types of quantification and mapping of fluorescent reporters in cells and tissue tosingle molecule classification on microarrays. My strongest expertise lies in image segmentation, which is a
crucial initial step in every kind of image analysis task, before quantitative information can be robustly
extracted from the objects of interest. The algorithms I developed for cell segmentation have served as afoundation for the open-source software CellProfiler and have proven very robust in high-throughput
applications. At the Department of Genetics and Pathology, my postdoctoral research was very applicationoriented, giving me broad experience in microscopy and issues related to sample preparation and handling in
relation to image quality and analysis.
As project manager for the image analysis part of the EU-financed ENLIGHT project I successfully managed
researchers from academia as well as industry in 5 different European countries, producing a number of peerreviewed collaborative publications. This highly interdisciplinary project taught me the importance ofcommunication between collaborators from different fields of expertise, which will be of importance for the
proposed project. My experience from supervising PhD students as assistant and associate professor at
Uppsala University has also helped me develop skills for project planning, execution and dissemination. My
most recent work at the Imaging Platform of the Broad Institute has given me insight about many aspects
related to high-throughput experiments, and the expertise available at the platform provides a strong support
for the proposed project.
Initial work on C. elegans images in collaboration with my co-investigators Polina Golland and Tammy RiklinRaviv at M.I.T. Computer Science and Artificial Intelligence Lab has already led to a peer-reviewed paper(Wählby et al., 2010) describing methods applied to data provided by our main collaborators Gary Ruvkun
and Fred Ausubel at Massachusetts General Hospital/ Harvard Medical School: a well establishedcollaboration initiated by my co-investigator Anne Carpenter, leading to the publication of the first automatedimage-based screen on C.elegans (Moy et al., ACS Chemical Biology, 2009).
To conclude, my strong computational experience in combination with experience from collaborativeinterdisciplinary projects together with well established contacts provides a basis for successful project
leadership.

Biosketches

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

B. Positions and Honors
Positions:
1998-2003

PhD Student, Uppsala University, Sweden, Centre for Image Analysis, supervised by professor
Ewert Bengtsson
2004-2009
Assistant Professor, the Centre for Image Analysis, Uppsala University, Sweden (part-timefrom 2005)
2005-2008
Postdoctoral Fellow / researcher (part time) at the Dept. Genetics and Pathology, Molecular
Medicine, Laboratory of Ulf Landegren, MD, PhD, and Mats Nilsson, PhD.
2009-present Associate Professor, Centre for Image Analysis, Uppsala University, Sweden.
2009-present Computational Biologist, Broad Institute of Harvard and MIT
Other Experience and Professional Memberships
International Association for Pattern Recognition (IAPR), since 1998, Swedish Society for Automated Image
Analysis (SSBA), since 1998, International Society for Analytical Cytology (ISAC), since 2001; on the EU
Project ENLIGHT Program Management Board, since 2006.
C. Selected peer-reviewed publications (chosen from 24)
Most relevant to the current application
1. Wählby C, Riklin-Raviv T, Ljosa V, Conery AL, Golland P, Ausubel FM, Carpenter AE (2010)
Resolving clustered worms via probablistic shape models. Accepted for publication in proceedings ofthe IEEE International Symposium on Biomedical Imaging (ISBI) 2010.
2. Allalou A, Pinidiyaarachchi A, Wählby C. Robust signal detection in 3D fluorescence microscopy.
Cytometry A. 2009 Apr;94(1):58-65. PMID: 19760746
3. Wählby C, Sintorn IM, Erlandsson F, Borgefors G, Bengtsson E. Combining intensity, edge and shapeinformation for 2D and 3D segmentation of cell nuclei in tissue sections. J Microsc. 2004
Jul;215(Pt1):67-76. PMID: 15230877
4. Wählby C, Lindblad J, Vondrus M, Bengtsson E, Björkesten L. Algorithms for cytoplasm segmentationof fluorescence labelled cells. Anal Cell Pathol. 2002;24(2-3):101-11. PMID: 12446959
5. Gavrilovic M, Wählby C. Quantification of colocalization and cross-talk based on spectral angles. J
Microsc. 2009 Jun;234(3):311-324. PMID: 19493110
Additional recent publications of importance to the field
6. Erlandsson F, Linnman (-Wählby) C, Ekholm S, Bengtsson E, Zetterberg A. A detailed analysis of
cyclin A accumulation at the G(1)/S border in normal and transformed cells. Exp Cell Res. 2000 A
25;259(1):86-95. PMID: 10942581
7. Wählby C, Erlandsson F, Bengtsson E, Zetterberg A. Sequential immunofluorescence staining andimage analysis for detection of large numbers of antigens in individual cell nuclei. Cytometry. 2002 Jan
1;47(1):32-41. PMID: 11774347
8. Erlandsson F, Wählby C, Ekholm-Reed S, Hellström AC, Bengtsson E, Zetterberg A. Abnormal
expression pattern of cyclin E in tumour cells. Int J Cancer. 2003 Apr 10;104(3):369-75. PMID:
12569561
9. Lindblad J, Wählby C, Bengtsson E, Zaltsman A. Image analysis for automatic segmentation ofcytoplasms and classification of Rac1 activation. Cytometry A. 2004 Jan;57(1):22-33. PMID: 14699602
10. Jarvius M, Paulsson J, Weibrecht I, Leuchowius KJ, Andersson AC, Wählby C, Gullberg M, Botling J,
Sjöblom T, Markova B, Ostman A, Landegren U, Söderberg O. In situ detection of phosphorylated
platelet-derived growth factor receptor beta using a generalized proximity ligation method. Mol Cell
Proteomics. 2007 Sep;6(9):1500-9. Epub 2007 Jun 12. PMID: 17565975
11. Jahangir Tafrechi RS, van de Rijke FM, Allallou A, Larsson C, Sloos WC, van de Sande M, Wählby C,
Janssen GM, Raap AK. Single-cell A3243G mitochondrial DNA mutation load assays for segregation
analysis. J Histochem Cytochem. 2007 Nov;55(11):1159-66. Epub 2007 Aug 6. PMID: 17679731
12. Allalou A, Wählby C. BlobFinder, a tool for fluorescence microscopy image cytometry. Comput
Methods Programs Biomed. 2008 Oct 16. [Epub ahead of print] PMID: 18950895

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13. Göransson J, Wählby C, Isaksson M, Howell WM, Jarvius J, Nilsson M. A single molecule array for
digital targeted molecular analyses. Nucleic Acids Res. 2009 Jan;37(1):e7. Epub 2008 Nov 25. PMID:
19033366
14. Pinidiyaarachchi A, Zieba A, Allalou A, Pardali K, Wählby C. A detailed analysis of 3D subcellularsignal localization. Cytometry A. 2009 Apr;75(4):319-28. PMID: 19006073
15. Zieba A, Wählby C, Hjelm F, Jordan L, Berg J, Landegren U, Pardali K. Bright-field microscopy
visualization of proteins and protein complexes by in situ proximity ligation with peroxidase detection.
Clin Chem. 2010 Jan;56(1):99-110. Epub 2009 Nov 19.PMID: 19926775

D. Research Support
Ongoing Research Support
Swedish Research Council Collaboration Grant, Medicine
01/01/08-12/31/10
A multidisciplinary approach to establish mechanisms for mitochondrial DNA segregation in human disease
In this project we apply a powerful fluorescent in situ hybridization (FISH) technology to follow
transmission of heteroplasmic mtDNA mutations in real tissues in situ and develop image analysis software to
do three-dimensional (3D) reconstructions of the distribution of mutated mtDNA molecules in mammaliantissues.
Role: co-Principal Investigator, with Mats Nilsson and Nils-Göran Larsson
Completed research support
EU-grant for SMEs in Health Research
08/01/06-07/31/09
Life sciences, genomics and biotechnology for health, ENLIGHT.
The aim of the project was to develop molecular methods and image analysis tools for analysis of cancer
biomarkers in situ.
Role: Project manager, WP2: Image analysis

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Honors1994 National Merit Scholar, Presidential Scholar semifinalist
1995 Pew Research scholarship (declined)
1996 Howard Hughes Medical Institute undergraduate research fellowship1997 Fellowships from Phi Kappa Phi, the University of Illinois, UI’s NIH training grant1998 Howard Hughes Medical Institute predoctoral fellowship (5 years)
1998 National Science Foundation fellowship (declined)
2003 BioVision World Leader delegate
2003 Merck/MIT Computational & Systems Biology Initiative fellowship2004 Life Sciences Research Foundation fellowship (3 years)
2006 L’Oreal USA Women in Science fellowship2007 Genome Technology “Rising young investigator”
2008 Featured in public television show “Bold Visions: Women in Science and Technology”
2008 Elected to be a fellow of the Massachusetts Academy of Sciences
2009 Awarded Bio-IT World Best Practices Award for CellProfiler software project

C. Selected Peer-reviewed publications (chosen from 36 since first paper in 2001)
Researchers contributing to the present proposal are underlined below
Most relevant to the current application
1. Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, Guertin DA, Chang JH, Lindquist
RA, Moffat J, Golland P, Sabatini DM (2006) CellProfiler: image analysis software for identifying andquantifying cell phenotypes. Genome Biology, 7:R100. PMID: 17076895 PMCID: PMC1794559
2. Vokes, MS, Carpenter AE (2008) Using CellProfiler for automatic identification and measurement of
biological objects in images. In: Ausubel FM et al., eds. Current Protocols in Molecular Biology 82:14.17.114.17.12. PMID: 18425761
3. Jones TR*, Carpenter AE* (the first two authors contributed equally), Lamprecht MR, Moffat J, Silver S,
Grenier J, Castoreno AB, Eggert US, Root DE, Golland P, Sabatini DM (2009) Scoring diverse cellularmorphologies in image-based screens with iterative feedback and machine learning. PNAS 106(6):18261831/doi:10.1073/pnas.0808843106. PMID: 19188593 PMCID: PMC2634799
4. Moy TI, Conery AL, Larkins-Ford J, Wu G, Mazitschek R, Casadei G, Lewis K, Carpenter AE, Ausubel FM(2009) High throughput screen for novel antimicrobials using a whole animal infection model. ACS Chemical
Biology 4/doi:10.1021/cb900084v. PMID: 19572548 PMCID: In process
5. Wählby C, Riklin-Raviv T, Ljosa V, Conery AL, Golland P, Ausubel FM, Carpenter AE (2010) Resolving
clustered worms via probabilistic shape models. Accepted for publication in proceedings of the IEEE
International Symposium on Biomedical Imaging (ISBI) 2010
Additional recent publications of importance to the field (in chronological order)
6. Nye, A Carpenter (former name), Rajendran RR, Stenoien DL, Mancini MA, Katzenellenbogen BS,
Belmont AS (2002) Alteration of large-scale chromatin structure by estrogen receptor. Molecular andCellular Biology, 22(10):3437-49. PMID: 11971975 PMCID: PMC133805
7. Carpenter AE, Sabatini DM (2004) Systematic genome-wide screens of gene function. Nature Reviews
Genetics, 5(1):11-22. PMID: 14708012
8. Carpenter AE, Memedula S, Plutz MJ, Belmont AS. Common effects of acidic activators on large-scalechromatin structure and transcription (2005) Molecular and Cellular Biology, 25(3):958-968. PMID:
15657424 PMCID: PMC544008
9. Jones TR, Carpenter AE, Golland P (2005) Voronoi-based segmentation of cells on image manifolds.
Proceedings of the Workshop on Computer Vision for Biomedical Image Applications (CVBIA). Yanxi Liu,
Tianzi Jiang, Changshui Zhang (Eds.). Beijing, China, October 21. Lecture Notes in Computer Science3765. Published by Springer-Verlag, Berlin, p. 535-543, ISBN 3-540-29411-2
10. Moffat J, Grueneberg DA, Yang X, Kim SY, Kloepfer AM, Hinkle G, Piqani B, Eisenhaure TM, Luo B,
Grenier JK, Carpenter AE, Foo SY, Stewart SA, Stockwell BR, Hacohen N, Hahn WC, Lander ES,
Sabatini DM, Root DE (2006) A lentiviral RNAi library for human and mouse genes applied to an arrayedviral high-content screen. Cell, 124(6):1283-98. PMID: 16564017
11. Jones TR, Carpenter AE, Sabatini DM, Golland P (2006) Methods for high-content, high-throughput
image-based cell screening Proceedings of the Workshop on Microscopic Image Analysis with
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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Applications in Biology (MIAAB). Metaxas DN, Whitaker RT, Rittcher J, Sebastian T (Eds). Copenhagen,Denmark, October 5, pp 65-72.
12. Lamprecht MR, Sabatini DM, Carpenter AE (2007) CellProfiler: free, versatile software for automatedbiological image analysis. Biotechniques. 42(1):71-75.
13. Carpenter AE (2007) Extracting rich information from images. In: Clemons PA et al., eds. Cell-Based
Assays for High-Throughput Screening, Methods in Molecular Biology 486:14. New York, NY: Humana
Press; 193-211. PMID: 19347625
14. Carpenter AE (2007) Image-based chemical screening. Nature Chemical Biology 3:461-465. PMID:
17637778
15. Jones, TR, Kang IH, Wheeler DB, Lindquist RA, Papallo A, Sabatini DM, Golland P, Carpenter AE (2008)
CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC
Bioinformatics 9(1):482/doi: 10.1186/1471-2105-9-482. PMID: 19014601 PMCID: PMC261443
D. Research Support
Ongoing Research Support
AstraZeneca Pharmaceuticals Collaboration (PI: Anne Carpenter)
09/08/08 - 10/31/10
Image Data Mining for Determining Small Molecule Mechanisms of Action
In this project, we are developing methods to analyze images from four image-based screens, provided by
AstraZeneca. The hundreds of morphological features extracted from these images are compared to data
AstraZeneca has already extracted, using commercial software, in terms of their power to discriminate
mechanisms of action among a small library of chemical compounds.
Role: PI
NIH R01-AI085581 (PI: Fred Ausubel)
09/28/09 - 08/31/14
Identifying novel anti-infectives by high throughput screening in whole animals
The goal of this project is to perform a screen of 250,000 chemical compounds to identify new classes of antiinfectives against Pseudomonas aeruginosa. Promising compounds will undergo characterization, efficacy
testing in other gram-negative bacteria (Klebsiella, Acinetobacter, Enterobacter), testing in mouse models of
infection, and in some cases molecular target identification.
Role: PI of Subaward
NIH RC2-OD-09-004 (PI: Kevin Eliceiri)
09/30/09 - 09/29/11
ImageJ as an extensible image processing framework
The major goals of this grant are to improve ImageJ’s core architecture, expand its functionality by interfacing
ImageJ with existing open-source programs, and grow community-driven development while maintaining
compatibility.
Role: PI of Subaward
NIH U54-HG005032-01 (PI: Stuart Schreiber)
09/01/08 – 05/31/14
Broad Institute Comprehensive Screening Center MLPCN
The BCSC will provide a wide range of assay development, assay adaptation/implementation, high-throughput
screening via automation, follow-up and medicinal chemistry, informatics, and project management throughfive core groups. The investigators with primary responsibilities for these functions have come together in an
open data-sharing environment to form a single, integrated pipeline where the concepts of one activity or
discipline affect the thinking of the other.
Role: Key personnel
NIH 5 RL1-CA133834-03 (PI: Todd Golub)
10/01/07 - 09/30/12
Genomics Based Drug Discovery
This project proposes to create a new approach to drug discovery involving four components: (1) Leadership;(2) a discovery pipeline; (3) target ID; and (4) testing on 6 driving medical projects.
Role: Key personnel

Biographical Sketches for each listed Senior/Key Person 2

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Completed Research Support
Broad Institute SPARC Proposal
08/01/06-02/28/09
Dramatically Enhancing the Throughput of C.elegans RNAi and Drug Screens
The overall goal of this project is to increase the throughput of C. elegans RNAi and chemical screens by 10 to
100 fold.
Role: participant (PIs: Ausubel and Ruvkun)
Eli Lilly Collaboration
Enabling phenotypic screens with multiple complex cellular morphologies
Role: PI

04/01/09-12/31/09

Culpeper Foundation Biomedical Pilot Grant
06/01/06-05/31/08
Testing for Drug Targets in Realistic Cell Environments. In this project, we explored a system to test multiple
genes for involvement in disease by implanting RNAi microarrays in whole animals, including image analysis toquantify the resulting cellular phenotypes.
Role: PI
Merck collaboration
10/01/06-12/30/07
RNAi /Compound Screening in Drosophila for Target/Pathway ID and to Identify Proteins Involved in DNA
Damage Repair. In this project, we screened Drosophila genes by RNAi cell microarrays and using advanced
image analysis methods to identify appropriate drug targets.
Role: co-PI, with David M. Sabatini

Biographical Sketches for each listed Senior/Key Person 2

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Please note that the application text is copyrighted. It may be used only for nonprofit educational purposes
provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
See more online: http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx

BIOGRAPHICAL SKETCH
Provide the following information for the key personnel and other significant contributors.
Follow this format for each person. DO NOT EXCEED FOUR PAGES.
NAMEPOSITION TITLE

Frederick M. Ausubel

Molecular Biologist, Massachusetts General
Hospital
Professor of Genetics, Harvard Medical School

eRA COMMONS USER NAMEXXXXXXXEDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, and include postdoctoral training.)
INSTITUTION AND LOCATION

University of Illinois, Urbana, IL
Massachusetts Institute of Technology,
Cambridge, MA

DEGREE(if applicable)

YEAR(s)

B.S.

1966

Chemistry

Ph.D.

1972

Biology

FIELD OF STUDY

A. Personal Statement
My research group will be responsible for completing the laboratory work for the C. elegans infection screens,which will provide images necessary for this project. We will also follow up on the small-molecule regulators of
infection and immunity that are identified in the screens. My group has pioneered the development of socalled multi-host pathogenesis systems that involve the infection of invertebrate hosts with human bacterial
pathogens. Together with Gary Ruvkun’s laboratory, we have also pioneered and assembled an automatedC. elegans sample preparation pipeline over the past three years to enable high-throughput image-based
screens. We used the system to screen 37,000 unique compounds for regulators of infection of C. elegans by
the important human opportunistic pathogen Enterococcus faecalis—to our knowledge, the first screen
utilizing adult C. elegans animals to employ automated image analysis. This work was completed incollaboration with the Broad Institute's Imaging Platform, another collaborator on this proposal. Members of
my group have been developing several new assays described in the proposal and have been working closelywith both the Imaging Platform and Dr. Carolina Wählby in this effort.

B. Positions and Honors
EXPERIENCE/EMPLOYMENT:
1966 - 1971:
Graduate Student, M.I.T. Purification and properties of bacteriophage lambda integrase.
1972 - 1973:
Instructor and Research Associate, M.I.T. Genetic analysis of nitrogen fixation genes.
1974 - 1975:
Research Fellow, Harvard University. Genetic analysis of nitrogen fixation genes.
1975 - 1982:
Assistant and Associate Professor of Biology, Department of Cellular and Developmental
Biology, Harvard University. Molecular-genetic analysis of nitrogen fixation genes.
1982 Professor of Genetics, Harvard Medical School. Molecular genetics of microbial pathogenesis and host defense.
AWARDS:
Member, National Academy of Sciences, 1994
Member, American Academy of Microbiology, 2002
Member, American Academy of Arts and Sciences, 2003

C. 15 selected relevant publications from a total of 297
1. Mahajan-Miklos, S., M.-W. Tan, L.G. Rahme and F.M. Ausubel (1999) Molecular mechanisms of bacterial
virulence elucidated using a Pseudomonas aeruginosa-Caenorhabdititis elegans pathogenesis model.
Cell 96:47-56. PMID: 9989496
2. Tan, M.-W., S. Mahajan-Miklos, and F.M. Ausubel (1999) Killing of C. elegans by P. aeruginosa used tomodel mammalian bacterial pathogenesis. Proc. Natl. Acad. Sci. USA 96:715-720. PMID: 9892699;
PMCID: PMC15202
3. Aballay, A., P. Yorgey and F.M. Ausubel (2000) Salmonella typhimurium proliferates and establishes apersistent infection in the intestine of Caenorhabditis elegans. Current Biology 10:1539-1542. PMID:
11114525
Biographical Sketches for each listed Senior/Key Person 3

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

4. Kim, D.H., R. Feinbaum, G. Alloing, F.E. Emerson, D.A. Garsin, H. Inoue, M. Tanaka-Hino, N. Hisamoto,
K. Matsumoto, M.-W. Tan and F.M. Ausubel (2002) A conserved p38 MAP kinase pathway in
Caenorhabditis elegans innate immunity. Science 297:623-626. PMCID: PMC128160
5. Garsin, D.A., J. Villanueva, J. Begun, C.D. Sifri, D.H. Kim, S.B. Calderwood, G.B. Ruvkun and F.M.
Ausubel (2003) Long-lived C. elegans daf-2 mutants are resistant to bacterial pathogens. Science
300:1921. PMID: 12817143
6. Liberati, N.T., K.A. Fitzgerald, D.H. Kim, R. Feinbaum, D.T. Golenbock, and F.M. Ausubel (2004)
Requirement for a conserved Toll/interleukin-1 resistance domain protein in the Caenorhabditis elegans
immmune response. Proc. Natl. Acad. Sci. USA 101:6593-6598. PMCID: PMC404090
7. Moy, T.I., A.R. Ball, Z. Anklesaria, G. Casadei, K. Lewis and F.M. Ausubel (2006). Identification of novel
antimicrobials using a live-animal infection model. Proc. Natl. Acad. Sci. USA 103:10414-10419. PMCID:
PMC1482800
8. Troemel, E.R., S.W. Chu, V. Reinke, S.S. Lee, F.M. Ausubel and D.H. Kim (2006) p38 MAPK regulates
expression of immune response genes and contributes to longevity in C. elegans. PLoS Genet. 2:e183.
PMCID: PMC1635533
9. Breger, J., G. Aperis, B.B. Fuchs, T.I. Moy, F.M. Ausubel and E. Mylonakis (2007) Antifungal chemicalcompounds identified using a C. elegans pathogenicity assay. PLoS Pathog. 3:e18.
doi:10.1371/journal.ppat.0030018. PMCID: PMC1790726
10. Troemel E.R., M.-A. Felix, N.K. Whiteman, A. Barriere, and F.M. Ausubel (2008) Microsporidia are natural
intracellular parasites of the nematode Caenorhabditis elegans. PLoS Biol 6:e309. PMCID: PMC2596862
11. Irazoqui, J.E., A. Ng, R.J. Xavier, and F.M. Ausubel (2008). Role for -catenin and HOX transcription
factors in Caenorhabditis elegans and mammalian host epithelial-pathogen interactions. Proc. Natl. Acad.
Sci. USA 105:17469-17474. PMCID: PMC2582251
12. Powell, J.R., D.H. Kim and F.M. Ausubel (2009) The G protein-coupled receptor FSHR-1 is required for
the Caenorhabditis elegans innate immune response. Proc. Natl. Acad. Sci. USA, 106:2782-2787.
PMCID: PMC2650343.
13. Moy, T.I., A.L. Connery, J. Larkins-Ford, G. Wu, R. Mazitschek, G. Casadei, K. Lewis, A.E. Carpenter and
F.M. Ausubel (2009) High throughput screen for antimicrobials using a whole animal infection model. ACS
Chem. Biol, 4:527-533. PMCID: PMC2745594
14. Estes, K., T. Dunbar, J. Powell, F.M. Ausubel and E.R. Troemel (2010) The bZIP transcription factor zip-2
mediates an early response to P. aeruginosa infection in C. elegans. Proc. Natl. Acad. Sci. USA, in press.
15. Wählby, C., T. Riklin-Raviv, V. Ljosa, A.L. Conery, P. Golland, F.M. Ausubel and A.E. Carpenter (2010)
Resolving clustered worms via probablistic shape models. Accepted for publication in proceedings of theIEEE International Symposium on Biomedical Imaging (ISBI) 2010.

D. Selected Research Support (ongoing or completed in past 3 years) Relevant to Proposal
NIAID 1 R01 AI072508-03 (F.M. Ausubel, PI)
12/15/06-11/30/09
Novel whole-animal screens for anti-microbials
The goal of this project was to develop high throughput screens for identifying compounds that will cure apersistent bacterial infection in the intestine of the nematode worm Caenorhabditis elegans.
NIAID 1P01AI083214-01 (M. Gilmore, PI) (subcontract Schepens Eye Institute)
09/01/09 – 08/31/11
Harvard-wide program on antibiotic resistanceThis is a multi-investigator program project grant. The title of the Ausubel subproject is: “Identification ofpathways that can be targeted for the development of novel therapies for MRSA.” The goal is to use wholeanimal high throughput assays to identify antimicrobial compounds that cure C. elegans of an MRSA infection.
NIAID 1 R01 AI076372-01A2 (K. Lewis, PI) (subcontract Northeastern University)
12/1/08-11/30/2013
A synergy-based therapy against C. difficile
This is a multi-investigator project. The aim of this project is to use a C. elegans infection model to identify low
molecular weight compounds that function as MDR pump inhibitors that can be used in combination with aweak antibiotic to potentiate the activity of the antibiotic.

Biographical Sketches for each listed Senior/Key Person 3

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

NIAID (Transformative) 1 R01 AI085581-01 (F.M. Ausubel, PI)
9/1/09-8/31/14
Identifying novel anti-infectives by high through-put screening in whole animals
The goal of this project is to use a high throughput screening assay involving whole C. elegans animals to
identify low molecular weight compounds that cure C. elegans of a Pseudomonas aeruginosa infection.

Biographical Sketches for each listed Senior/Key Person 3

Page 25

Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Please note that the application text is copyrighted. It may be used only for nonprofit educational purposes
provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
See more online: http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx

BIOGRAPHICAL SKETCH

NAME

POSITION TITLE

Golland, Polina

Associate Professor of Computer Science andElectrical Engineering

eRA COMMONS USER NAME (credential, e.g., agency login)

XXXXXXXEDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training andresidency training if applicable.)
DEGREEINSTITUTION AND LOCATION
MM/YY
FIELD OF STUDY(if applicable)

Technion, Haifa, Israel
Technion, Haifa, Israel
Massachusetts Institute of Technology, Cambridge, MA

B.A.
M.Sc.
Ph.D.

05/93
05/95
05/01

Massachusetts Institute of Technology, Cambridge, MA

Postdoc

10/03

Computer Science
Computer Science
Electrical Engineering andComputer Science (Biomedical
Image Analysis)
Biomedical Image Analysis

A. Personal Statement
My expertise is in biomedical image analysis. My group has demonstrated novel methods for image
segmentation and registration, shape analysis and fMRI modeling. In particular, we have developed novel
methods for shape analysis that enable us to capture shape variation within a population and techniques forimage segmentation that utilize prior information, such as shape models, to achieve accurate delineation of
objects of interest in the images. Our collaboration with the Imaging Platform at the Broad Institute dates backseveral years. We jointly developed methods for cell segmentation and analysis that represent the core of
CellProfiler, a software platform used by many biology groups for high throughput analysis of microcopy
images.
I will contribute to the proposed project by collaborating with Dr. Wählby and her team on developing methods
for worm segmentation in microscopy images. Specifically, we will apply shape modeling techniques to worm
segmentation, further refining the approach demonstrated in the preliminary studies. In addition, we will
actively collaborate with Dr. Wählby’s group on building population models based on shape and appearance of
the worms, to enable phenotype scoring in high throughput experiments. Dr. Riklin-Raviv is currently a postdoc
in my group; her expertise is in shape-based segmentation. She will develop proposed methods under myguidance and in direct collaboration with Dr. Wählby.

B. Positions and Honors
Positions and Employment
2001-2003
Postdoctoral Research Associate, Artificial Intelligence Laboratory, MIT, Cambridge, MA.
2003-2008
Assistant Professor, EECS Department, MIT, Cambridge, MA.
2008-present Associate Professor, EECS Department, MIT, Cambridge, MA.
2009-present Associate Member of the Broad Institute of Harvard and MIT, Cambridge, MA.
Other Experience and Professional Memberships
Member of the Board of Directors, International Society on Medical Image Computing and Computer-Assisted
Intervention.
Associate Editor, IEEE Transactions on Medical Imaging (TMI).
Member of the Editorial Board, Journal of Medical Image Analysis (MedIA).
Member of the Editorial Board, NeuroImage.
NIH Panel on Biomedical Imaging, member (2007), ad-hoc reviewer (2009).

Biographical Sketches for each listed Senior/Key Person 4

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

NSF Panel on Collaborative Research in Computational Neuroscience (CRCNS), member (2006,2008), ad-hoc
reviewer (2007).
Honors
1998
1999

2007
2007

2007-1020092009

Marr Prize, Honorable Mention for paper "Stereo Matching with Transparency and Matting",
ICCV: Sixth International Conference On Computer Vision.
Best Poster Award, Honorable Mention for paper "Statistical Shape Analysis Using Fixed Topology
Skeletons: Corpus Callosum Study", IPMI: International Conference on Information Processing andMedical Imaging.
NSF CAREER Award. “Computational Modeling of Spatial Activation Patterns in fMRI.”MICCAI Young Scientist Award, for paper “Effects of Registration Regularization and Atlas
Sharpness on Segmentation Accuracy”, B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, P.
Golland. MICCAI: International Conference on Medical Image Computing and Computer Assisted
Intervention.
Distinguished Alumnus (class of ‘64) Career Development Chair.
MICCAI Young Scientist Award, for paper “Joint Segmentation of Image Ensembles via Latent
Atlases”, T. Riklin Raviv, K. Van Leemput, W.M. Wells III, P. Golland. MICCAI: International
Conference on Medical Image Computing and Computer Assisted Intervention.
Erbsmann Award, Honorable Mention, for paper “Exploratory fMRI Analysis without Spatial
Normalization”, D. Lashkari and P. Golland. IPMI: International Conference on Information
Processing and Medical Imaging.

C. Selected Peer-reviewed Publications
Most relevant to the current application
T. R. Jones, A. E. Carpenter, D. M. Sabatini, and P. Golland. Methods for High-Content, High-Throughput
Image-Based Cell Screening. In Proc. the First MICCAI Workshop on Microscopic Image Analysis with
Applications in Biology, 65-72, 2006.
A.E. Carpenter, T.R. Jones, M.R. Lamprecht, C. Clarke, I.H. Kang, O. Friman, D.A. Guertin, J.H. Chang, R.A.
Lindquist, J. Moffat, P. Golland and D.M. Sabatini. CellProfiler: image analysis software for identifying andquantifying cell phenotypes. Genome Biology 7(10):R100, 2006.
T.R. Jones, I.-H. Kang, D.B. Wheeler, R.A. Lindquist, A. Papallo, D.M. Sabatini, P. Golland and A.E.
Carpenter. CellProfiler Analyst: data exploration and analysis software for complex image-based screens. BMC
Bioinformatics 9:482, 2008.
T.R. Jones, A.E. Carpenter, M.R. Lamprecht, J. Moffat, S.J. Silver, J.K. Grenier, A.B. Castoreno, U.S. Eggert,
D.E. Root, P. Golland, and D.M. Sabatini. Scoring diverse cellular morphologies in image-based screens with
iterative feedback and machine learning. PNAS, A 106(6):1826-1831, 2009.
T. Riklin Raviv, K. Van Leemput, W.M. Wells III, and P. Golland. Joint Segmentation of Image Ensembles via
Latent Atlases. In Proc. MICCAI: International Conference on Medical Image Computing and Computer
Assisted Intervention, LNCS 5761:272-280, 2009. MICCAI Young Scientist Award.
Additional recent publications of importance to the field
P. Golland, W.E.L. Grimson, M.E. Shenton, R. Kikinis. Detection and Analysis of Statistical Differences in
Anatomical Shape. Medical Image Analysis, 9(1):69-86, 2005.
P. Yu, P.E. Grant, Y. Qi, X. Han, F. Segonne, R. Pienaar, E. Busa, J. Pacheco, N. Makris, R.L. Buckner, P.
Golland, B. Fischl. Cortical Surface Shape Analysis Based on Spherical Wavelets. IEEE Transactions on
Medical Imaigng, 26(4):582-597, 2007.

Biographical Sketches for each listed Senior/Key Person 4

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

B.T.T. Yeo, M.R. Sabuncu, R. Desikan, B. Fischl, and P. Golland. Effects of registration regularization andatlas sharpness on segmentation accuracy. Medical Image Analysis, 12(5):603-615, 2008.
B.T.T. Yeo, W. Ou and P. Golland. On the Construction of Invertible Filter Banks on the 2-Sphere. IEEE
Transactions on Image Processing, 17(3):283-300, 2008.
Y. Golland, P. Golland, S. Bentin, and R. Malach. Data-driven clustering reveals a fundamental subdivision of
the human cortex into two global systems. Neuropsychologia, 46(2):540-553, 2008.
D. Lashkari and P. Golland. Convex Clustering with Exemplar-Based Models. Advances in Neural Information
Processing Systems, 20:825-832, 2008.
M.R. Sabuncu, S.K. Balci, M.E. Shenton, and P. Golland. Image-Driven Population Analysis Through Mixture
Modeling. IEEE Transactions on Medical Imaging, 28(9):1473 - 1487, 2009.
W. Ou, M.S. Hamäläinen, and P. Golland. A Distributed Spatio-Temporal EEG/MEG Inverse Solver.
NeuroImage, 44(3):932-946, 2009.
K. Van Leemput, A. Bakkour, T. Benner, G. Wiggins, L.L. Wald, J. Augustinack, B.C. Dickerson, P. Golland,
and B. Fischl. Automated Segmentation of Hippocampal Subfields from Ultra-High Resolution In Vivo MRI.
Hippocampus, 19:549-557, 2009.
B.T.T. Yeo, M.R. Sabuncu, T. Vercauteren, N. Ayache, B. Fischl, and P. Golland. Spherical Demons: Fast
Diffeomorphic Landmark-Free Surface Registration. IEEE Transactions on Medical Imaging, in press, 2009.
D. Research Support
Ongoing Research Support
NIH NAMIC U54-EB005149 (Kikinis PI)
11/1/04 – 7/31/10
National Alliance for Medical Image Analysis
The goal of this project is to create a national network of research centers focused on the creation of
integrated tools for medical image analysis. Primary responsibility is development of novel algorithms forimage segmentation and registration, and integration of those algorithms into a coordinated framework thatincorporates tools from other sites.
Role: MIT site PI
NIH 1-R01-NS051826 (Grimson PI)
2/15/05-1/31/11
Computational Modeling of Anatomical Shape Distributions
This project aims to further develop and validate computational methods for representing anatomical shapeand its variation in populations.
Role: Investigator
NSF IIS- 0642971 (Golland PI)
2/1/07-1/31/12
Computational Modeling of Spatial Activation Patterns in fMRI
This project aims to develop and deploy novel representations of spatial networks of co-activation in fMRI.
Role: PINIH NAC P41-RR13218 (Kikinis PI)
8/1/08 – 5/31/13
Neuroimaging Analysis Center (NAC)
The goal of this project is to develop fMRI analysis methods for characterizing functional connectivity
patterns in normal subjects and clinical populations.
Role: MIT site PI

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

NSF IIS/CRCNS 0904625 (Golland PI)
9/01/09-8/31/12
Finding Structure in the Space of Activation Profiles in fMRI
This project aims to develop novel representations for brain organization based on fMRI experiments withrich stimulus sets and to apply the representations and the related algorithms to better characterize theventral visual pathway using visual fMRI studies.
Role: PI

Completed Research Support
NIH mBIRN U24-RR021382 (Rosen PI)
10/1/04-5/31/09
Morphometry Biomedical Informatics Research Network (MBIRN)
The goals of this project are to develop a distributed computing platform for researchers in the field ofcomputational anatomy. The particular responsibilities are to make the tools of machine learning andsemantic information retrieval available to the computational anatomy community by incorporating theminto the common analysis platform of mBIRN.
Role: Investigator
MINT (MIT internal grant) (Kanwisher, Golland PIs)
9/1/08 – 1/31/10
Discovering Structure in the Space of Activation Profiles
This project aims to develop novel representations for brain organization based on fMRI experiments withrich stimulus sets and to apply the representations and the related algorithms to better characterize theventral visual pathway using visual fMRI studies.
Role: Co-PI

Biographical Sketches for each listed Senior/Key Person 4

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Please note that the application text is copyrighted. It may be used only for nonprofit educational purposes
provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
See more online: http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx

BIOGRAPHICAL SKETCH
Provide the following information for the key personnel and other significant contributors in the order listed on Form Page 2.
Follow this format for each person. DO NOT EXCEED FOUR PAGES.
NAMEPOSITION TITLE

Gary Ruvkun

Molecular Biologist, MGH andProfessor of Genetics, Harvard Medical School

eRA COMMONS USER NAMEXXXXXXXEDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, and include postdoctoral training.)
INSTITUTION AND LOCATION

University of California at Berkeley
Harvard University

DEGREE(if applicable)

YEAR(s)

A.B.
Ph.D.

1973
1982

FIELD OF STUDY

Biophysics
Biophysics

A. Personal Statement
My lab studies genetic pathways in the regulation of miRNA, siRNA, fat storage and aging. My laboratory has
conducted numerous screens using genome-scale RNAi libraries for C. elegans, uncovering dozens ofimportant genes with implications for human health that have been further investigated in my laboratory andmy former postdocs’ laboratories. Together with Fred Ausubel, I have worked to create an automated highthroughput C. elegans screening center, using fluorescence microscopy as the primary readout given theimportance of visual phenotypes in the analysis of complex biological processes like metabolism and aging.
The one remaining hurdle is image analysis. We have been collaborating with the Imaging Platform for three
years and with Dr. Wahlby for the past year to overcome this challenge. We will make all of the images wehave produced available for algorithm development, and we will use funding from other sources to complete
assay development and genome-scale RNAi screening for regulators of fat accumulation and metabolism
using oil red O staining, as well as the laboratory work to follow up on hits from this screen. We plan tocontribute to the proposal by annotating control images for the oil red O assay, transferring images from thefull-scale screen to the Wahlby group, and most importantly, to participate interactively in the development ofalgorithms for this assay by helping to assess the accuracy of automated scoring. Beyond the scope of the
proposed grant to complete this particular screen, my laboratory has dozens of other large-scale screens thatwill make use of the algorithms and software developed, and we therefore are most supportive of continuing
the collaboration.
B. Positions and Honors
1997-present
Professor of Genetics, Harvard Medical School
Other Professional Activities:
1995 - present
Editor, Developmental Biology
2004 - 2007
NIH National Advisory Council on Aging
Awards and Honors:
2002-present
NIH Merit Award
2005 Rosenstiel Award from Brandeis University shared with Victor Ambros, Andy Fire, and Craig Mello.
2008 Benjamin Franklin Medal, Franklin Institute, with Victor Ambros and David Baulcombe
2008 Gairdner International Award, with Victor Ambros.
2008 National Academy of Sciences
2008 Lasker Basic Medical Research Award, Lasker Foundation, with Victor Ambros and David Baulcombe
2008 Warren Triennial Prize, Massachusetts General Hospital, with Victor Ambros
2009 Louisa Gross Horwitz Prize, Columbia University, with Victor Ambros
2009 American Academy of Arts and Sciences
2009 Massry Prize, with Victor Ambros

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

C. Selected peer reviewed publications (in chronological order) 131 publications total:
1. Curran, S. P. and G. Ruvkun. Lifespan regulation by evolutionarily conserved genes essential for viability.
2007. PloS Genetics, 2007 Apr 6;3(4):e56. Epub 2007 Feb 27. PMCID: PMC1847696
2. Samuelson, A. V., Carr, C. E., and G. Ruvkun. 2007. Gene activities that mediate increased lifespan of C.
elegans insulin-like signaling mutants. Genes and Development, Nov 15;21(22):2976-94. PMCID:
PMC2049198
3. Parry, D.H., Xu, J. and G. Ruvkun. 2007. A whole-genome RNAi screen for C. elegans miRNA pathway
genes. Curr Biol. 2007 Dec 4;17(23):2013-22. Epub 2007 Nov 20. PMID: 18023351
4. Samuelson, A.V., R. R. Klimczak, D. Thompson, C. E. Carr, and G. Ruvkun. 2008 Identification of C.
elegans genes regulating longevity using enhanced RNAi-sensitive strains. Cold Spring Harbor Symp Quant
Biol.; 73: Circadian Rhythms. PMID: 18419309
5. Gabel, H. W. and G. Ruvkun. 2008. The exonuclease ERI-1 has a conserved dual role in 5.8S rRNA
processing and RNAi. Nat Struct Mol Biol. 2008 May;15(5):531-3. Epub 2008 Apr 27. PMID: 18438419
6. Fischer, S.E.J., M. D. Butler, Q. Pan and G. Ruvkun. Trans-splicing in C. elegans generates the negative
RNAi regulator ERI-6/7. Nature. 2008 Sep 25;455(7212):491-6. Epub 2008 Sep 10. PMID: 1878465
7. Wang, M. C., E. O’Rourke and G. Ruvkun. Fat metabolism links germline stem cells and longevity in C.
elegans.. Science. 2008 Nov 7;322(5903):957-60. PMID: 18988854
8. Soukas, A. A., E. A. Kane, C. E. Carr, J. A. Melo, and G. Ruvkun. 2009. Rictor/TORC2 regulates fat
metabolism, feeding, growth, and lifespan in Caenorhabditis elegans. Genes Dev. 2009 Feb 15;23(4):496-511.
PMC2648650
9. Curran SP, Wu X, Riedel CG, Ruvkun G. 2009. A soma-to-germline transformation in long-lived
Caenorhabditis elegans mutants. Nature 459: 1079-84. PMID: 19506556
10. O'Rourke EJ, Soukas AA, Carr CE, Ruvkun G. 2009. C. elegans major fats are stored in vesicles distinct
from lysosome-related organelles. Cell Metab. 2009 Nov;10(5):430-5. PMID: 19883620
D. Research Support
Ongoing Research Support

PI: Gary Ruvkun:
5 R01 AG16636-11 ARRA
04/01/99-08/31/11
NIH Genetic and molecular basis of longevity
Aims 1.1. Classify stress responses activated in long-lived C. elegans using stress gene GFP fusion genes.
1.2 Endocrine exploration of the growth arrest pathways
2.1. Analyze the association of essential gene inactivation induced developmental arrest and longevity
extension.
2.2. Identify components of the cct-6 chaperonin complex stress response/soma to germline pathway activatedin long-lived worms.
2.3. Molecular analysis of genes identified in the screens
3. Validation of C. elegans genes that act as co factors for daf-2-mediated increased longevity
5 R37 AG14161-14
05/01/96-08/31/11
NIH Inositol signaling in C. elegans senescence and diapause
Aim 1. Phenotypic characterization of gene inactivations and mutations in genes identified by RNAi that confer
long lifespan
Aim 2. Identification of genes regulating longevity using enhanced RNAi-sensitive (ERI) strains.
Aim 3. Pathway analysis of new genes identified.
2 R01 GM044619-18
05/01/91-06/30/12
NIH Control of C. elegans lineage by heterochronic genes
Aim 1. Validation and classification of C. elegans genes that act as co factors for miRNA function
Aim 2. Pathway analysis of C. elegans genes that act as co factors for RNA interference
Biographical Sketches for each listed Senior/Key Person 5

Page 31Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

2 R01 DK070147-05
09/15/04-08/31/13
NIH Genetic and functional genomic analysis of C. elegans fat regulatory pathways
Aim 1: Metabolic and satiety profiling of serotonin signaling mutants in fat deposition
Aim 2: Molecular analysis of genes identified in the screens
NNG05GK27G
National Aeronautics and Space Administration, NASAA Search for Extraterrestrial Genomes

06/01/05-05/31/10

NNX08AX15G
09/01/08-08/31/12
MIT/National Aeronautics and Space Administration, NASAA Search for Extraterrestrial Genomes (SETG): An In-situ Detector for Life on Mars Ancestrally Related
to Life on Earth
NNH08ZDA001N-MMAMA
06/01/09-12/01/10
National Aeronautics and Space Administration, NASA
REDGENES: Remotely Examining DNA with a Genetic Explorer in a Natural Extreme System
Glenn Foundation
Award for Research in Biological Mechanisms of Aging

03/01/09-03/15/11

3 R01 DK070147-06S1 ARRA
NIH Genomic analysis of C. elegans fat regulatory pathways

12/21/09-02/28/10

Completed Research Support

PI: Gary Ruvkun:
Broad Institute08/01/06-02/28/09
SPARC Proposal
Dramatically Enhancing the Throughput of C.elegans RNAi and Drug Screens
Aims: The overall goal of this project is to increase the throughput of C. elegans RNAi and chemical screensby 10 to 100 fold.

Biographical Sketches for each listed Senior/Key Person 5

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Please note that the application text is copyrighted. It may be used only for nonprofit educational purposes
provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
See more online: http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx

BIOGRAPHICAL SKETCH
Provide the following information for the key personnel and other significant contributors.
Follow this format for each person. DO NOT EXCEED FOUR PAGES.
NAMEPOSITION TITLE

Riklin-Raviv, Tamar

Postdoc researcher
Computer Science / Artificial Intelligence Laboratory
EECS department at MIT

eRA COMMONS USER NAME (credential, e.g., agency login)
XXXXXXXEDUCATION/TRAINING (Begin with baccalaureate or other initial professional education, such as nursing, include postdoctoral training andresidency training if applicable.)
DEGREEINSTITUTION AND LOCATION
MM/YY
FIELD OF STUDY(if applicable)

Hebrew University of Jerusalem, Israel
Hebrew University of Jerusalem, Israel
Tel-Aviv University, Israel
Massachusetts Institute of Technology

B.Sc.
M.Sc.
Ph.D.
Postdoc

09/93
12/99
02/08
current

Physics
Computer vision
Computer vision
Medical Image Analysis

A. Personal Statement
An essential component in the analysis of high-throughput images of C. elegans is the extraction of individual
worms from the images; many infection- and metabolism-related phenotypes in the proposal depend on this.
The almost identical appearance of the worms, due to their stereotypical development pattern, makes theirshape an ideal cue for facilitating the segmentation. In the past few years I have been addressing problems ofobject segmentation in the presence of morphological information. In particular I developed variational methods
based on an active contour paradigm known as level-set that allows the incorporation of implicit shapeinformation within a unified cost functional for object segmentation. These approaches have garnered attention
in the field; I have published 3 top-journal papers and 7 peer reviewed international conference papers (I am afirst author in most of them) on this particular research direction and related topics. One of these won theprestigious young scientist award at a leading conference in the field of biomedical image analysis, theInternational Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI).
Since January 2008, I have pursued and expanded these research interests as a postdoctoral associate in the
Medical Vision group of M.I.T., led by Prof. Polina Golland. I started collaborating with Dr. Wählby of the Broad
Institute in the spring of 2009, continuing a long-term collaboration between Dr. Golland’s group and the Broad.
Already, this work has been fruitful; our recent research on the segmentation of C. elegans in high-throughput
images has been accepted for publication in the proceedings of the IEEE International Symposium on
Biomedical Imaging (ISBI), and we have submitted a second paper together. Given my computational
background in addition to my experience with C. elegans images so far, I am thus well-suited to continue
working together with Dr. Wählby on developing novel algorithms for this important biological area.
B. Positions and Honors
Positions and Employment:
2003-2007
PhD student w/ Nahum Kiryati and Nir Sochen, School of Electrical Engineering,
Tel-Aviv University
2008-present Postdoctoral associate w/ Polina Golland, Department of Electrical Engineering and Computer
Science, Massachusetts Institute of Technology
Other Experience and Professional Memberships
Member, IEEE
2006- 2007 Coordinator of the Mathematical Visual Perception seminar at Tel-Aviv University
Ad-hoc journal reviewer: IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Signal ProcessingIEEE Transactions on Systems, Man and Cybernetics
Program Committee:
IEEE Conference on Computer Vision and Pattern Recognition 2006,2007,2009
IEEE International Conference on Computer Vision 2007
Biographical Sketches for each listed Senior/Key Person 6

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

European Conference in Computer Vision 2004, 2006, 2008
International Journal of Biomedical Imaging
International Journal of Image and Graphics
Journal of Visual Communication and Image Representation
Honors
1993
2005
2007
2007
2007
2008
2009

The Hebrew University of Jerusalem Dean’s list
Weinstein award for excellent paper, “Unlevel-Sets: Geometry and Prior-based Segmentation”
Fulbright Post-Doctoral fellowship
The Commercial & Industrial Club Illan Ramon Post-doctoral Scholar
Weinstein award for excellence in studies
Weinstein award for excellent paper, “Prior-based Segmentation and Shape Registration in the
Presence of Perspective Distortion”
MICCAI 2009 young scientist award for “Joint Segmentation of Image Ensembles via Latent Atlases”

C. Peer-reviewed publications
Total number of citations according to Google scholar as of January 2010 is over 450.
Journal papers – reverse chronological order
1. T. Riklin-Raviv, N. Sochen and N. Kiryati, On Symmetry, Perspectivity and Level-set based
segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). Vol 31(8)pp 1458-1471, August 2009
2. T. Riklin-Raviv, N. Sochen and N. Kiryati, Shape based Mutual Segmentation. International Journalof computer Vision (IJCV). Vol 79(3) pp 231-245, September 2008
3. T. Riklin-Raviv, N. Kiryati and N. Sochen, Prior-based Segmentation and Shape Registration in the
Presence of Perspective Distortion. International Journal of Computer Vision (IJCV). Vol 72(3) pp 309328 May 2007
4. A. Shashua and T. Rikiln-Raviv, The Quotient Image: Class Based Re-Rendering and Recognition With
Varying Illuminations. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI). Vol.
23(2) pp 129-139, February 2001.

Peer reviewed conference papers – reverse chronological order
5. C. Wählby, T. Riklin-Raviv, V. Ljosa, A.L. Conery, P. Golland, F.M. Ausubel, and A.E. Carpenter,
Resolving Clustered Worms via Probabilistic Shape Model, IEEE International Symposium on
Biomedical Imaging: From Nano to Micro (ISBI), April 2010, accepted for publication.
6. T. Riklin Raviv, K. Van-Leemput, W.M. Wells III and Polina Golland, Joint Segmentation of Image
Ensembles via Latent Atlases, International Conference on Medical Image Computing and Computer
Assisted Intervention (MICCAI), Part I, LNCS 5761, pp. 272–280, September 2009.
Received the MICCAI 09 Young Scientist Award.
7. T. Riklin Raviv, B.M. Menze, K. Van-Leemput, B. Stieltjes, M.A. Weber, N. Ayache, W.M. Wells III and
Polina Golland, Joint Segmentation via Patient-Specific Latent Anatomy Model, MICCAI workshop:
Probabilistic Models for Medical Imaging Analysis (PMMIA), September 2009.
8. T. Riklin Raviv, N. Ben-Zadok and N. Kiryati Interactive Level-set Segmentation for Image Guided
Therapy . IEEE International Symposium on Biomedical Imaging: From Nano to Micro (ISBI), pp. 10791082, June 2009.
9. N. Kiryati, T. Riklin Raviv, Y. Ivanchenko and S. Rochel, Real-time Abnormal Motion Detection in
Surveillance Video. International Conference on Pattern Recognition (ICPR), pp. 1-4, December 2008.

Biographical Sketches for each listed Senior/Key Person 6

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

10. T. Riklin-Raviv, N. Sochen, N. Kiryati, N. Ben-Zadok, S. Gefen, L. Bertand and J. Nissanov,
Propagating Distributions for Segmentation of Brain Atlas. IEEE International Symposium on
Biomedical Imaging: From Nano to Micro (ISBI), pp 1304-1307, April 2007.
11. T. Riklin-Raviv, N. Kiryati and N. Sochen, Segmentation with Level Sets and Symmetry. In Proc. ofIEEE Conference on Computer Vision and Pattern Recognition. (CVPR), pp 1015-1022, June 2006.
12. T. Riklin-Raviv, N. Sochen and N. Kiryati, Mutual Segmentation with Level Sets. In the 5th IEEE
Workshop on Perceptual Organization in Computer Vision (POCV) in conjunction with the CVPR. 2006.
13. T. Riklin-Raviv, N. Kiryati and N. Sochen, Prior-based Segmentation by Projective Registration andLevel Sets. In Proc. of the Tenth IEEE International Conference on Computer Vision (ICCV).pp 204211, October 2005.
14. T. Riklin-Raviv, N. Kiryati and N. Sochen, Unlevel-Sets: Geometry and Prior-based Segmentation. In
Proc. of the European Conference on Computer Vision (ECCV). pp 50-61, May 2004.
15. T. Riklin-Raviv and A. Shashua, The Quotient Image: Class Based Recognition and Synthesis Under
Varying Illumination Conditions. In Proc. of IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). pp 566-571, June 1999.
D. Research Support
none

Biographical Sketches for each listed Senior/Key Person 6

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

PHS 398 Cover Page Supplement

OMB Number: 0925-0001

Please note that the application text is copyrighted. It may be used only for nonprofit educational purposes
provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
See more online: http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx

1. Project Director / Principal Investigator (PD/PI)
Prefix:* First Name: Carolina

Middle Name:* Last Name:
Suffix:WahlbyPh.D.

2. Human Subjects
Clinical Trial?

No

Yes

* Agency-Defined Phase III Clinical Trial?

No

Yes

3. Applicant Organization Contact
Person to be contacted on matters involving this application* First Name:

Prefix:StaceyMiddle Name:* Last Name:

Donnelly

Suffix:* Phone Number: 617-714-7130

Fax Number: 617-714-8972

Email: awards@broadinstitute.org* Title: Director, Sponsored Research and Planning* Street1:

7 Cambridge Center

Street2:* City:Cambridge

County/Parish:* State:MA: Massachusetts

Province:* Country:* Zip / Postal Code: 02142-1401

USA: UNITED STATESClinical Trial & HESC

 

Page 36    !"#"Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

PHS 398 Cover Page Supplement

4. Human Embryonic Stem Cells* Does the proposed project involve human embryonic stem cells?

No

Yes

If the proposed project involves human embryonic stem cells, list below the registration number of thespecific cell line(s) from the following list: http://stemcells.nih.gov/research/registry/. Or, if a specificstem cell line cannot be referenced at this time, please check the box indicating that one from theregistry will be used:

Cell Line(s):Specific stem cell line cannot be referenced at this time. One from the registry will be used.

Clinical Trial & HESC

 

Page 37    !"#"Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Speciﬁc Aims
Microscopy has emerged as one of the most powerful and informative ways to analyze cell-based high-throughput
screening (HTS) samples in experiments designed to uncover novel drugs and drug targets. However, many diseases and biological pathways can be better studied in whole animals–particularly diseases that involve organsystems and multicellular interactions, such as metabolism and infection. The worm Caenorhabditis elegans
is a well-established and effective model organism that can be robotically prepared and imaged, but existing
image-analysis methods are insufﬁcient for most assays.
We propose to develop algorithms for the analysis of high-throughput C. elegans images, validating themin three speciﬁc experiments to identify chemicals to cure human infections and genetic regulators of host response to pathogens and fat metabolism. Novel computational tools for automated image analysis of C. elegans
assays will make whole-animal screening possible for a variety of biological questions not approachable by cellbased assays. Building on our expertise in developing image processing and machine learning algorithms forhigh-throughput screening, and on our established collaborations with leaders in C. elegans research, we will:
Aim 1: Develop algorithms for C. elegans viability assays to identify modulators of pathogen infection
Challenge: To identify individual worms in thousands of two-dimensional brightﬁeld images of worm populations infected by Microsporidia, and measure viability based on worm body shape (live worms are curvy
whereas dead worms are straight).
Approach: We will develop algorithms that use a probabilistic shape model of C. elegans learned fromexamples, enabling segmentation and body shape measurements even when worms touch or cross.
Impact: These algorithms will quantify a wide range of phenotypic descriptors detectable in individual
worms, including body morphology as well as subtle variations in reporter signal levels.
Aim 2: Develop algorithms for C. elegans lipid assays to identify genes that regulate fat metabolism
Challenge: To detect worms versus background, despite artifacts from sample preparation, and detect
subtle phenotypes of worm populations.
Approach: We will improve well edge detection, illumination correction, and detection of artifacts (e.g. bubbles and aggregates of bacteria) and enable image segmentation in highly variable image backgrounds usinglevel-set segmentation. We will also design feature descriptors that can capture worm population phenotypes.
Impact: These algorithms will provide detection for a variety of phenotypes in worm populations. They will
also improve data quality in other assays, such as those in Aims 1 and 3.
Aim 3: Develop algorithms for gene expression pattern assays to identify regulators of the response of
the C. elegans host to Staphylococcus aureus infection
Challenge: To map each worm to a reference and quantify changes in ﬂuorescence localization patterns.
Approach: We will develop worm mapping algorithms and combine them with anatomical maps to extract
atlas-based measurements of staining patterns and localization. We will then use machine learning to distinguish morphological phenotypes of interest based on the extracted features.
Impact: These algorithms will enable addressing a variety of biological questions by measuring complex
morphologies within individual worms.
In addition to discovering novel anti-infectives and genes involved in metabolism and pathogen resistance,
this work will provide the C. elegans community with (a) a versatile, modular, open-source toolbox of algorithms
readily usable by biologists to quantify a wide range of important high-throughput whole-organism assays, (b)a new framework for extracting morphological features from C. elegans populations for quantitative analysis of
this organism, and (c) the capability to discover disease-related pathways, chemical probes, and drug targets inhigh-throughput screens relevant to a variety of diseases.
Primary collaborators
Gary Ruvkun and Fred Ausubel, MGH/Harvard Medical School: Development, execution, and follow-up oflarge-scale C. elegans screens probing metabolism and infection. Polina Golland and Tammy Riklin-Raviv,
MIT Computer Science and Artiﬁcial Intelligence Lab: Illumination/bias correction, model-based segmentation,and statistical image analysis. Anne Carpenter, Broad Imaging Platform: Software engineering and support.
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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Research Strategy
A

SigniﬁcanceThe NIH is committed to translating basic biomedical research into clinical practice and thereby impacting global
human health 1 , and Francis Collins identiﬁes high-throughput technology as one of ﬁve areas of focus for the
NIH’s research agenda 2 . For many diseases, researchers have identiﬁed successful novel therapeutics or
research probes by applying technical advances in automation to high-throughput screening (HTS) using either
biochemical or cell-based assays 3–6 . Researchers are using genetic perturbations such as RNA interference orgene overexpression in cell-based HTS assays to identify genetic regulators of disease processes as potential
drug targets 7–9 . However, the molecular mechanisms of many diseases that deeply impact human healthworldwide are not well-understood and thus cannot yet be reduced to biochemical or cell-based assays.
Ideally, researchers could approach disease from a phenotypic direction, in addition to the traditional molecular approach, by searching for chemical or genetic regulators of disease processes in whole model organisms
rather than isolated cells or proteins. Moving HTS towards more intact, physiological systems also improves
the likelihood that the ﬁndings from such experiments accurately translate into the context of the human body(e.g., in terms of toxicity and bioavailability), simplifying the path to clinical trials and reducing the failure of potential therapeutics at later stages of testing. In fact, for some diseases, a whole organism screen may actually
be necessary to break new therapeutic ground; in the search for novel therapeutics for infectious agents, forexample, it is widely speculated that the traditional approach of screening for chemicals that directly kill bacteria in vitro has been largely exhausted 10 . Our work recently identiﬁed six novel classes of chemicals thatcure model organisms from infection by the important human pathogen E. faecalis through mechanisms distinct
from directly killing the bacterium itself 11 . Anti-infectives with new mechanisms of action are urgently needed tocombat widespread antibiotic resistance in pathogens.
Enabling HTS in whole organisms is therefore recognized as a high priority (NIH PAR-08-024) 12,13 . C.
elegans is a natural choice. Manually-analyzed RNAi and chemical screens are well-proven in this organism,with dozens completed 14–16 . Many existing assays can be adapted to HTS; instrumentation exists to handle
and culture C. elegans in HTS-compatible multi-well. Its organ systems have high physiologic similarity andgenetic conservation with humans 17,18 . C. elegans is particularly suited to assays involving visual phenotypes:
physiologic abnormalities and ﬂuorescent markers are easily observed because the worm is mostly transparent.
The worms follow a stereotypic development pattern that yields identically-appearing adults 19,20 , such thatdeviations from wild-type are more readily apparent.
The bottleneck that remains for tackling important human health problems using C. elegans HTS is image
analysis (NIH PA-07-320) 21,22 . It has been recently stated, “Currently, one of the biggest technical limitations
for large-scale RNAi-based screens in C. elegans is the lack of efﬁcient high-throughput methods to quantitate
lethality, growth rates, and other morphological phenotypes” 23 . Our proposal to develop image analysis
algorithms to identify regulators of infection and metabolism in high-throughput C. elegans assays
would bring image-based HTS to whole organisms, and have the following impact:• Identifying novel modulators of infection by the NIH priority pathogen Microsporidia (Aim 1). Microsporidia are emerging human pathogens whose infection mechanisms are almost completely unknown.
Further, they inﬂict agricultural damage and are on the EPA list of waterborne microbial contaminants ofconcern 24,25 . Identifying anti-microsporidian therapeutics is a special challenge because they are eukaryotes. Moreover, they are obligate intracellular pathogens so they are not amenable to traditional antibiotic
screens; screening for drugs to kill them requires the presence of a validated, infectible host whose immune system is homologous to mammals, such as C. elegans 26,27 . This screen could identify not only
useful chemical research probes and compounds that kill these pathogens outright, but also those thatblock microbial virulence, are modiﬁed by the host for full efﬁcacy (prodrugs), or enhance host immunity.
• Identifying novel regulators of fat metabolism (Aim 2). Disregulation of metabolism results in many
common and expensive chronic health conditions; diabetes alone affects 24 million Americans 28 . Energy
centers must receive and integrate nutritional information from multiple peripheral signals across multiple
tissues and cell types to elicit appropriate behavioral and metabolic responses; screening in a wholeorganism is important. In particular, screening with a strain of C. elegans with an RNAi-sensitive nervous
system will likely reveal novel energy regulators of therapeutic and research value.
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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina• Identifying novel regulators of infection by the pathogen Staphylococcus aureus (Aim 3). S. aureus
is life-threatening for immune-compromised patients. Recently, antibiotic-resistant MRSA strains havecreated an urgent need for therapeutics with a new mechanism of action 29 . We will identify genetic
regulators of the C. elegans host’s response to infection by S. aureus 30 . These will lead to potential drug
targets useful for boosting humans’ innate immunity.
• Enabling the automated analysis of a wide variety of C. elegans screens. Because C. elegans
has proven to be an excellent model for many human organs and processes, the impact of algorithms
for automated scoring for currently intractable C. elegans image-based screens on our understanding andtreatment of a variety of human diseases will be substantial. Adding novel C. elegans algorithms to existing
open-source software will create a ﬂexible toolbox that can be applied to other types of assays (including
alternative formats such as microﬂuidics chambers; see Yanik support letter) with minimal modiﬁcation:
Aim 1: The algorithms developed for Aim 1 will enable scoring viability and other body morphology assays
probing a number of biological processes. Our collaborators plan several RNAi and chemical screensusing live/dead assays to identify modulators of many other clinically relevant pathogens (see Ausubel
and Mylonakis support letters).
Aim 2: The algorithms developed for the fat metabolism assay can also be used to quantify the levels of
any stain within worms, to measure protein expression levels, the degree of staining by ﬂuorescent dyes
or antibodies, and promoter activity in reporter assays probing a wide range of biological processes.
Aim 3: Where localization patterns are of interest, the algorithms developed for the gene expression
pattern assay will often be directly applicable, especially given the proposed machine learning capabilities.
Many beneﬁts come from the automation of image analysis for such screens: (a) increased throughput so
as to enable genome-scale RNAi and large-scale chemical screens in whole animals; (b) quantitative results amenable to data mining 31–33 ; (c) increased objectivity and consistency; and (d) increased sensitivity
to subtle phenotypes, which often can not be scored reliably by eye. The requisite automation of sample
preparation and image acquisition has the welcome side effect of improving consistency and providing apermanent record of the experiment.
• Creating open-source software for the C. elegans community. C. elegans is used for studying complex multicellular biological processes by more than 11,000 researchers in 750 laboratories worldwide(http://www.WormBase.org, January 2010), and the close-knit community rapidly shares methods 17,18,34,35 .
Based on our experience developing the CellProﬁler software system (see Preliminary studies), packaging automated image analysis algorithms in user-friendly software encourages their use by the broader
research community. Although we developed CellProﬁler solely for high-throughput screening, 70% of
studies citing it actually used it to quantify low-throughput assays (fewer than 100 samples). In this proposal we focus on developing algorithms that are robust and efﬁcient for large-scale experiments, but weanticipate they will become an everyday tool for many researchers in the C. elegans community, a goodinvestment since many of these are funded by the NIH.
Thus, in addition to the discovery of potential drugs and drug targets related to metabolism and infection,
which could signiﬁcantly impact the global burden of human disease, our aims will yield open-source software
for automated, accurate, quantitative scoring for a wide range of C. elegans image-based assays that are
currently intractable. The impact will be multiplied by C. elegans laboratories worldwide using the resulting
software to study a wide variety of pathways relevant to basic biological research and human disease, in both
low-throughput and high-throughput experiments.

B

Innovation

In response to the strong demand for C. elegans screening, we propose to build on our technological innovations
in sample preparation and imaging and our computational innovations for cells and brains to now create a novel
technology for C. elegans. Our proposed work to develop novel algorithms for identifying and characterizing
worms in microscopy images will bridge the ﬁnal gap, for the ﬁrst time enabling widespread identiﬁcation ofgenetic and chemical regulators of human biological processes and diseases via whole-organism screening.
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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Assay development, high-throughput
sample preparation and imaging

Assay improvement
and validation

Algorithm
development

Follow up on infection andmetabolism regulators

Aim 1: Microsporidia viability
assay:
single worm segmentation

Ausubelgroup

Wählby group(Madden)

Wählby group(Genovesio)
Golland group(Riklin-Raviv)

Ausubelgroup

Aim 2: Fat metabolism lipid
assay: worm population
segmentation

Ruvkungroup

Wählby group(Madden)

Wählby group(Ljosa)Golland group(Riklin-Raviv)

Ruvkungroup

Aim 3: S. aureus infection
gene expression assay: worm
mapping, machine learning

Ausubelgroup

Wählby group(Madden)

Wählby group(Ljosa)Golland group(Riklin-Raviv)

Ausubelgroup

Software engineering,
dissemination,and tutorials

Carpenter group
Wählby group

Figure C.1: Project overview, including the contributions of collaborating groups.

Automated image analysis for high-throughput screening of C. elegans is, in itself, novel: screens have so
far been performed by eye due to the lack of suitable image analysis algorithms (excepting our simple E. faecalis
screen 11 ), limiting the number, types, and sizes of screens. Visual examination for a genome-wide RNAi screen
takes 0.5–4 people-years; a large chemical screen 1 requires more than 10 people-years. Using the algorithms
we will develop, such screens can be analyzed in weeks or months. Existing algorithms for C. elegans are
insufﬁcient; they were developed for low-throughput, high-resolution, 3-D, or time-lapse images 36–46 , or forembryos, which have a different appearance than adults 47–53 .
Several algorithmic innovations are necessary in order to quantify a variety of C. elegans phenotypes andattain the robustness required for routine high-throughput screening. We propose a novel, simpliﬁed representation for worm shapes that lends itself to a probabilistic interpretation. This allows us to adapt shape models
to identiﬁcation of worms in a high-throughput context, and leads to a novel algorithm for detangling worms bymorphology-guided graph search. We will also build upon methods from our work in deformation analysis 54
and per-cell classiﬁcation of cellular phenotypes by machine learning 55 to quantify phenotypic variation andﬂuorescence localization in individual worms.

C

Approach

Overview of the team and the approach
The proposed project is founded on several multi-year existing collaborations between groups studying infection
and metabolism using C. elegans (Ausubel and Ruvkun), and computational groups focused on developing
algorithms for biomedical research (Wahlby, Carpenter, and Golland), making us uniquely situated to accomplish
the proposed aims. As shown in Figure C.1, our interdisciplinary team is highly interactive and our approachto image assay development is a highly iterative process; typically the majority of the work is in multiple roundsof validation and testing of novel or existing algorithms while optimizing sample preparation protocols to ensure
robust real-world performance. Each proposed aim is independent, but in several instances, improvements
made for one aim will beneﬁt the others. Later sections detail our proposed algorithm development for each aim,
which will occur in the rich, collaborative, interdisciplinary environment of algorithm and software development
at the Broad Institute and MIT. Here we outline the team and the approach.
Project leadership and algorithm development: The PI, Carolina Wahlby, will lead and coordinate thecollaborating groups for the project. Based on Dr. Carpenter’s work with Golland’s group across the street
at MIT’s CSAIL (since 2004 55–59 ) and the Ausubel and Ruvkun C. elegans laboratories across the Longfellow
Bridge at MGH (since 2005 11,60 ), Dr. Wahlby was able to quickly take leadership of these projects in 2009,
start her own collaborations, and develop new ideas for C. elegans image analysis with the Golland group (seesupport letter). In less than one year, this collaboration resulted in a joint, peer-reviewed paper accepted for
publication 61 , another submitted, and the present proposal. The project’s success so far is due to Dr. Wahlby’s
strong computational background and previous experience managing highly interdisciplinary collaborations on
application-oriented image analysis (see Bengtsson and Ekström/Alderborn support letters).
Wet laboratory work: The Ausubel and Ruvkun groups are separately funded, equipped, and committed tocompleting the wet laboratory work to image thousands of samples for each assay (see Table C.1, Preliminary
studies section, Resources ﬁle, and support letters). Furthermore, the laboratories are dedicated to the studyof infection and metabolism and are separately funded to follow up on “hits” from the screens, in some cases
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Table C.1: Overview of image sets to be collected by Ausubel and Ruvkun groups.
Aim

Assay

Group

ImagesScale

1 Viability assay(Microsporidia)c

Ausubel &RuvkunBrightﬁeld only (shape reveals viability of worms in
response to infection)

5000–100,000
chemicals

2 Lipid assayb

RuvkunBrightﬁeld of oil red O (stains lipids)

Genome-wide RNAi

3 S. aureus-induced
expression patternc

AusubelBrightﬁeld + GFP-ﬂuorescence (reports expression ofclec-60:GFP in response to infection) + myo-2:mCherry

Genome-wide RNAi

Screens funded by: a NIH R01 AI085581-01 b NIH R01 DK070147-06 & Broad Institute c NIH R01 AI064332-05 & R01 AI072508-02.

collaborating with the Broad Institute’s Chemical Biology Platform, which has extensive experience in converting
hit compounds into usable research probes or drugs.
Software development, dissemination, resource sharing, and reproducible research: The Carpentergroup (see support letter) will implement, test, and disseminate the project team’s algorithms into readily usable
software following good software engineering practices. In keeping with the Broad Institute’s mission to create
advanced research tools for the scientiﬁc community, the Data/Software Sharing ﬁle details our plans for comprehensive sharing of both the data (images) and software produced. Speciﬁcally, the algorithms developed will
be made readily usable by biologists via the open-source CellProﬁler software project for high-throughput image
analysis 57 . A major advantage of this system is that each analysis run retains complete information about thealgorithms and settings used, enabling reproducible research 62 . CellProﬁler runs on Windows, PC, and Unix
systems, including computing clusters, and reads many image ﬁle formats via the BioFormats library 63 . The
C. elegans algorithms will also be available via ImageJ 64 , due to a funded project to interface it with CellProﬁler(Carpenter, Eliceiri, and Rasband). Building on this existing software eliminates the waste of building a separate
interface for worm algorithms and ensures longevity and dissemination for the algorithms.
In addition to software engineering for the project, the Carpenter group will also be primarily responsible forsoftware dissemination and support through direct training with other high-throughput C. elegans laboratories(see Roy, Mylonakis, and Yanik support letters, for example), via conferences (e.g., The International C. elegans
Meeting, Worm Genomics and Systems Biology Conference), via the Worm Breeder’s Gazette 35 , via online
tutorials, and via public C. elegans-speciﬁc tutorials to train biologists to use the software.
Timeline: Work on Aim 1 will take place during the ﬁrst two years. Work on Aim 2 will commence six
months after funding and will be ﬁnished by the end of the third year. Work on Aim 3 will begin halfway throughthe second year and will be ﬁnished by the end of year 5.

Preliminary studies supporting the approach
In this section, we describe the independent and collaborative research completed within and among the
Wahlby, Carpenter, Golland, Ausubel, and Ruvkun groups that provides the foundation for this proposal.
High-throughput C. elegans microscopy screen for regulators of Enterococcus faecalis infection:
We recently published the ﬁrst whole-animal C. elegans microscopy screen analyzed by automated image
analysis 11 . Building on a smaller, manually-scored screen 65 , we tested 37,214 chemicals for their ability torescue C. elegans worms from an otherwise lethal E. faecalis infection. We acquired ﬂuorescence images of thedead worms stained with SYTOX dye, plus brightﬁeld images showing the entire worm population. Although theimage-analysis approach was relatively simple, the screen uncovered six structural classes of compounds thatare “anti-infectives” and appear to cure C. elegans animals without directly affecting the growth of E. faecalis.
Three of these are novel structural classes of compounds that were not found in in vitro screens for antimicrobial
compounds. This validates a major premise of our proposal, that image-based screens in the whole organism
C. elegans will reveal compounds acting through novel mechanisms of action, in this case, mechanisms thatare only manifest when the complex host/pathogen relationship is intact.
High-throughput C. elegans sample preparation, image acquisition, and assay development: The Ruvkun and Ausubel labs, with help from the Carpenter group, have established the pioneering C. elegans HighThroughput Screening Core Facility 66 . Both groups have extensive experience in developing assays and conducting large-scale screens to probe important biological questions in C. elegans, having completed manuallyscored C. elegans screens relating to longevity 67–69 , E. faecalis infection 65,70 , metabolism 71,72 , RNA interResearch Strategy

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

ference 73 , Candida albicans infection 74 , synapses 75 , immune response 76 , molting 77 , miRNA 78 , diabetes 79 ,innate immune signaling 80 , neuroendocrinology 81 . The speciﬁc assays they developed for this proposal aredescribed later, in the context of each Aim.
The screening center uses a workﬂow in which a precise number of worms within a speciﬁed size/age range
are dispensed by a COPAS large particle sorter into 4–6 multi-well plates per hour, and subsequently processedusing automated plate washers and microscopes. The workﬂows enable both RNAi and chemical screening
and imaging at multiple wavelengths. The team is skilled at optimizing assay parameters such as genetic
background, readout, food source, salt concentration, temperature, timing, number of replicates, and numberof animals per well. Imaging is optimized by transfer from agar to liquid media to minimize imaging artifacts
and a paralytic drug is often added to slow worm movement, minimizing misalignment between subsequently
imaged channels. Microscopy imaging is the primary screening method: plate readers do not offer per-worm ormorphological readouts and are often not compatible even with bulk ﬂuorescence-level assays 66 ; customized
ﬂow cytometers can measure certain phenotypes 82,83 , but current equipment to retrieve worms from a 96-wellplate is too slow and inconsistent.
Both laboratories lead their ﬁelds and have productive records of pursuing hits from C. elegans screens. The
Ausubel laboratory’s reputation stems from pioneering discoveries that many human microbial pathogens also
kill C. elegans 84–89 , typically using similar virulence factors 84,86–92 , and that key features are shared between
C. elegans’ immune system and the innate immune systems of mammals 80,93–97 . The Ruvkun lab is wellrespected for work using C. elegans molecular genetics and genomics, leading to the discovery of microRNAs 98 ,the ﬁrst detection of microRNAs in other animals 99 , and the discovery of their role in gene regulation. Most
relevant to the proposed project is the discovery of key members of the insulin pathway that control metabolism
and longevity 100 , that were later found to be conserved in mammals.
Development of image analysis and machine learning algorithms for biomedicine: The Wahlby,
Carpenter, and Golland groups each have substantial experience developing and applying image analysis algorithms to important problems in cell biology and biomedical imaging. Our expertise spans the full spectrum
required for the proposed project: developing advanced image analysis algorithms, validating them in the context of real-world biological problems, and creating practical, useful software tools that are made publicly andfreely available.
Dr. Wahlby was one of the pioneers in developing advanced segmentation methods for phenotype quantiﬁcation in ﬂuorescence microscopy images of cells 101 , using nuclear stains for seeded segmentation of cytoplasms 102 , a widely used approach today. Our algorithms for accurate delineation of individual cells in culture
and tissue 103 have proven valuable in a number of our own image-based biological experiments 104–108 . The
algorithms have become widely used via a software tool 109 that also incorporates our novel algorithms for signal detection 110 . The algorithms are also a key component of CellProﬁler 57 as a result of our collaboration
with the Carpenter group in 2003. We have also developed a new apprach for quantiﬁcation of signal colocalization 111,112 and designed methods for quantitative measurements using novel staining techniques 113,114 .
Our recent work on C. elegans with this proposal’s collaborators produced a novel method for segmentation ofclusters of worms using a probabilistic shape model 61 .
The Carpenter and Golland groups began collaborating in 2004 to expand the range of cell types andphenotypes amenable to automated analysis for high-throughput screening. This produced algorithms for theaccurate identiﬁcation of cell edges based on Voronoi diagrams in an image-based metric space 56 , an approach
for illumination correction for ﬂuorescence microscopy images 58,115,116 , a combination of existing algorithms,
including Wahlby’s, for the accurate identiﬁcation of difﬁcult-to-segment nuclei 57 , a workﬂow for handling theunprecedented hundreds of numerical measurements for each of millions of cells in dozens of experiments 58 ,and a software infrastructure in which to incorporate these algorithms and approaches (detailed below). The
software, CellProﬁler, and some of its algorithms will be useful for the C. elegans work proposed here. Most
importantly, these algorithms have been cited in hundreds of papers in the past three years, demonstrating thatthey serve an unmet need in biomedical research. We directly collaborated in many important studies in a wide
variety of biological ﬁelds of study 55,57,59,117–126 .
Machine learning has become increasingly useful in our work on scoring phenotypes in image-based screensin cases when a complex combination of features is required to differentiate between classes. We adapted theprinciples of content-based image retrieval 127 and created a system for scoring complex phenotypes in highResearch Strategy

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

throughput image-based screens using iterative feedback and machine learning 55 . We have used this software
for large-scale screens for dozens of phenotypes that could not be scored by traditional methods 55,128 , many ofwhich are likely to be published in the next 1–2 years. Typically, no customization is required to accurately score
phenotypes, aside from initial segmentation and feature extraction, overcoming a signiﬁcant bottleneck in assay
development for screens. The approach and software should be equally successful for C. elegans screens,
once accurate measurements can be obtained from individual worms.
Aside from this collaborative work, the Golland group has established computational frameworks for imagebased statistical analysis of shapes as well as shape-based segmentation. Their shape analysis research 129–133
explores the morphological variability of brain structures across and within different populations, which led
to the development of a discriminative shape model 54 . The underlying mathematical frameworks are either
level-set or MRF models—both are state-of-the-art techniques for segmentation. While the analysis and extraction of brain structures has been the main focus of the group’s research 134 , the segmentation of natural
images with various forms of priors such as shape symmetry, GMM models and user interaction have also
been explored 135–141 . These two complementary lines of research cover most aspects of the problems athand—foreground/background segmentation, delineation of individual worms based on shape, and extractionof numerical measurements that are speciﬁc to worm phenotypes.
Modular open-source software for image analysis: Together, the Wahlby, Carpenter, and Golland groups
have a track record of producing user-friendly software that is valued by the scientiﬁc community and capable of generating useful biological discoveries. The Carpenter, Golland, and Sabatini groups launched theopen-source CellProﬁler software project to give biologists a user-friendly interface to mix and match advanced
image analysis algorithms (including our own, described above) in a modular way for high-throughput experiments 56–58,115,125,142–144 . We also created companion software, CellProﬁler Analyst, for the exploration andanalysis of multi-dimensional, image-based screening data which could not be handled by existing software,commercial or open-source 125 . These tools will be directly applicable to C. elegans-derived data.
CellProﬁler has been useful to the biological community by many measures: (a) It has been cited morethan 150 times in the 3 years since publication, including high-proﬁle studies unafﬁliated with our groups, 145–155(b) The CellProﬁler software is downloaded at a rate of 360/month, (c) There was widespread support fromscreening centers and laboratories around the world for our recent NIH R01 proposal to support CellProﬁler.

Aim 1: Algorithms for C. elegans viability assays to identify modulators of pathogen infection
To score chemical perturbants for their ability to rescue C. elegans from an otherwise lethal infection by thepathogen Microsporidia, we will develop algorithms to count live and dead worms in each sample. Thesealgorithms will delineate individual worms from clusters of worms and extract shape features that can distinguishcurvy, live worms from straight, dead worms.
The successful C. elegans viability screen described in Preliminary studies 11 relied on measuring a ﬂuorescent viability stain (SYTOX) across the population without needing to identify individual worms. However, for thisMicrosporidia assay, and other future live/dead screens, it is preferable to instead classify each animal as live
or dead based on its shape in brightﬁeld images; SYTOX staining adds reagent costs and sample preparation
time and it is a less reliable indicator of viability from a biological perspective 66 . In addition, SYTOX stains somepathogens we plan to screen as well as some types of debris, thus obscuring the signal from the worms.
Experimental approach
While non-touching worms can usually be delineated in brightﬁeld images based on the differences in intensities between foreground and background, image intensity alone is not sufﬁcient for touching and overlapping
worms. The high-throughput screening assays addressed here require algorithms that separate touching andoverlapping worms in static images, where motion cues are unavailable. Moreover, edges and intensity variations within the worms often mislead conventional segmentation algorithms. On the other hand, while thevarying postures of the worms introduce signiﬁcant extrinsic geometrical differences, the worms have similar
intrinsic geometrical properties (such as length and width proﬁle). We propose a probabilistic shape model that
captures this type of knowledge in an automated segmentation method. The key ideas are the constructionof a low-dimensional shape-descriptor space and the deﬁnition of a probability measure on it. Closely related
approaches for shape representation include the active shape model (ASM) and its variants 156 , and medial
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B
C
D

D

E

F

wormdiameter(pixels)

ypositionafteralignment

A

G

E

controlpointxpositionafteralignment

H

Figure C.2: Constructing a worm model. A: Rotated input image. B: Initial segmentation. C: Skeleton. D: Pruned skeleton
with local radii at control points. E: Parameterized shape, recreated from descriptor. F: Connected control points of N=454
training worms after mirroring, alignment by translation, and rotation. G: Variation in radius along the length of all N worms.
H: The effect of varying the weights of the “eigenworms” corresponding to the seven largest eigenvalues of the ﬁnal model.
Any 3-D properties of the worms will be captured as projections in our 2-D images. In fact, the sixth “eigenworm” appears
to capture the C. elegans lifting its head.

axis transform methods 157 for capturing shape variability in anatomical structures and other objects 158,159 andothers. We learn the possible shape variations from N training worms obtained by automated segmentation of
a subset of worms that do not touch or overlap.
1. Construct a low-dimensional worm shape descriptor from the skeleton of the shape and its distances
to the boundaries, given by the medial-axis transform 160 . Fig. C.2 exempliﬁes our proposed computationally
efﬁcient representation of the shape, where we extract the skeleton of each worm (Fig. C.2C), and prune spurs
by iteratively removing the shortest spur of every branch point of the skeleton. Once a non-branched skeleton
is obtained, we ﬁnd end points, and sample n control points uniformly along the skeleton. The original worm
shape can be approximately restored by placing discs with radius equal to the local worm width (Fig. C.2D) ateach control point, and smoothing the edges by the pair-wise convex hull of the discs (Fig. C.2E).
2. Reduce dimensionality by Principal Component Analysis (PCA): Align descriptors by similarity transformation (i.e., rotation and translation, no scale or skew) by minimizing the sum of the Euclidean distances ofcorresponding points along the skeletons. Thus, non-rigid components of the deformations are completely captured within the shape variations. To make variations in worm shape symmetrical, the training set is doubled to
2N by mirroring all samples. Fig. C.2F shows the aligned skeletons of the training set. The signiﬁcant similarityof the worms’ radii proﬁles (Fig. C.2G) allows representation of the differences in the radii by a single value,
which corresponds to the median thickness of the worm. The deformations of the postures are described by
the coordinates of the n aligned control points and the variation in thickness, resulting in a (2n + 1) dimensional
data space. We project the vector representations of the parameterized skeletons into a lower-dimensionalfeature space by PCA 161 . All the worms in the training set can be restored with good approximation by linear
combinations of the eigenvectors, or “eigenworms” (Fig. C.2H).
3. Find posture probabilities and resolve clusters by graph search algorithm: The weights w of
the training worms deﬁne a probability measure on the feature space of the worm deformations: p(x) ∝162 . After the input images have been partitioned into wormexp(−wT Σ−1
L w), where ΣL = diag(λ1 ... λL ) as inregions (individuals and clusters) and background (Fig. C.3B) as discussed later, we ﬁnd the skeleton of eachclusters using the medial-axis transform 160 (Fig. C.3C). We represent the skeleton by a sparse direct graphGs = {V , E}. The vertices V of the graph represent the skeleton segments (Fig. C.3C)) and the edges E connect pairs of vertices representing pairs of skeleton segments with common intersection points. We represent aworm candidate by a path p1 ... pN in the graph containing one or more vertices. Set K to the estimated numberof worms in a cluster (given by cluster area) and Let p1 ... pN denote the paths in the graph. We ﬁnd K out of N
paths in the graph by minimizing the cost functionalE(p1 ... pK ) = −

K

k =1

Research Strategy

log P(pk ) + α

−1
K k


|pk ∩ pl | + β|Ṽk |,k =2 l=1

Page 51

Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

A

B
v

v vv

v
v

C

v

D

Figure C.3: Resolving clusters. A: Input image, B:
binary image of cluster, and C: its pruned skeleton:vs indicate vertices; os indicate groups of edges. D:
Final segmentation result.

Figure C.4: Three examples of resolved clusters (bottom) shown
together with original images (top). Worms close to the well edge
were excluded from this analysis.

where | · | denote cardinality or size. The ﬁrst term is a requirement that the selected set of paths will have thehighest probability to represent true worm shapes. The second term is a requirement that the sum of pairwise
overlaps between the selected paths will be minimal. The third term is the number of vertices that are notincluded in the union of the selected paths, constraining the paths to cover the worm-cluster skeleton, and α
and β are scaling factors. A global minimum can by obtained by an exhaustive
search for all the subsets of K 
out of N paths in Gs . This is however a combinatorial problem of order KN . To reduce the computational time
we apply a greedy 163 strategy where at each stage we make a locally optimized choice of a path in the graph,
until we select K paths. We applied the proposed segmentation approach to images containing worm clusters
that could not be resolved based on gray-scale information alone. Most of the worms were correctly segmented
as veriﬁed by visual evaluation (Fig. C.3D and C.4).
4. Measure worm viability by scoring the live/dead phenotype as the worm’s length along the medial axis
divided by the straight distance between worm’s end points 37,164 . Initial studies also indicate that the shapecharacteristics described by the eigenworms provide a good measure of viability.
Validation, evaluation, and benchmarks
To validate and evaluate the proposed algorithm we will use a set of 6000 expert-annotated brightﬁeld images
from a previous screen 11 in addition to images from the Microsporidia screen itself. Overall, our goal is toachieve “screenability” in terms of both accuracy and computational speed. Accuracy: We will use metrics
accepted in the screening ﬁeld to assess accuracy based on the ability to distinguish control wells with worm
populations of known phenotype—hundreds of these controls are included in each experiment. If the assay
readout is Gaussian, we will aim for a Z’-factor 165 above 0.5 (>0.2 would still be acceptable); if not, we will use
classiﬁcation sensitivity and speciﬁcity, overall aiming to avoid visual examination for 90–95% of the samples.
During the iterative process of algorithm and assay development, we will also validate individual steps of theimage analysis pipeline (foreground/background segmentation, worm cluster resolution, live/dead scoring) as
appropriate, comparing algorithm results to “ground truth” provided by our worm experts. Speed: Image processing should keep pace with image acquisition; given current image acquisition rates and cluster computing
costs, our goal is 6 CPU-minutes or less per image on a typical CPU. The methods proposed are likely to meetthis goal, but there are many ways to reduce computational costs if needed.
Potential problems and alternative strategies
Initial foreground/background segmentation is a prerequisite for the proposed cluster separation. If
local adaptive thresholding is not sufﬁcient, we will rely on more advanced methods, such as level-sets forforeground/background separation (Aim 2).
Cluster skeletonization may not coincide with the centers of the worms, skewing the cluster separation.
Research Strategy

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may beCarolina
used only for nonprofit educational purposes
Principal
Investigator/Program
Director
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provided the document remains unchanged and the PI, the grantee organization, and NIAID are credited.
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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

MASSACHUSETTS
GENERAL HOSPITAL
Department of Molecular Biology
Richard B. Simches Research Center
185 Cambridge Street, 7th Floor
Boston, MA 02114
Tel: 617.726.5969, Fax: 617.726.5949
E-mail: ausubel@molbio.mgh.harvard.edu

HARVARD
MEDICAL SCHOOL
Frederick M. Ausubel, Ph.D.
Professor of Genetics
Harvard Medical School
Massachusetts General Hospital

Please note that the application text is copyrighted. It may be used only for
nonprofit educational purposes provided the document remains unchanged
and the PI, the grantee organization, and NIAID are credited. See more online:
http://funding.niaid.nih.gov/researchfunding/grant/pages/appsamples.aspx

January 22, 2010
Carolina Wählby
Broad Institute of Harvard and MIT
Uppsala University
Dear Carolina,

I am eager to continue our successful collaboration as part of your NIH R01 proposal, “Image
analysis algorithms for high-throughput C. elegans assays to identify regulators of infection andmetabolism”. Our work together over the past couple of years, which has been built on thefoundation of our long-term collaboration with Anne Carpenter’s group at the Broad Institute,has demonstrated that whole-organism screens using adult C. elegans in 384-well plates arefeasible and effective for identifying chemicals that affect the course of a bacterial infection.
Automating image analysis for distinguishing live and dead C. elegans by shape and fluorescent
staining is revolutionizing our ability to rapidly screen large chemical compound libraries andRNAi libraries more accurately and rapidly than has been possible, as well as enabling screensthat cannot be accomplished with current technology. I have a great deal of confidence in yourability to manage this large-scale project with its various contributing personnel.
My research group has pioneered the development of so-called multi-host pathogenesis
systems that involve the infection of invertebrate hosts with human bacterial pathogens.
Together with Gary Ruvkun, we have also pioneered and assembled an automated C. elegans
sample preparation pipeline over the past two years to enable high-throughput image-based
screens. We used the system to screen 37,000 chemicals for regulators of infection of C.
elegans by the important human opportunistic pathogen Enterococcus faecalis, the first screenof adult C. elegans to employ automated image analysis (Moy, T.I., A.L. Connery, J. LarkinsFord, G. Wu, R. Mazitschek, G. Casadei, K. Lewis, A.E. Carpenter and F.M. Ausubel (2009)
High throughput screen for antimicrobials using a whole animal infection model. ACS Chem.
Biol., 4:527-533). The screen was highly successful, leading to the identification of at least three
new chemical backbones with antimicrobial activities that are amenable to medicinal chemistry
analysis. In the Moy et al. paper (cited above), live and dead worms were distinguished bystaining dead worms with the fluorescent dye Sytox Orange. However, our recent joint paperthat was recently accepted for publication, substantially advances the image analysis
technology and sets the stage for developing assays that do not depend on staining deadworms with fluorescent dyes (Wählby, C., T. Riklin-Raviv, V. Ljosa, A.L. Conery, P. Golland,
F.M. Ausubel and A.E. Carpenter (2010) Resolving clustered worms via probabilistic shapemodels. Accepted for publication in proceedings of the IEEE International Symposium on
Biomedical Imaging (ISBI) 2010).
Now that we have successfully assembled the hardware and worked out the details of the liquidkilling assays, we will be able to work with you to develop more sophisticated image analysis

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

software that will enable us to carry out much larger screens. In addition to the images that wehave already collected for the E. faecalis screen, we also have collected control images for theother assays. All of these images are available for your work. The E. faecalis screen has laid thegroundwork for additional full-scale chemical screens employing new assays that we havedeveloped, which are funded through other grants in my laboratory:
1. A live/dead assay to identify modulators of infection of C. elegans by microsporidia,
obligate intracellular eukaryotic parasites that are an important emerging humanpathogen and that has recently been added to the NIH list of priority pathogens.
2. Live/dead assay to identify compounds that cure a Staphylococcus aureus or a
Pseudomonas aeruginosa infection of the C. elegans intestine.
3. A protein localization assay to identify modulators of the GFP-labeled proteins clec-60
and/or irg-1::GFP in response to infection of C. elegans by S. aureus or P. aeruginosa,
respectively. These GFP reporters change their pattern of expression in the C. elegans
intestine in response to infection and this screen will identify potential regulators oflocation-specific immunity pathways.
The funding that I have for these projects covers the development of C. elegans assays.
Therefore, my group will be able to assist you in developing automated image analysis for theassays described above. Specifically, we will be able to provide:
1. Expert annotation of control sets of images for the new assays.
2. Informatics support to assist transfer of images and annotations to your group.
3. Collaboration to assess the results of automated scoring.
To guide this work, my group members working on the project will attend at least a monthly
project meeting with your researchers, and I will join them at a twice-yearly meeting of the entire
project team, including PI’s. The scientists in my group who have developed the assays and thehigh-throughput automated methodology for sample preparation and image acquisition will becommitted to contributing to the project. These include Dr. Annie Lee-Conery, the project
manager and Mr. Jonah Ford-Larkins, a technician in my laboratory who has been the primarydeveloper of many of the automated robotic steps of the screening assays. As you know, wehave proven quite proficient at learning the methodology and software that your group produces
and are looking forward to next-generation algorithms to solve these more complex assays.
We will continue to work closely with you as you optimize the computational methods foranalyzing images for this important work. I wish you the best with your grant application.
Sincerely,

Frederick M. Ausubel

Letters of Support

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

7 Cambridge Center
Cambridge, MA 02142
T 617-714-7000 F 617-714-8972
www.broadinstitute.org

Dr. Carolina W hlby
Broad Institute & Uppsala University

January 22, 2010

Dear Carolina,
With great pleasure, I write to support your NIH R01 proposal, “Image analysis algorithms for highthroughput C. elegans assays to identify regulators of infection and metabolism”.
As you know, I became interested in developing image analysis algorithms for C. elegans throughcollaborations that began with Fred Ausubel and Gary Ruvkun in 2004. While helping them to build
the C. elegans Screening Center at Harvard/MGH and to identify novel regulators of infection
through this collaboration, it became clear there is a huge need for image analysis for this organism,
which enables screening in the physiological context of a whole animal. However, my group has been
unable to be devote the effort necessary to “solve” the many assays that C. elegans researchers havesince brought to us which would otherwise be ready-to-screen; my Imaging Platform has focused oncell-based assays.
I was therefore thrilled to learn that you decided to direct the computational expertise of your groupto this long-standing need in the field upon joining the Broad Institute. You have already madetremendous progress since being introduced to the C. elegans researchers and the challenges mygroup had encountered.
Based on my own group’s experience developing computational methods to extract information frommicroscopy images and helping biologists to apply the resulting software, we are well equipped toprovide assistance for the work you propose. My team will assist in the validation of the algorithms
your team develops, but primarily will focus on implementing the algorithms into our user-friendly
open-source software (CellProfiler) and disseminating the result to the C. elegans community. In
particular, we will organize workshops and tutorials to teach the software to those interested inimage-based screening in C. elegans, building on our more general workshops.
I have every confidence that your strong image processing background, combined with the expertiseof my group and the other collaborators, will produce a validated toolbox of algorithms for C. elegans
high-throughput assays and thus substantially transform the field.
I therefore look forward to a continued productive collaboration with you and your group.
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A Collaboration of Massachusetts Institute of Technology, Harvard University and the Harvard-Affiliated Hospitals

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January 25, 2010
Carolina Wahlby
Broad Institute of Harvard and MIT
Uppsala University
7 Cambridge Center
Cambridge, MA 02142

Dear Carolina,
I am writing to express enthusiastic support for your grant proposal “Image analysis algorithms forhigh-throughput C. elegans assays to identify regulators of infection and metabolism.” The aims of this
proposal represent a natural evolution of our recent joint work on automatic methods for microscopy
image analysis in C. elegans experiments. I look forward to our continuing collaboration on this truly
interdisciplinary project. My research focuses on computational modeling of biological shape, its
relationship with biological function and its statistical variability. Shape detection and analysis fromfluorescence microscopy images is an interesting challenge from the computational point of view.
Segmentation and shape characterization of C. elegans provides a great application for computationaland statistical models of biological shape.
This project builds on a long-term collaboration between my group and the Imaging Platform at theBroad Institute. As part of this collaboration, we contributed significantly to development of CellProfiler,a software system for high throughput analysis of cell images. I look forward to collaborating with yourgroup on developing the next generation of algorithms for segmentation and shape analysis of C.
elegans. I also enthusiastically support your plans to distribute the algorithms and the test image data
sets to the community. I believe that open source software development and data sharing enable much
faster scientific progress by engaging more researchers in development and validation of image
analysis methods. I will be happy to see the algorithms and the data created in this project shared withthe broad scientific community.
As part of this collaboration, my group will work closely with the researchers from your group to
develop algorithms for segmentation and shape characterization of C. elegans in high throughput
microscopy images. This problem is an instance of model-based segmentation and shape analysis,
which is the area of expertise of several members of my group. Specifically, Tamar Riklin Raviv is anexpert on implicit representations of shapes in images, shape priors and shape-based segmentation.
Her knowledge in this field is directly applicable to the problems we face in microscopy image analysis.
My expertise relevant to this project is in modeling shape distributions and characterizing statisticaldifferences in shape across populations. Our initial joint efforts to solve this problem led to two
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conference papers, and I expect many interesting publications to come out of this work in the future. I
expect your group to lead the algorithm development and testing, with our help and guidance on modelbased segmentation. I believe our prior experience in the field of shape modeling, detection andanalysis will be directly applicable to this problem.
I expect our groups to meet at least monthly to coordinate our effort, with more extensive semiannual
status update meetings. Your researchers are also welcome to continue attending the general scientificmeetings in my group, as they have been doing for the past several years. This close community
should provide a good venue for brainstorming and testing new ideas.
In addition to the biological benefits of this work, we anticipate that the algorithms developed in thecourse of this collaboration will in turn be useful for the biomedical image analysis domain, which is theprimary focus of our work. Having seen the many benefits of sharing algorithms and ideas between ourgroups in the past, I am eager to collaborate with you on this important and challenging problem.
Sincerely,

Polina Golland
Associate Professor
MIT EECS/CSAIL

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Eleftherios Mylonakis, M.D.
Associate Professor of Medicine
E-mail: emylonakis@partners.org

21 January 2010
Carolina Wahlby, Ph.D.
Broad Institute of Harvard and MIT
Dear Carolina,
I am writing to enthusiastically support your proposal, "Image analysis algorithms forhigh-throughput C. elegans assays to identify regulators of infection and metabolism" forNIH R01 funding.
I became aware of your work through Fred Ausubel’s group. The recent publication of
the first automatically-analyzed image-based screen C. elegans, a collaboration between
his group and members of your project team, was certainly exciting for my group to see,given that we also are scaling up our use of this model organism for high-throughput
screening.
My group is interested in the development of new antifungal therapies. We use C.
elegans in order to identify novel antifungal compounds in a whole animal high assay.
For this work, we find screening in a whole organism to be indispensible for probing thephenotypes of interest/studying this critical disease area.
Although we are not directly collaborating with you in this proposal, it is clear that theflexible, open-source software that you will develop will be directly applicable tomultiple screens planned in my laboratory. I will gladly share images that we collect inour group for various assays so that your algorithms can be designed to be broadly
applicable to other laboratories and assays.
I wish you the best in obtaining funding for your work and look forward to making
productive use of the software tools that result!
Sincerely,

E. Mylonakis, M.D., Ph.D., FIDSA

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FACULTY OF MEDICINE, DEPARTMENT OF MOLECULAR GENETICS
THE TERRENCE DONNELLY CENTRE FOR CELLULAR & BIOMOLECULAR RESEARCH

Dr. Peter J. Roy, Associate Professor

January22nd,2010
CarolinaWahlby,Ph.D.
BroadInstituteofHarvardandMIT
UppsalaUniversity
DearCarolina,IampleasedtosupportyourapplicationtotheNIH,entitled,“ImageanalysisalgorithmsforhighthroughputC.elegansassaystoidentifyregulatorsofinfectionandmetabolism”.Thisworkwould
benefitmylabandthemanyotherswhoareinterestedinmeasuringpropertiesofthisimportantmodelorganismtoanswerawidevarietyofbiologicalquestionsinawhole,livinganimal.
Asyouknow,myresearchgrouphasongoinginterestsinidentifyingandcharacterizingnewsmall
moleculestructuresthatperturbconservedregulatorsofanimaldevelopmentwiththeultimategoal
ofgeneratingnewmoleculartoolsforbiologicalinvestigationandpotentialdrugleads.Ourbasic
approachistococultivateC.eleganswiththesmallmoleculein24wellplateformatandthen
examineprogeny36dayslaterforphenotypesconsistentwithdefectsindevelopment.Wehave
screenedover70,000smallmoleculesinwormsoryeast(asaprescreen)andhaveover1000
bioactivemoleculesthathavepeakedourinterest.Wearecurrentlyinvestigatingnovelantagonists
oftheWntandinsulinsignalingpathwaysingreatdetailinasimilarfashionaswehavedone
previouslywithacalciumchannelantagonist(Kwoketal.,Nature,2006).
Todate,wehavemainlyanalyzedtheexperimentalsamplesfromourscreensmanually,andhave
archivedmostofourexperimentsusinganautomatedimagerthatwedeveloped(describedin
Burns,etal.,NatureProtocols,2006).Althoughwehavedevelopedimageanalysissoftwareto
measurecertainpropertiesoftheimages(ibid),thealgorithmdoesnotdistinguishindividualsmall
worms(L1andL2larvae)andcanencounterdifficultieswhenlargerwormsareespeciallydense.I
wouldbehappytocontinuetoshareanyorallofthese1000sofarchivedimageswithyoutohelp
guideyouralgorithmdevelopmentforarobustimageanalysissoftwarepackage.Iwaspleasedto
hearthattheimagesourgroupproducesaresimilarenoughtothoseofyourlocalcollaboratorsthatthealgorithmsyouplanshouldbeadaptabletothem.
Isuspectthatphenotypesmighthavebeenoverlookedinourpreviouslycollectedimagesetsthat
couldbefoundusingimageanalysisalgorithmsthatcouldbettermeasurethephysicalparameters
ofdifferentlysizedworms.Wewouldbeeagertousealgorithmsyourgroupdevelopstoreevaluate
ourpastimagesanduncoverthesesamplesforfurtherexperimentationinourgroup.Ifthisis
Room 1202, Donnelly Centre for Cellular and Biomolecular Research, 160 College St., Toronto, Ontario M5S 3E1(Office) 416-946-8395 (Fax) 416-978-8287 (email) peter.roy@utoronto.ca
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successful,ofcourseitwouldallowustorelymoreonautomatedalgorithmsinthefuturescreens
ourgroupenvisions.Beingabletoextractquantitativeinformationfromourimageswouldalsobe
usefulforcollaborationwithWilliamRyu,aphysicistrecentlyrecruitedtoourinstitutionwhoisinterestedinusingcomputationalapproachestodissectwormbehaviorandbuildmathematical
models.Havingthisnewcapabilityinourtoolboxwouldclearlybeenormouslyusefultoour
research.

Sincerely,PeterJ.Roy,Ph.D.


Room 1202, Donnelly Centre for Cellular and Biomolecular Research, 160 College St., Toronto, Ontario M5S 3E1(Office) 416-946-8395 (Fax) 416-978-8287 (email) peter.roy@utoronto.ca
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MASSACHUSETTS
GENERAL HOSPITAL
Department of Molecular Biology
Richard B. Simches Research Center
185 Cambridge Street, 7th Floor
Boston, MA 02114
Tel: 617.726.5959, Fax: 617.726.5949
E-mail: ruvkun@molbio.mgh.harvard.edu

HARVARD
MEDICAL SCHOOL
Gary Ruvkun, Ph.D.
Professor of Genetics
Harvard Medical School
Massachusetts General Hospital

January 25, 2010
Carolina Wahlby
Broad Institute of Harvard and MIT
Uppsala University
Dear Carolina,
I am writing to confirm our collaboration to develop image analysis software and algorithms toidentify and measure C. elegans in high-throughput microscopy images.
As you know, my group has been conducting large-scale RNAi screens probing various pathways
related to metabolism and aging, as well as the mechanisms underlying microRNA and RNAi. The
power of these screens has been phenomenal, uncovering dozens of important genes with implications
for human health that have been further investigated in my laboratory and the laboratories of scientists
in my group.
Together with Fred Ausubel, I have worked to create an automated high-throughput C. elegans
screening center, using fluorescence microscopy as the primary readout given the importance of visual
phenotypes in the analysis of complex biological processes like metabolism and aging. The one
remaining bottleneck is image analysis. While the Carpenter group successfully completed an
automated imaging screen for C. elegans viability in collaboration with Fred’s group, my group hasdozens of assays that remain intractable and must be analyzed by eye.
I am therefore very eager to collaborate in your current proposal, “Image analysis algorithms forhigh-throughput C. elegans assays to identify regulators of infection and metabolism”, to create
algorithms to address an important fat-regulation screen in development in my group. Because we havebeen collaborating with the Imaging Platform for three years and with you for the past year, assisting us
on optimizing automated sample preparation and microscopy for optimal image analysis, we have beenable to produce high-quality images that await your algorithm development.
As you know, all of the images we have produced are available for your work, and we areprepared to use funding from other sources to complete assay development and genome-scale RNAi
screening for regulators of fat accumulation and metabolism using Nile Red staining, as well as thelaboratory work to follow up on hits from this screen. We expect this screen to reveal novel regulators offat deposition and elimination, and are hopeful that many of these genes will be useful targets forclinical obesity treatment, given the high conservation between C. elegans and humans.
We plan to contribute to your proposal by annotating control images for the Nile Red assay andtransferring images from the full-scale screen to your group. We plan to participate interactively in thedevelopment of algorithms for this assay by helping you to assess the accuracy of automated scoring.
In the course of this collaboration, my researchers who are working on the project will meet with you atleast once monthly, myself included twice a year for project progress meetings.
Beyond the scope of your proposed grant to complete this particular screen, my laboratory has

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dozens of other large-scale screens that will make use of the algorithms and software you develop, andwe therefore are most supportive of continuing our collaboration with an emphasis now on automatedimage analysis for C. elegans.
Best wishes,

Gary Ruvkun
Professor of Genetics

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Mehmet Fatih Yanik, PhD
Assoc. Professor
Room 36-834

High-Throughput Neurotechnology Laboratory

Massachusetts Institute of Technology

yanik@mit.edu
617.253.1583—Tel
617.258.5846—Fax

Carolina Wahlby, Ph.D.
Broad Institute of Harvard and MIT
7 Cambridge Center, Cambridge, MA 02142
Dear Carolina,

Jan 27, 2010

I am writing to support your NIH proposal, "Image analysis algorithms for highthroughput C. elegans assays to identify regulators of infection and metabolism". As you
know, we recently developed the first on-chip high-throughput C. elegans screening
technologies using microfluidics. This technology allows us to immobilize awake
animals within fractions of a second to take sub-cellular resolution images as well as toperform femtosecond laser surgery for genome-wide neural regeneration/degeneration
studies. A great challenge however still lies in the analysis of highly rich data we
acquire. Several algorithms you propose to develop for multi-well plate-based highthroughput screening could be very useful to my group’s research goals over the next
years.
As we have discussed last time, it will help our work considerably for your group to
develop and systematize C. elegans-specific measurements, and to develop opensource algorithms for these in CellProfiler. We are also enthusiastic about testing in C.
elegans the machine-learning methods that you have successfully used for complex
cellular phenotypes. Several of my group members (e.g., Chris Rohde, whom you’ve
worked with) will be able to adapt the open-source code as needed. In particular, themodular code you develop will allow us to run analysis on-the-fly by building yourmodules into the image acquisition pipeline. Automating the image analysis andextracting information from each sample will enable us to pursue rich phenotypiccharacterization of neural degeneration and regeneration in whole, living organisms.
Your proposal is an excellent synergy to our technology development efforts andbiological goals. We look forward to your completion of this work and its application tosome of our ongoing research!
Best wishes with your grant application,

Mehmet Fatih Yanik, Ph. D.
Associate Professor
Massachusetts Institute of Technology
Department of Electrical Engineering and Computer Science
Program of Computational and Systems Biology
Broad Institute of MIT and Harvard

Massachusetts Institute of Technology

Letters of Support

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www.rle.mit.edu/Yanik

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Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Mehmet Fatih Yanik, PhD
Assoc. Professor
Room 36-834

High-Throughput Neurotechnology Laboratory

Massachusetts Institute of Technology

yanik@mit.edu
617.253.1583—Tel
617.258.5846—Fax

Biography: Dr. Yanik received his BS and MS at MIT Engineering in 2000, and PhD as Stanford Applied
Physics Fellow in 2006. He completed short postdoctoral work in Stanford Bioengineering and
Neurosurgery. He is currently serving as Assoc. Prof. at MIT’s Department of Engineering. His work on
high-throughput technologies, ultrafast optics, microfluidics, neuronal regeneration, coherent photonics
is recognized by NIH Director’s Innovator Award, Packard Award in Engineering, Alfred Sloan Award in
Neuroscience, NIH Eureka (Exceptional Unconventional Research Enabling Knowledge Acceleration)Award, Shillman Career Award, NSF Career Award, Silicon Valley Innovator's Challenge Award, Technology
Review Magazine’s “World’s top 35 innovators under age 35”, Junior Chamber International’s “Outstanding
Young Person", and Technology Research News Magazine’s “Top ten advances of the year”. His studies
have been highlighted in ABC, The Economist, Scientific American, Nature, New Scientist, Biophotonics
International, Popular Mechanics, Genome Technology, and others. www.rle.mit.edu/Yanik

Massachusetts Institute of Technology

Letters of Support

77 Massachusetts Avenue
Cambridge, MA 02139-4307

Room 36-834
www.rle.mit.edu/Yanik

Page 81Principal Investigator/Program Director (Last, first, middle): Wahlby, Carolina

Resource Sharing Plans
It is the mission of the Broad Institute to create comprehensive tools for genomics, proteomics, chemical biology,
and medicine and make them broadly available to the scientiﬁc community. In keeping with this mission, we havemade plans for comprehensive sharing of both the data (images) and software produced.

Data Sharing Plan
All the raw microscopy images for the three screens will be made available to other researchers. In addition, foreach assay we will compile a representative set of images and their metadata and make them available as partof the Broad Bioimage Benchmark Collection (http://www.broadinstitute.org/bbbc/) so that other researchers
can download them and use them for algorithm development, validation, and comparison.

Software Sharing Plan
Software implementations of the algorithms will be incorporated into the Carpenter group’s CellProﬁler system,
which also enables their use via ImageJ (See Research Strategy Section C, Preliminary Studies, and Softwaredevelopment, dissemination, resource sharing, and reproducible research for details.) In order to encourage
participation by the community in its development, CellProﬁler is available under the GNU General Public License (GPL) version 2 (http://www.gnu.org/licenses/old-licenses/gpl-2.0.txt). This is an open-source licensewith the following properties:
1. It makes the software freely available to everyone.
2. It permits the dissemination and commercialization of enhanced or customized versions of the software,or incorporation of the software or pieces of it into other software packages, with the restriction that thesource code of the resulting software also be made available under the GPL. If this restriction becomes alimiting factor, we are willing to dual-license the software, i.e., grant a commercial license while continuing
to release under the GPL for the beneﬁt of the community. If incorporating CellProﬁler or pieces of it intoanother open-source, non-GPL’ed software package would be beneﬁcial to the scientiﬁc community, we
will consider changing CellProﬁler’s license to the less restrictive BSD license. Note that CellProﬁler’s plugin architecture allows many kinds of enhancements to be written as independently distributed modules, towhich the GPL’s restriction does not apply.
3. It permits anyone to continue development of the software. This preserves utility to the community in theevent that we are unable or unwilling to continue development.
4. It permits anyone to modify the source code and to share modiﬁcations with others. We plan to continue
to develop and release new ofﬁcial versions of the software, and have a track record of doing so: in thepast 4 years, we have released 19 versions of CellProﬁler.
We are more than happy to manage and disseminate the improvements or customizations of our tools byothers by incorporating improvements made by others into the ofﬁcial versions of our software. To facilitate such
community involvement, we have set up a publicly available source code repository and developer mailing list.
We also plan to make our bug-tracking system publicly available.
CellProﬁler has a plug-in architecture that makes it easy for users to enhance the software. Several labs
and companies around the world have written their own CellProﬁler plug-ins (“modules”), e.g., to interface withan instrument, perform a specialized analysis for their experiment, or tie into their infrastructure for data storage
and analysis.
Timeline: The software will be made available as soon as it is tested and validated, and before publication.
Congruent with the timeline in Section C, the software for Aim 1 should be available by the end of the second
year, the software for Aim 2 by the end of the third year, and the software for Aim 3 by the end of the ﬁfth year.

Resource Sharing Plan

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PHS 398 Checklist
OMB Number: 0925-0001

1. Application Type:
From SF 424 (R&R) Cover Page. The responses provided on the R&R cover page are repeated here for your reference, as you answer
the questions that are specific to the PHS398.
* Type of Application:
New

Resubmission

RenewalContinuation

Revision

Federal Identifier:

2. Change of Investigator / Change of Institution Questions
Change of principal investigator / program director

Name of former principal investigator / program director:
Prefix:* First Name:
Middle Name:* Last Name:
Suffix:Change of Grantee Institution* Name of former institution:

3. Inventions and Patents* Inventions and Patents:(For renewal applications only)

Yes

No

If the answer is "Yes" then please answer the following:* Previously Reported:

Yes

No

Checklist

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4. * Program Income
Is program income anticipated during the periods for which the grant support is requested?

Yes

No

If you checked "yes" above (indicating that program income is anticipated), then use the format below to reflect the amount andsource(s). Otherwise, leave this section blank.
*Budget Period*Source(s)*Anticipated Amount ($)

5. * Disclosure Permission Statement
If this application does not result in an award, is the Government permitted to disclose the title of your proposed project, and the name,address, telephone number and e-mail address of the official signing for the applicant organization, to organizations that may beinterested in contacting you for further information (e.g., possible collaborations, investment)?
Yes

No

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