FCM Data Management and Analysis in ImmPort

Richard H. Scheuermann, Ph.D.

Department of Pathology and Division of Biomedical Informatics

U.T. Southwestern Medical Center, Dallas, TX

Outline

• The Immunology Database and Analysis Portal – ImmPort

• Data management challenges– Support for large projects using diverse experiment

methodologies

– Extensive clinical data

• Automated FCM data analysis challenges– Cross-sample comparison

• Linkage of automated FCM analysis results with knowledge about cell types– Use of the Cell Ontology

ImmPort Purpose and History

• NIH/NIAID/DAIT would like to:– maximize the return on the public investment in basic, translational and clinical

research

– allow investigators to more effectively extract meaningful information from the vast amounts of data generated from advanced research technologies

– => data sharing policy

• Bioinformatics Integration Support Contract (BISC) to support data sharing for all DAIT-funded programs - basic, translational and clinical research

• Immunology Database and Analysis Portal (ImmPort) - www.ImmPort.org – Archive and manage basic and clinical research data

– Integrate these research data with extensive biological knowledge

– Support analysis of these integrated data

Home page

www.immport.org

• Support for many large projects that use a variety of different experiment methodologies, including FCM

Challenge 1

Immune Function and Biodefense in Children, Elderly, and Immunocompromised Populations Program

Population Genetics Analysis Program: Immunity to Vaccines/Infections Program

HLA Region Genetics in Immune-Mediated Diseases Program

Other Consortium Projects

Immune Modeling Centers

ImmPort Research Data | My Work Bench

Browse Data/ ImmPort Research Data/ ImmPort Supported Programs

Grants/Contracts/Projects:

Immune Function and Biodefense in Children, Elderly, and Immunocompromised Populations Program

Project Title InstitutionPrinciple

Investigator ObjectiveNumber of

Subjects Mechanistic Assays

An Improved Influenza A Vaccine for Rapid Protection of the Elderly

The Wistar InstituteHildegund C. J. Ertl, M.D.

To conduct pre-clinical studies needed to develop a vaccine for the elderly that would provide protection in the event of a bioterror attack with influenza A virus.

600ELISA, ELISPOT, Flow Cytometry

Protective Immunity in Transplant Recipients Emory Transplant CenterLarsen, Christian, Ph.D.

To determine the effects of chronic immunosuppressive therapies on adaptive, innate and specific immunity

90ELISA, ELISPOT, Microarry, RT - PCR

Kinetic analysis of immunologic repletion and influenza vaccine responsiveness

Children´s Hospital of Philadelphia

Sullivan, Kathleen, Ph.D.

To propose a comprehensive analysis of the immunologic response to killed trivalent influenza vaccine in different immunocompromised populations in order to understand how to improve vaccine responses

54ELISPOT, Sequencing, Flow Cytometry

Immune Function and Biodefense in Children, Elderly, and Immunocompromised Populations: TLRs in Innate Immunity and the Induction of Adaptive Immunity in the Neonate and Infant

University of WashingtonWilson, Christopher, M.D.

To define comprehensively and in molecular and cellular detail differences in recognition and response to microbe-derived danger signals between adults, neonates and infants, and how these, in turn, contribute to differences in innate immunity and the induction of antigen-specific (adaptive) immunity

17 adults, 17 neonates

RT-PCR, ELISA, Flow Cytometry, Microarray

Responses to Influenza Vaccination in Systemic Lupus

Oklahoma Medical Research Foundation (OMRF)

Thompson, Linda, Ph.D.

To understand why many patients with systemic lupus erythematosus (SLE) fail to make adequate responses to immunization with the influenza vaccine.

60ELISPOT, ELISA, multiplex RT-PCR

Immune function and Biodefense in Children, Elderly and Immunocompromised Populations

Oregon Health and Science University

Nikolich-Zugich, Janko, M.D., Ph.D.

To characterize immune markers and mechanisms in the elderly that determine their vulnerability to infectious and bioterrorism agents in categories A-C.

130Flow Cytometry, ELISA, RT-PCR, Gene expression,

Immune Response to Virus Infection During Pregnancy

Mt. Sinai School of Medicine

Moran, Thomas, Ph.D.

To determine whether the different trimesters of pregnancy, characterized by unique hormonal environments, are associated with (a) identifiable, discrete changes in maternal systemic immunity and/or (b) recognizable alterations in susceptibility to select bio-defense pathogens and/or (c) differential responses to influenza vaccination

75Flow Cytometry, multiplex ELISA, RT-PCR

Rochester Biodefense University of Rochester Sanz, Ignacio, M.D.To identify the specific immune defects that make immunocomprised populations specially susceptible at bioterrorists attack.

280 Flow Cytometry

Innate Immune Responsiveness in the Elderly and the Immunosuppressed

Yale School of Medicine Fikrig, Erol, M.D.

This proposal will explore the hypothesis that altered innate immune responsiveness in the elderly and the immunosuppressed contributes to vaccine unresponsiveness or disease susceptibility.

1160SNP genotyping, Flow Cytometry, ELISA

ImmPort Research Data | My Work Bench

Browse Data/ ImmPort Research Data/ ImmPort Supported Programs

• Extensive clinical data for correlative analysis

Challenge 2

AUTOMATED FCM ANALYSIS

Challenge 3

• Identification of same cell populations in multiple samples

Challenge 4

• Linkage of automated results with knowledge about known cell types

N1-3

UM1-2

UM3-4PB GSM

GNSM

DNM

CD27

IgD

B220

CD24

CD38

IgG

A

17 B Cell Populations in Blood

Population characteristics

Populationa Colorb CD27c IgDc IgGc CD38c CD24c B220c Proportiond Putative cell typea

N1 Gray - + - + int + 48.94% naïve (CD38+)[Bm2?]N2 Magenta - + - - + + 4.69% naïve (CD38-)N3 Purple - + - + + low 4.41% naïve (CD38+B220low)

UM1 Darkred + + - + + + 1.55% unswitched memory (CD38+)UM2 Salmon + + - - + + 0.94% unswitched memory (CD38-)[Bm1?]UM3 Darkblue + int - + + low 6.16% IgDlow unswitched memory (CD38+)UM4 Green + int - - + low 11.50% IgDlow unswitched memory (CD38-)

GSM1 Grayishgreen + + + + + + 0.36% switching memory (IgD+IgG+CD38+)GSM2 Yellow + - + + + low 4.05% switched memory (CD38+)[early Bm5?]GSM3 Blue + - + - + low 4.40% switched memory (CD38-)[late Bm5?]

GNSM1 Cyan + - - + + low 4.84% IgD-IgG- memoryGNSM2 Darkgreen + - - - + low 3.84% IgD-IgG- memoryGNSM3 Teal + - - + + + 1.30% IgD-IgG- memoryGNSM4 Orange + - - - - low 0.51% IgD-IgG- memory

DNSM1 Pink - - + - - + 0.85% double negative memory (IgG+)DNSM2 Darkgray - - - - - + 0.91% double negative memory (IgG-)

PB Red high - - high - low 0.75% plasmablasts

Summary Statistics

B cell component of the Cell Ontology

http://www.obofoundry.org/

FCM Data Challenges

• Data management challenges– Support for large projects using diverse experiment

methodologies

– Extensive clinical data for correlative analysis

• Automated FCM data analysis challenges– Cross-sample comparison

• Linkage of automated FCM analysis results with knowledge about cell types– Use of the Cell Ontology

UT SouthwesternYu (Max) QianDavid DougallMegan KongJamie LeeJennifer CaiJie HuangNishanth MarthandanDiane XiangYoung Bun KimPaula GuidryEva Sadat

Ignacio Sanz (Rochester)Chungwen Wei (Rochester)Tim Mosmann (Rochester)Adam Seegmiller (UTSW)Nitin Karandikar (UTSW)Christine Martens (Emory)Chris Ding (UTA)

Alex Diehl (Jackson Labs)Terry Meehan (Jackson Labs)Martin Zand (Rochester)

Supported by NIH N01AI40076 and N01AI40041

Northrop GrummanJohn CampbellCarl DahlkeYue LiuLiz ThompsonJeff WiserMike Attasi

Immune Tolerance NetworkDave ParrishKeith BoyceTom CasaleJeff Bluestone

Acknowledgments