FCM Data Management and Analysis in ImmPort Richard H. Scheuermann, Ph.D. Department of Pathology and Division of Biomedical Informatics U.T. Southwestern.

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<ul><li><p>FCM Data Management and Analysis in ImmPortRichard H. Scheuermann, Ph.D. Department of Pathology and Division of Biomedical InformaticsU.T. Southwestern Medical Center, Dallas, TX</p></li><li><p>OutlineThe Immunology Database and Analysis Portal ImmPortData management challengesSupport for large projects using diverse experiment methodologiesExtensive clinical dataAutomated FCM data analysis challengesCross-sample comparisonLinkage of automated FCM analysis results with knowledge about cell typesUse of the Cell Ontology</p></li><li><p>ImmPort Purpose and History NIH/NIAID/DAIT would like to:maximize the return on the public investment in basic, translational and clinical researchallow investigators to more effectively extract meaningful information from the vast amounts of data generated from advanced research technologies=&gt; data sharing policy</p><p>Bioinformatics Integration Support Contract (BISC) to support data sharing for all DAIT-funded programs - basic, translational and clinical research</p><p>Immunology Database and Analysis Portal (ImmPort) - www.ImmPort.org Archive and manage basic and clinical research dataIntegrate these research data with extensive biological knowledgeSupport analysis of these integrated data</p></li><li><p>Home pagewww.immport.org</p></li><li><p>Support for many large projects that use a variety of different experiment methodologies, including FCMChallenge 1</p></li><li><p>Immune Function and Biodefense in Children, Elderly, and Immunocompromised Populations ProgramPopulation Genetics Analysis Program: Immunity to Vaccines/Infections ProgramHLA Region Genetics in Immune-Mediated Diseases ProgramOther Consortium ProjectsImmune Modeling CentersImmPort Research Data | My Work BenchBrowse Data/ ImmPort Research Data/ ImmPort Supported Programs</p></li><li><p>Grants/Contracts/Projects:Immune Function and Biodefense in Children, Elderly, and Immunocompromised Populations ProgramImmPort Research Data | My Work BenchBrowse Data/ ImmPort Research Data/ ImmPort Supported Programs</p><p>Project TitleInstitutionPrinciple InvestigatorObjectiveNumber of SubjectsMechanistic AssaysAn Improved Influenza A Vaccine for Rapid Protection of the ElderlyThe 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 CytometryProtective Immunity in Transplant RecipientsEmory Transplant CenterLarsen, Christian, Ph.D.To determine the effects of chronic immunosuppressive therapies on adaptive, innate and specific immunity90ELISA, ELISPOT, Microarry, RT - PCRKinetic analysis of immunologic repletion and influenza vaccine responsivenessChildrens Hospital of PhiladelphiaSullivan, 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 responses54ELISPOT, Sequencing, Flow CytometryImmune Function and Biodefense in Children, Elderly, and Immunocompromised Populations: TLRs in Innate Immunity and the Induction of Adaptive Immunity in the Neonate and InfantUniversity 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) immunity17 adults, 17 neonatesRT-PCR, ELISA, Flow Cytometry, MicroarrayResponses to Influenza Vaccination in Systemic LupusOklahoma 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-PCRImmune function and Biodefense in Children, Elderly and Immunocompromised PopulationsOregon Health and Science UniversityNikolich-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 PregnancyMt. Sinai School of MedicineMoran, 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 vaccination75Flow Cytometry, multiplex ELISA, RT-PCRRochester BiodefenseUniversity of RochesterSanz, Ignacio, M.D.To identify the specific immune defects that make immunocomprised populations specially susceptible at bioterrorists attack.280Flow CytometryInnate Immune Responsiveness in the Elderly and the Immunosuppressed Yale School of MedicineFikrig, 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</p></li><li><p>Extensive clinical data for correlative analysisChallenge 2</p></li><li><p>AUTOMATED FCM ANALYSIS</p></li><li><p>Challenge 3Identification of same cell populations in multiple samples</p></li><li><p>Challenge 4Linkage of automated results with knowledge about known cell types</p></li><li><p>N1-3UM1-2UM3-4PBGSMGNSMDNMCD27IgDB220CD24CD38IgGA17 B Cell Populations in Blood</p></li><li><p>Population characteristics</p><p>PopulationaColorbCD27cIgDcIgGcCD38cCD24cB220cProportiondPutative cell typeaN1Gray-+-+int+48.94%nave (CD38+)[Bm2?]N2Magenta-+--++4.69%nave (CD38-)N3Purple-+-++low4.41%nave (CD38+B220low)</p><p>UM1Darkred++-+++1.55%unswitched memory (CD38+)UM2Salmon++--++0.94%unswitched memory (CD38-)[Bm1?]UM3Darkblue+int-++low6.16%IgDlow unswitched memory (CD38+)UM4Green+int--+low11.50%IgDlow unswitched memory (CD38-)</p><p>GSM1Grayishgreen++++++0.36%switching memory (IgD+IgG+CD38+)GSM2Yellow+-+++low4.05%switched memory (CD38+)[early Bm5?]GSM3Blue+-+-+low4.40%switched memory (CD38-)[late Bm5?]</p><p>GNSM1Cyan+--++low4.84%IgD-IgG- memoryGNSM2Darkgreen+---+low3.84%IgD-IgG- memoryGNSM3Teal+--+++1.30%IgD-IgG- memoryGNSM4Orange+----low0.51%IgD-IgG- memory</p><p>DNSM1Pink--+--+0.85%double negative memory (IgG+)DNSM2Darkgray-----+0.91%double negative memory (IgG-)</p><p>PBRedhigh--high-low0.75%plasmablasts</p></li><li><p>Summary Statistics</p></li><li><p>B cell component of the Cell Ontologyhttp://www.obofoundry.org/</p></li><li><p>FCM Data ChallengesData management challengesSupport for large projects using diverse experiment methodologiesExtensive clinical data for correlative analysisAutomated FCM data analysis challengesCross-sample comparisonLinkage of automated FCM analysis results with knowledge about cell typesUse of the Cell Ontology</p></li><li><p>UT SouthwesternYu (Max) QianDavid DougallMegan KongJamie LeeJennifer CaiJie HuangNishanth MarthandanDiane XiangYoung Bun KimPaula GuidryEva Sadat</p><p>Ignacio Sanz (Rochester)Chungwen Wei (Rochester)Tim Mosmann (Rochester)Adam Seegmiller (UTSW)Nitin Karandikar (UTSW)Christine Martens (Emory)Chris Ding (UTA)</p><p>Alex Diehl (Jackson Labs)Terry Meehan (Jackson Labs)Martin Zand (Rochester)Supported by NIH N01AI40076 and N01AI40041Northrop GrummanJohn CampbellCarl DahlkeYue LiuLiz ThompsonJeff WiserMike Attasi</p><p>Immune Tolerance NetworkDave ParrishKeith BoyceTom CasaleJeff Bluestone</p><p>Acknowledgments</p><p>****FLOCK *****</p></li></ul>