Ruth R Montgomery, PhD
Professor of Medicine and Professor of Epidemiology (Microbial Diseases)DownloadHi-Res Photo
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Additional Titles
Associate Dean for Scientific Affairs, Dept Clinical: Internal Medicine
Administrative Support
Publications Overview
- 174 Publications
- 13,989 Citations
- 117 Yale Co-Authors
Additional Titles
Associate Dean for Scientific Affairs, Dept Clinical: Internal Medicine
Administrative Support
Publications Overview
- 174 Publications
- 13,989 Citations
- 117 Yale Co-Authors
Additional Titles
Associate Dean for Scientific Affairs, Dept Clinical: Internal Medicine
Administrative Support
Publications Overview
- 174 Publications
- 13,989 Citations
- 117 Yale Co-Authors
About
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Titles
Professor of Medicine and Professor of Epidemiology (Microbial Diseases)
Associate Dean for Scientific Affairs, Dept Clinical: Internal Medicine
Biography
Ruth R. Montgomery is a cellular immunologist with particular expertise in use of novel technology for human translational studies. Her research employs systems wide studies to identify individual differences in immune responses that lead to divergent outcomes to infection. Her group focuses on effects of aging on innate immunity and individual variation influencing susceptibility to West Nile, dengue, Zika and COVID-19 viruses, and inflammatory profiling of patients with sickle cell disease. She has overseen studies of immune responsiveness in human cohorts with successful enrollment of >2000 healthy individuals. Dr. Montgomery’s work is notable for her use of primary human cells to demonstrate immune related mechanisms and illuminate potential avenues for therapeutic interventions. She launched the CyTOF facility at Yale in 2013, was co-chair of the University Provost’s ITS Advisory Committee (ITSAC), and serves as Associate Dean for Scientific Affairs.
Last Updated on January 17, 2024.
Appointments
Office of the Dean, School of Medicine
Associate DeanDualRheumatology
ProfessorPrimaryEpidemiology of Microbial Diseases
ProfessorSecondaryPathology
ProfessorSecondary
Other Departments & Organizations
- All Institutions
- Cancer Immunology
- Epidemiology of Microbial Diseases
- Human and Translational Immunology Program
- Internal Medicine
- Mobile @ Yale
- Molecular Medicine, Pharmacology, and Physiology
- Office of the Dean, School of Medicine
- Pathology
- Pathology and Molecular Medicine
- Pathology Research
- Program in Translational Biomedicine (PTB)
- Rheumatic Diseases Research Core
- Rheumatology
- Rheumatology, Allergy, & Immunology
- Yale Center for Research on Aging (Y-Age)
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
- Yale Institute for Global Health
- Yale School of Public Health
- Yale-BI Biomedical Data Science Fellowship
Education & Training
- Postdoctoral Fellow
- Yale University (1991)
- PhD
- Rockefeller University (1987)
- BA
- University of Pennsylvania, Biology (1981)
Research
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Overview
Immune profiling; Single cell mass cytometry
Medical Research Interests
Aging; Asthma; COVID-19; Immune System; Immune System Diseases; Immunity, Innate; Macrophages; Neutrophils; Pathology; Public Health; Rheumatology; Virus Diseases; West Nile virus
Public Health Interests
Immunology
ORCID
0000-0002-8661-4454
Research at a Glance
Yale Co-Authors
Frequent collaborators of Ruth R Montgomery's published research.
Publications Timeline
A big-picture view of Ruth R Montgomery's research output by year.
Research Interests
Research topics Ruth R Montgomery is interested in exploring.
Erol Fikrig, MD
Albert C Shaw, MD, PhD
Steven Kleinstein, PhD
David A. Hafler, MD, FANA, MSc
Xiaomei Wang
Akiko Iwasaki, PhD
174Publications
13,989Citations
West Nile virus
Aging
Immunity, Innate
Macrophages
COVID-19
Neutrophils
Publications
2026
Empiric azithromycin alters the upper respiratory microbiome and resistome without anti-inflammatory benefit in COVID-19
Glascock A, Maguire C, Phan H, Lydon E, Schaenman J, Calfee C, Melamed E, Greenland J, Corry D, Kheradmand F, Baden L, Sekaly R, McComsey G, Haddad E, Cairns C, Geng L, Pulendran B, Fernandez-Sesma A, Simon V, Metcalf J, Agudelo Higuita N, Messer W, Davis M, Nadeau K, Kraft M, Bime C, Erle D, Atkinson M, Brakenridge S, Ehrlich L, Montgomery R, Shaw A, Hough C, Hafler D, Augustine A, Becker P, Peters B, Ozonoff A, Hoch A, Kim-Schulze S, Krammer F, Bosinger S, Eckalbar W, Altman M, Wilson M, Guan L, Maecker H, Steen H, Diray-Arce J, Rouphael N, Kleinstein S, Jayavelu N, Reed E, Levy O, Chu V, Langelier C. Empiric azithromycin alters the upper respiratory microbiome and resistome without anti-inflammatory benefit in COVID-19. Nature Microbiology 2026, 1-13. PMID: 41840216, DOI: 10.1038/s41564-026-02285-8.Peer-Reviewed Original ResearchAltmetricConceptsUpper respiratory microbiomeRespiratory microbiomeAnti-inflammatory benefitsPatients treated with azithromycinProspective multicentre cohortViral respiratory infectionsSystemic immune responsesAssociated with commensalismInflammatory gene expressionResistance gene expressionAzithromycin treatmentMulticentre cohortGene expressionRespiratory infectionsAzithromycinHospitalized patientsImmune responsePatientsNasal swabsAntibioticsPotential pathogensCOVID-19Microbiome compositionResistance genesExpressionAuthor Correction: Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors
Doni Jayavelu N, Samaha H, Wimalasena S, Hoch A, Gygi J, Gabernet G, Ozonoff A, Liu S, Milliren C, Levy O, Baden L, Melamed E, Ehrlich L, McComsey G, Sekaly R, Cairns C, Haddad E, Schaenman J, Shaw A, Hafler D, Montgomery R, Corry D, Kheradmand F, Atkinson M, Brakenridge S, Agudelo Higuit N, Metcalf J, Hough C, Messer W, Pulendran B, Nadeau K, Davis M, Geng L, Fernandez Sesma A, Simon V, Krammer F, Kraft M, Bime C, Calfee C, Erle D, Langelier C, Guan L, Maecker H, Peters B, Kleinstein S, Reed E, Augustine A, Diray-Arce J, Becker P, Rouphael N, Altman M. Author Correction: Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors. Communications Medicine 2026, 6: 125. PMID: 41730996, PMCID: PMC12929789, DOI: 10.1038/s43856-026-01425-9.Peer-Reviewed Original ResearchMachine learning models predict long COVID outcomes based on baseline clinical and immunologic factors
Doni Jayavelu N, Samaha H, Wimalasena S, Hoch A, Gygi J, Gabernet G, Ozonoff A, Liu S, Milliren C, Levy O, Baden L, Melamed E, Ehrlich L, McComsey G, Sekaly R, Cairns C, Haddad E, Schaenman J, Shaw A, Hafler D, Montgomery R, Corry D, Kheradmand F, Atkinson M, Brakenridge S, Agudelo Higuit N, Metcalf J, Hough C, Messer W, Pulendran B, Nadeau K, Davis M, Geng L, Fernandez Sesma A, Simon V, Krammer F, Kraft M, Bime C, Calfee C, Erle D, Langelier C, Guan L, Maecker H, Peters B, Kleinstein S, Reed E, Augustine A, Diray-Arce J, Becker P, Rouphael N, Altman M. Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors. Communications Medicine 2026, 6: 1. PMID: 41484172, PMCID: PMC12764860, DOI: 10.1038/s43856-025-01230-w.Peer-Reviewed Original ResearchCitationsAltmetricConceptsAntibody titersBaseline clinical characteristicsViral load measurementsLong-term public health impactHospital admissionAcute COVID-19Low antibody titersMethodsClinical dataViral loadClinical characteristicsImmunological factorsClinical dataFemale sexEstablished biomarkersImpact of SARS-CoV-2Risk factorsSignificant health issueSub-phenotypesPatient outcomesDiagnostic precisionAUROC valuesSARS-CoV-2Post-acute sequelaeInfected individualsDisease mechanisms
2025
MTHFR allele and one-carbon metabolic profile predict severity of COVID-19
Petrova B, Syphurs C, Culhane A, Chen J, Chen E, Cotsapas C, Esserman D, Montgomery R, Kleinstein S, Smolen K, Mendez K, Network I, Lasky-Su J, Steen H, Levy O, Diray-Arce J, Kanarek N. MTHFR allele and one-carbon metabolic profile predict severity of COVID-19. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2509118122. PMID: 41410771, PMCID: PMC12745694, DOI: 10.1073/pnas.2509118122.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsSevere COVID-19High riskPublic health burdenHigh risk of severe diseaseEarly detection of patientsC677T alleleRisk of severe diseaseStudy of COVID-19 patientsEarly identificationRisk of severe COVID-19Early detectionDetection of patientsVaccine-acquired immunityHigher risk of severe COVID-19One-carbon metabolism pathwayBurden of SARS-CoV-2 infectionSARS-CoV-2 infectionSeverity of COVID-19COVID-19 patientsTargeted metabolite profilingVariant statusC677TRespiratory statusOne-carbon metabolismT alleleUnravelling the Immunological Enigma of Sickle Cell Disease: Current Understanding and Future Directions
Jamwal S, Calhoun C, Mohanty S, Montgomery R, Krishnamurti L, Shaw A, Yildirim I. Unravelling the Immunological Enigma of Sickle Cell Disease: Current Understanding and Future Directions. Immunology 2025, 177: 445-456. PMID: 41387179, PMCID: PMC12826409, DOI: 10.1111/imm.70080.Peer-Reviewed Original ResearchConceptsMemory B cellsSickle cell diseaseImmune dysregulationT cellsB cellsChronic inflammationImmunological enigmaImmune activationClass-switched memory B cellsAntigen-specific T cell responsesAntigen-specific T cellsImmune responseCell diseaseAntigen-specific immune activationAntigen-specific immune responsesImmune-targeted therapiesSickle cell disease patientsT-cell countsT-cell abnormalitiesT-cell phenotypeMemory B cell differentiationT cell responsesB cell compartmentAdaptive immune dysfunctionSickle cell disease pathophysiologyMultiparameter Profiling Distinguishes Functional Subsets of Platelets in Aging
Montgomery R, Tanguay A, Mohanty S, Shelar A, Rondina M, Meng H, Kleinstein S, Shaw A. Multiparameter Profiling Distinguishes Functional Subsets of Platelets in Aging. Innovation In Aging 2025, 9: igaf122.3627. PMCID: PMC12761676, DOI: 10.1093/geroni/igaf122.3627.Peer-Reviewed Original ResearchConceptsExpression of CD62PTLR-induced activationAntibodies to surface markersSurface markersDifferential expression of markersElevated expressionAberrant immune responseAdhesion to endotheliumPlatelet-rich plasmaSubsets of plateletsExpression of markersResponse to stimulationActivation of plateletsImmune agingIL-10Differential expressionCD107bImmune responseFlow cytometryPlatelet aggregationMass cytometryFlowSOM clusteringCD62PPlateletPlatelet populationMinimalistic transcriptomic signatures permit accurate early prediction of COVID-19 mortality
Narendra R, Lydon E, Van Phan H, Spottiswoode N, Neyton L, Diray-Arce J, Network I, Consortium C, Consortium E, Becker P, Kim-Schulze S, Hoch A, Pickering H, van Zalm P, Cairns C, Altman M, Augustine A, Bosinger S, Eckalbar W, Guan L, Jayavelu N, Kleinstein S, Krammer F, Maecker H, Ozonoff A, Peters B, Rouphael N, Montgomery R, Reed E, Schaenman J, Steen H, Levy O, Haller S, Erle D, Hendrickson C, Krummel M, Matthay M, Woodruff P, Haddad E, Calfee C, Langelier C. Minimalistic transcriptomic signatures permit accurate early prediction of COVID-19 mortality. JCI Insight 2025, 10: e195436. PMID: 41212055, PMCID: PMC12643502, DOI: 10.1172/jci.insight.195436.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsPeripheral blood mononuclear cellsArea under the receiver operating characteristic curveSARS-CoV-2 viral loadCOVID-19 cohortViral loadViral infection pathogenesisNasal swabsNational Institute of Allergy and Infectious DiseasesSARS-CoV-2Transcriptomic signaturesBlood mononuclear cellsReceiver operating characteristic curveHost gene expressionOperating characteristics curvePeripheral bloodPrognostic classifierContemporary cohortGene expressionPrognostic assaysImmunophenotypic assessmentMononuclear cellsFatal outcomePrognostic toolInfection pathogenesisNational InstituteIdentifying one-carbon metabolism as a predictor of COVID-19 severity in the IMPACC cohort 3975
Syphurs C, Petrova B, Diray-Arce J, Culhane A, Chen J, Chen E, Montgomery R, Kleinstein S, Smolen K, Mendez K, Lasky-Su J, Steen H, Levy O, Kanarek N. Identifying one-carbon metabolism as a predictor of COVID-19 severity in the IMPACC cohort 3975. The Journal Of Immunology 2025, 214 DOI: 10.1093/jimmun/vkaf283.1713.Peer-Reviewed Original ResearchConceptsSevere SARS-CoV-2 infectionAdult COVID-19 patientsOne-carbon metabolism pathwaySARS-CoV-2 infectionCOVID-19 cohortCOVID-19 patientsEarly indicatorMTHFR geneMethionine cycleImmunophenotypic assessmentOne-carbon metabolismMTHFR variantsTrajectory groupsDisease progressionLongitudinal analysisMetabolic profilePredictors of COVID-19Respiratory illnessPatientsMetabolic characterizationLiquid chromatography-mass spectrometryCOVID-19 managementGenetic contributorsMethylation of DNACohortEvaluating COVID-19 severity prediction and immune dynamics with NULISAseq: Insights from the IMPACC study
Abe K, Holmes T, Nguyen T, Abe K, Holmes T, Nguyen T, Kim-Schulze S, Levy O, Baden L, Melamed E, Ehrlich L, McComsey G, Sekaly R, Cairns C, Haddad E, Shaw A, Hafler D, Montgomery R, Corry D, Kheradmand F, Atkinson M, Brakenridge S, Higuita N, Metcalf J, Hough C, Messer W, Pulendran B, Nadeau K, Davis M, Fernandez-Sesma A, Simon V, Kraft M, Bime C, Calfee C, Erle D, Schaenman J, Reed E, Ozonoff A, Peters B, Kleinstein S, Augustine A, Diray-Arce J, Becker P, Rouphael N, Maecker H, Kim-Schulze S, Levy O, Baden L, Melamed E, Ehrlich L, McComsey G, Sekaly R, Cairns C, Haddad E, Shaw A, Hafler D, Montgomery R, Corry D, Kheradmand F, Atkinson M, Brakenridge S, Higuita N, Metcalf J, Hough C, Messer W, Pulendran B, Nadeau K, Davis M, Fernandez-Sesma A, Simon V, Kraft M, Bime C, Calfee C, Erle D, Schaenman J, Reed E, Ozonoff A, Peters B, Kleinstein S, Augustine A, Diray-Arce J, Becker P, Rouphael N, Maecker H. Evaluating COVID-19 severity prediction and immune dynamics with NULISAseq: Insights from the IMPACC study. The Journal Of Immunology 2025, 214: 3310-3320. PMID: 41166719, PMCID: PMC12726064, DOI: 10.1093/jimmun/vkaf263.Peer-Reviewed Original ResearchMeSH Keywords and ConceptsCirculating endothelial signatures correlate with worse outcomes in COVID-19, respiratory failure and ARDS
Costa Monteiro A, Pickering H, Sarma A, Taylor C, Jenkins M, Hsu F, Nadel B, Levy O, Baden L, Melamed E, Ehrlich L, McComsey G, Sekaly R, Cairns C, Haddad E, Shaw A, Hafler D, Montgomery R, Corry D, Kheradmand F, Atkinson M, Brakenridge S, Higuita N, Metcalf J, Hough C, Messer W, Pulendran B, Nadeau K, Davis M, Geng L, Fernandez-Sesma A, Simon V, Krammer F, Kraft M, Bime C, Calfee C, Erle D, Bosinger S, Eckalbar W, Maecker H, Rahman A, Guan L, Peters B, Kleinstein S, Augustine A, Diray-Arce J, Becker P, Rouphael N, Agus M, Kulkarni H, Schaenmann J, Salehi-Rad R, Matthay M, Reed E, Sapru A. Circulating endothelial signatures correlate with worse outcomes in COVID-19, respiratory failure and ARDS. Critical Care 2025, 29: 432. PMID: 41088445, PMCID: PMC12522733, DOI: 10.1186/s13054-025-05596-0.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsRespiratory failureNon-invasive evaluationEndothelial damageNon-survivorsRespiratory outcomesInvasive mechanical ventilationAdult hospitalized patientsMultivariate logistic regressionAssociated with mortalityPresence of endothelial cellsRespiratory trajectoriesPediatric patientsValidation cohortMechanical ventilationCell signaturesPrimary outcomeMethodsTo testEndothelial cellsCOVID-19 non-survivorsPatientsProteomic phenotypesLogistic regressionARDSOutcomesBaseline
Clinical Trials
Current Trials
Immune Response Analysis in Lymph Node Tissue
IRB ID2000032631RoleSub InvestigatorPrimary Completion Date04/30/2027Recruiting ParticipantsImpact of HIV Infection on Immunologic, Transcriptomic, and Metabolomic Signatures
IRB ID1608018239RoleSub InvestigatorPrimary Completion Date09/01/2021Recruiting ParticipantsGenderBothAge18+ years
Academic Achievements & Community Involvement
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Activities
activity Immunity & Ageing
02/01/2019 - PresentJournal ServiceAssociate EditorDetailsBoard memberactivity Coalition for Epidemic Preparedness Innovations (CEPI)
02/15/2019 - PresentProfessional OrganizationsCommittee MemberDetailsSystems Immunology Task Forceactivity Phenomics
11/01/2020 - PresentJournal ServiceEditorial Board Memberactivity CIVICs NIAID Collaborative Influenza Vaccine Innovation Centers (CIVICs) Program
08/01/2020 - 01/01/2025Advisory BoardsExpert Panel MemberDetailsCIVICs NIAID Collaborative Influenza Vaccine Innovation Centers (CIVICs) Programactivity Society of Leukocyte Biology
2018 - 2021Professional OrganizationsCouncil MemberDetailsCouncil Member
Honors
honor Fellow
03/27/2025National AwardAAAS American Association for the Advancement of ScienceDetailsUnited Stateshonor Member
07/01/2020Regional AwardConnecticut Academy of Science and EngineeringDetailsUnited States
News & Links
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Media
- An in-depth single cell analysis identifies multiple distinct subtypes of airway immune cells and provides insights of cellular function relevant for pathogenesis of asthma. J Leuk Biol. 2020;108:1555-64.
- 360 cell type/functional marker combinations were rank-ordered by P value comparing virus-induced changes with dengue (top panel) or Zika (bottom panel). Enrichment scores for innate cell types showed striking differences between naive and recall responses. PLoS Negl Trop Dis. 2020;14:e0008112.
- Increase in diversity of NK cell receptors following infection with virus. Sci Trans Med 7:297ra115. PMCID:PMC4547537
- Ixodes tick, vector of Lyme disease
News
- March 27, 2025Source: Yale News
Eight Yale Faculty Members Named AAAS Fellows
- November 20, 2024
How Does Aging Affect Innate Immunity?
- May 01, 2024
COVID-19: New ‘Omics’ Models Show Why Some People Are at Greater Risk of Severe Disease, Death
- March 19, 2024
Why Do Some People Experience Asthma Symptoms Despite Treatment?
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Rheumatology
300 Cedar St
New Haven, CT 06520
United States
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