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Steven Kleinstein, PhD

Anthony N Brady Professor of Pathology; Co-Director of Graduate Studies, Computational Biology and Bioinformatics

Research Summary

We are a computational immunology group with a combination of big data analysis and immunology domain expertise. Our interests include both developing new computational methods and applying these methods to study human immune responses. Specific areas of focus include:

We are always open to new collaboration opportunities with basic science and clinical research groups. Please send us an email if interested. For research updates, follow us on twitter (@skleinstein).

Extensive Research Description

High-throughput B cell receptor (BCR) repertoire sequencing

Next-generation sequencing (NGS) technologies have revolutionized our ability to carry out large-scale adaptive immune receptor repertoire sequencing (AIRR-seq) experiments. AIRR-seq is increasingly being applied to gain insights into immune responses in healthy individuals and those with a range of diseases, including autoimmunity, infection, allergy, cancer and aging. As NGS technologies improve, these experiments are producing ever larger datasets, with tens- to hundreds-of-thousands of single cells (or tens- to hundreds-of-millions of BCR sequences when run in bulk), requiring the development of new computational methods to manage and analyze these “Big Data”. For an overview, please check out our review.

We have developed many widely used computational methods for bulk and single-cell AIRR-seq data processing and analysis. These methods are available to the wider scientific community through the Immcantation framework, which provides a start-to-finish analytical ecosystem for high-throughput AIRR-seq datasets, with a focus on B cell receptor (BCR) repertoire profiling. Working in close collaboration with basic experimental and clinical groups, we have been applying our methods to gain biological insights in several systems, including: infection (SARS-CoV-2, HIV, Salmonella, West Nile virus, Lyme), vaccination (influenza), allergy (allergic rhinitis, atopic asthma) and autoimmune disease (Multiple Sclerosis, Myasthenia Gravis). We are also active members of the AIRR Community.

Multi-omic immune signatures of human infection and vaccination responses

Individual variations in immune status and function produce significant heterogeneity in infection and vaccination responses. For example, West Nile virus infection is usually asymptomatic, but can cause severe neurological disease and death, particularly in older patients. Our research leverages recent advances in immune profiling methods to characterize diverse states of human immune system (in health and disease, and following infection and vaccination). We have developed several computational methods for large-scale genetic network modeling, including:

  • QuSAGE, which quantifies pathway activity from high-throughput transcriptional profiling data while accounting for gene-gene correlations
  • LogMiNeR, which leverages prior knowledge networks to improve model interpretability in the analysis of high-throughput transcriptional profiling data.
  • SPEC, which predicts the specific cellular source (e.g., B cells, T cells, etc.) of a gene expression signature using data from total PBMCs
  • TIDAL, which integrates genome-wide expression kinetics and time-dependent promoter analysis to reconstruct transcriptional regulatory networks

For a complete list, check out our software page.

A major biological focus area for this research is the response to influenza infection and vaccination. As part of the multi-institutional Program for Research on Immune Modeling and Experimentation (PRIME), we developed data-driven models for the response of multiple human cell types to infection with different strains of influenza (including the infamous 1918 pandemic strain). We also study influenza vaccination responses as part of the NIH/NIAID Human Immunology Project Consortium (HIPC) and the SARS-CoV-2 infection response as part of IMPACC.

Coauthors

Research Interests

Autoimmune Diseases; Computing Methodologies; Immune System Diseases; Influenza Vaccines; Information Science; Lyme Disease; Pattern Recognition, Automated; Virus Diseases; West Nile virus; Computational Biology; Autoimmune Diseases of the Nervous System; Immune System Phenomena; Mathematical Concepts; Patient-Specific Modeling

Public Health Interests

Bioinformatics; Genetics, Genomics, Epigenetics; Infectious Diseases; Influenza; Vaccines; Viruses; Mosquito-borne Diseases; Tick-borne Diseases

Research Image

Selected Publications

  • Featured CoverKonstorum A, Mohanty S, Zhao Y, Melillo A, Vander Wyk B, Nelson A, Tsang S, Blevins T, Belshe R, Chawla D, Rondina M, Gill T, Montgomery R, Allore H, Kleinstein S, Shaw A. Featured Cover Aging Cell 2023, 22 PMCID: PMC9924937, DOI: 10.1111/acel.13801.
  • CEDARMusen M, Sansone S, Cheung K, Kleinstein S, Crafts M, Schürer S, Graybeal J. CEDAR 2018, 427-428. DOI: 10.1145/3184558.3186200.
  • Graded Vector Representations of Immunoglobulins Produced in Response to West Nile VirusCohen T, Widdows D, Heiden J, Gupta N, Kleinstein S. Graded Vector Representations of Immunoglobulins Produced in Response to West Nile Virus 2017, 10106: 135-148. DOI: 10.1007/978-3-319-52289-0_11.
  • Single cell variability in pro-inflammatory and antiviral gene responses in dendritic cellsFribourg Casajuana M, Hartmann B, Tabbaa O, Ramos I, Zaslavsky E, Nudelman G, Albrecht R, Merad M, Hayot F, Jayaprakash C, Kleinstein S, Garcia-Sastre A, Sealfon S. Single cell variability in pro-inflammatory and antiviral gene responses in dendritic cells The Journal Of Immunology 2016, 196: 202.29-202.29. DOI: 10.4049/jimmunol.196.supp.202.29.
  • VDJML – tools for capturing the results of inferring immune receptor rearrangementsToby I, Breden F, Buntzman A, Christley S, Corrie B, Fonner J, Gupta N, Hershberg U, Jordan C, Kim M, Kleinstein S, Marthandan N, Mock S, Monson N, Rounds W, Rojas M, Rosenfeld A, Rubelt F, Scarborough W, Scheuermann R, Scott J, Uduman M, Heiden J, Cowell L. VDJML – tools for capturing the results of inferring immune receptor rearrangements The Journal Of Immunology 2016, 196: 209.24-209.24. DOI: 10.4049/jimmunol.196.supp.209.24.
  • The transcription factors ZEB2 and T-bet cooperate to program cytotoxic T cell terminal differentiation in response to LCMV viral infectionDominguez C, Amezquita R, Guan T, Marshall H, Joshi N, Kleinstein S, Kaech S. The transcription factors ZEB2 and T-bet cooperate to program cytotoxic T cell terminal differentiation in response to LCMV viral infection Journal Of Cell Biology 2015, 211: 2113oia258. DOI: 10.1083/jcb.2113oia258.
  • A model of somatic hypermutation targeting in mice based on high-throughput immunoglobulin sequencing data (TECH2P.910)Cui A, Diniro R, Briggs A, Adams K, Vander Heiden J, O'Connor K, Vigneault F, Shlomchik M, Kleinstein S. A model of somatic hypermutation targeting in mice based on high-throughput immunoglobulin sequencing data (TECH2P.910) The Journal Of Immunology 2015, 194: 206.20-206.20. DOI: 10.4049/jimmunol.194.supp.206.20.
  • Long-lived IgM plasma cells confer host protection against viral challenge (LYM6P.717)Bohannon C, Powers R, Satyabhama L, Cui A, Tipton C, Michaeli M, Mehr R, Mittler R, Kleinstein S, Sanz I, Jacob J. Long-lived IgM plasma cells confer host protection against viral challenge (LYM6P.717) The Journal Of Immunology 2015, 194: 135.5-135.5. DOI: 10.4049/jimmunol.194.supp.135.5.
  • DNA Demethylation By Activation-Induced Cytidine Deaminase in B Cell LymphomaXi Y, Shivarov V, Yaari G, Kleinstein S, Strout M. DNA Demethylation By Activation-Induced Cytidine Deaminase in B Cell Lymphoma Blood 2014, 124: 3549-3549. DOI: 10.1182/blood.v124.21.3549.3549.
  • pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires (TECH1P.863)Vander Heiden J, Yaari G, Uduman M, Stern J, O'Connor K, Halfer D, Vigneault F, Kleinstein S. pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires (TECH1P.863) The Journal Of Immunology 2014, 192: 69.31-69.31. DOI: 10.4049/jimmunol.192.supp.69.31.
  • Investigating immunological pathways and diseases with a comprehensive compendium of human data (HUM8P.347)Zaslavsky E, Gorenshteyn D, Fribourg M, Park C, Kleinstein S, Sealfon S, Troyanskaya O. Investigating immunological pathways and diseases with a comprehensive compendium of human data (HUM8P.347) The Journal Of Immunology 2014, 192: 185.22-185.22. DOI: 10.4049/jimmunol.192.supp.185.22.
  • Quantifying selection in high-throughput Immunoglobulin sequencing datasets (58.4)Yaari G, Uduman M, Kleinstein S. Quantifying selection in high-throughput Immunoglobulin sequencing datasets (58.4) The Journal Of Immunology 2012, 188: 58.4-58.4. DOI: 10.4049/jimmunol.188.supp.58.4.
  • P1-06-23: Changes in Gene Expression after One Dose of Trastuzumab (T) in HER2+ Breast Cancer Cell Lines Predict Novel Pathways of Response in HER2 Positive Early Stage Breast Cancer.Sprecher E, Lezon-Geyda K, Sarkar S, Bossuyt V, Narayaan M, Kumar A, Krop I, Winer E, Tuck D, Kleinstein S, Harris L. P1-06-23: Changes in Gene Expression after One Dose of Trastuzumab (T) in HER2+ Breast Cancer Cell Lines Predict Novel Pathways of Response in HER2 Positive Early Stage Breast Cancer. Cancer Research 2011, 71: p1-06-23-p1-06-23. DOI: 10.1158/0008-5472.sabcs11-p1-06-23.
  • PS2-108. Infection with Hepatitis C down-regulates the expression of cytokine genes in peripheral bloodTaylor M, Bolen C, Kleinstein S, Brodsky L. PS2-108. Infection with Hepatitis C down-regulates the expression of cytokine genes in peripheral blood Cytokine 2011, 56: 94. DOI: 10.1016/j.cyto.2011.07.274.
  • Inter-follicular germinal center B cell and T follicular helper cell development precedes follicular Tfh maintenance (63.23)Kerfoot S, Yaari G, Patel J, Johnson K, Gonzalez D, Kleinstein S, Haberman A. Inter-follicular germinal center B cell and T follicular helper cell development precedes follicular Tfh maintenance (63.23) The Journal Of Immunology 2011, 186: 63.23-63.23. DOI: 10.4049/jimmunol.186.supp.63.23.
  • Association between response to brief trastuzumab exposure in cell lines and early stage HER2+ breast tumorsSprecher E, Sarkar S, Kleinstein S, Narayan M, Winer E, Tuck D, Lezon-Geyda K, Krop I, Harris L. Association between response to brief trastuzumab exposure in cell lines and early stage HER2+ breast tumors 2011, 446-450. DOI: 10.1145/2147805.2147867.
  • Activated Germinal-Center B Cells Undergo Directed MigrationO'Connor M, Hauser A, Haberman A, Kleinstein S. Activated Germinal-Center B Cells Undergo Directed Migration 2009, 327-331. DOI: 10.1109/bibm.2009.61.
  • Nonuniform Sampling for Global Optimization of Kinetic Rate Constants in Biological PathwaysKleinstein S, Bottino D, Georgieva A, Sarangapani R, Lett G. Nonuniform Sampling for Global Optimization of Kinetic Rate Constants in Biological Pathways 2006, 1611-1616. DOI: 10.1109/wsc.2006.322934.
  • Simulating the immune systemKleinstein S, Seiden P. Simulating the immune system Computing In Science & Engineering 2000, 2: 69-77. DOI: 10.1109/5992.852392.