Research Departments & Organizations
Autoimmune Diseases; Autoimmune Diseases of the Nervous System; Carcinoma, Hepatocellular; Computational Biology; Computing Methodologies; Immune System Diseases; Immune System Phenomena; Influenza Vaccines; Information Science; Leukemia; Lymphoma, Non-Hodgkin; Mathematical Concepts; Patient-Specific Modeling; Pattern Recognition, Automated; Virus Diseases
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:
- High-throughput B cell receptor (BCR) repertoire profiling (AIRR-seq or Rep-seq)
- Immune signature of human infection and vaccination responses
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-millions of BCR sequences, 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 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 (HIV, Salmonella, West Nile virus), vaccination (influenza), allergy (allergic rhinitis, atopic asthma) and autoimmune disease (Multiple Sclerosis, Myasthenia Gravis). We are also active members of the AIRR Community.
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 are developing 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).
Somatic hypermutation has been a particular focus on my work starting from my Ph.D. thesis, which involved developing models of B cell affinity maturation. Since then I have made significant advances in understanding germinal center population dynamics and spatial migration patterns, somatic hypermutation targeting and selection dynamics. These advances have been driven by the development of numerous computational and statistical models that have provided robust interpretations of experiments probing specific aspects of somatic mutation and AID targeting, and directly led to the design and implementation of new experiments.
In collaboration with David Schatz, I demonstrated that somatic hypermutation can act genome-wide and thus represents a risk for genomic instability. This finding has led me to further investigate the extent to which these processes could be involved in B cell cancers. We are now carrying out next-generation whole-exome sequencing of tumors from CLL (collaboration with Matthew Strout) and non-Hodgkins Lymphoma (collaboration with David Hudnall) patients.
HCV and Hepatocellular Carcinoma
A second significant cancer-related research effort of my lab focus on chronic infection with hepatitis C virus (HCV), which leads to significant liver diseases such as cirrhosis and hepatocellular carcinoma. In collaboration with Michael Robek, we investigated the ability of IFN-alpha or IFN-gamma and IL-29 (IFN-lambda 1) to individually and cooperatively inhibit HCV virus replication, and determined how this relates to gene expression changes using an HCV replicon system. My lab is also using gene expression profiling of blood samples from chronic HCV patients as a way to understand (and hopefully predict) the response to clinical therapy.
Practical guidelines for B-cell receptor repertoire sequencing analysis.
Yaari G, Kleinstein SH. Practical guidelines for B-cell receptor repertoire sequencing analysis. Genome Medicine 2015, 7:121. 2015
Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles.
Gadala-Maria D, Yaari G, Uduman M, Kleinstein SH. Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles. Proceedings Of The National Academy Of Sciences Of The United States Of America 2015, 112:E862-70. 2015
Hierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data.
Gupta NT, Adams KD, Briggs AW, Timberlake SC, Vigneault F, Kleinstein SH. Hierarchical Clustering Can Identify B Cell Clones with High Confidence in Ig Repertoire Sequencing Data. Journal Of Immunology (Baltimore, Md. : 1950) 2017, 198:2489-2499. 2017
Quantifying selection in high-throughput Immunoglobulin sequencing data sets.
Yaari G, Uduman M, Kleinstein SH. Quantifying selection in high-throughput Immunoglobulin sequencing data sets. Nucleic Acids Research 2012, 40:e134. 2012
B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes.
Stern JN, Yaari G, Vander Heiden JA, Church G, Donahue WF, Hintzen RQ, Huttner AJ, Laman JD, Nagra RM, Nylander A, Pitt D, Ramanan S, Siddiqui BA, Vigneault F, Kleinstein SH, Hafler DA, O'Connor KC. B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes. Science Translational Medicine 2014, 6:248ra107. 2014
Two levels of protection for the B cell genome during somatic hypermutation.
Liu M, Duke JL, Richter DJ, Vinuesa CG, Goodnow CC, Kleinstein SH, Schatz DG. Two levels of protection for the B cell genome during somatic hypermutation. Nature 2008, 451:841-5. 2008
Multiple network-constrained regressions expand insights into influenza vaccination responses.
Avey S, Mohanty S, Wilson J, Zapata H, Joshi SR, Siconolfi B, Tsang S, Shaw AC, Kleinstein SH. Multiple network-constrained regressions expand insights into influenza vaccination responses. Bioinformatics (Oxford, England) 2017, 33:i208-i216. 2017
Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations.
Yaari G, Bolen CR, Thakar J, Kleinstein SH. Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations. Nucleic Acids Research 2013, 41:e170. 2013
Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses.
HIPC-CHI Signatures Project Team., HIPC-I Consortium.. Multicohort analysis reveals baseline transcriptional predictors of influenza vaccination responses. Sci Immunol 2017, 2. 2017