Aparna Nathan
Aparna Nathan, PhD
Lecturer in Biomedical Informatics
Associate Director, Master of Biomedical Informatics (MBI) Program

Aparna Nathan studies T cell states in the context of infectious and autoimmune diseases through multimodal analysis of single-cell data from disease cohorts. She earned her PhD in Bioinformatics and Integrative Genomics from Harvard University and conducted her thesis research in Soumya Raychaudhuri’s lab. In her graduate work, she identified T cell states associated with tuberculosis progression and rheumatoid arthritis, and developed a single-cell-resolution model of state-dependent expression quantitative trait loci.

Aparna currently teaches and oversees capstone projects in the Master of Biomedical Informatics program. Her current research is focused on leveraging single-cell data to characterize state-dependent regulatory effects of genetic variants implicated in disease.

DBMI Courses
Immunosuppression causes dynamic changes in expression QTLs in psoriatic skin.
Authors: Xiao Q, Mears J, Nathan A, Ishigaki K, Baglaenko Y, Lim N, Cooney LA, Harris KM, Anderson MS, Fox DA, Smilek DE, Krueger JG, Raychaudhuri S.
Nat Commun
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Methods and Insights from Single-Cell Expression Quantitative Trait Loci.
Authors: Kang JB, Raveane A, Nathan A, Soranzo N, Raychaudhuri S.
Annu Rev Genomics Hum Genet
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Single-cell genomics meets human genetics.
Authors: Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE.
Nat Rev Genet
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The T cell receptor sequence influences the likelihood of T cell memory formation.
Authors: Lagattuta KA, Nathan A, Rumker L, Birnbaum ME, Raychaudhuri S.
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The Chromatin Landscape of Pathogenic Transcriptional Cell States in Rheumatoid Arthritis.
Authors: Weinand K, Sakaue S, Nathan A, Jonsson AH, Zhang F, Watts GFM, Zhu Z, Rao DA, Anolik JH, Brenner MB, Donlin LT, Wei K, Raychaudhuri S.
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Clonal associations of lymphocyte subsets and functional states revealed by single cell antigen receptor profiling of T and B cells in rheumatoid arthritis synovium.
Authors: Dunlap G, Wagner A, Meednu N, Zhang F, Jonsson AH, Wei K, Sakaue S, Nathan A, Bykerk VP, Donlin LT, Goodman SM, Firestein GS, Boyle DL, Holers VM, Moreland LW, Tabechian D, Pitzalis C, Filer A, Raychaudhuri S, Brenner MB, McDavid A, Rao DA, Anolik JH.
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Mapping the dynamic genetic regulatory architecture of HLA genes at single-cell resolution.
Authors: Kang JB, Shen AZ, Sakaue S, Luo Y, Gurajala S, Nathan A, Rumker L, Aguiar VRC, Valencia C, Lagattuta K, Zhang F, Jonsson AH, Yazar S, Alquicira-Hernandez J, Khalili H, Ananthakrishnan AN, Jagadeesh K, Dey K, Daly MJ, Xavier RJ, Donlin LT, Anolik JH, Powell JE, Rao DA, Brenner MB, Gutierrez-Arcelus M, Raychaudhuri S.
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Single-cell eQTL models reveal dynamic T cell state dependence of disease loci.
Authors: Nathan A, Asgari S, Ishigaki K, Valencia C, Amariuta T, Luo Y, Beynor JI, Baglaenko Y, Suliman S, Price AL, Lecca L, Murray MB, Moody DB, Raychaudhuri S.
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Repertoire analyses reveal T cell antigen receptor sequence features that influence T cell fate.
Authors: Lagattuta KA, Kang JB, Nathan A, Pauken KE, Jonsson AH, Rao DA, Sharpe AH, Ishigaki K, Raychaudhuri S.
Nat Immunol
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Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics.
Authors: Reshef YA, Rumker L, Kang JB, Nathan A, Korsunsky I, Asgari S, Murray MB, Moody DB, Raychaudhuri S.
Nat Biotechnol
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