Aparna Nathan
Aparna Nathan, PhD
Lecturer in Biomedical Informatics

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
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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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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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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Maximizing statistical power to detect differentially abundant cell states with scPOST.
Authors: Millard N, Korsunsky I, Weinand K, Fonseka CY, Nathan A, Kang JB, Raychaudhuri S.
Cell Rep Methods
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Efficient and precise single-cell reference atlas mapping with Symphony.
Authors: Kang JB, Nathan A, Weinand K, Zhang F, Millard N, Rumker L, Moody DB, Korsunsky I, Raychaudhuri S.
Nat Commun
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Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease.
Authors: Nathan A, Beynor JI, Baglaenko Y, Suliman S, Ishigaki K, Asgari S, Huang CC, Luo Y, Zhang Z, Lopez K, Lindestam Arlehamn CS, Ernst JD, Jimenez J, Calderón RI, Lecca L, Van Rhijn I, Moody DB, Murray MB, Raychaudhuri S.
Nat Immunol
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Deep transcriptome sequencing of subgenual anterior cingulate cortex reveals cross-diagnostic and diagnosis-specific RNA expression changes in major psychiatric disorders.
Authors: Akula N, Marenco S, Johnson K, Feng N, Zhu K, Schulmann A, Corona W, Jiang X, Cross J, England B, Nathan A, Detera-Wadleigh S, Xu Q, Auluck PK, An K, Kramer R, Apud J, Harris BT, Harker Rhodes C, Lipska BK, McMahon FJ.
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IFN-? and TNF-a drive a CXCL10+ CCL2+ macrophage phenotype expanded in severe COVID-19 lungs and inflammatory diseases with tissue inflammation.
Authors: Zhang F, Mears JR, Shakib L, Beynor JI, Shanaj S, Korsunsky I, Nathan A, Donlin LT, Raychaudhuri S.
Genome Med
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Associations between neighborhood-level factors and opioid-related mortality: A multi-level analysis using death certificate data.
Authors: Flores MW, Lê Cook B, Mullin B, Halperin-Goldstein G, Nathan A, Tenso K, Schuman-Olivier Z.
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BraInMap Elucidates the Macromolecular Connectivity Landscape of Mammalian Brain.
Authors: Pourhaghighi R, Ash PEA, Phanse S, Goebels F, Hu LZM, Chen S, Zhang Y, Wierbowski SD, Boudeau S, Moutaoufik MT, Malty RH, Malolepsza E, Tsafou K, Nathan A, Cromar G, Guo H, Al Abdullatif A, Apicco DJ, Becker LA, Gitler AD, Pulst SM, Youssef A, Hekman R, Havugimana PC, White CA, Blum BC, Ratti A, Bryant CD, Parkinson J, Lage K, Babu M, Yu H, Bader GD, Wolozin B, Emili A.
Cell Syst
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