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Disentangling tissue context
What is the role of tissue context in influencing cell states?
Our work analyzing single-cell datasets across tissue contexts has uncovered shared immune and stromal cells. We are interested in decoding the origins and plasticity of such cells. Learn more in Subramanian et al., Cell Reports 2024, and Geraslan,.., Subramanian et al., Science 2022.
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Comparative transcriptomics
How did cell states evolve?
One of our areas of focus is developing methods for cell state comparisons between species, model systems and human data to determine conserved or divergent cell states. Such comparisons are essential for translating insights from pre-clinical models to humans. We have compared mouse and human kidney disease (Subramanian et al., Cell Reports 2024), and human kidney organoids with native kidney tissue (Subramanian et al., Nature Communications 2019).
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Form and function
How does variability in cell states in the population influence traits?
We are interested in probing and characterizing such variability and its impacts on population-scale attributes. We have developed metrics for assessing variability in cell states in human kidney organoids derived from multiple donors (Subramanian et al., Nature Communications 2019).
Our work bridges basic science and translational questions
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Diabetes and obesity-associated kidney disease
At least 1 in 7 Americans have chronic kidney diseases. Our recent work (Subramanian et al., Cell Reports 2024) highlighted the putative protective role of a macrophage subset expressing the receptor TREM2 (Triggering Receptor Expressed on Myeloid cells-2) in the setting of obesity and diabetes induced kidney injury.
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COVID-19 and virus-associated conditions
We have developed statistical approaches to predict and compare host factors relevant to COVID-19 across cell types and issues of entry (Muus* ,..,Subramanian* et al., Nature Methods 2021, Delorey* ,..,Subramanian* et al., Nature 2021).
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Cancer heterogeneity and evolution
Cancer cells have unstable genomes leading to evolution of cell states and microenvironments. We have developed machine learning models to infer cell heterogeneity, and track the evolution of cell states using phylogenetics (Subramanian, Shackney and Schwartz ISBRA 2012; IEEE TCBB 2013, Subramanian and Schwartz BMC Genomics 2015)