Research

Understanding Cell Lineage and Fate Dynamics

Reconstructing cell lineages that lead to the formation of tissues and organs is of crucial importance in developmental biology. Recent studies combine two high-throughput technologies, single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at an unprecedented scale. We employ cutting-edge analysis tools and develop novel statistical learning algorithms to leverage these complementary molecular datasets and better characterize the cell lineages, cell-state trajectories and the mechanisms driving the cell fate decisions. Our analysis approaches rely on formulating probabilistic or deep generative models which account for underlying biological mechanisms as well as data-specific artefacts. 

Developing Computational Methods for Omics Data

Recent advances in sequencing technologies have enabled the generation of large-scale single-cell and spatial omics datasets that measure single or multiple modalities (chromatin accessibility, DNA sequence, gene expression, surface proteins, etc.) simultaneously from multitude of single cells or tissue locations. Such datasets help to curate catalogs of cellular identities across tissues and organisms and provide the substrate for uncovering the relationships between biomolecules in cells in their tissue context. We develop computational methods for understanding these interactions and cellular states by handling the massive-scale, multi-modal, and noisy single-cell and spatial omics data.

Multiomics and Evolutionary Analyses of Cancer Types of High Indian Prevalence

Certain cancer types such as oral cancer, cervical cancer are more prevalent in India. Despite their high prevalence, the overall survival of these cancer patients has not improved in recent years, indicating a challenge in diagnostics, prognosis and treatment avenues. We employ multiomic and evolutionary analyses to characterize the genomic and transcriptional heterogeneity of these cancer patients and explore their implications in cancer progression, disease prognosis, and patient survival. By generating single-cell datasets from Indian patient cohorts, we plan to develop the cell atlases for these cancer types to investigate the role of cellular heterogeneity in cancer biology and identify novel therapeutic targets.