Rupa Sridharan

This study introduces CRISPRm, a dCas9-metabolic enzyme fusion platform, to investigate how nuclear metabolic perturbations affect gene expression and chromatin states in HEK293T cells.

The paper shows that inducing DOT1L in serum/LIF ESCs increases RNAPII accumulation at transcription start sites, mimicking 2i/LIF conditions, but without altering steady-state RNA expression.

The paper introduces MERLIN+P+TFA to robustly estimate transcription factor activity and infer gene regulatory networks from bulk and single-cell data, showing improved accuracy in yeast and mammalian systems.

The paper shows that AF10, a DOT1L interaction partner, regulates H3K79 methylation levels and RNA polymerase II distribution to maintain cell identity and prevent reprogramming.

The paper shows that low H3K79 methylation in ESCs promotes transcription elongation at housekeeping genes, and inhibiting DOT1L enhances iPSC reprogramming by increasing H3K9ac.

This paper introduces scMTNI, a framework to infer cell type-specific gene regulatory networks on cell lineages using scRNA-seq and scATAC-seq data, showing its effectiveness on simulated and real datasets.

This study shows that inhibiting Dot1L, an H3K79 methyltransferase, enhances reprogramming of somatic cells to iPSCs but does not improve transdifferentiation to neurons or muscle cells.

This paper introduces DRMN, a method to infer cell type-specific transcriptional networks from multi-omic data, showing it accurately predicts cis-regulators and models network dynamics.

This study identifies reprogramming checkpoints in iPSC generation by analyzing single-cell transcriptomes and reveals key gene interactions and regulatory mechanisms.