Ongoing Projects
Ongoing Projects
Embedded Files
Leveraging single-cell and spatial transcriptomics data to identify novel therapeutic targets in Pancreatic Ductal Adenocarcinoma (PDAC):
Leveraging single-cell and spatial transcriptomics data to identify novel therapeutic targets in Pancreatic Ductal Adenocarcinoma (PDAC):
PDAC tumor epithelial cells exist within unique transcriptional states, predictive of differential sensitivities to different therapeutics. Further, fibrotic desmoplasia (scar-like) shields these tumor cells from immune response.
PDAC tumor epithelial cells exist within unique transcriptional states, predictive of differential sensitivities to different therapeutics. Further, fibrotic desmoplasia (scar-like) shields these tumor cells from immune response.
Through leveraging single-cell and spatial biology with pharmacotranscriptomic tools we've developed, we seek to identify novel therapeutics that take into account both intra-tumor heterogeneity and the reduction of fibrosis to enhance immunotherapeutic options.
Through leveraging single-cell and spatial biology with pharmacotranscriptomic tools we've developed, we seek to identify novel therapeutics that take into account both intra-tumor heterogeneity and the reduction of fibrosis to enhance immunotherapeutic options.
Leveraging gene expression data to identify novel synergetic small molecule combinations for brain cancers:
Leveraging gene expression data to identify novel synergetic small molecule combinations for brain cancers:
We have shown that bulk gene expression data of glioblastoma and medulloblastoma tumors can be used to predict efficacy of multiple small molecule based treatments and combinations.
We have shown that bulk gene expression data of glioblastoma and medulloblastoma tumors can be used to predict efficacy of multiple small molecule based treatments and combinations.
Through leveraging single-cell and spatial biology, we seek to identify novel therapeutics which take into account both intratumor heterogeneity and inherent plasticity which characterizes these cancers
Through leveraging single-cell and spatial biology, we seek to identify novel therapeutics which take into account both intratumor heterogeneity and inherent plasticity which characterizes these cancers
Pharmacotranscriptomic approaches to promote regeneration following Spinal Cord Injury:
Pharmacotranscriptomic approaches to promote regeneration following Spinal Cord Injury:
We've found the tools we've developed in the context of cancers, also predict small molecules which revert transcriptionally resolvable phenotypes
We've found the tools we've developed in the context of cancers, also predict small molecules which revert transcriptionally resolvable phenotypes
As co-investigator in an NINDS-funded multi-disciplinary project, we are applying single-cell and spatial pharmacotranscriptomics approaches to prioritize small molecules which promote a favorable cellular environment for regeneration following spinal cord injury
As co-investigator in an NINDS-funded multi-disciplinary project, we are applying single-cell and spatial pharmacotranscriptomics approaches to prioritize small molecules which promote a favorable cellular environment for regeneration following spinal cord injury
The development of novel pharmacotranscriptomics tools:
The development of novel pharmacotranscriptomics tools:
As we progress in our goal of understanding the intracellular dynamics of cancers and injury through integrative pharmacotranscriptomics, we are building new software tools.
As we progress in our goal of understanding the intracellular dynamics of cancers and injury through integrative pharmacotranscriptomics, we are building new software tools.
Importantly, as a lab, we approach this with an open-source mindset, with a focus on making this type of analysis more accessible for the broad research community.
Importantly, as a lab, we approach this with an open-source mindset, with a focus on making this type of analysis more accessible for the broad research community.
Ongoing and future directions include the development of novel AI and ML applications conducive to de novo drug discovery using NIH Common Fund datasets and other 'big data' resources.
E: RKS82(at)Georgetown.edu
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