Biology subjects

Uttam, S.

Publications and source records attributed to Uttam, S..

3 recordsLinked to original sources

bioRxiv2020

Single-cell RNA sequencing reveals time- and sex-specific responses of spinal cord microglia to peripheral nerve injury and links ApoE to neuropathic pain

Activation of microglia in the spinal cord following peripheral nerve injury is critical for the development of long-lasting pain hypersensitivity. However, it remains unknown whether distinct microglia subpopulations or states contribute to different stages of pain development and maintenance. We show, using single-cell RNA-sequencing, that nerve injury induces the generation of a male-specific inflammatory microglia subtype, and demonstrate increased proliferation of microglia in males as compared to females. We also show time- and sex-specific transcriptional changes in different microglial subpopulations following injury. Apolipoprotein E (Apoe) is the top upregulated gene in microglia at chronic time points after nerve injury in mice and polymorphisms in the APOE gene in humans are associated with chronic pain. Single-cell analysis of human spinal cord microglia reveals a subpopulation with a disease-related transcriptional signature. Our data provide a detailed analysis of transcriptional states of mouse and human spinal cord microglia, and identify a previously unrecognized role for ApoE in neuropathic pain.

neuroscience↗

bioRxiv2020

Unsupervised cellular phenotypic hierarchy enables spatial intratumor heterogeneity characterization, recurrence-associated microdomains discovery, and harnesses network biology from hyperplexed in-situ fluorescence images of colorectal carcinoma

LEAPH is an unsupervised machine learning algorithm for characterizing in situ phenotypic heterogeneity in tissue samples. LEAPH builds a phenotypic hierarchy of cell types, cell states and their spatial configurations. The recursive modeling steps involve determining cell types with low-ranked mixtures of factor analyzers and optimizing cell states with spatial regularization. We applied LEAPH to hyperplexed (51 biomarkers) immunofluorescence images of colorectal carcinoma primary tumors (N=213). LEAPH, combined with pointwise mutual information (PMI), enables the discovery of phenotypically distinct microdomains, composed of spatially configured computational phenotypes. LEAPH identified a subset of microdomains visualized as the spatial configuration of recurrence-specific signaling networks whose intracellular and intercellular interactions support cancer stem cell maintenance and immunosuppression in the evolving tumor microenvironment. The LEAPH framework, when combined with microdomain discovery and microdomain-specific network biology, has the potential to provide insights into pathophysiological mechanisms, identify novel drug targets and inform therapeutic strategies for individual patients.

cancer biology↗

bioRxiv2019

A spatial analytics computational and systems biology platform predicts risk of colorectal cancer recurrence and identifies emergent spatial domain networks associated with recurrence

An unmet clinical need in solid tumor cancers is the ability to harness the intrinsic spatial information in primary tumors that can be exploited to optimize prognostics, diagnostics and therapeutic strategies for precision medicine. We have developed a transformational spatial analytics (SpAn) computational and systems biology platform that predicts clinical outcomes and captures emergent spatial biology that can potentially inform therapeutic strategies. Here we apply SpAn to primary tumor tissue samples from a cohort of 432 chemo-naive colorectal cancer (CRC) patients iteratively labeled with a highly multiplexed (hyperplexed) panel of fifty-five fluorescently tagged antibodies. SpAn predicted the 5-year risk of CRC recurrence with a mean area under the ROC curve of 88.5% (SE of 0.1%), significantly better than current state-of-the-art methods. SpAn also inferred the emergent network biology of the tumor spatial domains revealing a synergistic role of known features from CRC consensus molecular subtypes that will enhance precision medicine.

cancer biology↗