Biology subjects

Palshikar, M. G.

Publications and source records attributed to Palshikar, M. G..

3 recordsLinked to original sources

bioRxiv2025

Powerful and accurate case-control analysis of spatial molecular data with deep learning-defined tissue microniches

As spatial molecular data grow in scope and resolution, there is a pressing need to identify key spatial structures associated with disease. Current approaches typically make restrictive assumptions such as representing tissue regions by local abundances of manually typed, discrete cell types, or representing samples in terms of abundances of manually called, discrete spatial structures; this risks overlooking important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method that combines deep learning with principled statistics to discover associated spatial features with greater flexibility and precision. VIMA trains an ensemble of variational autoencoders to extract numerical "fingerprints" from small tissue patches that capture their biological content. It uses these fingerprints to define a large number of data-dependent "microniches" - small, potentially overlapping groups of tissue patches with highly similar biology that span multiple samples. It then meta-analyzes across the autoencoders to identify microniches whose abundance correlates with case-control status while controlling for multiple testing. We show in simulations that VIMA is well calibrated. We then apply VIMA to spatial datasets spanning three different diseases and spatial modalities: a 7-marker immunofluorescence (IF) microscopy dataset in rheumatoid arthritis (RA), a 52-marker CO-Detection by indEXing (CODEX) dataset in ulcerative colitis (UC), and a 140-gene spatial transcriptomics dataset in dementia. In each case, we recapitulate known biology and identify novel spatial features of disease that were not discoverable with current state-of-the-art methods.

genomics↗

bioRxiv2024

Integrating spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns

Spatial transcriptomics allows for the analysis of a cells gene expression in the context of its physical location. With spatial transcriptomics data, investigators often want to find genes of interest whose spatial patterns are biologically relevant in multiple samples. However, due to confounding factors in spatial data that produce noise across samples, datasets, and technologies, it is challenging to visualize genes and their spatial patterns across samples. We present Crescendo, an integration algorithm that performs correction directly on gene expression counts to reduce variation from technical confounders. We first apply Crescendo to a 3-sample spatial transcriptomics mouse brain dataset to show how Crescendo enables accurate visualization of gene expression across these spatial transcriptomic samples. We then demonstrate Crescendos scalability by integrating a 16-sample immuno-oncology dataset of 7 million cells. Finally, we show that Crescendo can perform cross-technology integration by merging a colorectal cancer (CRC) scRNA-seq dataset with two CRC spatial transcriptomics samples. By transferring information between technologies, Crescendo can impute poorly expressed genes to improve detection of gene-gene colocalization, such as ligand-receptor interactions.

bioinformatics↗

bioRxiv2019

Cyclosporine A Directly Affects Human and Mouse B cell Migration in vitro by Disrupting a HIF-1α Dependent, O2 Sensing, Molecular Switch

BackgroundHypoxia is a potent molecular signal for cellular metabolism, mitochondrial function, and migration. Conditions of low oxygen tension trigger regulatory cascades mediated via the highly conserved HIF-1 post-translational modification system. In the adaptive immune response, B cells (Bc) are activated and differentiate under hypoxic conditions within lymph node germinal centers, and subsequently migrate to other compartments. During migration, they traverse through changing oxygen levels, ranging from 1-5% in the lymph node to 5-13% in the peripheral blood. Interestingly, the calcineurin inhibitor cyclosporine A is known to stimulate prolyl hydroxylase activity, resulting in HIF-1 destabilization and may alter Bc responses directly. Over 60% of patients taking calcineurin immunosuppressant medications have hypo-gammaglobulinemia and poor vaccine responses, putting them at high risk of infection with significantly increased morbidity and mortality.\n\nResultsWe demonstrate that O2 tension is a previously unrecognized Bc regulatory switch, altering CXCR4 chemokine receptor signaling in activated Bc through HIF-1 expression and controlling critical aspects of Bc migration. Our data demonstrate that calcineurin inhibition hinders this O2 regulatory switch in primary human Bc.\n\nConclusionThis previously unrecognized effect of calcineurin inhibition directly on human Bc has significant and direct clinical implications.

immunology↗