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Biology subjects

Batchu, S.

Publications and source records attributed to Batchu, S..

2 recordsLinked to original sources

Single-cell analysis reveals diversity of tumor-associated macrophages and their interactions with T lymphocytes in glioblastoma

Glioblastoma (GBM) is an aggressive primary CNS malignancy and clinical outcomes have remained stagnant despite introduction of new treatments. Understanding the tumor microenvironment (TME) in which tumor associated macrophages (TAMs) interact with T cells has been of great interest. Although previous studies examining TAMs in GBM have shown that certain TAMs are associated with specific clinical and/or pathologic features, these studies used an outdated M1/M2 paradigm of macrophage polarization and failed to include the continuum of TAM states in GBM. Perhaps most significantly, the interactions of TAMs with T cells have yet to be fully explored. Our study uses single-cell RNA sequencing data from adult IDH-wildtype glioblastoma, with the primary aim of deciphering the cellular interactions of the 7 TAM subtypes with T cells in the GBM TME. Furthermore, the interactions discovered herein are compared to IDH-mutant astrocytoma, allowing for focus on the cellular ecosystem unique to GBM. The resulting ligand-receptor interactions, signaling sources, and global communication patterns discovered provide a framework for future studies to explore methods of leveraging the immune system for treating GBM.

cancer biology↗

kimma: flexible linear mixed effects modeling with kinship for RNA-seq data

We introduce kimma (Kinship In Mixed Model Analysis), an open-source R package for flexible linear mixed effects modeling of RNA-seq including covariates, weights, random effects, covariance matrices, and fit metrics. In simulated datasets, kimma detects differentially expressed genes (DEGs) with similar specificity, sensitivity, and computational time as limma unpaired and dream paired models. Unlike other software, kimma supports covariance matrices as well as fit metrics like AIC. Utilizing genetic kinship covariance, kimma revealed that kinship impacts model fit and DEG detection in a related cohort. Thus, kimma equals or outcompetes current DEG pipelines in sensitivity, computational time, and model complexity.

bioinformatics↗