Biology subjects

Chintalapudi, H.

Publications and source records attributed to Chintalapudi, H..

5 recordsLinked to original sources

bioRxiv2026

Glioblastoma Invasion Remodels Neural Circuits and Drives Persistent GABAergic Dysfunction in Human Brain Organoids

BackgroundGlioblastoma (GBM) is characterized by neurological dysfunction caused by tumor cells that interact with and alter neuronal circuits. However, the specific neuronal populations and molecular mechanisms most susceptible to GBM invasion remain poorly understood. MethodsWe created a human tumor-brain organoid model by combining U87 glioblastoma cells with iPSC-derived neural organoids. This system enabled us to study tumor-neural interactions over an extended period under standard temozolomide (TMZ) treatment. We used single-cell transcriptomics to monitor cell-type-specific responses. ResultsOur model recapitulated the diffuse infiltration observed in patients, leading to extensive structural remodeling and a profound loss of neuronal and glial populations. Single-cell analysis revealed that TMZ suppressed proliferative and biosynthetic programs but enriched for stress-responsive, mesenchymal-like, and therapy-adapted tumor states. Notably, GABAergic neurons exhibited the greatest transcriptional vulnerability, with [~]36% (7,499 of 20,659) of genes differentially expressed. Invasion triggered endoplasmic reticulum stress and shut down metabolic, respiratory, synaptic, and ion-homeostatic pathways. Crucially, SLC12A5-expressing GABAergic neurons plummeted from 31% to 12%, accompanied by a sharp decline in KCC2 protein expression. While TMZ partially rescued neuronal metabolic and electron transport chain function, it failed to restore SLC12A5/KCC2 expression or inhibitory signaling. ConclusionsGBM invasion leads to a continued imbalance of chloride in GABAergic networks, and this disruption remains even after undergoing tumor-targeted chemotherapy. This human iPSC-derived tumor-brain organoid platform provides a reliable and scalable system for studying complex tumor-neural interactions and exploring therapeutic approaches that aim to eliminate the tumor while preserving neural function.

cancer biology↗

bioRxiv2025

A Reference Atlas of the Human Dorsal Root Ganglion

Somatosensory perception largely emerges from diverse peripheral sensory neurons whose cell bodies reside in dorsal root ganglia (DRG). Damage or dysfunction of DRG neurons is a major cause of chronic pain and sensory loss. In mice, deep single-cell transcriptomic profiling and genetically defined models have offered important clues into DRG function, but in humans, the cellular and molecular landscape of DRG neurons remains less understood. Here, we constructed a reference cell atlas of the human DRG by profiling transcriptomes of cells and nuclei from 126 donors sampled across cervical, thoracic, and lumbar DRGs. This atlas resolves 22 neuronal subtypes, including known and previously unrecognized subtypes linked to nociception, mechanosensation, thermosensation, and proprioception, as well as 10 types of non-neuronal cells. Cross-species integration, spatial transcriptomics, and microneurography enabled cell-type-specific comparisons of soma size and conduction velocity between species. Human DRG somata are larger across all cell types than their mouse counterparts, and the conduction velocities of human hair follicle innervating A-fibers are faster than in mice, suggesting a functional shift in rapid mechanical detection in humans. This integrated human DRG reference cell atlas provides a resource for exploring new molecular and physiological features of human DRG, which could help identify new strategies for treating chronic pain and other diseases of the peripheral nervous system.

neuroscience↗

bioRxiv2025

EVscope: A Comprehensive Bioinformatics Pipeline for Accurate and Robust Analysis of Total RNA Sequencing from Extracellular Vesicles

MotivationExtracellular vesicle (EV) RNA sequencing has emerged as a powerful approach for studying RNA biomarkers and intercellular communication. Nevertheless, the extremely low abundance, fragmented nature and ubiquitous tissue origin of EV RNAs, alongside potential contamination from co-isolated materials, such as free DNA and bacterial RNA, pose substantial analytical challenges. These complexities highlight a pressing need for a standardized, computational workflow that ensures robust quality control and EV RNA characterization. ResultsHere, we present EVscope, an open-source bioinformatics pipeline designed specifically for processing EV RNA-seq datasets. EVscope employs an optimized genome-wide expectation-maximization (EM) algorithm that significantly improves multi-mapping read assignment at single-base resolution by effectively leveraging alignment scores (AS) and local read coverage, specifically tailored for fragmented and low-abundance EV RNAs. Notably, EVscope uniquely generates EM-based BigWig files for downstream analysis, a capability currently unavailable in existing EM-based BigWig quantification tools. The pipeline systematically integrates 27 major steps, including quality control, analysis of library structure, contamination assessment, read alignment, read strandedness detection, UMI-based deduplication, RNA quantification, genomic DNA (gDNA) contamination correction, cellular and tissue source inference and visualization with a comprehensive HTML report. EVscope incorporates a comprehensive, updated annotation covering 19 distinct RNA biotypes, encompassing protein-coding genes, lncRNAs, miRNAs, piRNAs, retrotransposons (LINEs, SINEs, ERVs), and additional non-coding RNAs (tRNAs, rRNAs, snoRNAs). Furthermore, it leverages two highly balanced circRNA detection algorithms for robust circular RNA identification. Notably, a downstream module enables the inference of the tissue/cellular origins of EV RNAs using bulk and single-cell RNA-seq reference datasets. EVscope is implemented as a convenient, single-command Bash pipeline leveraging Conda-managed standard software packages and custom scripts, ensuring reproducibility and straightforward deployment. Availability and implementationCode, documentation, and tutorials are available at GitHub (https://github.com/TheDongLab/EVscope) and archived on Zenodo (https://zenodo.org/records/15577789).

bioinformatics↗

bioRxiv2024

A common set of structurally distinct endogenous retroviruses predict survival in multiple human tumor types

Accumulating evidence suggests that viral mimicry induction in cancer cells through the expression of human endogenous retroviral elements (ERVs) enhances tumor immunogenicity and improves response to immunotherapy. However, the characterization of ERV expression across multiple tumor types, their clinical relevance, and their role in tumor immunity remain limited. Here, we assessed the prognostic and immune-related consequences of ERV expression across 8 different tumor types from The Cancer Genome Atlas (TCGA). We applied a recently developed tool, Telescope, to quantify the expression of 14,968 ERVs in bulk tumor RNA-sequencing data. Approximately 15-22% of Telescopes ERVs (navg = 2,736) were expressed in each tumor cohort. We identified both tissue-specific and commonly expressed ERVs across cohorts. Over 50% of tissue-specific ERVs were ubiquitously expressed (n = 1,709) across all 8 tumor types. Using patient clinical data, we identified a subset of 94 ERVs whose expression levels were significantly associated with overall survival, of which 42 were favorably prognostic. Prognostic ERVs displayed unique structural characteristics, including more frequent inverted repeats and more stable secondary structures. Patients were classified based on ERV expression, allowing identification of patient cohorts with greater than 6-fold differences in overall survival. We also found that expression of positively prognostic ERVs was associated with enrichment for selected anti-tumor immune response gene expression signatures. This analysis extends previous results identifying patterns of ERV expression in bulk tumor RNA-seq datasets, providing further insight into the role of ERVs in tumor clinicopathology and immunogenicity.

cancer biology↗

bioRxiv2021

Pancreatic cancer prognosis is predicted by a novel ATAC-array technology for assessing chromatin accessibility

We investigated tumor-cell-intrinsic chromatin accessibility patterns of pancreatic ductal adenocarcinoma (PDAC) by ATAC-seq on EpCAM+ PDAC malignant epithelial cells, sorted from 54 freshly resected human tumors, and discovered a signature of 1092 chromatin loci displaying differential accessibility between patients with disease free survival (DFS) < 1 year and patients with DFS > 1 year. Analyzing transcription factor (TF) binding motifs within these loci, we identified two TFs (ZKSCAN1 and HNF1b) displaying differential nuclear localization between patients with short vs. long DFS. We further developed a novel chromatin accessibility microarray methodology termed "ATAC-Array", an easy-to-use platform obviating the time and cost of next generation sequencing. Applying this novel methodology to the original ATAC-seq libraries as well as independent libraries generated from patient-derived organoids, we validated ATAC-array technology in both the original ATAC-Seq cohort as well as in an independent validation cohort. We conclude that PDAC prognosis can be predicted by ATAC-array, which represents a novel, lowcost, clinically feasible technology for assessing chromatin accessibility profiles.

cancer biology↗