Biology subjects

Jayakumar, R.

Publications and source records attributed to Jayakumar, R..

7 recordsLinked to original sources

bioRxiv2026

TRACE: a framework for traceable biomedical resource construction demonstrated through single-cell glaucoma data

The growing availability of public omics datasets presents opportunities for biomedical research, yet constructing research question-driven resources from distributed studies remains challenging due to heterogeneous data, incomplete metadata and limited documentation of curation decisions. Here, we present TRACE (Traceable Resource Assembly and Curation Ecosystem), a general framework that links systematic knowledge discovery, data curation and resource delivery while preserving provenance throughout resource construction. We demonstrate TRACE through TRACE-scGlaucoma, a curated single-cell transcriptomics resource for glaucoma and optic nerve biology. Systematic screening identified 57 eligible studies, of which 42 provided accessible datasets comprising approximately 3.3 million cells. The resource provides harmonised metadata, standardised cell-type annotations and reusable expression objects while retaining original study information and curation provenance. We demonstrate its analytical utility through cellular enrichment of glaucoma GWAS-associated genes, comparing candidate drug-repurposing signals across distinct biological contexts and assessment of cell-type annotation reliability. TRACE provides a systematic and traceable approach to transforming fragmented public datasets into reusable biomedical resources, supporting reproducible analyses and future extensions across disease areas and data modalities.

bioinformatics↗

bioRxiv2026

CCIDeconv: Hierarchical model for deconvolution of subcellular cell-cell interactions in single-cell data

Cell-cell interaction (CCI) underlies several fundamental biological processes, including development, homeostasis and disease progression. Subcellular spatial transcriptomics (sST) provides an opportunity to examine whether CCI-associated signals show compartment-specific patterns within cells. Assessing CCI at subcellular level can help us gain insights into the distinct pathway activation and signalling patterns. We developed a novel approach that deconvolutes CCI into subcellular CCI (sCCI) information from non-spatial single-cell transcriptomics (scRNA- seq) based CCI using a modified CellChat-derived communication score. By estimating communication scores separately for cytoplasmic and nuclear compartments, we identified compartment-associated sCCI. We then deconvolved whole-cell communication scores into subcellular compartments using a hierarchical classification and regression framework, which we call CCIDeconv. To ensure biological fidelity, we integrated protein localization data from the Human Protein Atlas in our deconvolution model. Across nine publicly available human sST datasets, leave-one- dataset-out validation achieved a median composite score of 0.75, with mean R2 values of 0.87 and 0.80 for cytoplasmic- and nuclear-associated scores, respectively. Performance without spatial features approached that of spatial models as the number of training datasets increased, supporting application to non-spatial scRNA-seq data. This highlighted the potential for prediction of sCCI from scRNA-seq, given a sufficiently large number of training datasets. Overall, our method can attribute whole-cell CCI to its subcellular compartments, allowing researchers to dissect sCCI patterns and gain insights into the underlying biology of healthy and disease tissues. Keywords Cell-Cell Communication, Single Cell RNA-seq, Predictive Modeling, Bioinformatics, Transcriptomics, Machine Learning

bioinformatics↗

bioRxiv2025

Large Language Models for Accessible Reporting of Bioinformatics Analyses in Interdisciplinary Contexts

Health and life scientists frequently rely on quantitative experts to perform complex data analyses, yet interpretation of these results often becomes a bottleneck due to communication barriers across disciplines. Large Language Models (LLMs) have the potential to act as intermediaries that translate analytical outputs into accessible scientific narratives, but their ability to reliably interpret outputs from real-world biomedical data analyses across disciplines remains unclear. In this study, we investigate how state-of-the-art LLMs behave when integrated into real bioinformatics analysis workflows as report-generation assistants for interdisciplinary teams. We evaluated LLM-generated summaries using both automated and human evaluation frameworks to ensure holistic evaluation. Automated assessment employed multiple choice questions designed using Bloom's taxonomy to assess multiple levels of understanding, while human evaluation tasked scientists to score summaries for factual consistency, lack of harmfulness, comprehensiveness, and coherence. All generally produced readable and largely safe summaries, confirming their value for first-pass translation of technical analyses, however frequently misinterpreted visualisations, produced verbose summaries and rarely offered novel insights beyond what was already contained in the analytics. Our findings suggest that LLMs are best suited for easing interdisciplinary communication rather than replacing domain expertise and human oversight remains essential to guarantee accuracy, interpretative depth, and the generation of genuinely novel scientific insights.

bioinformatics↗

bioRxiv2024

Molecular profiling of frontal and occipital subcortical white matter hyperintensities in Alzheimer's disease

White matter hyperintensities (WMHs) are commonly detected on T2-weighted magnetic resonance imaging (MRI) scans, occurring in both typical aging and Alzheimers disease. Despite their frequent appearance and their association with cognitive decline, the molecular factors contributing to WMHs remain unclear. In this study, we investigated the transcriptomic profiles of two commonly affected brain regions with coincident AD pathology--frontal subcortical white matter (frontal-WM) and occipital subcortical white matter (occipital-WM)--and compared with age-matched healthy controls. Through RNA-sequencing in frontal- and occipital-WM bulk tissues, we identified an upregulation of genes associated with brain vasculature function in AD white matter. To further elucidate vasculature-specific transcriptomic features, we performed RNA-seq analysis on blood vessels isolated from these white matter regions, which revealed an upregulation of genes related to protein folding pathways. Finally, comparing gene expression profiles between AD individuals with high-versus low-WMH burden showed an increased expression of pathways associated with immune function. Taken together, our study characterizes the diverse molecular profiles of white matter changes in AD compared to normal aging and provides new mechanistic insights processes underlying AD-related WMHs.

neuroscience↗

bioRxiv2023

Endothelial Cells are Heterogeneous in Different Brain Regions and are Dramatically Altered in Alzheimer's Disease

Vascular endothelial cells play an important role in maintaining brain health, but their contribution to Alzheimers disease (AD) is obscured by limited understanding of the cellular heterogeneity in normal aged brain and in disease. To address this, we performed single nucleus RNAseq on tissue from 32 AD and non-AD donors each with five cortical regions: entorhinal cortex, inferior temporal gyrus, prefrontal cortex, visual association cortex and primary visual cortex. Analysis of 51,586 endothelial cells revealed unique gene expression patterns across the five regions in non-AD donors. Alzheimers brain endothelial cells were characterized by upregulated protein folding genes and distinct transcriptomic differences in response to amyloid beta plaques and cerebral amyloid angiopathy (CAA). This dataset demonstrates previously unrecognized regional heterogeneity in the endothelial cell transcriptome in both aged non-AD and AD brain. Significance StatementIn this work, we show that vascular endothelial cells collected from five different brain regions display surprising variability in gene expression. In the presence of Alzheimers disease pathology, endothelial cell gene expression is dramatically altered with clear differences in regional and temporal changes. These findings help explain why certain brain regions appear to differ in susceptibility to disease-related vascular remodeling events that may impact blood flow.

neuroscience↗

bioRxiv2022

Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer's disease

Astrocytes play a critical role in brain homeostasis and normal functions but their changes along the spatiotemporal progression of Alzheimers disease (AD) neuropathology remain largely unknown. Here we performed single-nucleus RNA-sequencing on brain regions along the stereotypical progression of AD pathology from donors ranging the entire normal aging-AD continuum comprising 628,943 astrocyte nuclei from 32 donors across 5 brain regions. We discovered temporal gene-expression-trajectories with gene sets differentially activated at various disease stages. Surprisingly, a gene set enriched in proteostasis and energy metabolism, was upregulated in late-stage but unexpectedly returned to baseline levels in end-stage, suggesting exhaustion of response in "burnt-out" astrocytes. The spatial gene-expression-trajectories revealed that astrocytic genes of tripartite synapses are dysregulated in parallel to the stereotypical progression of tangle pathology across regions. We identified astrocyte heterogeneity across brain regions with a continuum from homeostatic to reactive cells through "intermediate" transitional states. These findings suggest complex astrocytic dysfunction in AD neurodegeneration.

neuroscience↗

bioRxiv2019

Spooky interaction at a distance in cave and surface dwelling electric fishes

Glass knifefish (Eigenmannia) are a group of weakly electric fishes found throughout the Amazon basin. We made recordings of the electric fields of two populations of freely behaving Eigenmannia in their natural habitats: a troglobitic population of blind cavefish (Eigenmannia vicentespelaea) and a nearby epigean (surface) population (Eigenmannia trilineata). These recordings were made using a grid of electrodes to determine the movements of individual fish in relation to their electrosensory behaviors. The strengths of electric discharges in cavefish were larger than in surface fish, which may be a correlate of increased reliance on electrosensory perception and larger size. Both movement and social signals were found to affect the electrosensory signaling of individual Eigenmannia. Surface fish were recorded while feeding at night and did not show evidence of territoriality. In contrast, cavefish appeared to maintain territories. Surprisingly, we routinely found both surface and cavefish with sustained differences in electric field frequencies that were below 10 Hz despite being within close proximity of less than one meter. A half century of analysis of electrosocial interactions in laboratory tanks suggest that these small differences in electric field frequencies should have triggered the jamming avoidance response. Fish also showed significant interactions between their electric field frequencies and relative movements at large distances, over 1.5 meters, and at high differences in frequencies, often greater than 50 Hz. These interactions are likely envelope responses in which fish alter their EOD frequency in relation to changes in the depth of modulation of electrosocial signals.

neuroscience↗