Biology subjects

Sinha, H.

Publications and source records attributed to Sinha, H..

17 recordsLinked to original sources

bioRxiv2026

spatiAlytica: Viewer-Grounded Multimodal Agentic System for Interactive Spatial Omics Analysis

Spatial transcriptomics and proteomics map tissue architecture and cellular interactions, but analysis remains limited by programming demands and text-centered AI agents that lack viewer grounding and cross-turn context. We present spatiAlytica, a viewer-centric multimodal interactive agentic system embedded in the Napari viewer that enables non-programmer biologists to perform iterative, hypothesis-driven spatial omics analysis via natural language. spatiAlytica couples viewer-state serialization, agentic memory, biological concept-to-data-field mapping, code generation and debugging, Spatial VQA, and grounded interpretation to support an exploratory analysis and interpretive reasoning workflow. We introduce spatiAlyticaBench, a comprehensive benchmark spanning 222 single-turn spatial analytical coding questions, 178 multi-turn sequential workflow questions, and 7,350 image-grounded reasoning questions. spatiAlytica outperformed strong agentic baselines, while using less time and tokens. Case studies across Kaposis sarcoma, colorectal cancer, and ovarian cancer recapitulated known spatial patterns and uncovered progressive CD8 T-cell dysfunction during KS progression.

bioinformatics↗

bioRxiv2026

STiLE: Automated Tissue Microarray Dearraying for Spatial Transcriptomics

Tissue microarrays (TMAs) enable high-throughput spatial transcriptomic profiling of dozens of tissue cores on a single slide. However, existing dearraying methods operate on histological images and do not support the coordinate-based outputs of spatial transcriptomics platforms. Therefore, task of assigning cells to their respective cores (dearraying) remains a manual bottleneck. We present STiLE, a tool for automated TMA dearraying that operates solely on cell centroid coordinates. By eliminating dependence on image data, STiLE is robust to artifacts such as variable staining quality and uneven illumination. The algorithm combines connectivity-based component detection, density-based clustering (HDBSCAN), component-guided cluster merging, and optional grid-based peak detection. Validation on eleven public TMA samples (50-150 cores, three platforms) achieved ARI > 0.99, while systematic benchmarking on 396 synthetic datasets with realistic artifacts demonstrated consistently robust performance (mean ARI = 0.992). STiLE accepts standard formats (AnnData, CSV) and is platform-agnostic, supporting diverse platforms including Vizgen MERSCOPE, 10x Xenium, and NanoString CosMx. An interactive Streamlit interface enables parameter tuning, visual inspection, and region-based processing for large slides. Availability and Implementationhttps://pypi.org/project/stile; source at https://github.com/Huang-AI4Medicine-Lab/stile Contactyu Supplementary InformationSupplementary data are available at Bioinformatics online

bioinformatics↗

bioRxiv2026

Interpretable machine learning meets systems biology to decode genotype-phenotype maps

Resolving causal genes from quantitative trait loci (QTL) remains fundamentally limited by linkage disequilibrium. We developed an interpretable machine learning framework that captures higher-order nonlinear genotype-phenotype relationships and allows conditional evaluation of genetic variants, enabling statistical decorrelation of linked loci. Applied to Saccharomyces cerevisiae segregants across chemical stress conditions, our method achieved >75% prediction accuracy and identified known causal genes, including MKT1 (genotoxic stress) and IRA2 (osmotic stress). SHAP-based analysis recovered 56% of the validated pleiotropic genes, compared with 36% by conventional contingency testing. Integration with genome-scale metabolic models revealed pathway enrichments distinguishing high-growing strains, including carbon transport, glycolysis, and oxidative phosphorylation. Notably, gene regulatory network analysis identified a novel function for PDR8 in protein mannosylation and cell wall integrity--functions extending beyond its role in drug resistance. This framework demonstrates that interpretable machine learning, coupled with systems biology, transforms QTL associations into mechanistic biological insight.

systems biology↗

bioRxiv2026

Antagonism Rather Than Synergy: Ibuprofen-Antifungal Interactions Depend on Strain Genetics and Nutrient Environment

Drug interaction outcomes-synergism, additivity, or antagonism-represent complex phenotypes. While drug repurposing aims to identify compounds that potentiate conventional antifungals, when given in combination, the modulators of these interactions in fungi remain largely unexplored. We hypothesize that the response to repurposed-conventional antifungal pairs is a complex trait modulated by genetic and environmental factors. To study the impact of genotype on the outcome, we screened six diverse Saccharomyces cerevisiae isolates, including clinical, wild, and fermentation strains, for their responses to combinations of ibuprofen with either clotrimazole or caspofungin. We evaluated the role of the environment using rich and minimal media and assessed the influence of assay type by comparing solid- and liquid-rich media assays. Our results reveal that ibuprofen-clotrimazole interactions are highly dynamic, predominantly antagonistic, with limited synergy observed. These outcomes are significantly modulated by genetic background, media composition, assay type, and, in specific genotypes, even by the drug dosage, reflecting a complex, multi-parametric phenotype. However, the ibuprofen-caspofungin combination is more predictable, exhibiting only synergy or additivity. Interaction outcomes correlate with baseline sensitivity to caspofungin: caspofungin-resistant isolates consistently demonstrate synergy, while sensitive strains exhibit additivity. These findings shift the paradigm of drug discovery by demonstrating that synergism and antagonism are not static properties of drug pairs but are dynamic, context-dependent outcomes. This study highlights the need to use clinically relevant models and patient-specific isolates before clinical application, as drug interactions cannot be generalized from a single dosage, strain, or environmental condition.

microbiology↗

bioRxiv2025

Spatial Single-Cell Atlas Reveals KSHV-Driven Broad Cellular Reprogramming, Progenitor Expansion, Immune and Vascular Remodeling in Kaposi's Sarcoma

Kaposis sarcoma (KS) is a highly inflammatory, angiogenic tumor driven by Kaposis sarcoma-associated herpesvirus (KSHV), yet the origins of tumor cells and mechanisms of progression remain unclear. Here, we present the first spatial single-cell atlas of KS, profiling 256 samples across patch, plaque, and nodular lesions and normal controls. We identify CD34 progenitor lymphatic endothelial cells (LECs) as the primary targets of KSHV, whose clonal expansion drives tumor growth. KSHV infection induces widespread cellular reprogramming across the tumor microenvironment, including LECs, vascular endothelial cells, fibroblasts, and macrophages, generating hybrid phenotypes that support angiogenesis, inflammation, and immune modulation. KSHV macrophages are enriched in tumor-proximal niches, further promoting a proangiogenic, immunosuppressive environment. Spatial analysis reveals evolving tumor-associated niches, with a core-to-periphery gradient correlating with infection, immune modulation, and cellular remodeling. We identify disease progression predictive signatures, offering mechanistic insights into KS pathogenesis and potential new therapeutic strategies by reprogramming the tumor microenvironment. Highlights CD34+ progenitor lymphatic endothelial cells are the primary KSHV target cells, with their clonal expansion driving Kaposis sarcoma growth. KSHV infection reprograms broad cell types into hybrid identities, and drives tumor-specific niche and vascular remodeling, endothelial plasticity, and immune modulation. KSHV-reprogrammed macrophages drive inflammation, angiogenesis, and immune modulation. Spatially resolved molecular and cellular signatures predict Kaposis sarcoma progression, offering novel therapeutic strategies targeting the tumor microenvironment.

cancer biology↗

bioRxiv2025

A single-cell atlas of ribosomal protein heterogeneity across human tissues reveals phenotypes of biological and clinical significance

Ribosomes, once considered homogenous, exhibit dynamic compositional heterogeneity driven by differential ribosomal protein (RP) gene expression, modulating translational control. The exact cellular contributions to ribosomal heterogeneity in human tissues and their biological or clinical significance remain largely unknown. This study addresses these by mapping the expression of 76 cytoplasmic RP genes across 161 cell types from 15 human tissues using single-cell RNA sequencing from the Human Cell Atlas. We reveal extensive tissue- and cell-type-specific RP expression patterns, with RPL23, RPS20, RPS17 and RPL27A showing variability across most tissues. The testis cells exhibited the greatest variability and the largest number of variable RP genes, with distinct RP signatures distinguishing germ and somatic lineages; these signatures form temporally coordinated expression modules throughout spermatogenesis. In the context of disease, a comparative analysis of male infertility patients revealed widespread RP gene dysregulation in testicular cell types, highlighting the importance of proper RP composition for reproductive health. Furthermore, given the link between RP gene mutations and inherited anaemia syndromes, we investigated RP gene expression during erythropoiesis. We observed disrupted RP gene expression in Diamond-Blackfan Anaemia patients, contrasting with stable patterns in normal erythropoiesis. Our findings underscore the underappreciated cellular specificity and dynamic regulation of RP gene expression, strongly implicating ribosome compositional heterogeneity as fundamental to both cellular identity and disease pathogenesis.

systems biology↗

bioRxiv2025

Microbial communities on station and train surfaces in Chennai Metro: Insights into urban transit microbiome

BackgroundUrban public transport systems, particularly metro networks, serve as key hubs for microbial transmission, yet the urban microbiome in densely populated regions like India remains poorly characterized. These environments harbour diverse microbial communities, including beneficial and pathogenic species, which can influence public health. The COVID-19 pandemic has further underscored the need to monitor microbial ecosystems, particularly with respect to antimicrobial resistance (AMR) genes, which may have escalated due to the increased use of antibiotics during health crises. In a first-of-its-kind study in India, we comprehensively characterized microbial communities and the prevalence of AMR genes in the Chennai Metro system. We collected 96 surface swab samples from 12 stations across two metro lines, targeting four surfaces: bannisters, kiosks, rods, and ticket counters. Forty-seven samples passed quality control and were analyzed using whole-genome metagenomic sequencing. We employed taxonomic classification and conducted comparative and diversity analyses, as well as differential taxa profile analyses, across the sample collection objects. We studied the prevalence and abundance of AMR genes using AMR annotations from the CARD database. We performed pangenome analysis by constructing Metagenome Assembled Genomes (MAGs) from the collected samples and comparing them with the NCBI reference genome. ResultsComparative analysis with global urban microbiome datasets revealed distinct microbial profiles, including nine species that are differentially prevalent in Chennai samples. Surface type significantly influenced microbial diversity, with kiosks exhibiting the highest diversity. We successfully reconstructed several high-quality MAGs, providing insights into the genomic potential and adaptability of dominant taxa in this environment. While the overall prevalence of AMR genes was minimal, genes associated with Sulfonamide and Rifamycin resistance were detected. ConclusionThese findings highlight unique microbial signatures and emphasize the need for ongoing surveillance and targeted interventions to mitigate microbial transmission risks in densely populated urban areas.

ecology↗

bioRxiv2024

Interaction of Genetic Variants Activates Latent Metabolic Pathways in Yeast

Genetic interactions are fundamental to the architecture of complex traits, yet the molecular mechanisms by which variant combinations influence cellular pathways remain poorly understood. Here, we answer the question of whether interactions between genetic variants can activate unique pathways and if such pathways can be targeted to modulate phenotypic outcomes. The model organism Saccharomyces cerevisiae was used to dissect how two causal single-nucleotide polymorphisms (SNPs), MKT189G and TAO34477C, interact to modulate metabolic and phenotypic outcomes during sporulation. By integrating time-resolved transcriptomics, absolute proteomics, and targeted metabolomics in isogenic allele replacement yeast strains, we show that the combined presence of these SNPs uniquely activates the arginine biosynthesis pathway and suppresses ribosome biogenesis, reflecting a metabolic trade-off that enhances sporulation efficiency. Functional validation demonstrates that the arginine pathway is essential for mitochondrial activity and efficient sporulation only in the double-SNP background. Our findings reveal how genetic variant interactions can rewire core metabolic networks, providing a mechanistic framework for understanding polygenic trait regulation and the emergence of additive effects in complex traits.

systems biology↗

bioRxiv2024

A Digital Microfluidic Platform for the Microscale Production of Functional Immune Cell Therapies

Genetically engineering human immune cells has been shown to be an effective approach for developing novel cellular therapies to treat a wide range of diseases. To expand the scope of these cellular therapies while solving persistent challenges, extensive research and development is still required. Electroporation has recently emerged as one of the most popular techniques for inserting biological payloads into human immune cells to perform genetic engineering. However, several recent studies have reported that electroporation can negatively impact cell functionality. Additionally, the requirement to use large amounts of cells and expensive payloads to achieve efficient delivery can drive up the costs of development efforts. Here we use a digital microfluidic enabled electroporation system (referred to as triDrop) and compare them against two state-of-the-art commercially available systems for the engineering of human T cells. We describe the ability to use triDrop for highly viable, highly efficient transfection while using substantially fewer cells and payload. Subsequently, we perform transcriptomic analysis on cells engineered with each of the three systems and show that electroporation with triDrop lead to less dysregulation of several functionally relevant pathways. Finally, in a direct comparison of immunotherapeutic functionality, we show that T cells engineered with triDrop have an improved ability to mount an immune response when presented with tumor cells. These results show that the triDrop platform is uniquely suited to produce functionally engineered immune cells while also reducing the costs of cell engineering compared to other commercially available systems.

bioengineering↗

bioRxiv2024

Unveiling Genomic Complexity: A Framework for Genome Graph Structural Analysis and Optimised Variant Calling Workflows

MotivationGenome graphs represent genetic diversity by highlighting polymorphic regions, but current methods lack the ability to characterize and compare their complex structures effectively. ResultsOur study introduces GViNC: a framework for Genome graph Visualisation, Navigation, and Comparison. GViNC maps genomic coordinates onto genome graph nodes, facilitating subgraph partitioning by regions, which aids in navigating and comparing genetic data. Applied to multiple genome graphs from the 1,000 Genomes Project, we observed that genomic complexity varies by ancestry and chromosomes, with rare variants increasing variability significantly. GViNC identified key regions like HLA and DEFB loci, revealing population-specific heterogeneity linked to essential biological functions. Its versatility and scalability support extensive research on genetic diversity across different cohorts or species. Availability and ImplementationGViNC, automated with Snakemake, is available at https://github.com/IBSE-IITM/GViNC. Contact(K.R.) kraman@iitm.ac.in, (M.N) nmanik@cse.iitm.ac.in, (H.S.) sinha@iitm.ac.in Supplementary informationA supplementary document with tables and figures accompanies this manuscript.

genomics↗

bioRxiv2024

Steroidal glycoalkaloid biosynthesis and fungal tolerance are regulated by ELONGATED HYPOCOTYL 5, SlHY5, in tomato

Tomato (Solanum lycopersicum L.) is one of the highest consumable fruit crops, rich in nutrients, and has been an important target for enhancing the accumulation of various metabolites. Tomato also contains cholesterol-derived molecules, steroidal glycoalkaloids (SGAs), which contribute to pathogen defence but are toxic to humans and considered anti-nutritional compounds. Previous studies suggest the role of various transcription factors in SGA biosynthesis; however, the role of light and associated regulatory factors has not been studied in tomatoes. Here, we demonstrated that SGA biosynthesis is regulated by light through the ELONGATED HYPOCOTYL 5 homolog, SlHY5, by binding to light-responsive G-boxes present in the promoters of the structural and regulatory genes. Our analysis suggests that SlHY5 could complement the Arabidopsis thaliana and Nicotiana tabacum, hy5 mutants at molecular, morphological, and biochemical levels. We report the development of CRISPR/Cas9-based knockout mutant plants of tomato, slhy5CR, and show down-regulation of the SGA and phenylpropanoid pathway genes leading to a significant reduction in SGA (-tomatine and dehydrotomatine) and flavonol contents, whereas SlHY5 overexpression (SlHY5OX) plants show opposite effect. An enhanced SGA and flavonol levels in SlHY5OX lines provided tolerance against Alternaria solani fungus, while SlHY5CR was susceptible to the pathogen. This study advances our understanding of the HY5-dependent light-regulated biosynthesis of SGAs and flavonoids and their role in biotic stress in tomatoes. One Sentence SummaryLight-associated transcription factor, ELONGATED HYPOCOTYL 5, regulates biosynthesis of anti-nutrient molecules, steroidal glycoalkaloids, and fungal tolerance in tomato

molecular biology↗

bioRxiv2023

Panera: A novel framework for surmounting uncertainty in microbial community modelling using Pan-genera metabolic models

Over the last decade, microbiome research has witnessed exponential growth, largely driven by the widespread availability of metagenomic data. Despite this influx of data, 16S targeted amplicon sequencing, which offers relatively lower resolution, still dominates the landscape over whole-genome shotgun sequencing. Existing algorithms for constructing metabolic models of microbial communities primarily rely on whole-genome sequences and do not fully harness the potential of 16S datasets. In this study, we report Panera, a novel framework designed to model microbial communities under uncertainty and yet perform inferences by building pan-genus metabolic models. We tested the performance of the models from our approach by analysing their ability to capture the functionality of the entire genus and individual species within a genus. We further exercise the model to explore the comprehensive metabolic abilities of a genus, shedding light on metabolic commonalities between microbial groups. Furthermore, we showcase its application in characterising microbial community models using 16S data. Our hybrid community models, which combine both GSMM and pan-genus metabolic models, exhibit a 10% reduction in prediction error, with error rates diminishing as community size increases. Overall, the Panera framework represents a potent and effective approach for metabolic modelling, enabling robust predictions of the metabolic phenotypes of microbial communities, even when working with limited 16S data. This advancement has the potential to greatly impact the field of microbiome research, offering new insights into the metabolic dynamics of diverse microbial ecosystems.

systems biology↗

bioRxiv2023

Genome-scale metabolic modelling reveals functional effects of quantitative trait variant combinations

Understanding how genetic variations influence cellular function remains a major challenge in genetics. Genome-scale metabolic models (GEMs) are powerful tools used to understand the functional effects of genetic variants. While GEMs have illuminated genotype-phenotype relationships, the impact of single nucleotide polymorphisms (SNPs) in transcription factors and their interactions on metabolic fluxes remains largely unexplored. We used gene expression data from a yeast allele replacement panel to construct co-expression networks and SNP-specific GEMs. The analysis of these models helped us to understand how genetic interactions affect yeast sporulation efficiency, a quantitative trait. Our findings revealed that SNP-SNP interactions have a significant impact on the connectivity of key metabolic regulators involved in steroid biosynthesis, amino acid metabolism and histidine biosynthesis. By integrating gene expression data into GEMs and conducting genome-scale differential flux analysis, we were able to identify causal reactions within six major metabolic pathways, providing mechanistic explanations for variations in sporulation efficiency. Notably, we found that in specific SNP combinations, the pentose phosphate pathway was differentially regulated. In models where the pentose phosphate pathway was inactive, the autophagy pathway was activated, likely compensating by providing critical precursors such as nucleotides and amino acids. This compensatory mechanism may enhance sporulation efficiency by supporting processes that are dependent on the pentose phosphate pathway. Our study sheds light on how transcription factor polymorphisms interact to shape metabolic pathways in yeast and offers valuable insights into genetic variants associated with metabolic traits in genome-wide association studies.

systems biology↗

bioRxiv2023

SCI-VCF: A cross-platform application to summarise, compare and design interactive visualisations of the variant call format

As genomics advances swiftly and its applications extend to diverse fields, bioinformatics tools must enable researchers and clinicians to work with genomic data irrespective of their programming expertise. We developed SCI-VCF, a Shiny-based comprehensive analysis utility to summarise, compare, inspect, analyse, and design interactive visualisations of the genetic variants from the variant call format. With an intuitive GUI, SCI-VCF aims to bridge the approachability gap in genomics that arises from the existing predominantly command-line utilities. SCI-VCF is written with R and is freely available at https://github.com/HimanshuLab/SCI-VCF. For installation-free access, users can avail of an online version at https://ibse.shinyapps.io/sci-vcf-online/.

bioinformatics↗

bioRxiv2023

Solving the Pervasive Problem of Protocol Non-Compliance in MRI using an Open-Source tool mrQA

AO_SCPLOWBSTRACTC_SCPLOWPooling data across diverse sources acquired by multisite consortia requires compliance with a predefined reference protocol i.e., ensuring different sites and scanners for a given project have used identical or compatible MR physics parameter values. Traditionally, this has been an arduous and manual process due to difficulties in working with the complicated DICOM standard and lack of resources allocated towards protocol compliance. Moreover, issues of protocol compliance is often overlooked for lack of realization that parameter values are routinely improvised/modified locally at various sites. The inconsistencies in acquisition protocols can reduce SNR, statistical power, and in the worst case, may invalidate the results altogether. An open-source tool, mrQA was developed to automatically assess protocol compliance on standard dataset formats such as DICOM and BIDS, and to study the patterns of non-compliance in over 20 open neuroimaging datasets, including the large ABCD study. The results demonstrate that the lack of compliance is rather pervasive. The frequent sources of non-compliance include but are not limited to deviations in Repetition Time, Echo Time, Flip Angle, and Phase Encoding Direction. It was also observed that GE and Philips scanners exhibited higher rates of non-compliance relative to the Siemens scanners in the ABCD dataset. Continuous monitoring for protocol compliance is strongly recommended before any pre/post-processing, ideally right after the acquisition, to avoid the silent propagation of severe/subtle issues. Although, this study focuses on neuroimaging datasets, the proposed tool mrQA can work with any DICOM-based datasets.

bioinformatics↗

bioRxiv2023

MKT1 alleles regulate stress responses through post-transcriptional modulation of Puf3 targets in budding yeast

MKT1 is a pleiotropic stress response gene identified by several quantitative trait studies with MKT189G as a causal variant, contributing to growth advantage in multiple stress environments. MKT1 has been shown to regulate HO endonuclease post-transcriptionally via the Pbp1-Pab1 complex. RNA-binding protein Puf3 modulates a set of nuclear-encoded mitochondrial transcripts whose expression was found to be affected by MKT1 alleles. This study attempts to relate the MKT1 allele-derived growth advantage with the stability of Puf3 targets during stress and elucidate the roles of Pbp1 and Puf3 in this mechanism. Our results showed that the growth advantage of the MKT189G allele in cycloheximide and H O was PBP1-dependent, whereas in 4NQO, the growth advantage was dependent on both PUF3 and PBP1. We compared the mRNA decay kinetics of a set of Puf3 targets in multiple stress environments to understand the allele-specific regulation by MKT1. In oxidative stress, the MKT189G allele modulated the differential expression of nuclear-encoded mitochondrial genes in a PBP1 and PUF3-dependent manner. Additionally, MKT189G stabilised Puf3 targets, namely, COX17, MRS1, and RDL2, in an allele and stress-specific manner. Our results showed that COX17, MRS1, and RDL2 had a stress-specific response in stress environments, with the MKT189G allele contributing to better growth; this response was both PBP1 and PUF3-dependent. Our results indicate that the common allele, MKT189G, regulates stress responses by differentially stabilising Puf3-target mitochondrial genes, which allows for the strains better growth in stress environments. TAKE AWAYMKT1 alleles vary stress responses by post-transcriptional modulation of Puf3 targets. PBP1 and PUF3 influence the regulation of Puf3 target stability under stress. Nuclear-encoded mitochondrial Puf3 targets COX17, MRS1 and RDL2, contribute to the growth advantage of MKT189G allele in oxidative stress.

genetics↗

bioRxiv2021

Identification and validation of putative target genes regulated by miR-34 in cervical cancer

PurposeWe aimed to identify the epithelial-mesenchymal transition and associated putative targets of the miR-34 family in transcriptomic data of cervical epithelial squamous cell carcinoma (CESC) to find new therapeutic targets for better disease management. MethodsA combined computational analysis of the miR-34 family; gene expression in heterogeneous primary CESCs derived from TCGA; and the integration of miR-34b and EMT-regulated genes was performed. Four EMT-associated miR-34b gene targets were analysed in primary human CESC and non-cancerous cervical tissues by qRT-PCR. Effects of endogenous miR-34b expression and its associated gene modulations in cervical cancer cells (C33A and HeLa) were analysed using qRT-PCR, western blotting and immunofluorescence, transwell migration and invasion assays. ResultsThe results showed that the miR-34 family might regulate the mTOR pathway, cell cycle (CCND2) and cell adhesion functions (FZD4). Further, we showed that a low negative correlation (r2= -0.07) between miR-34b/EM[I] score and four EMT signature genes (E>MP7, CAV1, ID2, FN1) were significantly regulated by miR-34b. Also, FN1 was indicated as its putative target with the highest negative EMT score and high binding energy between MRE/3UTR. Further, these transcriptomic signatures in CESC revealed a significant inverse correlation across the stages of primary human CESC. These genes were repressed at transcriptional and translational levels in miR-34b-3p expressing C33A and HeLa cells, contributing to their reduced cell migration and invasive properties. ConclusionsOur studies revealed the potential targets of the miR-34 family, especially miR-34b, that can emerge as potential biomarkers and promising therapeutic targets in CESC disease management.

cancer biology↗