Biology subjects

Jain, Y.

Publications and source records attributed to Jain, Y..

15 recordsLinked to original sources

bioRxiv2026

Bridging Biomedical Atlas Ecosystem: Cross-Atlas Alignment And Scalable Tissue Specimen Registration

Over the last five years, over 13,000 tissue datasets with 200+ million cells from 20 consortia have been spatially registered into the Human Reference Atlas (HRA) common coordinate framework (CCF). The shared 3D spatial and semantic reference system enables exploration of datasets in the context of all other data across organs, assay types, and spatial scales. However, manual registration of individual samples remains resource intensive, posing feasibility challenges exacerbated by the proliferation of samples, assays, and atlasing efforts. This paper presents two approaches to scale up HRA construction: (1) projecting data across biomedical reference atlas systems and (2) using millitomes to bulk register tissue blocks into a reference organ. Both methods use the AutoMated Alignment and Projection (AMAP) pipeline to align 3D mesh models using point cloud registration. We demonstrate the evolving HRA-aligned atlas ecosystem for 6 models from the SPARC Program (heart), Gut Cell Atlas (large intestine), 500-subject consensus kidneys, and the Julich Brain Atlas. Additionally, we used AMAP to project 7 millitome models across 5 organs onto the HRA ecosystem, integrating 300+ tissue extraction sites. AMAP enables scalable tissue registration of data across atlas ecosystems enabling the construction of detailed reference maps of the human body.

bioinformatics↗

bioRxiv2026

Evolutionary turnover of protein structural disorder drives aberrant proteome remodelling in naked mole rat

The naked mole rat is an evolutionary outlier among mammals, exhibiting extreme longevity, cancer resistance, hypoxia tolerance, pain insensitivity, eusociality, poikilothermy and other distinctive physiological traits, most of which likely resulted from its adaptation to highly adverse subterranean habitat. Despite accumulating data, the genetic and molecular basis underlying these traits remain poorly understood. Through analyses of 18 distinct protein attributes and allied datasets across hystricomorphs, myomorphs, carnivores, and primates, we observed lineage-specific evolutionary divergence in intrinsic protein disorder in the naked mole rat. The disorder turnover exhibited functional dichotomy. The gain of disorder preferentially associated with proteostasis, immune regulation, neurodevelopment, skeletal growth and tumour suppressive properties, while loss of disorder modulated mostly the cardiac development. The proteins that gained disorder in NMR exhibited lower degradation rates, consistent with stabilization through phase-separation, while the proteins losing disorder show pronounced divergence in gene expression. The disorder turnover was primarily driven by indels affecting functional regions including Pfam domains, ANCHOR-predicted binding sites, short linear motifs, stress induced modifications of Tyr, Met, and Cys residues. Notably, the gained disordered regions were inferred to be redox-sensitive, aligning to exceptional stress tolerance in naked mole rats. Collectively, our results highlight an unusual and previously overlooked large-scale proteome remodelling that drives the molecular evolution of extraordinary traits of naked mole rat. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=169 SRC="FIGDIR/small/720964v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@644d1dorg.highwire.dtl.DTLVardef@102c9e4org.highwire.dtl.DTLVardef@14c770org.highwire.dtl.DTLVardef@31bc5e_HPS_FORMAT_FIGEXP M_FIG C_FIG

evolutionary biology↗

bioRxiv2026

SenNet Portal: Build, Optimization and Usage

Cellular senescence is a hallmark of aging and a driver of functional decline across tissues, yet its heterogeneity and context dependence have limited systematic study. The Common Funds Cellular Senescence Network (SenNet) Program addresses this challenge by generating multimodal, multi-tissue datasets that profile senescent cells across the human lifespan and complementary mouse models. The SenNet Data Portal (https://data.sennetconsortium.org) serves as the public gateway to these resources, providing open access to harmonized single-cell, spatial, imaging, transcriptomic, and proteomic data; senescence biomarker catalogs; and standardized protocols that can be used to comprehensively identify and characterize senescent cells in mouse and human tissue. As of April 2026, the portal hosts 2,041 publicly available human and mouse datasets across 15 organs using 6 general assay types. Experts from 13 Tissue Mapping Centers (TMCs) and 12 Technology Development and Application (TDAs) components contribute tissue data, analyze data, identify senescent biomarkers, and agree on panels for cross-tissue antibody harmonization. They also register human tissue data into the Human Reference Atlas (HRA) and develop user interfaces for the multiscale and multimodal exploration of this data. Built on a scalable hybrid cloud microservices architecture by the Consortium Organization and Data Coordinating Center (CODCC), the Portal enables data submission, management, integrated analysis, spatial context mapping, and harmonized access to cross-species data critical for aging research. This paper presents user needs, the Portals architecture, data processing workflows, and senescence-focused analytical tools; usage scenarios illustrating applications in biomarker discovery, quality benchmarking, hypothesis generation, spatial analysis, cost-efficient profiling, and cell distance distribution analysis; and utility and usage by the larger researcher community. Current limitations and planned extensions--including expanded spatial-omics releases and improved tools for senotype characterization--are discussed. SenNet protocols, code, and user interfaces are freely available on https://docs.sennetconsortium.org/apis.

bioinformatics↗

bioRxiv2025

PD-1 blockade during T cell priming enhances long-term protection against metastatic tumors by epigenetically tuning T cell exhaustion

In cancer, CD8+ T cell responses are dominated by exhausted T cells, which can be reinvigorated using immune checkpoint blockade therapy and can control large tumors. However, it remains unclear which T cell fate best supports long-term immunity following tumor regression or clearance and a period of minimal antigen load. This question is particularly relevant following surgical tumor resection, when tuning the immune system could prevent recurrence. To determine which T cell fate provides durable protection following surgery and metastatic rechallenge, we modulated T cell priming using anti-PD-1, IFN-{beta} or agonistic anti-CD40 and assessed effects on CD8+ T cell differentiation and overall survival. IFN-{beta} and anti-CD40 promoted effector and memory-like T cell states, respectively, whereas anti-PD-1 did not markedly alter T cell differentiation, yet conferred the greatest survival benefit against metastatic tumors. Notably, anti-PD-1 induced epigenetic remodeling, which was detectable upon metastatic recall, consistent with the maintenance of a circulatory intermediate-exhausted T cell state. Thus, while effector and memory precursor-like T cells could be generated with IFN-{beta} and agonistic anti-CD40, only the intermediate-exhausted T cell state driven by anti-PD-1 supported durable anti-tumor immunity. SummaryThis study shows that PD-1 blockade during T cell priming promotes a circulatory intermediate-exhausted CD8 T cell state that uniquely supports durable anti-tumor immunity after surgical resection and metastatic challenge, outperforming effector or memory-like T cell responses generated by IFN-{beta} or CD40 agonist treatment, respectively.

immunology↗

bioRxiv2025

Machine Learning-Driven Discovery of Synergistic Protein Interactions Identifies ATL3 as a Putative Biomarker for Cancer Drug Response

BackgroundResistance to targeted molecular therapies--both primary and acquired--remains a major obstacle to effective cancer treatment. Despite extensive research, the molecular determinants of treatment resistance are still incompletely understood, underscoring the need to identify robust resistance drivers to improve therapeutic outcomes. Recently, the ProCan and Wellcome Sanger Institute released the worlds largest pan-cancer proteomic dataset to date, comprising 949 cancer cell lines treated with 625 anti-cancer agents. Despite progress in identifying single protein markers, scalable methods for detecting synergistic protein interactions driving drug susceptibility remain limited. Machine learning models such as random forests may offer a robust framework for capturing non-linear protein interactions across diverse cancer types. ResultsOur study presents synerOmics, a scalable framework for identifying putative synergistic protein interactions by leveraging parent-child co-occurrences in random forest regression trees to reduce the interaction search space. We validated our approach using simulated data and two independent cancer proteomic datasets, identifying both pan-cancer and breast cancer-specific markers associated with drug susceptibility. Synergistic interactions that consistently replicated across datasets were enriched for endoplasmic reticulum stress pathways. Notably, shared targets included established sensitivity markers for tyrosine kinase inhibitors (TKIs) and revealed a resistance-associated network centred on ATL3, which demonstrated both prognostic and predictive relevance. ConclusionsApplying synerOmics to the largest cancer cell line proteomic dataset to date, we identified ATL3 as a strong candidate biomarker for lapatinib resistance in breast cancer, with superior predictive performance compared to the current gold standard sensitivity biomarker, ERBB2. HighlightsO_LIWe present a machine learning framework for drug response biomarker discovery, leveraging the largest proteomic cancer cell line dataset, ProCan-DepMapSanger C_LIO_LIATL3 is proposed as a candidate predictive biomarker for lapatinib resistance in breast cancer C_LIO_LIElevated expression of a novel resistance-conferring subcluster centered on ATL3 is associated with poor prognosis across 11 TCGA cancer types C_LIO_LIBaseline drug resistance may be broadly driven by stress pathways, with ER stress and protein ATL3 implicated in ER-phagy in response to TKIs. C_LI

cancer biology↗

bioRxiv2025

Exploring endothelial cell environments across organs in spatially resolved omics data

Endothelial cells are ubiquitously present in the human body and line the luminal surface of blood and lymphatic vessels. The oxygen-dependence of cells impacts their proximity to blood vessels, and consequently, to endothelial cells depending on their functional properties and priorities. This paper presents cell-to-nearest-endothelial-cell distance distributions for various cell types using 399 spatially resolved omics datasets from 14 studies comprising 12 tissue types with a total of 47,349,496 cells. Additionally, we developed an open-source web-based interactive tool, Cell Distance Explorer, that allows researchers to interactively visualize cell graphs and linkages in 2D and 3D datasets. Finally, we present a hierarchical neighborhood analysis focused on the endothelial cell neighborhoods in small and large intestine datasets. This paper provides an open-access resource (datasets, tools, and analyses) to characterize and compare cell distances and cell neighborhoods in spatially resolved omics data.

bioinformatics↗

bioRxiv2025

Constructing and Using Cell Type Populations of the Human Reference Atlas

The human body contains [~]27-36 trillion cells of up to 10,000 cell types (CTs) within a volume of [~]62-120 liters (males) and 52-89 liters (females). The Human Reference Atlas (HRA) v2.3 provides a quantitative 3D framework of CTs across 73 reference organs and 1,283 3D anatomical structures (ASs). The HRA Cell Type Population (HRApop) effort has quantified CTs per AS using high-quality single-cell datasets processed through scalable, reproducible workflows and cell type annotation (CTann) tools. HRApop v1.0 includes reference CT populations for 73 ASs (112 when sex-specific) using 662 datasets spatially registered to 230 locations across 17 organs (31 when sex-specific). For 558 single-cell (sc-)transcriptomics datasets (11,042,750 cells), CTs and biomarker expressions were computed using Azimuth, CellTypist, and popV. To test generalizability, 104 sc-proteomics datasets (16,576,863 cells) were integrated. In total, HRApop includes 27,619,613 cells and serves as a healthy reference for researchers aiming to elucidate mechanisms underlying cellular interactions, FTU operations, and cellular and tissue level disease progression, which may facilitate advancements in basic discovery and lead to new therapeutic strategies.

bioinformatics↗

bioRxiv2024

Generalized cell phenotyping for spatial proteomics with language-informed vision models

We present DeepCell Types, a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels collected across different platforms. Our approach utilizes a transformer with channel-wise attention to create a language-informed vision model; this models semantic understanding of the underlying marker panel enables it to learn from and adapt to heterogeneous datasets. Leveraging a curated, diverse dataset named Expanded TissueNet with cell type labels spanning the literature and the NIH Human BioMolecular Atlas Program (HuBMAP) consortium, our model demonstrates robust performance across various cell types, tissues, and imaging modalities. Comprehensive benchmarking shows that our method outperforms existing approaches on cell-type prediction and, from the same model, predicts marker positivity competitively with a dedicated specialist; it further matches manual expert gating and adapts to new data with modest fine-tuning, well past what baselines reach when trained from scratch. This work equips the spatial proteomics community with a single, continuously improvable phenotyping model that generalizes to new marker panels and can be fine-tuned efficiently when needed. We release both DeepCell Types and Expanded TissueNet as open-source resources.

bioinformatics↗

bioRxiv2024

Vasculature segmentation in 3D hierarchical phase-contrast tomography images of human kidneys

Efficient algorithms are needed to segment vasculature in new three-dimensional (3D) medical imaging datasets at scale for a wide range of research and clinical applications. Manual segmentation of vessels in images is time-consuming and expensive. Computational approaches are more scalable but have limitations in accuracy. We organized a global machine learning competition, engaging 1,401 participants, to help develop new deep learning methods for 3D blood vessel segmentation. This paper presents a detailed analysis of the top-performing solutions using manually curated 3D Hierarchical Phase-Contrast Tomography datasets of the human kidney, focusing on the segmentation accuracy and morphological analysis, thereby establishing a benchmark for future studies in blood vessel segmentation within phase-contrast tomography imaging.

bioinformatics↗

bioRxiv2024

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRAs Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

bioinformatics↗

bioRxiv2023

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the "Hacking the Human Body" machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We showcase how 1,175 teams from 78 countries engaged in community- driven, open-science code development that resulted in machine learning models which successfully segment anatomical structures across five organs using histology images from two consortia and that will be productized in the HuBMAP data portal to process large datasets at scale in support of Human Reference Atlas construction. We discuss the benchmark data created for the competition, major challenges faced by the participants, and the winning models and strategies.

bioinformatics↗

bioRxiv2021

Robust and generalizable segmentation of human functional tissue units

The Human BioMolecular Atlas Program aims to compile a reference atlas for the healthy human adult body at the cellular level. Functional tissue units (FTU, e.g., renal glomeruli and colonic crypts) are of pathobiological significance and relevant for modeling and understanding disease progression. Yet, annotation of FTUs is time consuming and expensive when done manually and existing algorithms achieve low accuracy and do not generalize well. This paper compares the five winning algorithms from the "Hacking the Kidney" Kaggle competition to which more than a thousand teams from sixty countries contributed. We compare the accuracy and performance of the algorithms on a large-scale renal glomerulus Periodic acid-Schiff stain dataset and their generalizability to a colonic crypts hematoxylin and eosin stain dataset. Results help to characterize how the number of FTUs per unit area differs in relationship to their position in kidney and colon with respect to age, sex, body mass index (BMI), and other clinical data and are relevant for advancing pathology, anatomy, and surgery.

bioinformatics↗

bioRxiv2019

BitEpi: A Fast and Accurate Exhaustive Higher-Order Epistasis Search

MotivationComplex genetic diseases may be modulated by a large number of epistatic interactions affecting a polygenic phenotype. Identifying these interactions is difficult due to computational complexity, especially in the case of higher-order interactions where more than two genomic variants are involved. ResultsIn this paper, we present BitEpi, a fast and accurate method to test all possible combinations of up to four bi-allelic variants (i.e. Single Nucleotide Variant or SNV for short). BitEpi introduces a novel bitwise algorithm that is 2.1 and 56 times faster for 3-SNV and 4-SNV search, than established software. The novel entropy statistic used in BitEpi is 44% more accurate to identify interactive SNVs, incorporating a p-value-based significance testing. We demonstrate BitEpi on real world data of 4,900 samples and 87,000 SNPs. We also present EpiExplorer to visualize the potentially large number of individual and interacting SNVs in an interactive Cytoscape graph. EpiExplorer uses various visual elements to facilitate the discovery of true biological events in a complex polygenic environment.

bioinformatics↗

bioRxiv2019

VariantSpark, A Random Forest Machine Learning Implementation for Ultra High Dimensional Data

The demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random Forests are suitable for \"wide\" datasets, current implementations such as Googles PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random Forest. This paper introduces CursedForest, a novel Random Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. CursedForest is 9 and up to 89 times faster than Googles PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features.

bioinformatics↗

bioRxiv2017

Evolutionary Dynamics Of Genome-Wide Position Effects In Mammals

Conserved noncoding elements (CNEs) have significant regulatory influence on their neighbouring genes. Loss of synteny to CNEs through genomic rearrangements can, therefore, impact the transcriptional states of the cognate genes. Yet, the evolutionary implications of such chromosomal position effects have not been studied. Through genome-wide analysis of CNEs and the cognate genes of representative species from 5 different mammalian orders, we observed significant loss of synteny to CNEs in rat lineage. The CNEs and genes losing synteny had significant association with the fetal, but not the post-natal, brain development as assessed through ontology terms, developmental gene expression, chromatin marks and genetic mutations. The loss of synteny correlated with the independent evolutionary loss of fetus-specific upregulation of genes in rat brain. DNA-breakpoints implicated in brain abnormalities of germ-line origin had significant representation between CNE and the gene that exhibited loss of synteny, signifying the underlying developmental tolerance of genomic rearrangements that had allowed the evolutionary splits of CNEs and the cognate genes in rodent lineage. These observations highlighted the non-trivial impact of chromosomal position-effect in shaping the evolutionary dynamics of mammalian brain development and might explain loss of brain traits, like cerebral folding of cortex, in rodent lineage.\n\nAuthor SummaryExpression of genes is regulated by proximally located non-coding regulatory elements. Loss of linear proximity between gene and its regulatory element thus can alter the expression of gene. Such a phenomenon can be tested at whole genome scale using evolutionary methods. We compared the positions of genes and regulatory elements in 5 different mammals and identified the significant loss of proximities between gene and their regulatory elements in rat during evolution. Brain development related function was selectively enriched among the genes and regulatory elements that had lost the proximity in rat. The observed separation of genes and their regulatory elements was strongly associated with the evolutionary loss of developmental gene expression pattern in rat brain, which coincided with the loss of brain traits in rodents. The study highlighted the importance of relative chromosomal positioning of genes and their gene regulatory elements in the evolution of phenotypes.

genomics↗