Biology subjects

Ang, K. S.

Publications and source records attributed to Ang, K. S..

7 recordsLinked to original sources

bioRxiv2026

Uni-TINT: Unifying T and Innate Lymphoid Cell Taxonomy with a high resolution, pan-disease and pan-tissue single-cell transcriptomic atlas

T cells and innate lymphoid cells (ILCs) exhibit extensive phenotypic diversity across tissues and diseases, yet inconsistent annotation limits cross-study comparisons and biological interpretation. We present Uni-TINT, an integrated pan-disease and pan-tissue atlas comprising 3.46 million cells from 1,869 samples spanning 194 studies, 166 disease subtypes, and 66 tissue types. Through systematic, hierarchical manual annotation, Uni-TINT establishes a unified and context-aware taxonomy of T and ILC populations, resolving 207 cell subtypes and states across conventional and unconventional T cells, natural killer (NK) cells, helper ILCs, thymocytes, and hematopoietic progenitors. We identified an immunosuppressive, tumour-associated CD4 T regulatory population which we validated with spatial transcriptomics. Other rare and unconventional populations characterised included CD8 regulatory T cells, invariant NKT cells and memory-like NK cells. Integration of T cell receptor sequencing suggested functional associations between {gamma}{delta} T cell co-receptor expression and TRDV gene usage. Finally, a comparative analysis of healthy and diseased immature cells identified a small population of malignant hematopoietic stem cells carrying chromosomal aberrations and enriched in acute leukaemia of mixed phenotype. Together, Uni-TINT provides a unified reference framework for immune annotation and discovery across health and disease.

immunology↗

bioRxiv2025

A Population-Scale Single-Cell Atlas of the Human Heart Reveals Cellular Remodeling and Cell-Cell Communication in Aging and Cardiac Disease

Previous single-cell and single-nucleus heart atlases, often limited by small sample sizes, lack the statistical power needed for phenotype association analysis, particularly for cardiovascular diseases and cardiac aging. To address this, we integrated data from 436 samples across 12 single-cell studies, harmonized the corresponding sample metadata, and constructed a comprehensive heart atlas comprising 355,762 cells and 1,436,719 nuclei. Consensus annotation identified 10 broad cell types and 54 fine-grained subsets. Associating gene expression patterns and cell type proportions with phenotypic data, we identified NRG1-expressing endocardial cells linked to multiple cardiac diseases and found that interferon (IFN) response signatures mark aging in multiple heart cell types. Importantly, we also developed PopComm, a novel computational method for inferring ligand-receptor (LR) interactions from population-scale single-cell data and quantifying interaction strength for individual samples. Using PopComm, we revealed a close association between the IFN response state and altered cell-cell communication during cardiac aging.

bioinformatics↗

bioRxiv2024

Mosaic integration of spatial multi-omics with SpaMosaic

With the advent of spatial multi-omics, mosaic integration of diverse datasets with partially overlapping modalities enables construction of comprehensive multi-modal spatial atlases from heterogeneous sources. Here, we present SpaMosaic, a tool that employs contrastive learning and graph neural networks to build a modality-agnostic and batch-corrected latent space for spatial domain identification and missing modality imputation. We systematically benchmarked SpaMosaic against existing integration methods using simulated data and experimentally acquired datasets spanning RNA and protein abundance, chromatin accessibility, and histone modifications from brain, embryo, tonsil, and lymph node tissues. SpaMosaic consistently outperformed other methods in identifying coherent spatial domains by reducing noise and mitigating batch effects across diverse technologies and developmental stages. Computationally, SpaMosaic is highly scalable, capable of integrating over 100 sections and processing a single section with more than 800,000 spots. Beyond robust integration, the unified latent space generated by SpaMosaic enables accurate imputation of missing modalities. In a mosaic mouse brain dataset, the imputed histone modifications not only recapitulated expected transcriptome-epigenome correlations but also uncovered more region-specific regulatory links compared to the measured chromatin accessibility data, demonstrating the ability to infer relationships between modalities without co-profiling. In summary, SpaMosaic provides a versatile framework for unifying the rapidly accumulating heterogeneous spatial omics data into comprehensive biological atlases.

bioinformatics↗

bioRxiv2023

Integrated analysis of spatial multi-omics with SpatialGlue

Integration of multiple data modalities in a spatially informed manner remains an unmet need for exploiting spatial multi-omics data. We introduce SpatialGlue, a graph neural network with dual-attention mechanism, to learn each modalitys significance at cross-omics and intra-omics integration. We demonstrate that SpatialGlue can accurately aggregate cell types into spatial domains at a higher resolution on different tissue types and technology platforms, as well as gain insights into cross-modality spatial correlations.

bioinformatics↗

bioRxiv2022

DeepST: A versatile graph contrastive learning framework for spatially informed clustering, integration, and deconvolution of spatial transcriptomics

Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining its spatial context. Effective exploitation of this data combination requires spatially informed analysis tools to perform three key tasks, spatial clustering, multi-sample integration, and cell type deconvolution. Here, we present GraphST, a novel graph self-supervised contrastive learning method that incorporates spatial location information and gene expression profiles to accomplish all three tasks in a streamlined process while outperforming existing methods in each task. GraphST combines graph neural networks with self-supervised contrastive learning to learn informative and discriminative spot representations by minimizing the embedding distance between spatially adjacent spots and vice versa. With GraphST, we achieved 10% higher clustering accuracy on multiple datasets than competing methods, and better delineated the fine-grained structures in tissues such as the brain and embryo. Moreover, GraphST is the only method that can jointly analyze multiple tissue slices in both vertical and horizontal integration while correcting for batch effects. Lastly, compared to other methods, GraphSTs cell type deconvolution achieved higher accuracy on simulated data and better captured spatial niches such as the germinal centers of the lymph node in experimentally acquired data. We further showed that GraphST can recover the immune cell distribution in different regions of breast tumor tissue and reveal spatial niches with exhausted tumor infiltrating T cells. Through our examples, we demonstrated that GraphST is widely applicable to a broad range of tissue types and technology platforms. In summary, GraphST is a streamlined, user friendly and computationally efficient tool for characterizing tissue complexity and gaining biological insights into the spatial organization within tissues.

bioinformatics↗

bioRxiv2020

Single cell RNA-seq reveals immunosuppressive gastric stem-like cancer cells as a poor prognostic factor

ObjectiveGastric cancer (GC) tumors are highly heterogenous with different subpopulations of epithelial cells. We employed single cell RNA sequencing (scRNA-seq) to dissect the heterogeneity and identified subpopulations of cancer cells with stem-like properties. We further investigated their resistance to oxaliplatin chemotherapy and their contribution to gastric cancer outcome. DesignWe performed scRNA-seq on FACS sorted epithelial and immune cells from paired samples of GC tumors and normal adjacent tissues. We identified two epithelial subpopulations (STMN1+IQGAP3+ and STMN1+IQGAP3-) with stem-like properties. We characterized and compared them to known healthy gastric stem cell populations. We also cultivated GC derived organoids to study the chemoresistance of similarly marked populations. Lastly, we employed immunohistochemistry (IHC) staining to ascertain the predicted immunosuppressive interactions. ResultsThe STMN1+IQGAP3+ subpopulation showed a higher tumor mutation burden, upregulated proliferative pathways and transcriptomically resembled proliferative healthy gastric isthmus stem cells. The STMN1+IQGAP3- subpopulation were comparatively quiescent and transcriptomically resembled enteroendocrine cells. Both transcriptomic signatures were associated with worse mortality than other epithelial subpopulations with the quiescent being associated with the poorest patient survival. GC tissue derived organoids were dominated by STMN1+IQGAP3+ cells but the STMN1+IQGAP3- compartment was more resistant to chemotherapy. We also verified the likely suppression of CD8 T cell cytotoxicity by STMN1+IQGAP3+ cells through the NECTIN2/TIGIT interaction. ConclusionsCancer cells with stem-like characteristics are associated with poor survival through chemoresistance and immunosuppression. Reactivating the immune system through checkpoint blockade is an opportunity to eliminate these cells. What is already known on this topicMultiple gastric stem cell populations have been identified and linked to tumor initiation in rodent-based studies. However, none of them have been conclusively proven in human tumors. Isolating and characterizing tumor cells with stem-like properties will help shed light on their possible origin and possible mitigation strategies. What this study addsHere we identified two sets of stem-like gastric cancer cells that are associated with poorer patient prognosis. One set is highly proliferative and exhibits oxaliplatin susceptibility. It also engages in immunosuppressive interactions such as NECTIN2/TIGIT. The other set is quiescent and highly resistant to oxaliplatin. How this study might affect research, practice or policyThe transcriptome signatures of the identified stem-like cells can aid in patient prognosis and identify patients who can benefit from checkpoint blockade therapy to reactivate their immune response towards gastric cancer cells.

immunology↗

bioRxiv2019

On the impact of biomass composition in constraint-based flux analysis

The biomass equation is a critical component in genome-scale metabolic models (GEMs): it is used as the de facto objective function in flux balance analysis (FBA). This equation accounts for the quantities of all known biomass precursors that are required for cell growth based on the macromolecular and monomer compositions measured at certain conditions. However, it is often reported that the macromolecular composition of cells could change across different environmental conditions; the use of the same single biomass equation in FBA, under multiple conditions, is questionable. Thus, we first investigated the qualitative and quantitative variations of macromolecular compositions of three representative host organisms, Escherichia coli, Saccharomyces cerevisiae and Cricetulus griseus, across different environmental/genetic variations. While macromolecular building blocks such as DNA, RNA, protein, and lipid composition vary notably, variations in fundamental biomass monomer units such as nucleotides and amino acids are not appreciable. We further observed that while macromolecular compositions are similar across taxonomically closer species, certain monomers, especially fatty acids, vary substantially. Based on the analysis results, we subsequently propose a new extension to FBA, named "Flux Balance Analysis with Ensemble Biomass (FBAwEB)", to embrace the natural variation in selected components of the biomass equation. The current study clearly highlights that certain components of the biomass equation are very sensitive to different conditions, and the ensemble representation of biomass equation in the FBA framework enables us to account for such natural variations accurately during GEM-guided in silico simulations.

systems biology↗