Biology subjects

Ho, S. Y.

Publications and source records attributed to Ho, S. Y..

3 recordsLinked to original sources

bioRxiv2024

Multiomic analysis reveals developmental dynamics of the human heart in health and disease

Developmental dynamics involve the specification of diverse cell types and their spatial organization into multicellular niches. Here, we combine single-cell and spatial multiomics to define 19 distinct tissue niches in the developing heart, leading to the development of a context-aware, resolution-agnostic niche classification tool (TissueTypist). Applying high-resolution spatial profiling to the developing sinoatrial node, we resolve three pacemaker cell subtypes arrayed along a linear axis. First trimester subpopulations, such as the pacemaker cells in the sinus horn and sinoatrial node head region, display neuro-attractant programmes and interact with parasympathetic neurons via interactions including Semaphorin-Plexin signalling. Temporal trajectories map maturation of atrial and ventricular cardiomyocytes, uncovering a lipid-metabolic switch and potential key regulators of cell type identity. In the ventricle, we identify cellular and transcriptional gradients along both pseudotime and transmural axes, offering new molecular insights into myocardial compaction and maturation. Comparative profiling of euploid and trisomy 21 hearts shows a depletion of compact cardiomyocytes and heightened apoptosis, validated in isogenic-matched trisomy 21 and euploid iPSC-derived cardiomyocytes. This implicates disrupted myocardial growth may be a mechanism for Downs syndrome-associated congenital heart disease. Overall, we deliver a spatially resolved framework of human cardiac development, enabling systematic exploration of developmental niches in health and disease.

developmental biology↗

bioRxiv2023

Spatially resolved multiomics of human cardiac niches

A cells function is defined by its intrinsic characteristics and its niche: the tissue microenvironment in which it dwells. Here, we combine single-cell and spatial transcriptomic data to discover cellular niches within eight regions of the human heart. We map cells to micro-anatomic locations and integrate knowledge-based and unsupervised structural annotations. For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors, and cell interactions using a custom CellPhoneDB.org module. We show that the sinoatrial node is compartmentalised, with a core of pacemaker cells, fibroblasts and glial cells supporting paracrine glutamatergic signalling. We introduce a druggable target prediction tool, drug2cell, which leverages single-cell profiles and drug-target interactions, providing unexpected mechanistic insights into the chronotropic effects of drugs, including GLP-1 analogues. In the epicardium, we show enrichment of both IgG+ and IgA+ plasma cells forming immune niches which may contribute to infection defence. We define a ventricular myocardial-stress niche enriched for activated fibroblasts and stressed cardiomyocytes, cell states that are expanded in cardiomyopathies. Overall, we provide new clarity to cardiac electro-anatomy and immunology, and our suite of computational approaches can be deployed to other tissues and organs.

genomics↗

bioRxiv2019

Bayesian Evaluation of Temporal Signal in Measurably Evolving Populations

Phylogenetic methods can use the sampling times of molecular sequence data to calibrate the molecular clock, enabling the estimation of evolutionary rates and timescales for rapidly evolving pathogens and data sets containing ancient DNA samples. A key aspect of such calibrations is whether a sufficient amount of molecular evolution has occurred over the sampling time window, that is, whether the data can be treated as having come from a measurably evolving population. Here we investigate the performance of a fully Bayesian evaluation of temporal signal (BETS) in sequence data. The method involves comparing the fit to the data of two models: a model in which the data are accompanied by the actual (heterochronous) sampling times, and a model in which the samples are constrained to be contemporaneous (isochronous). We conducted simulations under a wide range of conditions to demonstrate that BETS accurately classifies data sets according to whether they contain temporal signal or not, even when there is substantial among-lineage rate variation. We explore the behaviour of this classification in analyses of five empirical data sets: modern samples of A/H1N1 influenza virus, the bacterium Bordetella pertussis, coronaviruses from mammalian hosts, ancient DNA from Hepatitis B virus and mitochondrial genomes of dog species. Our results indicate that BETS is an effective alternative to other tests of temporal signal. In particular, this method has the key advantage of allowing a coherent assessment of the entire model, including the molecular clock and tree prior which are essential aspects of Bayesian phylodynamic analyses.

bioinformatics↗