Biology subjects

Sinniah, E.

Publications and source records attributed to Sinniah, E..

5 recordsLinked to original sources

bioRxiv2026

Protocol-dependent cardiomyocyte states determine disease modelling capacity of human iPSCs

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are widely used to model cardiovascular disease, yet numerous differentiation protocols generate cardiomyocytes with heterogeneous molecular and functional properties, complicating experimental design. Here we systematically compare sixteen commonly used cardiomyocyte differentiation protocols and characterize their resulting cell states using single-nucleus RNA sequencing, functional phenotyping and computational integration with human genetic data. Despite similar cardiomyocyte yields, protocols produced distinct transcriptional programs, subtype compositions and physiological properties. By integrating protocol-specific gene expression signatures with genome-wide association studies of cardiovascular traits, we identify cardiomyocyte states enriched for genetic architectures underlying specific diseases. These analyses accurately predict protocols most suitable for modelling particular disease contexts, including electrophysiological defects associated with Brugada syndrome and metabolic vulnerability relevant to myocardial infarction. Our results demonstrate that differentiation protocols encode biologically distinct cardiomyocyte states with differential disease relevance and establish a framework for aligning stem-cell differentiation strategies with human complex trait genetics to guide model selection. This approach enables rational design of iPSC-based disease models and highlights how population-scale genetic data can inform experimental systems in stem cell biology.

systems biology↗

bioRxiv2025

Interrogating the Regulatory Function of HAQERs during Human Cortical Development

BackgroundSequence divergence within gene regulatory elements has been proposed to play an important role in the evolution of human-specific traits, including cortical expansion. However, the mutational processes that efficiently modify gene regulatory elements and the target genes upon which they act are poorly understood. We investigated the regulatory function and origins of the fastest evolved regions in the human genome, termed Human Ancestor Quickly Evolved Regions (HAQERs), in their native genomic context during human cerebral cortex development. ResultsWe identified 50 HAQERs with accessible chromatin in developing human cortex, largely arising from previously unconstrained ancestral sequences. To test the necessity of these HAQERs for gene regulation, we established an all-in-one CRISPRi lentiviral vector and linked 26 HAQERs to nearby target genes across cell types and Wnt pathway activation contexts. Rapid gains of CpGs distinguished HAQERs active during cortical development and displaying human-specific epigenomic marks. As a high density of CpG sites can drive formation of permissive chromatin, we identified 107 HAQERs with at least 17 human-specific CpG gains per kb, termed HAQER CpG Beacons. These HAQERs emerged via contributions from GC-biased gene conversion (gBGC) with evidence for selection preferentially fixing CpG sites. Notably, the CHL1 and DPP10 loci, both implicated in human neurological disorders, each harbored two gene-linked HAQER CpG Beacons. ConclusionsOur findings reveal HAQER target genes and support a model where gBGC and natural selection jointly drive regulatory-altering CpG variants to fixation, forging regulatory innovations in the human cortex.

evolutionary biology↗

bioRxiv2024

Epigenetic constraint of cellular genomes evolutionarily links genetic variation to function

Cellular diversity is a product of evolution acting to drive divergent regulatory programs from a common genome. Here, we use cross-cell-type epigenetic conservation to gain insight into the impact of selective constraints on genome function and phenotypic variation. By comparing chromatin accessibility across hundreds of diverse cell-types, we identify 1.4% of the human genome safeguarded by conserved domains of facultative heterochromatin, which we term regions under "cellular constraint". We calculate single-base resolution cellular constraint scores and demonstrate robust prediction of functionally important coding and non-coding loci in a cell-type-, trait-, and disease-agnostic manner. Cellular constraint annotation enhances causal variant identification, drug discovery, and clinical diagnostic predictions. Furthermore, cell-constrained sequences share paradoxical evolutionary signals of positive and negative selection, suggesting a dynamic role in driving human adaptation. Overall, this study demonstrates that evolutionary chromatin dynamics can be leveraged to inform the translation of genetic discoveries into effective biological, therapeutic, and clinical outcomes.

genetics↗

bioRxiv2022

Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity

Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. TRIAGE-Cluster integrates patterns of repressive chromatin deposited across diverse cell types with weighted density estimation to determine cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method that evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.

bioinformatics↗

bioRxiv2019

Comparative analysis of diverse cell states establishes an epigenetic basis for inferring regulatory genes governing cell identity

Determining genes orchestrating cell differentiation in development and disease remains a fundamental goal of cell biology. This study establishes a genome-wide metric based on the gene-repressive tri-methylation of histone 3 lysine 27 (H3K27me3) across hundreds of diverse cell types to identify genetic regulators of cell differentiation. We introduce a computational method, TRIAGE, that uses discordance between gene-repressive tendency and expression to identify genetic drivers of cell identity. We apply TRIAGE to millions of genome-wide single-cell transcriptomes, diverse omics platforms, and eukaryotic cells and tissue types. Using a wide range of data, we validate TRIAGEs performance for identifying cell-type specific regulatory factors across diverse species including human, mouse, boar, bird, fish, and tunicate. Using CRISPR gene editing, we use TRIAGE to experimentally validate RNF220 as a regulator of Ciona cardiopharyngeal development and SIX3 as required for differentiation of endoderm in human pluripotent stem cells. A record of this papers Transparent Peer Review process is included in the Supplemental Information.

cell biology↗