Biology subjects

Cranley, J.

Publications and source records attributed to Cranley, J..

7 recordsLinked to original sources

bioRxiv2025

An atlas of TF driven gene programs across human cells

Combinations of transcription factors (TFs) regulate gene expression and determine cell fate. Much effort has been devoted to understanding TF activity in different tissues and how tissue-specificity is achieved. However, ultimately gene regulation occurs at the single cell level and the recent explosion in the availability of single cell gene expression data now makes it possible to understand TF activity at this granular level of resolution. Here, we leverage a large collection of Human Cell Atlas (HCA) single cell data to explore TF activity by examining cell-type and tissue-specific sets of target genes, or regulons. We compile a regulon atlas, CellRegulon, and map the activity of TFs in an extensive set of healthy adult and foetal tissues spanning hundreds of cell types. Using CellRegulon, we describe dynamic patterns of co-regulation, associate TF-modules with different cellular functions and characterise the distribution of active TFs and TF families across cell types. We show that CellRegulon can link disease gene expression signatures to cell types and TFs relevant to the disease. Finally, using a newly generated multiome dataset of the adult lung, we show how CellRegulon can be extended into an enhancer-gene regulatory network (eGRN) to improve cell-type associations with genetic risk loci for diseases, such as childhood onset asthma, COPD and IPF, and to identify high risk gene modules. Our database for easy download and interactive exploration allows researchers to understand key gene modules activated at cell type transitions and will therefore be valuable for tasks such as cell type engineering (https://www.cellregulondb.org).

genetics↗

bioRxiv2024

inVAE: Conditionally invariant representation learning for generating multivariate single-cell reference maps

Single-cell data is driving new insights into the spatiotemporal dynamics of cells and individual disease susceptibility. However, accurately identifying cell states across diverse cohorts remains challenging, as both biological variation and technical biases cause distributional shifts in the data. Separating these effects is crucial for capturing cellular heterogeneity and ensuring interpretability. To address this, we developed inVAE, a conditionally invariant deep generative model based on variational autoencoders. inVAE models the latent space as a combination of invariant variables, encoding true biological signals, and spurious variables, capturing technical biases. By conditioning the prior distribution of cells on biological covariates, such as disease variants, inVAE identifies high-resolution cell states in the invariant representation. Enforcing independence between the two representations disentangles biological signals from noise, enabling a more interpretable and generalizable model with a causal semantic. inVAE outperformed existing methods across four human cellular atlases of the human heart and lung, while uncovering novel cell states. It precisely stratified cell atlas donors based on the genetic impact of pathogenic variants, and excelled in predicting cell types and disease in unseen data, proving its generalizability as a reference model for label transfer. Furthermore, inVAE accurately identified temporal cell states and trajectories from developmental datasets, and captured spatial cell states in a spatially-resolved atlas. In summary, inVAE provides a powerful method for integrating multivariate single-cell transcriptomics data. By leveraging prior knowledge such as metadata, it effectively accounts for biological variation and improves latent space interpretability by disentangling biological and technical sources of variation. These capabilities enable deeper insights into cellular heterogeneity and its role in disease progression.

bioinformatics↗

bioRxiv2024

A multiomic atlas of human early skeletal development

Bone and joint formation in the developing skeleton rely on co-ordinated differentiation of progenitors in the nascent developing limbs and joints. The cell states, epigenetic processes and key regulatory factors underlying their lineage commitment to osteogenic and other mesenchymal populations during ossification and joint formation remain poorly understood and are largely unexplored in human studies. Here, we apply paired single-nuclei transcriptional and epigenetic profiling of 336,000 droplets, in addition to spatial transcriptomics, to construct a comprehensive atlas of human bone, cartilage and joint development in the shoulder, hip, knee and cranium from 5 to 11 post-conception weeks. Spatial mapping of cell clusters to our highly multiplexed in situ sequencing (ISS) data using our newly developed tool ISS-Patcher revealed new cellular mechanisms of zonation during bone and joint formation. Combined modelling of chromatin accessibility and RNA expression allowed the identification of the transcriptional and epigenetic regulatory landscapes that drive differentiation of mesenchymal lineages including osteogenic and chondrogenic lineages, and novel chondrocyte cell states. In particular, we define regionally distinct limb and cranial osteoprogenitor populations and trajectories across the fetal skeleton and characterise differential regulatory networks that govern intramembranous and endochondral ossification. We also introduce SNP2Cell, a tool to link cell-type specific regulatory networks to numerous polygenic traits such as osteoarthritis. We also conduct in silico perturbations of genes that cause monogenic craniosynostosis and implicate potential pathogenic cell states and disease mechanisms involved. This work forms a detailed and dynamic regulatory atlas of human fetal skeletal maturation and advances our fundamental understanding of cell fate determination in human skeletal development.

developmental biology↗

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↗

bioRxiv2024

High-resolution atlas of the developing human heart and the great vessels

The human heart and adjoining great vessels consist of multiple cell types essential for life, yet many remain uncharacterised molecularly during development. Here, we performed a high-resolution profiling of the developing heart and great vessels between 4 and 20 post-conception weeks using single-cell and spatial transcriptomics defining 63 cell types with distinct identity and location-specific signatures. We reveal previously unreported molecular identities in cell types, including the pericardium and the ductus arteriosus. In the cardiomyocytes, we identify signatures of the trabeculated-compact, and right-left axes of ventricular cardiomyocytes. In vessels, we distinguish the constituents belonging to either coronary or great vessels. We confirm our transcriptional findings spatially, revealing nuanced signatures with specific zonation patterns and validating this atlas as a curated transcriptional reference for future studies. We leverage the temporal scope of the presented atlas to build CMageR, a predictive pipeline for scRNA-seq combining cardiac cell annotation with a transcriptional cardiac clock of single-cell developmental age for each cell type. Our cardiomyocyte clock captures dynamic biology, revealing core functional changes and novel markers of maturity during the first and second trimester. Finally, we benchmark in vitro models, suggesting a transcriptional right-chamber bias in stem cell derived cardiomyocytes with the oldest model age-matched to 12 post-conception weeks. Collectively, our work provides a high-resolution atlas of human cardiac development to enhance our understanding of function in development, health, and disease, and a foundation for building a rich reference to benchmark and improve in vitro models.

developmental biology↗

bioRxiv2023

Multidimensional Analysis of the Adult Human Heart in Health and Disease using Hierarchical Phase-Contrast Tomography (HiP-CT)

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Current clinical imaging modalities provide resolution adequate for diagnosis but are unable to provide detail of structural changes in the heart, across length-scales, necessary for understanding underlying pathophysiology of disease. Hierarchical Phase-Contrast Tomography (HiP-CT), using new (4th) generation synchrotron sources, potentially overcomes this limitation, allowing micron resolution imaging of intact adult organs with unprecedented detail. In this proof of principle study (n=2), we show the utility of HiP-CT to image whole adult human hearts ex-vivo: one control without known cardiac disease and one with multiple known cardiopulmonary pathologies. The resulting multiscale imaging was able to demonstrate exemplars of anatomy in each cardiac segment along with novel findings in the cardiac conduction system, from gross (20 um/voxel) to cellular scale (2.2 um/voxel), non-destructively, thereby bridging the gap between macroscopic and microscopic investigations. We propose that the technique represents a significant step in virtual autopsy methods for studying structural heart disease, facilitating research into abnormalities across scales and age-groups. It opens up possibilities for understanding and treating disease; and provides a cardiac blueprint with potential for in-silico simulation, device design, virtual surgical training, and bioengineered heart in the future.

pathology↗

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↗