Biology subjects

Whitfield, H. J.

Publications and source records attributed to Whitfield, H. J..

5 recordsLinked to original sources

bioRxiv2026

An integrated single cell and spatial omics atlas of human prenatal development

Single cell genomics has enabled analysis of human prenatal development at unprecedented resolution. However, most studies have relied on dissociated tissues during restricted windows of development, limiting insights into how spatially distributed networks of cells, and multicellular niches emerge and adapt to distinct organ microenvironments in situ. Moreover, existing human developmental atlases have not yet been harmonised, and we thus lack a comprehensive catalogue of known cell types in the developing human body. Here, we introduce the Human Developmental Cell Atlas (HDCA), a unified structural, cellular and molecular resource for prenatal human development. The HDCA integrates published and unpublished single cell/nucleus RNAseq atlases across prenatal organs, and includes a newly generated, spatially resolved, multimodal cell atlas of intact human embryos. Spanning 4-22 post conceptional weeks, capturing embryonic and early to mid fetal stages, the HDCA contains [~]4.6 million cells/nuclei which resolve into [~]450 cell types, explorable with a bespoke web portal. For a global overview of the human embryos multicellular communities, we applied unsupervised deep learning to our intact human embryo spatial data, charting 114 tissue niches that are structural and signalling hubs for the cellular interactions of the embryo. Guided by these niches, we profiled cellular networks over space and time, not examinable using single-organ atlases. In so doing, we revealed tissue-specific fibroblast patterning from previously undescribed mesenchyme progenitors, early diversification of organ-specific blood capillaries and lymphatic vasculature, emergence of neural crest cell fates, the formation of placode-and neural crest-derived peripheral sensory neurons, and how tissue niches guide peripheral neuron maturation and axonal migration. The HDCA thus serves as a comprehensive step towards a comprehensive understanding of human prenatal development, and a template towards unravelling the biology of congenital disorders.

developmental biology↗

bioRxiv2025

Single cell transcriptional evolution of myeloid leukaemia of Down syndrome

Children with Down syndrome have a 150-fold increased risk of developing myeloid leukaemia (ML-DS). Unusually for a childhood leukaemia, ML-DS arises from a preleukaemic state, termed transient abnormal myelopoiesis (TAM), via a conserved sequence of mutations. Here, we examined the relationship between the genetic and transcriptional evolution of ML-DS from natural variation; a rich collection of primary patient samples and fetal tissues with a range of constitutional karyotypes. We distilled transcriptional consequences of each genetic step in ML-DS evolution, utilising single cell mRNA sequencing, complemented by phylogenetic analyses in progressive disease. We found that transcriptional changes induced by the TAM-defining GATA1 mutations are retained in, and account for most of the ML-DS transcriptome. The GATA1 transcriptome pervaded all stages of ML-DS, including progressive disease that had undergone genetic evolution. Our approach delineates the transcriptional evolution of ML-DS and provides an analytical blueprint for distilling consequences of mutations within their pathophysiological context.

cancer biology↗

bioRxiv2024

A developmental cell atlas of the human thyroid gland

The primary function of the thyroid gland is the synthesis and release of thyroid hormones, which are essential for health from embryogenesis to adulthood. Thyroid disorders occur frequently and include congenital hypothyroidism, which occurs due to aberrant thyroid development (thyroid dysgenesis) or impaired hormone synthesis and is particularly prevalent in trisomy 21 (T21). In contrast, thyroid carcinoma, an acquired disorder, is the most common endocrine malignancy in both paediatric and adult populations. Understanding the molecular basis of thyroid dysgenesis and paediatric thyroid carcinoma remains challenging, and requires an improved understanding of foetal thyroid development. To address this, we generated a comprehensive spatiotemporal atlas of the human thyroid during the first and second trimesters of pregnancy. Profiling over 200,000 cells with single-cell sequencing revealed key cell types involved in thyroid gland development, including the hormone-producing thyrocytes. We discovered that foetal thyroid follicular cells are heterogeneous epithelial populations consisting of two main functional subtypes (fTFC1, fTFC2), with fTFC2 expressing increased levels of PAX8, and spatial transcriptomics revealed subtype co-occurrence within individual follicles. While both fTFC1 and fTFC2 persist in adult thyroid, fTFC2 is a minor population amongst additional PAX8-positive follicular cell subsets. We observed thyroid dysgenesis in T21 age-matched specimens, and T21 thyrocytes showed transcriptional signatures of cytoskeletal disorganisation and altered interactions with the extracellular matrix, as well as compensatory activation of metabolic stress gene programs and upregulation of thyroid biosynthetic genes. In line with the altered proportions of fTFC2 in healthy foetal and adult thyroid, papillary thyroid cancer in children is transcriptionally enriched for the fTFC2 signature compared to that in adults. All together, these findings reveal thyrocyte heterogeneity across the lifespan and provide insights into thyroid development in health and disease, informing potential therapeutic interventions.

genomics↗

bioRxiv2023

Identification of cell types, states and programs by learning gene set representations

As single cell molecular data expand, there is an increasing need for algorithms that efficiently query and prioritize gene programs, cell types and states in single-cell sequencing data, particularly in cell atlases. Here we present scDECAF, a statistical learning algorithm to identify cell types, states and programs in single-cell gene expression data using vector representation of gene sets, which improves biological interpretation by selecting a subset of most biologically relevant programs. We applied scDECAF to scRNAseq data from PBMC, Lung, Pancreas, Brain and slide-tags snRNA of human prefrontal cortex for automatic cell type annotation. We demonstrate that scDECAF can recover perturbed gene programs in Lupus PBMC cells stimulated with IFNbeta and TGFBeta-induced cells undergoing epithelial-to-mesenchymal transition. scDECAF delineates patient-specific heterogeneity in cellular programs in Ovarian Cancer data. Using a healthy PBMC reference, we apply scDECAF to a mapped query PBMC COVID-19 case-control dataset and identify multicellular programs associated with severe COVID-19. scDECAF can improve biological interpretation and complement reference mapping analysis, and provides a method for gene set and pathway analysis in single cell gene expression data.

bioinformatics↗

bioRxiv2022

vissE: A versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis

Functional analysis of high throughput experiments using pathway analysis is now ubiquitous. Though powerful, these methods often produce thousands of redundant results owing to knowledgebase redundancies upstream. This scale of results hinders extensive exploration by biologists and often leads to investigator biases due to previous knowledge and expectations. To address this issue, we present vissE, a flexible network-based analysis method that summarises redundancies into biological themes and provides various analytical modules to characterise and visualise them with respect to the underlying data, thus providing a comprehensive view of the biological system. We demonstrate vissEs versatility by applying it to three different technologies: bulk, single-cell and spatial transcriptomics. Applying vissE to a factor analysis of a breast cancer spatial transcriptomic data, we identified stromal phenotypes that support tumour dissemination. Its adaptability allows vissE to enhance all existing gene-set enrichment and pathway analysis workflows, removing investigator bias from molecular discovery. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=175 SRC="FIGDIR/small/483195v1_ufig1.gif" ALT="Figure 1"> View larger version (64K): org.highwire.dtl.DTLVardef@1c384b0org.highwire.dtl.DTLVardef@13c6bd3org.highwire.dtl.DTLVardef@1cb1d91org.highwire.dtl.DTLVardef@24cbf_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗