Biology subjects

Baldock, R.

Publications and source records attributed to Baldock, R..

4 recordsLinked to original sources

bioRxiv2026

The Human Gut Cell Atlas v1.0: A Unified, Community-BuiltReference of Cellular Diversity Across the Gut

Single-cell RNA-sequencing (scRNA-seq) studies have transformed our understanding of cellular diversity in the human intestine, but differences in anatomical sampling, experimental processing and cell-type nomenclature limit comparison across datasets. Here, we present the Human Gut Cell Atlas (HGCA) V1, a community-curated reference of 944,390 healthy and disease-adjacent intestinal cells from 502 samples, 265 donors and 24 landmark studies, linked to detailed anatomical, clinical, and technical metadata. As this metadata is of critical importance to human biology, we developed MetaManager, an open source data management framework for standardized metadata collection and tracking. A five-level consensus taxonomy resolves 94 homeostatic cell identities and enables transferrable, high-resolution annotation of new gut datasets. By assembling cell identity, sampling metadata, and independent spatial validation, intestinal segment and radial tissue depth were revealed to systematically shape cellular composition and transcriptional state, enabling inference of the presence of sampling-dependent tissue niches, including lymphoid follicles. Reference mapping accurately and rapidly recovered cohort-level Crohn's disease and treatment signatures without de novo annotation, while mapping human intestinal organoids quantified cell-type-specific identity, regional specification, maturation, and transcriptional fidelity to native tissue. The HGCA V1 provides a standardized cellular, anatomical coordinate, and metadata system for reproducible, high-quality analysis of intestinal biology across healthy tissue, disease-associated remodelling, and experimental models, providing a continuously evolving community reference for cumulative discovery in human intestinal biology.

systems biology↗

bioRxiv2025

NanoDel: a long-read sequencing pipeline for identifying large-scale mitochondrial DNA deletions validated in patient samples clinically diagnosed with mitochondrial disease and evaluated in glioblastoma.

MotivationTraditional methods for detecting large-scale mitochondrial DNA (mtDNA) deletions (LSMDs) in cells present challenges, i.e. a priori information, high DNA inputs, poor sensitivity and are not always quantitative. Mitigation can be achieved through high throughput DNA sequencing using e.g. Illumina and Oxford Nanopore Technologies (ONT), in combination with LSMD breakpoint identification and quantification using bioinformatic tools. Splice-aware RNA alignment tools increase the sensitivity for detecting LSMD breakpoints compared with DNA aligners. Long-read sequencing (LRS) also offers potential advantages over short read sequencing, e.g. greater read lengths and capturing variants on single reads. No existing pipelines capture the benefits of both a splice-aware alignment tool and LRS. ResultsWe developed "NanoDel", a LRS pipeline, to sensitively and accurately detect cellular LSMDs. Using artificial datasets, "NanoDel" was more sensitive and accurate than other pipelines. In samples diagnosed with mitochondrial disease, it identified both known and previously uncharacterised (including mixtures) of LSMDs, without a priori information. LSMD breakpoints were found in mt-co1, mt-cyb, mt-nd6 and mt-nd5 genes. Analysis of selected LSMDs revealed proximity to repeat and putative G-quadruplex motifs, and occurrence in a range of healthy and pathological tissues, indicating potential for a shared vulnerability landscape in mtDNA, shaped by sequence motifs and structural constraints. "NanoDel" combined with one-amplicon, not two-amplicon, LR-PCR offers a robust strategy with clinical application for detecting LSMDs across a variety of cell/tissue samples, and its application across a broader range of samples, will yield new mechanistic insights into LSMD formation, and further our understanding of mtDNA instability.

bioinformatics↗

bioRxiv2023

Spatial transcriptomics reveals novel genes during the remodelling of the embryonic human arterial valves

Abnormalities of the arterial valves, including bicuspid aortic valve (BAV) are amongst the most common congenital defects and are a significant cause of morbidity as well as predisposition to disease in later life. Despite this, and compounded by their small size and relative inaccessibility, there is still much to understand about how the arterial valves form and remodel during embryogenesis, both at the morphological and genetic level. Here we set out to address this in human embryos, using Spatial Transcriptomics (ST). We show that ST can be used to investigate the transcriptome of the developing arterial valves, circumventing the problems of accurately dissecting out these tiny structures from the developing embryo. We show that the transcriptome of CS16 and CS19 arterial valves overlap considerably, despite being several days apart in terms of human gestation, and that expression data confirm that the great majority of the most differentially expressed genes are valve-specific. Moreover, we show that the transcriptome of the human arterial valves overlaps with that of mouse atrioventricular valves from a range of gestations, validating our dataset but also highlighting novel genes, including four that are not found in the mouse genome and have not previously been linked to valve development. Importantly, our data suggests that valve transcriptomes are under-represented when using commonly used databases to filter for genes important in cardiac development; this means that causative variants in valve-related genes may be excluded during filtering for genomic data analyses for, for example, BAV. Finally, we highlight "novel" pathways that likely play important roles in arterial valve development, showing that mouse knockouts of RBP1 have arterial valve defects. Thus, this study has confirmed the utility of ST for studies of the developing heart valves and broadens our knowledge of the genes and signalling pathways important in human valve development. Non-Technical SummaryCongenital heart defects, particularly those affecting the valves and septa of the heart, are very common. Despite this, few gene variants have been confirmed as disease-causing in human congenital heart (including valve) disease patients. Here we utilise spatial transcriptomics technology, which allows the identification of genes expressed in tissue slices, on embryonic human heart valves and identify a gene dataset that is human arterial valve-specific. We confirm the localisation of key novel genes to the arterial valves and highlight the relevance of the dataset by showing that mice mutant for RBP1, a novel gene identified as being highly differentially expressed in our valve dataset, have previously unidentified arterial valve defects. Using commonly used bioinformatic databases we show that filtering patient genomic data using these terms would likely exclude valve genes and thus may not identify the causative genes. Thus, we confirm that spatial transcriptomics technology can be used to study gene expression in tiny structures such as the developing heart valves and provide a new human embryonic valve dataset that can be used in future genomic studies of patients with congenital valve defects.

developmental biology↗

bioRxiv2022

Towards a Clinically-based Common Coordinate Framework for the Human Gut Cell Atlas - The Gut Models

BackgroundThe Human Cell Atlas resource will deliver single cell transcriptome data spatially organised in terms of gross anatomy, tissue location and with images of cellular histology. This will enable the application of bioinformatics analysis, machine learning and data mining revealing an atlas of cell types, sub-types, varying states and ultimately cellular changes related to disease conditions. To further develop the understanding of specific pathological and histopathological phenotypes with their spatial relationships and dependencies, a more sophisticated spatial descriptive framework is required to enable integration and analysis in spatial terms. MethodsWe describe a conceptual coordinate model for the Gut Cell Atlas (small and large intestines). Here, we focus on a Gut Linear Model (1-dimensional representation based on the centreline of the gut) that represents the location semantics as typically used by clinicians and pathologists when describing location in the gut. This knowledge representation is based on a set of standardised gut anatomy ontology terms describing regions in situ, such as ileum or transverse colon, and landmarks, such as ileo-caecal valve or hepatic flexure, together with relative or absolute distance measures. We show how locations in the 1D model can be mapped to and from points and regions in both a 2D model and 3D models, such as a patients CT scan where the gut has been segmented. ResultsThe outputs of this work include 1D, 2D and 3D models of the human gut, delivered through publicly accessible Json and image files. We also illustrate the mappings between models using a demonstrator tool that allows the user to explore the anatomical space of the gut. All data and software is fully open-source and available online. ConclusionsSmall and large intestines have a natural "gut coordinate" system best represented as a 1D centreline through the gut tube, reflecting functional differences. Such a 1D centreline model with landmarks, visualised using viewer software allows interoperable translation to both a 2D anatomogram model and multiple 3D models of the intestines. This permits users to accurately locate samples for data comparison.

bioinformatics↗