Yale School of Public Health Symposium on tissue imaging mass spectrometry: illuminating phenotypic heterogeneity and drug disposition at the molecular level.

‘A picture is worth a thousand words’ is an idiom from the English language (‘borrowed’ from on old Chinese proverb) that conveys the notion that a complex idea can be succinctly and fully described by a single image. Never has this expression been truer than in the clinical and pharmaceutical arenas. Enormous strides have been made by the scientific community in the evolving field of biomedical imaging with the aim of representing and/or quantifying aspects of disease and drug action by using tools such as radiography, MRI, PET, and ultrasound. Yet linking the phenotypical data generated by these systems to the genome is a challenging task. Identifying the link between the mechanism of disease or failed drug response to the genome of an individual is difficult, because central pieces of information are missing. However, imaging mass spectrometry (IMS) can overcome this issue. IMS aims to detect the molecular constituents of the tissue; these can then be correlated with genome-related characteristics, such as gene expression patterns and possible mutations, and ultimately provide a phenotypic molecular link to the complex disease biology. The big data technology of IMS can generate spatial information of thousands of metabolites and proteins from within a tissue, facilitating a deeper understanding of the connections between the genome, phenotypic characteristics and the biological response. It is a technology that has the potential to serve as a segue between gene expression and observed biological signal

Automated annotation and visualisation of high-resolution spatial proteomic mass spectrometry imaging data using HIT-MAP.

Spatial proteomics has the potential to significantly advance our understanding of biology, physiology and medicine. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a powerful tool in the spatial proteomics field, enabling direct detection and registration of protein abundance and distribution across tissues. MALDI-MSI preserves spatial distribution and histology allowing unbiased analysis of complex, heterogeneous tissues. However, MALDI-MSI faces the challenge of simultaneous peptide quantification and identification. To overcome this, we develop and validate HIT-MAP (High-resolution Informatics Toolbox in MALDI-MSI Proteomics), an open-source bioinformatics workflow using peptide mass fingerprint analysis and a dual scoring system to computationally assign peptide and protein annotations to high mass resolution MSI datasets and generate customisable spatial distribution maps. HIT-MAP will be a valuable resource for the spatial proteomics community for analysing newly generated and retrospective datasets, enabling robust peptide and protein annotation and visualisation in a wide array of normal and disease contexts

TGFβR signalling determines CD103⁺CD11b⁺ dendritic cell development in the intestine

CD103+CD11b+ dendritic cells (DCs) are unique to the intestine, but the factors governing their differentiation are unclear. Here we show that transforming growth factor receptor 1 (TGFβR1) has an indispensable, cell intrinsic role in the development of these cells. Deletion of Tgfbr1 results in markedly fewer intestinal CD103+CD11b+ DCs and a reciprocal increase in the CD103−CD11b+ dendritic cell subset. Transcriptional profiling identifies markers that define the CD103+CD11b+ DC lineage, including CD101, TREM1 and Siglec-F, and shows that the absence of CD103+CD11b+ DCs in CD11c-Cre.Tgfbr1fl/fl mice reflects defective differentiation from CD103−CD11b+ intermediaries, rather than an isolated loss of CD103 expression. The defect in CD103+CD11b+ DCs is accompanied by reduced generation of antigen-specific, inducible FoxP3+ regulatory T cells in vitro and in vivo, and by reduced numbers of endogenous Th17 cells in the intestinal mucosa. Thus, TGFβR1-mediated signalling may explain the tissue-specific development of these unique DCs

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage

\ua9 The Author(s) 2025. The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Human Reference Atlas (HRA) of the healthy adult body. Experts from 20+ consortia collaborate to develop a Common Coordinate Framework (CCF), knowledge graphs and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes and biomarkers) and to use the HRA to characterize changes that occur with aging, disease and other perturbations. HRA v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types and 2,089 biomarkers (such as genes, proteins and lipids) from 33 ASCT+B tables and 65 3D Reference Objects linked to ontologies. New experimental data can be mapped into the HRA using (1) cell type annotation tools (for example, Azimuth), (2) validated antibody panels or (3) by registering tissue data spatially. This paper describes HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interfaces, flexible hybrid cloud infrastructure and previews atlas usage applications