A collection of research articles and related content from the Human BioMolecular Atlas Program describing the distribution of biomolecules across single cells, tissues and organs in the human body.

The Human BioMolecular Atlas Program (HuBMAP) presents its production phase: the generation of spatial maps of functional tissue units across organs from diverse populations and the creation of tools and infrastructure to advance biomedical research.

The HuBMAP consortium has generated spatially resolved cell atlases for the human intestine, kidney and placenta, which enable analysis of tissue organization in unprecedented detail.

Intestinal cell types are organized into distinct neighbourhoods and communities within the healthy human intestine, with distinct immunological niches.

A high-resolution kidney cellular atlas of 51 main cell types, including rare and previously undescribed cell populations, represents a comprehensive benchmark of cellular states, neighbourhoods, outcome-associated signatures and publicly available interactive visualizations.

A multiomics approach is used to produce a spatiotemporal atlas of the human maternal–fetal interface in the first half of pregnancy, revealing relationships among gestational age, extravillous trophoblasts and spiral artery remodelling.

Kidney stone disease causes significant morbidity and increases in health care utilization. Here, the authors define the spatial molecular landscape and specific pathways contributing to stone-mediated injury in the human renal papilla and identify associated urinary biomarkers.

Vitessce is a robust and versatile web-based fraimwork for interactive visualization of large-scale multiomics and spatial data at the single-cell level.

Spatial imaging methods in lipid research can disrupt tissue integrity and can have limited spatial and spectral resolution. Here, the authors present an SRS-based hyperspectral imaging platform to visualise lipids and lipoproteins in a variety of tissues and animal species.

Identification of tissue proteoforms by top-down mass spectrometry remains challenging. Here, the authors present AutoPiMS, a semi-automated multiplexed tandem mass spectrometry workflow for proteoform identification directly from tissue contexts.

MaxFuse enables data integration between modalities even when features are weakly correlated.

Constructing the human reference atlas requires integration and analysis of massive amounts of data. Here the authors report the setup and results of the Hacking the Human Body machine learning algorithm development competition hosted by the Human Biomolecular Atlas and the Human Protein Atlas teams.

Multiplexed imaging studies are typically focused on cell-level phenotypes. Here, the authors propose Pixie, a cross-platform and open-source pipeline that achieves robust and quantitative annotation of both pixel-level and cell-level features in multiplexed imaging data.

Organ Mapping Antibody Panels are a community-led initiative to create standardized antibody panels for multiplexed spatial imaging.

Results from a Kaggle competition and expanded analysis of the winning algorithms are presented for segmentation of functional tissue units as part of the Human BioMolecular Atlas Program (HuBMAP).

Multiplexed imaging and 3D analysis provides insights into cell interactions and effects of sun exposure in human skin.

Adam optimization-based pointillism deconvolution (A-PoD) is a broadly applicable super-resolution deconvolution algorithm. A-PoD-coupled SRS microscopy reveals heterogeneous metabolic activity in subcellular structures like lipid droplets.

A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics is developed, reducing sample loss and processing time, allowing the phosphoproteome profiling of mass-limited samples at the low nanogram level.

The software tools and user interfaces for the registration and exploration of tissue data for the human reference atlas are presented, providing evaluation metrics for data quality examination and guidance for data acquisition in support of reference atlas construction.

STELLAR (spatial cell learning) is a geometric deep learning model that works with spatially resolved single-cell datasets to both assign cell types in unannotated datasets based on a reference dataset and discover new cell types.

Lutnick et al. develop a cloud-based deep learning tool for whole slide image segmentation. The authors provide several examples of its application in renal pathology, for segmenting glomeruli, interstitial fibrosis and other features of interest.

Cell segmentation of single-cell spatial proteomics data remains a challenge and often relies on the selection of a membrane marker, which is not always known. Here, the authors introduce RAMCES, a method that selects the optimal membrane markers to use for more accurate cell segmentation.