The Human BioMolecular Atlas Program (HuBMAP) aims to create a multi-scale spatial atlas of the healthy human body at single-cell resolution by applying advanced technologies and disseminating resources to the community. As the HuBMAP moves past its first phase, creating ontologies, protocols and pipelines, this Perspective introduces the production phase: the generation of reference spatial maps of functional tissue units across many organs from diverse populations and the creation of mapping tools and infrastructure to advance biomedical research.HuBMAP was founded with the goal of establishing state-of-the-art frameworks for building spatial multiomic maps of non-diseased human organs at single-cell resolution 1 . During the first phase (2018)(2019)(2020)(2021)(2022), the priorities of the project included the validation and development of assay platforms; workflows for data processing, management, exploration and visualization; and the establishment of protocols, quality control standards and standard operating procedures. Extensive infrastructure was established through a coordinated effort among the various HuB-MAP integration, visualization and engagement teams, tissue-mapping centres, technology and tools development and rapid technology implementation teams and working groups 1 . Single-cell maps, predominantly consisting of two-dimensional (2D) spatial data as well as data from dissociated cells, were generated for several organs. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) was established for open access to experimental tissue data and reference atlas data.The infrastructure was augmented with software tools for tissue data registration, processing, annotation, visualization, cell segmentation and automated annotation of cell types and cellular neighbourhoods from spatial data. Computational methods were developed for integrating multiple data types across scales and interpretation 2 . Standard reference terminology and a common coordinate framework spanning anatomical to biomolecular scales were established to ensure interoperability across organs, research groups and consortia 3 . Guidelines to capture high-quality multiplexed spatial data 4 were established including validated panels of cell-and structure-specific antibodies 5 . The first phase produced a large number of manuscripts (https://commonfund.nih.gov/ publications?pid=43) including spatially resolved single-cell maps [6][7][8][9][10][11] .The production phase of HuBMAP was launched in the autumn of 2022. The focus is on scaling data production spanning diverse biological variables (for example, age and ethnicity) and deployment and enhancement of analytical, visualization and navigational tools to generate high-resolution 3D accessible maps of major functional tissue units from more than 20 organs. This phase involves over 60 institutions and 400 researchers with opportunities for active intra-and inter-consortia collaborations and building a foundational resource for new biological insights and precision medicine. Below, ...
The search for molecular species that are differentially expressed between biological states is an important step towards discovering promising biomarker candidates. In imaging mass spectrometry (IMS), performing this search manually is often impractical due to the large size and high-dimensionality of IMS datasets. Instead, we propose an interpretable machine learning workflow that automatically identifies biomarker candidates by their mass-to-charge ratios, and that quantitatively estimates their relevance to recognizing a given biological class using Shapley additive explanations (SHAP). The task of biomarker candidate discovery is translated into a feature ranking problem: given a classification model that assigns pixels to different biological classes on the basis of their mass spectra, the molecular species that the model uses as features are ranked in descending order of relative predictive importance such that the top-ranking features have a higher likelihood of being useful biomarkers. Besides providing the user with an experiment-wide measure of a molecular species’ biomarker potential, our workflow delivers spatially localized explanations of the classification model’s decision-making process in the form of a novel representation called SHAP maps. SHAP maps deliver insight into the spatial specificity of biomarker candidates by highlighting in which regions of the tissue sample each feature provides discriminative information and in which regions it does not. SHAP maps also enable one to determine whether the relationship between a biomarker candidate and a biological state of interest is correlative or anticorrelative. Our automated approach to estimating a molecular species’ potential for characterizing a user-provided biological class, combined with the untargeted and multiplexed nature of IMS, allows for the rapid screening of thousands of molecular species and the obtention of a broader biomarker candidate shortlist than would be possible through targeted manual assessment. Our biomarker candidate discovery workflow is demonstrated on mouse-pup and rat kidney case studies.HighlightsOur workflow automates the discovery of biomarker candidates in imaging mass spectrometry data by using state-of-the-art machine learning methodology to produce a shortlist of molecular species that are differentially expressed with regards to a user-provided biological class.A model interpretability method called Shapley additive explanations (SHAP), with observational Shapley values, enables us to quantify the local and global predictive importance of molecular species with respect to recognizing a user-provided biological class.By providing spatially localized explanations for a classification model’s decision-making process, SHAP maps deliver insight into the spatial specificity of biomarker candidates and enable one to determine whether (and where) the relationship between a biomarker candidate and the class of interest is correlative or anticorrelative.
The need for deep molecular and spatial characterization of tissues is growing rapidly to study complex diseases and disorders, particularly in the context of relationships between the cellular and molecular architecture from single cells to functional and anatomical regions of tissue. The human kidney is composed of over 26 cell types that actively coordinate to form higher order structures, such as the nephron. It is not yet understood how these structures vary molecularly throughout a single organ or among organs within the human population. Towards this end, we have developed an extensive molecular and cellular atlas of the human kidney consisting of over 3 million cells comprising 75,000 functional units from 13 human patients, using a spatially registered and integrated suite of technologies consisting of imaging mass spectrometry, histological staining, autofluorescence microscopy, and multiplexed immunofluorescence to comprehensively probe centimeter-sized areas of tissue. Through this approach, the cellular organization and metabolomic profiles of glomeruli, proximal tubules, distal tubules, and collecting ducts were determined as well as their intra- and inter-patient variance. Relating these lipid profiles to canonical cell types allowed us to hypothesize the functional role of specific phospholipids that have not previously been described, particularly how they relate to differences in BMI and sex. These integrated data provide a valuable reference molecular atlas for kidney researchers and are publicly available through the NIH Human Biomolecular Atlas Program (https://portal.hubmapconsortium.org/).
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