Biology subjects

Herr, B. W.

Publications and source records attributed to Herr, B. W..

15 recordsLinked to original sources

bioRxiv2026

Bridging Biomedical Atlas Ecosystem: Cross-Atlas Alignment And Scalable Tissue Specimen Registration

Over the last five years, over 13,000 tissue datasets with 200+ million cells from 20 consortia have been spatially registered into the Human Reference Atlas (HRA) common coordinate framework (CCF). The shared 3D spatial and semantic reference system enables exploration of datasets in the context of all other data across organs, assay types, and spatial scales. However, manual registration of individual samples remains resource intensive, posing feasibility challenges exacerbated by the proliferation of samples, assays, and atlasing efforts. This paper presents two approaches to scale up HRA construction: (1) projecting data across biomedical reference atlas systems and (2) using millitomes to bulk register tissue blocks into a reference organ. Both methods use the AutoMated Alignment and Projection (AMAP) pipeline to align 3D mesh models using point cloud registration. We demonstrate the evolving HRA-aligned atlas ecosystem for 6 models from the SPARC Program (heart), Gut Cell Atlas (large intestine), 500-subject consensus kidneys, and the Julich Brain Atlas. Additionally, we used AMAP to project 7 millitome models across 5 organs onto the HRA ecosystem, integrating 300+ tissue extraction sites. AMAP enables scalable tissue registration of data across atlas ecosystems enabling the construction of detailed reference maps of the human body.

bioinformatics↗

bioRxiv2026

Multiscale harmonization and semantic integration of biomedical data enable biological insights through immersive exploration

Single-cell atlassing efforts like the Human BioMolecular Atlas Program (HuBMAP) and the Cellular Senescence Network (SenNet) are producing multiscale datasets across the healthy, adult, human body, but this data is typically explored only on 2D screens with limited 3D affordances, even though understanding a cells location within a tissue, organ, and body requires reasoning across many orders of spatial magnitude. The Human Reference Atlas (HRA) provides standard terminologies and a Common Coordinate Framework (CCF) for harmonizing such data spatially and semantically. Building on the HRA Organ Gallery in virtual reality (VR) application, we present "HRA: Powers of Ten," which integrates, harmonizes, and visualizes biological data from these efforts immersively in VR using a Multiscale Elevator System that lets users descend, like riding an elevator through an inverted skyscraper, from a whole body view of 81 reference organs to datasets across 5 organs (lymph node, brain, large intestine, small intestine, liver), 5 assay types (CODEX, Visium, v-CyCIF, Xenium, SBF-SEM), and 4 spatial scales. Contributed by Data Providers, these VR scenes enable biological insights into senescence patterns, cellular neighborhoods, 3D tissue reconstruction, and subcellular liver architecture. A standard operating procedure supports adding further datasets. Freely available on the Meta Store to over 20 million headset owners, the application, data, and code are open-source.

bioinformatics↗

bioRxiv2026

SenNet Portal: Build, Optimization and Usage

Cellular senescence is a hallmark of aging and a driver of functional decline across tissues, yet its heterogeneity and context dependence have limited systematic study. The Common Funds Cellular Senescence Network (SenNet) Program addresses this challenge by generating multimodal, multi-tissue datasets that profile senescent cells across the human lifespan and complementary mouse models. The SenNet Data Portal (https://data.sennetconsortium.org) serves as the public gateway to these resources, providing open access to harmonized single-cell, spatial, imaging, transcriptomic, and proteomic data; senescence biomarker catalogs; and standardized protocols that can be used to comprehensively identify and characterize senescent cells in mouse and human tissue. As of April 2026, the portal hosts 2,041 publicly available human and mouse datasets across 15 organs using 6 general assay types. Experts from 13 Tissue Mapping Centers (TMCs) and 12 Technology Development and Application (TDAs) components contribute tissue data, analyze data, identify senescent biomarkers, and agree on panels for cross-tissue antibody harmonization. They also register human tissue data into the Human Reference Atlas (HRA) and develop user interfaces for the multiscale and multimodal exploration of this data. Built on a scalable hybrid cloud microservices architecture by the Consortium Organization and Data Coordinating Center (CODCC), the Portal enables data submission, management, integrated analysis, spatial context mapping, and harmonized access to cross-species data critical for aging research. This paper presents user needs, the Portals architecture, data processing workflows, and senescence-focused analytical tools; usage scenarios illustrating applications in biomarker discovery, quality benchmarking, hypothesis generation, spatial analysis, cost-efficient profiling, and cell distance distribution analysis; and utility and usage by the larger researcher community. Current limitations and planned extensions--including expanded spatial-omics releases and improved tools for senotype characterization--are discussed. SenNet protocols, code, and user interfaces are freely available on https://docs.sennetconsortium.org/apis.

bioinformatics↗

bioRxiv2025

Exploring endothelial cell environments across organs in spatially resolved omics data

Endothelial cells are ubiquitously present in the human body and line the luminal surface of blood and lymphatic vessels. The oxygen-dependence of cells impacts their proximity to blood vessels, and consequently, to endothelial cells depending on their functional properties and priorities. This paper presents cell-to-nearest-endothelial-cell distance distributions for various cell types using 399 spatially resolved omics datasets from 14 studies comprising 12 tissue types with a total of 47,349,496 cells. Additionally, we developed an open-source web-based interactive tool, Cell Distance Explorer, that allows researchers to interactively visualize cell graphs and linkages in 2D and 3D datasets. Finally, we present a hierarchical neighborhood analysis focused on the endothelial cell neighborhoods in small and large intestine datasets. This paper provides an open-access resource (datasets, tools, and analyses) to characterize and compare cell distances and cell neighborhoods in spatially resolved omics data.

bioinformatics↗

bioRxiv2025

Constructing and Using Cell Type Populations of the Human Reference Atlas

The human body contains [~]27-36 trillion cells of up to 10,000 cell types (CTs) within a volume of [~]62-120 liters (males) and 52-89 liters (females). The Human Reference Atlas (HRA) v2.3 provides a quantitative 3D framework of CTs across 73 reference organs and 1,283 3D anatomical structures (ASs). The HRA Cell Type Population (HRApop) effort has quantified CTs per AS using high-quality single-cell datasets processed through scalable, reproducible workflows and cell type annotation (CTann) tools. HRApop v1.0 includes reference CT populations for 73 ASs (112 when sex-specific) using 662 datasets spatially registered to 230 locations across 17 organs (31 when sex-specific). For 558 single-cell (sc-)transcriptomics datasets (11,042,750 cells), CTs and biomarker expressions were computed using Azimuth, CellTypist, and popV. To test generalizability, 104 sc-proteomics datasets (16,576,863 cells) were integrated. In total, HRApop includes 27,619,613 cells and serves as a healthy reference for researchers aiming to elucidate mechanisms underlying cellular interactions, FTU operations, and cellular and tissue level disease progression, which may facilitate advancements in basic discovery and lead to new therapeutic strategies.

bioinformatics↗

bioRxiv2025

Benchmarking single cell transcriptome matching methods for incremental growth of reference atlases

BackgroundThe advancement of single cell technologies has driven significant progress in constructing a multiscale, pan-organ Human Reference Atlas for healthy human cells. Many multi-faceted cell atlases for different organs, species, and diseases now exist, though challenges remain in harmonizing cell types and unifying nomenclature among respective cell atlases. Multiple machine learning and artificial intelligence methods, including models pre-trained on large-scale cell atlas datasets, are publicly available for single cell community users to computationally map their cell clusters to the cell atlases. ResultsThis study benchmarks seven computational tools for cell type matching and label transfer - Azimuth, CellTypist, CellHint, FR-Match, scArches, scPred, and singleR - in ten organ systems. Using healthy lung as an exemplary organ, when matching the well-annotated cell types in two atlases - the Human Lung Cell Atlas (HLCA) and the LungMAP Single-Cell Reference (CellRef), variations in the matching accuracy were observed, especially in rare cell types, underlining the need for a consensus strategy using a selective set of computational methods. In the meta-analysis, the benchmarked methods were used to incrementally integrate 61 cell types from HLCA and 48 from CellRef, resulting in a cell meta-atlas of 41 matched, 20 HLCA-, and 7 CellRef-specific cell types. Similar approach revealed 25 matched cell types existed in two independent kidney atlases. Generalizability of the benchmarking performances were further demonstrated in a variety of organ systems. ConclusionThis study reveals complementing strengths of the benchmarked methods and presents a framework for incremental growth of cell types in cell atlases.

bioinformatics↗

bioRxiv2024

Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph for Linked Open Data

The Human Reference Atlas (HRA) for the healthy, adult body is being developed by a team of international, interdisciplinary experts across 20+ consortia. It provides standard terminologies and data structures for describing specimens, biological structures, and spatial positions of experimental datasets and ontology-linked reference anatomical structures (AS), cell types (CT), and biomarkers (B). We introduce the HRA Knowledge Graph (KG) as central data resource for HRA v2.2, supporting cross-scale, biological queries to Resource Description Framework graphs using SPARQL. In February 2025, the HRA KG covered 71 organs with 5,800 AS, 2,268 CT, 2,531 B; it had 10,064,033 nodes, 171,250,177 edges, and a size of 125.84 GB. The HRA KG comprises 13 types of Digital Objects (DOs) using the Common Coordinate Framework Ontology to standardize core concepts and relationships across DOs. We (1) provide data and code for HRA KG construction; (2) detail HRA KG deployment by Linked Open Data principles; and (3) illustrate HRA KG usage via application programming interfaces, user interfaces, data products. A companion website is at https://cns-iu.github.io/hra-kg-supporting-information.

bioinformatics↗

bioRxiv2024

A general strategy for generating expert-guided, simplified views of ontologies

Annotation of biomedical entities with widely used, well-structured ontologies and ontology-aware tools ensures data and analyses are Findable, Accessible, Interoperable, and Reusable (FAIR). Standardized terms with synonyms support lexical search, while ontology structure enables biologically meaningful grouping of annotations, such as by location and type. However, ontologies serving diverse communities are often more complex than needed for specific applications, creating barriers to adoption by researchers and resource developers. For example, cell atlases often attempt simplifications by manually building term hierarchies linking to cell type and anatomy ontologies, but these may include relationship types unsuitable for grouping annotations. We present tools for validating human expert curated term hierarchies, developed in two human reference atlas projects, against ontology structures. The tools provide tabular statistics plus graphical views of matching and non-matching terms and relationships to support discussion and conflict resolution. The HuBMAP Human Reference Atlas (HRA) effort is used to validate the approach and tools, and the Human Developmental Cell Atlas is featured as a use case.

bioinformatics↗

bioRxiv2024

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRAs 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 understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies and 2D/3D reference objects. New experimental data can be mapped into the HRA using (1) three cell type annotation tools (e.g., Azimuth) or (2) validated antibody panels (OMAPs), or (3) by registering tissue data spatially. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and previews atlas usage applications.

bioinformatics↗

bioRxiv2024

Ontology-based modeling, integration, and analysis of heterogeneous clinical, pathological, and molecular kidney data for precision medicine

Many resources are now generating, processing, storing, or providing kidney-related molecular, pathological, and clinical data. Reference ontologies offer an opportunity to support knowledge and data organization and integration. The Kidney Precision Medicine Project (KPMP) team contributed to the representation and addition of 329 kidney phenotype terms to the Human Phenotype Ontology (HPO) and identified many subcategories of acute kidney injury (AKI) or chronic kidney disease (CKD). The Kidney Tissue Atlas Ontology (KTAO) imports and integrates kidney-related terms from existing ontologies (e.g., HPO, CL, and Uberon) and represents 259 kidney-related biomarkers. We also developed a precision medicine metadata ontology (PMMO) to integrate 50 variables from KPMP and CellxGene resources and applied PMMO for integrative analysis. The gene expression profiles of kidney gene biomarkers were specifically analyzed in healthy controls or AKI/CKD disease states. This work demonstrates how ontology-based approaches support multi-domain data and knowledge organization and integration to advance precision medicine.

bioinformatics↗

bioRxiv2023

Functional Tissue Units in the Human Reference Atlas.

Functional tissue units (FTUs) form the basic building blocks of organs and are important for understanding and modeling the healthy physiological function of the organ and changes during disease states. In this first comprehensive catalog of FTUs, we document the definition, physical dimensions, vasculature, and cellular composition of 22 anatomically correct, nested functional tissue units (FTUs) in 10 healthy human organs. The catalog includes datasets, illustrations, an interactive online FTU explorer, and a large printable poster. All data and code are freely available. This is part of a larger ongoing international effort to construct a Human Reference Atlas (HRA) of all cells in the human body.

bioinformatics↗

bioRxiv2022

Specimen, Biological Structure, and Spatial Ontologies in Support of a Human Reference Atlas

The Human Reference Atlas (HRA) is defined as a comprehensive, three-dimensional (3D) atlas of all the cells in the healthy human body. It is compiled by an international team of experts that develop standard terminologies linked to 3D reference objects describing anatomical structures. The third HRA release (v1.2) covers spatial reference data and ontology annotations for 26 organs. Experts access the HRA annotations via spreadsheets and view reference models in 3D editing tools. This paper introduces the Common Coordinate Framework Ontology (CCFO) v2.0.1 that interlinks specimen, biological structure, and spatial data together with the CCF API which makes the HRA programmatically accessible and interoperable with Linked Open Data (LOD). We detail how real-world user needs and experimental data guide CCFO design and implementation, present CCFO classes and properties together with examples of their usage, and report on technical validation performed. The CCFO graph database and API are used in the HuBMAP portal, Virtual Reality Organ Gallery, and other applications that support data queries across multiple, heterogeneous sources.

bioinformatics↗

bioRxiv2022

Anatomical structures, cell types, and biomarkers of the healthy human blood vasculature

More than 150 scientists from 17 consortia are collaborating on an international project to build a Human Reference Atlas, which maps all 37 trillion cells in the healthy adult human body. The initial release of this atlas provided hierarchical lists of the anatomical structures, cell types, and biomarkers in 11 organs. Here, we describe the methods we used as part of this initiative to build the first open, computer-readable, and comprehensive database of the adult human blood vasculature, called the Human Reference Atlas-Vasculature Common Coordinate Framework (HRA-VCCF). It includes 961 vessels and their branching connections, 10 cell types, and 10 biomarkers. With this paper we are releasing additional details on vessel types and subtypes, branching sequence, anastomoses, portal systems, microvasculature, functional tissue units, mappings to regions vessels supply or drain, and links to 3D reference objects. Future versions will add variants, geometric properties of vessels, and connections to the lymph vasculature; and, it will iteratively expand and improve the database as additional experimental data become available through the participating consortia.

cell biology↗

bioRxiv2021

Tissue Registration and Exploration User Interfaces in support of a Human Reference Atlas

Several international consortia are collaborating to construct a human reference atlas, which is a comprehensive, high-resolution, three-dimensional atlas of all the cells in the healthy human body. Laboratories around the world are collecting tissue specimens from donors varying in sex, age, ethnicity, and body mass index. However, integrating and harmonizing tissue data across 20+ organs and more than 15 bulk and spatial single-cell assay types poses diverse challenges. Here we present the software tools and user interfaces developed to annotate ("register") and explore the collected tissue data. A key part of these tools is a common coordinate framework, which provides standard terminologies and data structures for describing specimens, biological structures, and spatial positions linked to existing ontologies. As of December 2021, the "registration" user interface has been used to harmonize and make publicly available data on 6,178 tissue sections from 2,698 tissue blocks collected by the Human Biomolecular Atlas Program, the Stimulating Peripheral Activity to Relieve Conditions program, the Human Cell Atlas, the Kidney Precision Medicine Project, and the Genotype Tissue Expression project. The second "exploration" user interface enables consortia to evaluate data quality and coverage, explore tissue data in the context of the human body, and guide data acquisition.

bioinformatics↗

bioRxiv2021

Anatomical Structures, Cell Types, and Biomarkers Tables Plus 3D Reference Organs in Support of a Human Reference Atlas

1.This paper reviews efforts across 16 international consortia to construct human anatomical structures, cell types, and biomarkers (ASCT+B) tables and three-dimensional reference organs in support of a Human Reference Atlas. We detail the ontological descriptions and spatial three-dimensional anatomical representations together with user interfaces that support the registration and exploration of human tissue data. Four use cases are presented to demonstrate the utility of ASCT+B tables for advancing biomedical research and improving health.

cell biology↗