Biology subjects

Börner, K.

Publications and source records attributed to Börner, K..

8 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

Organism-scale annotation with Pan-human Azimuth

Single-cell atlases now span many human tissues, but inconsistent annotations across studies limit their utility as a unified reference. We introduce Pan-human Azimuth, a supervised neural network that maps human cells from diverse tissues and datasets onto a single hierarchical organism-scale typology. Developed through NIH HuBMAP, the model is trained on a uniquely curated corpus designed to maximize diversity across tissues and technologies while enforcing uniform, interpretable annotations and stringent quality control. The use of a single organism-wide reference enables us to map tens of millions of cells in the Tabula Sapiens and scBaseCamp repositories, perform cross-tissue comparisons across thousands of samples, and identify striking tissue specialization among fibroblast states. Pan-human Azimuth naturally extends to annotating spatial transcriptomic data, recovering canonical kidney cortical structures and distinguishing glomerular states consistent with expert pathology. We release Pan-human Azimuth alongside cloud, R, and Python interfaces to facilitate standardized organism-wide single-cell analysis.

genomics↗

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

AI-Supported Extraction of Functional Tissue Unit Properties for Human Reference Atlas Construction

The Human Reference Atlas (HRA) effort brings together experts from more than 25 international consortia to capture the multiscale organization of the human body--from large anatomical organ systems (macro) to the single-cell level (micro). Functional tissue units (FTU, meso) in 10 organs have been detailed and 2D illustrations have been created by experts. Comprehensive FTU property characterization is essential for the HRA, but manual review of the vast number of scholarly publications is impractical. Here, we introduce Large-Model Retrieval-Augmented Generation for HRA FTUs (HRAftu-LM-RAG), an AI-driven framework for scalable and automated extraction of FTU-relevant properties from scholarly publications. This validated framework integrates Large Language Models for textual reasoning, Large Vision Models for visual interpretation, and Retrieval Augmented Generation for knowledge grounding, offering a balanced trade-off between accuracy and processing efficiency. We retrieved 244,640 PubMed Central publications containing 1,389,168 figures for 22 FTUs and identified 617,237 figures with microscopy and schematic images. From these images and associated text, we automatically extracted 331,189 scale bars and 1,719,138 biological entity mentions, along with donor metadata such as sex and age. The resulting data facilitates the design, review, and approval of future FTU illustrations during HRA construction.

bioinformatics↗

bioRxiv2023

Publication, Funding, and Experimental Data in Support of Human Reference Atlas Construction and Usage

Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts (clinicians, pathologists, anatomists, single-cell experts), published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemes and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, called HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 2,689 anatomical structures, 590 cell types, 1,770 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit represents 23 tables with 21,704,001 records including 7 junction tables with 13,042,188 relationships. We demonstrate how HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage. Data and code are at https://github.com/cns-iu/hra-literature.

bioinformatics↗

bioRxiv2023

Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms

The development of a reference atlas of the healthy human body requires automated image segmentation of major anatomical structures across multiple organs based on spatial bioimages generated from various sources with differences in sample preparation. We present the setup and results of the "Hacking the Human Body" machine learning algorithm development competition hosted by the Human Biomolecular Atlas (HuBMAP) and the Human Protein Atlas (HPA) teams on the Kaggle platform. We showcase how 1,175 teams from 78 countries engaged in community- driven, open-science code development that resulted in machine learning models which successfully segment anatomical structures across five organs using histology images from two consortia and that will be productized in the HuBMAP data portal to process large datasets at scale in support of Human Reference Atlas construction. We discuss the benchmark data created for the competition, major challenges faced by the participants, and the winning models and strategies.

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↗

bioRxiv2020

Cone-shaped HIV-1 capsids are transported through intact nuclear pores

Human immunodeficiency virus (HIV-1) remains a major health threat. Viral capsid uncoating and nuclear import of the viral genome are critical for productive infection. The size of the HIV-1 capsid is generally believed to exceed the diameter of the nuclear pore complex (NPC), indicating that capsid uncoating has to occur prior to nuclear import. Here, we combined correlative light and electron microscopy with subtomogram averaging to capture the structural status of reverse transcription-competent HIV-1 complexes in infected T cells. We demonstrate that the diameter of the NPC in cellulo is sufficient for the import of apparently intact, coneshaped capsids. Subsequent to nuclear import, we detected disrupted and empty capsid fragments, indicating that uncoating of the replication complex occurs by breaking the capsid open, and not by disassembly into individual subunits. Our data directly visualize a key step in HIV-1 replication and enhance our mechanistic understanding of the viral life cycle.

microbiology↗