von Francesco Palumbo ; Mis̆a Gunjak ; Patty J. Lee ; Stefan Günther ; Anne Hilgendorff ; István Vadász ; Susanne Herold ; Werner Seeger ; Christian Axel Mühlfeld ; Rory E. Morty
Flow cytometry and fluorescence-activated cell sorting are widely used to study endothelial cells, for which the generation of viable single-cell suspensions is an essential first step. Two enzymatic approaches, collagenase A and dispase, are widely employed for endothelial cell isolation. In this study, the utility of both enzymatic approaches, alone and in combination, for endothelial cell isolation from juvenile and adult mouse lungs was assessed, considering the number, viability, and subtype composition of recovered endothelial cell pools. Collagenase A yielded an 8-12-fold superior recovery of viable endothelial cells from lung tissue from developing mouse pups, compared to dispase, although dispase proved superior in efficiency for epithelial cell recovery. Single-cell RNA-Seq revealed that the collagenase A approach yielded a diverse endothelial cell subtype composition of recovered endothelial cell pools, with broad representation of arterial, capillary, venous, and lymphatic lung endothelial cells; while the dispase approach yielded a recovered endothelial cell pool highly enriched for one subset of general capillary endothelial cells, but poor representation of other endothelial cells subtypes. These data indicate that tissue dissociation markedly influences the recovery of endothelial cells, and the endothelial subtype composition of recovered endothelial cell pools, as assessed by single-cell RNA-Seq.
von Octavia-Andreea Ciora ; Tanja Seegmüller ; Johannes S. Fischer ; Theresa Wirth ; Friederike Häfner ; Sophia Stoecklein ; Andreas W. Flemmer ; Kai Förster ; Alida Kindt ; Dirk Bassler ; Christian F. Poets ; Narges Ahmidi ; Anne Hilgendorff
von Lukas Heumos ; Philipp Ehmele ; Tim Treis ; Julius Upmeier zu Belzen ; Eljas Roellin ; Lilly May ; Altana Namsaraeva ; Nastassya Horlava ; Vladimir A. Shitov ; Xinyue Zhang ; Luke Zappia ; Rainer Knoll ; Niklas J. Lang ; Leon Hetzel ; Isaac Virshup ; Lisa Sikkema ; Fabiola Curion ; Roland Eils ; Herbert B. Schiller ; Anne Hilgendorff ; Fabian J. Theis
With progressive digitalization of healthcare systems worldwide, large-scale collection of electronic health records (EHRs) has become commonplace. However, an extensible framework for comprehensive exploratory analysis that accounts for data heterogeneity is missing. Here we introduce ehrapy, a modular open-source Python framework designed for exploratory analysis of heterogeneous epidemiology and EHR data. ehrapy incorporates a series of analytical steps, from data extraction and quality control to the generation of low-dimensional representations. Complemented by rich statistical modules, ehrapy facilitates associating patients with disease states, differential comparison between patient clusters, survival analysis, trajectory inference, causal inference and more. Leveraging ontologies, ehrapy further enables data sharing and training EHR deep learning models, paving the way for foundational models in biomedical research. We demonstrate ehrapy’s features in six distinct examples. We applied ehrapy to stratify patients affected by unspecified pneumonia into finer-grained phenotypes. Furthermore, we reveal biomarkers for significant differences in survival among these groups. Additionally, we quantify medication-class effects of pneumonia medications on length of stay. We further leveraged ehrapy to analyze cardiovascular risks across different data modalities. We reconstructed disease state trajectories in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) based on imaging data. Finally, we conducted a case study to demonstrate how ehrapy can detect and mitigate biases in EHR data. ehrapy, thus, provides a framework that we envision will standardize analysis pipelines on EHR data and serve as a cornerstone for the community.
Nature medicine [New York, NY] : Springer Nature, 1995 30(2024), 11, Seite 3369-3380 Online-Ressource
von Friederike Häfner ; Caroline Johansson ; Larissa Schwarzkopf ; Kai Förster ; Yvonne Kraus ; Andreas W. Flemmer ; Georg Hansmann ; Hannes Sallmon ; Ursula Felderhoff-Müser ; Sabine Witt ; Lars Schwettmann ; Anne Hilgendorff
