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Schnitzerlein, M.

Publications and source records attributed to Schnitzerlein, M..

2 recordsLinked to original sources

Quantifying distribution shifts in single-cell data with scXMatch

A basic task that frequently arises when analyzing single-cell data is to assess if there is a global distribution shift between the data profiles of cells from two different conditions. Widely used approaches to address this task such as visual inspection of two-dimensional representations or clustering-based workflows lack a solid statistical underpinning and are notoriously unstable and prone to confirmation bias. To promote more rigorous analysis, we here present the scverse-compatible Python tool scXMatch. scXMatch is based on a non-parametric graph-based test to quantify distribution shifts in arbitrary data spaces for which a suitable distance measure is available. We evaluated scXMatch on single-cell gene expression, chromatin accessibility, and imaging-derived cell morphology data, showing that it can robustly detect distribution shifts for different types of single-cell data. scXMatch thus aims to set a new standard in the single-cell biology field, replacing easy-to-manipulate semi-manual distribution shift quantification workflows by principled statistical testing.

bioinformatics↗

Cell morphology as a quantifier for functional states of resident tissue macrophages

Resident tissue macrophages (RTMs) are essential for tissue homeostasis. Their diverse functions, from monitoring interstitial fluids to clearing cellular debris, are accompanied by characteristic morphological changes that reflect their functional status. While current knowledge of macrophage behaviour comes primarily from in vitro studies, their dynamic behavior in vivo is fundamentally different, necessitating a more physiologically relevant approach to their understanding. In this study, we employed intravital imaging to generate dynamic data from peritoneal RTMs in mice under various conditions and developed a comprehensive image processing pipeline to quantify RTM morphodynamics over time, defining human-interpretable cell size and shape features. These features allowed for the quantitative and qualitative differentiation of cell populations in various functional states, including pro- and anti-inflammatory activation and endosomal dysfunction. The study revealed that under steady-state conditions, RTMs exhibit a wide range of morphodynamical phenotypes, constituting a naive morphospace of behavioral motifs. Upon challenge, morphodynamic patterns changed uniformly at the population level but predominantly within the constraints of this naive morphospace. Notably, aged animals displayed a markedly shifted naive morphospace, indicating drastically different behavioral patterns compared to their young counterparts. The developed method also proved valuable in optimizing explanted tissue setups, bringing RTM behavior closer to the physiological native state. Our versatile approach thus provides novel insights into the dynamic behavior of bona fide macrophages in vivo, enabling the distinction between physiological and pathological cell states and the assessment of functional tissue age on a population level. Author summaryIn this study, we combine state-of-the-art in vivo imaging with advanced computational analysis to reveal the dynamic behavior of peritoneal resident tissue macrophages (RTMs) in their natural environment. These sentinel cells, which are crucial for tissue homeostasis, constantly monitor their environment and, in the process, undergo dynamic morphological changes that have remained largely uninvestigated due to technical limitations. Using two-photon microscopy, we captured time-lapse images of RTMs in the peritoneal serosa under various experimental conditions. Our customized image processing pipeline allowed a comprehensive assessment of cell morphology and dynamics and provided unprecedented insights into the behavior of RTMs in vivo, enabling us to distinguish cell populations in different physiological and pathological states. Our work opens up new avenues for the dynamic in situ phenotyping of macrophage functionality in disease contexts without their extraction from tissues and provides a novel perspective on the behavior of RTMs in their natural microenvironment. This versatile tool promises to advance our understanding of tissue homeostasis and macrophage function in health and disease, with potential applications in both basic research and clinical settings.

immunology↗