Biology subjects

Rausch, L.

Publications and source records attributed to Rausch, L..

7 recordsLinked to original sources

bioRxiv2026

Distinct nanoscale dynamics of growth receptor complexes link hormone perception to rapid cell wall remodeling

Cell elongation is a fundamental process allowing plants to change size and shape, a process governed by several growth promoting hormones. While hormones such as brassinosteroids (BRs) and phytosulfokines (PSKs) have been shown to play important roles in elongation growth, the early steps of signal perception remains elusive, especially with regard to PSK. Here we report a rapid mechanism by which PSK alter cell wall mechanical properties in elongating hypocotyls. Notably, this mode of action differs from that of BR. Making use of atomic force microscopy and fluorescence lifetime imaging microscopy we demonstrate how hallmarks of growing plant cells such as mechanical wall properties, porosity and apoplastic pH are differentially affected by PSK compared to BR. Using super-resolution microscopy, we show that the receptor complex components for BR and PSK display individual spatiotemporal movement and organization patterns. The BR receptor BRI1 transitions to a faster diffusive state upon ligand perception, while the PSK receptor PSKR1 associates in tighter clusters. The shared co-receptor BAK1 displays a selective decrease in cluster density after PSK treatment. We found that the putative cell wall state sensor RLP44 is required for the observed changes, however the spatiotemporal dynamics of RLP44 are not altered during signaling. We propose a model of how cell walls are specifically tuned by BRI1- and PSKR1-centered signaling hubs as potential prerequisites for and during cell elongation initiation.

plant biology↗

bioRxiv2024

Contrasting Effects of Cytoskeleton Disruption on Plasma Membrane Receptor Dynamics: Insights from Single-Molecule Analyses

Traditional models such as the fluid mosaic model or the lipid raft hypothesis have shaped our understanding of plasma membrane (PM) organization. However, recent discoveries have extended these paradigms by pointing to the existence of micro- and nanodomains. Here, we investigated the role of the cytoskeleton in general and whether the picket fence model, established in animal cells, is transferable to the plant cell system. By using single-particle tracking photoactivated localization microscopy (sptPALM) in combination with genetically encoded enzymatic tools for the targeted disruption of the cytoskeleton, we studied the dynamics and nanoscale organization of a selection of PM receptor-like kinases (RLKs) and receptor-like proteins (RLPs). Our findings show that the disintegration of actin filaments leads to decreased diffusion, more restrictive motion patterns, and enlarged clusters, whereas the disintegration of microtubules results in increased diffusion, more unconstrained diffusive behavior, and decreased cluster sizes of the tested RLKs and RLPs. These results underscore the potential unique regulatory functions of cytoskeleton components in plants and suggest an altered mechanism compared to the picket fence model of the animal cell system. Our qualitative data can serve as the foundation for further investigations aimed at developing a comprehensive and refined model of protein dynamics and organization in plant cells.

plant biology↗

bioRxiv2023

GreenMemTraX: A User-Friendly Software for Super-Resolution Analysis of Membrane Protein Dynamics and Nanoscale Organization in Plants

Super-resolution microscopy (SRM) approaches revolutionize cell biology by providing insights into the nanoscale organization and dynamics of macromolecular assemblies and single molecules in living cells. A major hurdle limiting SRM democratization is post-acquisition data analysis which is often complex and time-consuming. Here, we present OneFlowTraX, a user-friendly and open-source software dedicated to the analysis of single-molecule localization microscopy (SMLM) approaches such as single-particle tracking photoactivated localization microscopy (sptPALM). Through an intuitive graphical user interface, OneFlowTraX provides an automated all-in-one solution for single-molecule localization, tracking, as well as mobility and clustering analyses. OneFlowTraX allows the extraction of diffusion and clustering parameters of millions of molecules in a few minutes. Finally, OneFlowTraX greatly simplifies data management following the FAIR (Findable, Accessible, Interoperable, Reusable) principles. We provide a detailed step-by-step manual and guidelines to assess the quality of single-molecule analyses. Applying different fluorophores including mEos3.2, PA-GFP, and PATagRFP, we exemplarily used OneFlowTraX to analyze the dynamics of plant plasma membrane-localized proteins including an aquaporin, the brassinosteroid receptor Brassinosteroid Insensitive 1 (BRI1) and the Receptor-Like Protein 44 (RLP44).

plant biology↗

bioRxiv2023

nf-root: a best-practice pipeline for deep learning-based analysis of apoplastic pH in microscopy images of developmental zones in plantroot tissue

Here we report nextflow-root (nf-root), a novel best-practice pipeline for deep learning-based analysis of fluorescence microscopy images of plant root tissue, aimed at studying hormonal mechanisms associated with cell elongation, given the vital role that plant hormones play in the development and growth of plants. This bioinformatics pipeline performs automatic identification of developmental zones in root tissue images, and analysis of apoplastic pH measurements of tissue zones, which is useful for modeling plant hormone signaling and cell physiological responses. Mathematical models of physiological responses of plant hormones, such as brassinolide, have been successfully established for certain root tissue types, by evaluating apoplastic pH via fluorescence imaging. However, the generation of data for this modeling is time-consuming, as it requires the manual segmentation of tissue zones and evaluation of large amounts of microscopy data. We introduce a high-throughput, highly reproducible Nextflow pipeline based on nf-core standards that automates tissue zone segmentation by implementing a deep-learning module, which deploys deterministically trained (i.e. bit-exact reproducible) convolutional neural network models, and augments the segmentation predictions with measures of prediction uncertainty and model interpretability, aiming to facilitate result interpretation and verification by experienced plant biologists. To train our segmentation prediction models, we created a publicly available dataset composed of confocal microscopy images of A. thaliana root tissue using the pH-sensitive fluorescence indicator, and manually annotated segmentation masks that identify relevant tissue zones. We applied this pipeline to analyze exemplary data, and observed a high statistical similarity between the manually generated results and the output of nf-root. Our results indicate that this approach achieves near human-level performance, and significantly reduces the time required to analyze large volumes of data, from several days to hours.

bioinformatics↗

bioRxiv2021

Binding of phosphatidylserine-positive microparticles by PBMCs classifies disease severity in COVID-19 patients

Infection with SARS-CoV-2 is associated with thromboinflammation, involving thrombotic and inflammatory responses, in many COVID-19 patients. In addition, immune dysfunction occurs in patients characterized by T cell exhaustion and severe lymphopenia. We investigated the distribution of phosphatidylserine (PS), a marker of dying cells, activated platelets, and platelet-derived microparticles (PMP), during the clinical course of COVID-19. We found an unexpectedly high amount of blood cells loaded with PS+ PMPs for weeks after the initial COVID-19 diagnosis. Elevated frequencies of PS+PMP+ PBMCs correlated strongly with increasing disease severity. As a marker, PS outperformed established laboratory markers for inflammation, leucocyte composition, and coagulation, currently used for COVID-19 clinical outcome prognosis. PS+ PMPs preferentially bound to CD8+ T cells with gene expression signatures of proliferating effector rather than memory T cells. As PS+ PMPs carried programmed death-ligand 1 (PD-L1), they may affect T cell expansion or function. Our data provide a novel marker for disease severity and show that PS, which can trigger the blood coagulation cascade, the complement system, and inflammation, resides on activated immune cells. Therefore, PS may serve as a beacon to attract thromboinflammatory processes toward lymphocytes and cause immune dysfunction in COVID-19.

immunology↗

bioRxiv2021

Impaired function and delayed regeneration of dendritic cells in COVID-19

Disease manifestations in COVID-19 range from mild to severe illness associated with a dysregulated innate immune response. Alterations in function and regeneration of dendritic cells (DC) and monocytes may contribute to immunopathology and influence adaptive immune responses in COVID-19 patients. We analyzed circulating DC and monocyte subsets in 65 hospitalized COVID-19 patients with mild/moderate or severe disease from acute disease to recovery and in healthy controls. Persisting reduction of all DC subpopulations was accompanied by an expansion of proliferating Lineage- HLADR+ cells lacking DC markers. Increased frequency of the recently discovered CD163+ CD14+ DC3 subpopulation in patients with more severe disease was associated with systemic inflammation, activated T follicular helper cells, and antibody-secreting cells. Persistent downregulation of CD86 and upregulation of PD-L1 in conventional DC (cDC2 and DC3) and classical monocytes associated with a reduced capacity to stimulate naive CD4+ T cells correlated with disease severity. Long-lasting depletion and functional impairment of DCs and monocytes may have consequences for susceptibility to secondary infections and therapy of COVID-19 patients.

immunology↗

bioRxiv2020

In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning

The in vivo detection of dead cells remains a major challenge due to technical hurdles. Here we present a novel method, where injection of fluorescent Milk fat globule-EGF factor 8 protein (MFG-E8) in vivo combined with imaging flow cytometry and deep learning allows the identification of dead cells based on their surface exposure of phosphatidylserine (PS) and other image parameters. A convolutional autoencoder (CAE) was trained on defined pictures and successfully used to identify apoptotic cells in vivo. However, unexpectedly, these analyses also revealed that the great majority of PS+ cells were not apoptotic, but rather live cells associated with PS+ extracellular vesicles (EVs). During acute viral infection apoptotic cells increased slightly, while up to 30% of lymphocytes were decorated with PS+ EVs of Dendritic cell exosomal origin. The combination of recombinant fluorescent MFG-E8 and the CAE-method will greatly facilitate analyses of cell death and EVs in vivo.

cell biology↗