Biology subjects

Krater, M.

Publications and source records attributed to Krater, M..

6 recordsLinked to original sources

bioRxiv2024

Cell viscosity influences hematogenous dissemination and metastatic extravasation of tumor cells

Metastases arise from a multi-step process during which tumor cells change their mechanics in response to microenvironmental cues. While such mechanical adaptability could influence metastatic success, how tumor cell mechanics directly impacts intravascular behavior of circulating tumor cells (CTCs) remains poorly understood. In the present study, we demonstrate how the deformability of CTCs affects hematogenous dissemination and identify the mechanical profiles that favor metastatic extravasation. Combining intravital microscopy with CTC-mimicking elastic beads and mechanically-tuned tumor cells, we demonstrate that the inherent properties of circulating objects dictate their ability to enter constraining vessels. We identify cellular viscosity as the key property that governs CTC circulation and arrest patterns. We further demonstrate that cellular viscosity is required for efficient extravasation and find that properties that favor extravasation and subsequent metastatic outgrowth can be opposite. Altogether, we identify CTC viscosity as a key biomechanical parameter that shapes several steps of metastasis.

cancer biology↗

bioRxiv2021

Physical phenotype of blood cells is altered in COVID-19

Clinical syndrome coronavirus disease 2019 (COVID-19) induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is characterized by rapid spreading and high mortality worldwide. While the pathology is not yet fully understood, hyper-inflammatory response and coagulation disorders leading to congestions of microvessels are considered to be key drivers of the still increasing death toll. Until now, physical changes of blood cells have not been considered to play a role in COVID-19 related vascular occlusion and organ damage. Here we report an evaluation of multiple physical parameters including the mechanical features of five frequent blood cell types, namely erythrocytes, lymphocytes, monocytes, neutrophils, and eosinophils. More than 4 million blood cells of 17 COVID-19 patients at different levels of severity, 24 volunteers free from infectious or inflammatory diseases, and 14 recovered COVID-19 patients were analyzed. We found significant changes in erythrocyte deformability, lymphocyte stiffness, monocyte size, and neutrophil size and deformability. While some of these changes recovered to normal values after hospitalization, others persisted for months after hospital discharge, evidencing the long-term imprint of COVID-19 on the body.

biophysics↗

bioRxiv2020

AIDeveloper: deep learning image classification in life science and beyond

Publications on artificial intelligence (AI)-based image analysis have increased drastically in recent years. However, all applications use individual solutions highly specialized for a particular task. Here, we present an easy-to-use, adaptable, open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming. The software provides a variety of NN-architectures that can be simply selected for training. AID allows the user to apply trained models on new data, obtain metrics for classification performance, and export final models to different formats. The working principles of AID are first illustrated by training a convolutional neural net (CNN) on a large dataset consisting of images of different objects (CIFAR-10). We further explore the potential of AID by training a model to distinguish areas of differentiated and non-differentiated mesenchymal stem cells (MSCs) in culture. Additionally, we compare a conventional clinical whole blood cell count with a whole blood cell count performed by an NN-trained, using a dataset of more than 1.2 million images obtained by real-time deformability cytometry, delivering comparable results. Finally, we demonstrate how AID can be used for label-free classification of B- and T-cells derived from human blood, which currently requires costly and time-consuming sample preparation. Thus, AID can empower anyone to develop, train, and apply NNs for image classification. Moreover, models can be generated by non-programmers, exported, and used on different devices, which allows for an interdisciplinary use.

bioinformatics↗

bioRxiv2019

3D microenvironment stiffness regulates tumor spheroid growth and mechanics via p21 and ROCK

Mechanical properties of cancer cells and their microenvironment contribute to breast cancer progression. While mechanosensing has been extensively studied using two-dimensional (2D) substrates, much less is known about it in a physiologically more relevant 3D context. Here we demonstrate that breast cancer tumor spheroids, growing in 3D polyethylene glycol-heparin hydrogels, are sensitive to their environment stiffness. During tumor spheroid growth, compressive stresses of up to 2 kPa built up, as quantitated using elastic polymer beads as stress sensors. Atomic force microscopy (AFM) revealed that tumor spheroid stiffness increased with hydrogel stiffness. Also, constituent cell stiffness increased in a ROCK- and F-actin-dependent manner. Increased hydrogel stiffness correlated with attenuated tumor spheroid growth, a higher proportion of cells in G0/G1 phase and elevated levels of the cyclin-dependent kinase inhibitor p21. Drug-mediated ROCK inhibition reversed not only cell stiffening upon culture in stiff hydrogels but also increased tumor spheroid growth. Taken together, we reveal here a mechanism by which the growth of a tumor spheroid can be regulated via cytoskeleton rearrangements in response to its mechanoenvironment. Thus, our findings contribute to a better understanding of how cancer cells react to compressive stress when growing under confinement in stiff environments and provide the basis for a more in-depth exploration of the underlying mechanosensory response.

bioengineering↗

bioRxiv2018

Spheroid culture of mesenchymal stromal cells results in morpho-rheological properties appropriate for improved microcirculation

Human bone marrow mesenchymal stromal cells (MSCs) have been used in clinical trials for the treatment of systemic inflammatory diseases due to their regenerative and immunomodulatory properties. However, intravenous administration of MSCs is hampered by cell trapping within the pulmonary capillary networks. Here, we hypothesize that traditional twodimensional (2D) plastic-adherent cell expansion fails to result in appropriate morphorheological properties required for cell-circulation. To address this issue, we adapted a novel method to culture MSCs in non-adherent three-dimensional (3D) spheroids (mesenspheres). The biological properties of mesensphere-cultured MSCs remained identical to conventional 2D cultures. Morpho-rheological analyses revealed a smaller size and lower cell stiffness of mesensphere-derived MSCs compared to plastic-adherent MSCs, measured using real-time deformability cytometry (RT-DC) and atomic force microscopy, resulting in an increased ability to pass through micro-constrictions in an ex vivo microcirculation assay. This ability was confirmed in vivo by analysis of cell accumulation in various organ capillary networks after intravenous injection of mesensphere-derived MSCs in mouse. Our findings generally identify cellular morpho-rheological properties as attractive targets to improve microcirculation and specifically suggest mesensphere cultures as a promising approach for optimized MSC-based therapies.

biophysics↗

bioRxiv2017

Detection Of Human Disease Conditions By Single-Cell Morpho-Rheological Phenotyping Of Whole Blood

Blood is arguably the most important bodily fluid and its analysis provides crucial health status information. A first routine measure to narrow down diagnosis in clinical practice is the differential blood count, determining the frequency of all major blood cells. What is lacking to advance initial blood diagnostics is an unbiased and quick functional assessment of blood that can narrow down the diagnosis and generate specific hypotheses. To address this need, we introduce the continuous, cell-by-cell morpho-rheological (MORE) analysis of whole blood, without labeling, enrichment or separation, at rates of 1,000 cells/sec. In a drop of blood we can identify all major blood cells and characterize their pathological changes in several disease conditions in vitro and in patient samples. This approach takes previous results of mechanical studies on specifically isolated blood cells to the level of application directly in whole blood and adds a functional dimension to conventional blood analysis.

biophysics↗