Biology subjects

Toepfner, N.

Publications and source records attributed to Toepfner, N..

5 recordsLinked to original sources

bioRxiv2026

Impact of chemotherapy on leukocyte stiffness -A longitudinal RT-DC study in a breast cancer patient

BackgroundThe physical properties of leukocytes, such as cell size and stiffness, are critical for their circulation in microcapillary networks where rapid shape changes are required to squeeze through vascular constrictions. Alterations in the cells physical phenotype can promote venous thromboembolism (VTE), a common cause of death in cancer patients receiving chemotherapy. While biochemical VTE predictors are well studied, physical properties of blood cells receive less attention. MethodsUsing real-time deformability cytometry (RT-DC), we monitored for the first time the physical phenotype of leukocytes in a longitudinal study of a breast cancer patient treated with epirubicin/cyclophosphamide (EC) and paclitaxel (Pax). ResultsThe leukocyte counts extracted from RT-DC were in good agreement with standard clinical leukograms and EC had no immediate effect on leukocyte properties. However, Pax caused a significant softening of granulo/monocytes and a stiffening of lymphocytes immediately after administration. Leukocyte size was constant throughout the therapy, but we observed an overall increase in leukocyte stiffness, which was restored to normal values 45 weeks post treatment. ConclusionTaken together, our data reveal chemotherapy-induced specific alterations of leukocyte stiffness potentially critical for microcirculation. Thus, RT-DC measurements can add important, yet currently not available information to VTE prediction in cancer patients.

biophysics↗

bioRxiv2021

De novo identification of universal cell mechanics regulators

Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

biophysics↗

bioRxiv2019

Using real-time fluorescence and deformability cytometry and deep learning to transfer molecular specificity to label-free sorting

The identification and separation of specific cells from heterogeneous populations is an essential prerequisite for further analysis or use. Conventional passive and active separation approaches rely on fluorescent or magnetic tags introduced to the cells of interest through molecular markers. Such labeling is time- and cost-intensive, can alter cellular properties, and might be incompatible with subsequent use, for example, in transplantation. Alternative label-free approaches utilizing morphological or mechanical features are attractive, but lack molecular specificity. Here we combine image-based real-time fluorescence and deformability cytometry (RT-FDC) with downstream cell sorting using standing surface acoustic waves (SSAW). We demonstrate basic sorting capabilities of the device by separating cell mimics and blood cell types based on fluorescence as well as deformability and other image parameters. The identification of blood sub-populations is enhanced by flow alignment and deformation of cells in the microfluidic channel constriction. In addition, the classification of blood cells using established fluorescence-based markers provides hundreds of thousands of labeled cell images used to train a deep neural network. The trained algorithm, with latency optimized to below 1 ms, is then used to identify and sort unlabeled blood cells at rates of 100 cells/sec. This approach transfers molecular specificity into label-free sorting and opens up new possibilities for basic biological research and clinical therapeutic applications.

biophysics↗

bioRxiv2017

Real-time fluorescence and deformability cytometry - flow cytometry goes mechanics

Cell mechanical characterization has recently approached the throughput of conventional flow cytometers. However, this very sensitive, label-free approach still lacks the specificity of molecular markers. Here we combine real-time 1D-imaging fluorescence and deformability cytometry (RT-FDC) to merge the two worlds in one instrument -- opening many new research avenues. We demonstrate its utility using sub-cellular fluorescence localization to identify mitotic cells and test for their mechanical changes in an RNAi screen.

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↗