Publications Cell Physics Division

The integration of optical imaging and machine learning on microfluidic platforms makes it possible to achieve high-content minimally invasive characterization of a population of samples on a single chip As the analysis of this high-content information is typically conducted in the electronic domain optoelectronic conversion speeds and the bandwidth available for data processing put a limitation on the throughput of these methods In this work we present an analysis system based on diffractive neural networks with the potential for integration in microfluidic systems for high-content classification of objects with high sampling rates We show that such a system can distinguish objects by size through passive optical inference with a numerical test accuracy of 98.2 and an experimental test accuracy of 83.4 in an environment compatible with a microfluidic chamber This development paves the way for novel approaches in high-speed phenotyping of large cell populations based on all-optical or hybrid optoelectronic neuromorphic information processing.

The mechanical properties of cells are dynamic, allowing them to adjust to different needs in different biological contexts. In recent years, advanced biophysical techniques have enabled the rapid, high-throughput assessment of single-cell mechanics, providing new insights into the regulation of the mechanical cell phenotype. However, the molecular mechanisms by which cells maintain and regulate their mechanical properties remain poorly understood. Here, we present a genome-scale RNA interference (RNAi) screen investigating the roles of kinase and phosphatase genes in regulating single-cell mechanics using Real-Time Fluorescence and Deformability Cytometry (RT-FDC). Our screen identified 82 known and novel mechanical regulators across diverse cellular functions from 214 targeted genes, leveraging RT-FDC’s unique capabilities for comprehensive, high-throughput mechanical phenotyping with single-cell and cell cycle resolution. These findings refine our understanding of how signaling pathways coordinate structural determinants of cell mechanical phenotypes and provide a starting point for uncovering new molecular targets involved in biomechanical regulation across diverse biological systems.

Deformability cytometry (DC) is a powerful biophysical technique that enables cost-effective, high-throughput characterization of disease-associated changes in blood cell mechanics. Mechanical profiling of living white blood cells (WBCs) is particularly valuable due to their critical role in the immune response. However, reliably identifying and classifying WBC subtypes in a label-free manner remains a significant challenge. Until now, the analysis pipeline has relied on manual gating by trained experts, limiting scalability and reproducibility. In this study, we present a fully automated and generalizable framework for WBC classification in shear flow DC experiments, based on box filters and unsupervised clustering of cell populations. Both box filters and unsupervised clustering rely on cell shape features derived from high-accuracy segmentation and on cell texture features derived from bright-field images. This unsupervised approach not only improves reproducibility and reduces processing time but also overcomes key limitations of supervised models that require extensive training data and often suffer from reduced performance under varying imaging conditions. We validated our method by comparing cell features obtained through manual gating and automated classification across six experimental sets. These sets incorporated variations in blood donors, anticoagulants (EDTA and citrate), blood collection sources (capillary and venous), and device brightness settings. Each set included five repeated measurements. The results consistently confirmed the reliability and robustness of the method across all tested conditions and WBC types. Importantly, this automated pipeline enables the inclusion of WBCs with membrane protrusions—typically excluded from standard analyses—allowing for morphological characterization of potentially activated cells. Moreover, by using shape features derived from the original contour rather than the convex hull, we improve morphological accuracy and reduce measurement variability. This approach thus enhances the accuracy, consistency, and scalability of WBC mechanophenotyping and enables high-throughput analysis across large cohorts.

INTRODUCTION: Pathogenic variants in myosin heavy chain 9 (MYH9) encoding the heavy chain of nonmuscle myosin IIA (NMMIIA) cause autosomal-dominant MYH9-related disease that may include proteinuric kidney disease macrothrombocytopenia cataract sensorineural deafness and elevated liver enzymes. METHODS: Whole exome sequencing and segregation analysis were performed in a patient with end-stage renal disease Histology of kidney and liver biopsies was assessed and blood smears were examined for the presence of Döhle-like bodies Deformability cytometry and monocyte migration assays were performed Immortalized podocytes and primary skin fibroblasts of 1 patient were transfected with plasmids containing MYH9 wild type (WT) or the p.(Arg424Gly) variant Biochemical studies using recombinantly produced proteins were conducted to assess the variant’s impact on adenosine triphosphate (ATP) turnover and motor function. RESULTS: We identified the likely pathogenic heterozygous MYH9 variant c.1270C>G p.(Arg424Gly) in all affected members of a nonconsanguineous family Typical microscopic findings such as Döhle-like bodies or NMMIIA conglomerates were absent Nonetheless all patients presented with proteinuric kidney disease elevated liver enzymes and intermittent thrombocytopenia The altered protein showed increased ATP turnover in the presence of actin and enhanced motor activity under both unloaded and loaded conditions. CONCLUSION: We identified a novel fully segregating MYH9 variant causing MYH9-related disease Based on biochemical findings we report the first gain-of-function variant of MYH9 We propose that the enhanced intrinsic motor activity of the p.(Arg424Gly) variant is a key contributor to the disease mechanism Incorporation of the p.(Arg424Gly) variant into nonmuscle myosin IIA filaments and higher-order actomyosin assemblies may in principle affect actomyosin dynamics.

Shaping polyacrylamide (PAAm) hydrogels via droplet microfluidics enables production of microgels that mimic cellular physical properties, advancing mechanobiology research. Controlling microgel size and elasticity is essential but challenging, as multiple factors influence polymerization and network formation. Although chemical reactions in microdroplets are generally faster and more uniform than in bulk, these microreactors are highly sensitive: small changes in chemical or physical conditions can cause significant variations in microgel properties. Our study identifies flow conditions as a crucial factor affecting both microgel elasticity and size by modulating interfacial transport during gelation. Using a flow-focusing microfluidic chip, we generated pre-gel droplets with the same composition in an oil phase, systematically varying the PAAm-to-oil flow rate ratio while maintaining a constant total flow rate. This method produced droplets with minimal size variation (<1 µm), but beads exhibited distinct Young’s moduli despite identical monomer concentrations. Further analysis showed that catalyst transport across the oil–water interface strongly impacts polymerization efficiency and network structure. These findings demonstrate that while droplet polymerization offers advantages, reproducible microgel properties demand precise flow control. This work emphasizes the critical role of microfluidic parameter tuning in advancing PAAm microgel applications in biophysics.