Paul Müller

Deformability cytometry (DC) is a type of imaging flow cytometry, which uses a camera-equipped device to measure cellular stiffness in addition to other cellular properties at high throughput. Cellular properties such as area and elongation can identify cell types, but this requires prior knowledge of distinguishing properties and cannot be applied to clinically important cell aggregates. Using DC data, we evaluated conventional multi-class (MC) classification and introduced a multi-label (ML) approach for identifying blood cells and their aggregates. In particular, an ML classifier can simultaneously assign multiple cell-type labels to a single imaged event. We show that, unlike MC classification, ML classification can identify cell aggregates not represented in the training data. It also avoids the need for exhaustive, strictly defined aggregate labels, thereby simplifying and speeding up annotation. Since automated blood analyzers do not reliably analyze cell aggregates, our approach may help address this clinical gap.

Biomolecular condensates have been identified as a ubiquitous means of intracellular organization, exhibiting very diverse material properties. However, techniques to characterize these material properties and their underlying molecular interactions are scarce. Here, we introduce two optical techniques – Brillouin microscopy and quantitative phase imaging (QPI) – to address this scarcity. We establish Brillouin shift and linewidth as measures for average molecular interaction and dissipation strength, respectively, and we used QPI to obtain the protein concentration within the condensates. We monitored the response of condensates formed by FUS and by the low-complexity domain of hnRNPA1 (A1-LCD) to altering temperature and ion concentration. Conditions favoring phase separation increased Brillouin shift, linewidth, and protein concentration. In comparison to solidification by chemical crosslinking, the ion-dependent aging of FUS condensates had a small effect on the molecular interaction strength inside. Finally, we investigated how sequence variations of A1-LCD, that change the driving force for phase separation, alter the physical properties of the respective condensates. Our results provide a new experimental perspective on the material properties of protein condensates. Robust and quantitative experimental approaches such as the presented ones will be crucial for understanding how the physical properties of biological condensates determine their function and dysfunction.

Extracellular matrix (ECM) deposition after central nervous system (CNS) injury leads to inhibitory scarring in humans and other mammals, whereas it facilitates axon regeneration in the zebrafish. However, the molecular basis of these different fates is not understood. Here, we identify small leucine-rich proteoglycans (SLRPs) as a contributing factor to regeneration failure in mammals. We demonstrate that the SLRPs chondroadherin, fibromodulin, lumican, and prolargin are enriched in rodent and human but not zebrafish CNS lesions. Targeting SLRPs to the zebrafish injury ECM inhibits axon regeneration and functional recovery. Mechanistically, we find that SLRPs confer mechano-structural properties to the lesion environment that are adverse to axon growth. Our study reveals SLRPs as inhibitory ECM factors that impair axon regeneration by modifying tissue mechanics and structure, and identifies their enrichment as a feature of human brain and spinal cord lesions. These findings imply that SLRPs may be targets for therapeutic strategies to promote CNS regeneration.

An improved lookup table is introduced based on three-dimensional finite element simulations with a hyperelastic neo-Hookean solid and non-Newtonian fluid to extract the cell stiffness in real-time deformability cytometry (RT-DC).

Extracellular matrix (ECM) deposition after central nervous system (CNS) injury leads to inhibitory scarring in mammals, whereas it facilitates axon regeneration in the zebrafish. However, the molecular basis of these different fates is not understood. Here, we identify small leucine-rich proteoglycans (SLRPs) as a causal factor in regeneration failure. We demonstrate that the SLRPs Chondroadherin, Fibromodulin, Lumican, and Prolargin are enriched in human, but not zebrafish, CNS lesions. Targeting SLRPs to the zebrafish injury ECM inhibits axon regeneration and functional recovery. Mechanistically, we find that SLRPs confer structural and mechanical properties to the lesion environment that are adverse to axon growth. Our study reveals SLRPs as previously unknown inhibitory ECM factors in the human CNS that impair axon regeneration by modifying tissue mechanics and structure. ONE SENTENCE SUMMARY: Composition, structural organization, and mechanical properties of the injury ECM direct central nervous system regeneration.

Stretching individual living cells with light is a standard method to assess their mechanical properties. Yet, heat introduced by the laser light of optical stretchers may unwittingly change the mechanical properties of cells therein. To estimate the temperature induced by an optical trap, we introduce cell-sized, elastic poly(N-isopropylacrylamide) (PNIPAAm) microgels that relate temperature changes to hydrogel swelling. For their usage as a standardized calibration tool, we analyze the effect of free-radical chain-growth gelation (FCG) and polymer-analogous photogelation (PAG) on hydrogel network heterogeneity, micromechanics, and temperature response by Brillouin microscopy and optical diffraction tomography. Using a combination of tailor-made PNIPAAm macromers, PAG, and microfluidic processing, we obtain microgels with homogeneous network architecture. With that, we expand the capability of standardized microgels in calibrating and validating cell mechanics analysis, not only considering cell and microgel elasticity but also providing stimuli-responsiveness to consider dynamic changes that cells may undergo during characterization.

In processes such as development and regeneration, where large cellular and tissue rearrangements occur, cell fate and behaviour are strongly influenced by tissue mechanics. While most well-established tools probing mechanical properties require an invasive sample preparation, confocal Brillouin microscopy captures mechanical parameters optically with high resolution in a contact-free and label-free fashion. In this work, we took advantage of this tool and the transparency of the highly regenerative axolotl to probe its mechanical propertiesin vivofor the first time. We mapped the Brillouin frequency shift with high resolution in developing limbs and regenerating digits, the most studied structures in the axolotl. We detected a gradual increase in the cartilage Brillouin frequency shift, suggesting decreasing tissue compressibility during both development and regeneration. Moreover, we were able to correlate such an increase with the regeneration stage, which was undetected with fluorescence microscopy imaging. The present work evidences the potential of Brillouin microscopy to unravel the mechanical changes occurringin vivoin axolotls, setting the basis to apply this technique in the growing field of epimorphic regeneration.

Background: Quantitative phase imaging (QPI) is an established tool for the marker-free classification and quantitative characterization of biological samples. For spherical objects, such as cells in suspension, microgel beads, or liquid droplets, a single QPI image is sufficient to extract the radius and the average refractive index. This technique is invaluable, as it allows the characterization of large sample populations at high measurement rates. However, until now, no universal software existed that could perform this type of analysis. Besides the choice of imaging modality and the variety in imaging software, the main difficulty has been to automate the entire analysis pipeline from raw data to ensemble statistics. Results: We present DryMass, a powerful tool for QPI that covers all relevant steps from loading experimental data (multiple file formats supported), computing the phase data (built-in, automated hologram analysis), performing phase background corrections (offset, tilt, second order polynomial) to fitting scattering models (light projection, Rytov approximation, Mie simulations) to spherical phase objects for the extraction of dry mass, radius, and average refractive index. The major contribution of DryMass is a user-convenient, reliable, reproducible, and automated analysis pipeline for an arbitrary number of QPI datasets of arbitrary sizes. Conclusion: DryMass is a leap forward for data analysis in QPI, as it not only makes it easier to visualize raw QPI data and reproduce previous results in the field, but it also opens up QPI analysis to users without a background in programming or phase imaging.

Although label-free cell sorting is desirable for providing pristine cells for further analysis or use, current approaches lack molecular specificity and speed. Here, we combine real-time fluorescence and deformability cytometry with sorting based on standing surface acoustic waves and transfer molecular specificity to image-based sorting using an efficient deep neural network. In addition to general performance, we demonstrate the utility of this method by sorting neutrophils from whole blood without labels.

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.