A pH-driven transition of the cytoplasm from a fluid- to a solid-like state promotes entry into dormancy

Cells can enter into a dormant state when faced with unfavorable conditions. However, how cells enter into and recover from this state is still poorly understood. Here, we study dormancy in different eukaryotic organisms and find it to be associated with a significant decrease in the mobility of organelles and foreign tracer particles. We show that this reduced mobility is caused by an influx of protons and a marked acidification of the cytoplasm, which leads to widespread macromolecular assembly of proteins and triggers a transition of the cytoplasm to a solid-like state with increased mechanical stability. We further demonstrate that this transition is required for cellular survival under conditions of starvation. Our findings have broad implications for understanding alternative physiological states, such as quiescence and dormancy, and create a new view of the cytoplasm as an adaptable fluid that can reversibly transition into a protective solid-like state.

Detection of human disease conditions by single-cell morpho-rheological phenotyping of 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 diluted whole blood, without labeling, enrichment or separation, at rates of 1000 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 blood and adds a functional dimension to conventional blood analysis.

Real-time fluorescence and deformability cytometry

The throughput of cell mechanical characterization has recently approached that of conventional flow cytometers. However, this very sensitive, label-free approach still lacks the specificity of molecular markers. Here we developed an approach that combines real-time 1D-imaging fluorescence and deformability cytometry in one instrument (RT-FDC), thus opening many new research avenues. We demonstrated its utility by using subcellular fluorescence localization to identify mitotic cells and test for mechanical changes in those cells in an RNA interference screen.

Mechanical deformation induces depolarization of neutrophils

The transition of neutrophils from a resting state to a primed state is an essential requirement for their function as competent immune cells. This transition can be caused not only by chemical signals but also by mechanical perturbation. After cessation of either, these cells gradually revert to a quiescent state over 40 to 120 min. We use two biophysical tools, an optical stretcher and a novel microcirculation mimetic, to effect physiologically relevant mechanical deformations of single nonadherent human neutrophils. We establish quantitative morphological analysis and mechanical phenotyping as label-free markers of neutrophil priming. We show that continued mechanical deformation of primed cells can cause active depolarization, which occurs two orders of magnitude faster than by spontaneous depriming. This work provides a cellular-level mechanism that potentially explains recent clinical studies demonstrating the potential importance, and physiological role, of neutrophil depriming in vivo and the pathophysiological implications when this deactivation is impaired, especially in disorders such as acute lung injury.

Standardized microgel beads as elastic cell mechanical probes

Cell mechanical measurements are gaining increasing interest in biological and biomedical studies. However, there are no standardized calibration particles available that permit the cross-comparison of different measurement techniques operating at different stresses and time-scales. Here we present the rational design, production, and comprehensive characterization of poly-acrylamide (PAAm) microgel beads mimicking size and overall mechanics of biological cells. We produced mono-disperse beads at rates of 20-60 kHz by means of a microfluidic droplet generator, where the pre-gel composition was adjusted to tune the beads' elasticity in the range of cell and tissue relevant mechanical properties. We verified bead homogeneity by optical diffraction tomography and Brillouin microscopy. Consistent elastic behavior of microgel beads at different shear rates was confirmed by AFM-enabled nanoindentation and real-time deformability cytometry (RT-DC). The remaining inherent variability in elastic modulus was rationalized using polymer theory and effectively reduced by sorting based on forward-scattering using conventional flow cytometry. Our results show that PAAm microgel beads can be standardized as mechanical probes, to serve not only for validation and calibration of cell mechanical measurements, but also as cell-scale stress sensors.

Linear complexions: Confined chemical and structural states at dislocations

For 5000 years, metals have been mankind's most essential materials owing to their ductility and strength. Linear defects called dislocations carry atomic shear steps, enabling their formability. We report chemical and structural states confined at dislocations. In a body-centered cubic Fe-9 atomic percent Mn alloy, we found Mn segregation at dislocation cores during heating, followed by formation of face-centered cubic regions but no further growth. The regions are in equilibrium with the matrix and remain confined to the dislocation cores with coherent interfaces. The phenomenon resembles interface-stabilized structural states called complexions. A cubic meter of strained alloy contains up to a light year of dislocation length, suggesting that linear complexions could provide opportunities to nanostructure alloys via segregation and confined structural states.

Targeting Mechanoresponsive Proteins in Pancreatic Cancer: 4-Hydroxyacetophenone Blocks Dissemination and Invasion by Activating MYH14

Metastasis is complex, involving multiple genetic, epigenetic, biochemical, and physical changes in the cancer cell and its microenvironment. Cells with metastatic potential are often characterized by altered cellular contractility and deformability, lending them the flexibility to disseminate and navigate through different microenvironments. We demonstrate that mechanoresponsiveness is a hallmark of pancreatic cancer cells. Key mechanoresponsive proteins, those that accumulate in response to mechanical stress, specifically nonmuscle myosin IIA (MYH9) and IIC (MYH14), α-actinin 4, and filamin B, were highly expressed in pancreatic cancer as compared with healthy ductal epithelia. Their less responsive sister paralogs-myosin IIB (MYH10), α-actinin 1, and filamin A-had lower expression differential or disappeared with cancer progression. We demonstrate that proteins whose cellular contributions are often overlooked because of their low abundance can have profound impact on cell architecture, behavior, and mechanics. Here, the low abundant protein MYH14 promoted metastatic behavior and could be exploited with 4-hydroxyacetophenone (4-HAP), which increased MYH14 assembly, stiffening cells. As a result, 4-HAP decreased dissemination, induced cortical actin belts in spheroids, and slowed retrograde actin flow. 4-HAP also reduced liver metastases in human pancreatic cancer-bearing nude mice. Thus, increasing MYH14 assembly overwhelms the ability of cells to polarize and invade, suggesting targeting the mechanoresponsive proteins of the actin cytoskeleton as a new strategy to improve the survival of patients with pancreatic cancer. SIGNIFICANCE: This study demonstrates that mechanoresponsive proteins become upregulated with pancreatic cancer progression and that this system of proteins can be pharmacologically targeted to inhibit the metastatic potential of pancreatic cancer cells.

Combining structural and chemical information at the nanometer scale by correlative transmission electron microscopy and atom probe tomography

In many cases, the three-dimensional reconstructions from atom probe tomography (APT) are not sufficiently accurate to resolve crystallographic features such as lattice planes, shear bands, stacking faults, dislocations or grain boundaries. Hence, correlative crystallographic characterization is required in addition to APT at the exact same location of the specimen. Also, for the site-specific preparation of APT tips containing regions of interest (e.g. grain boundaries) correlative electron microscopy is often inevitable. Here we present a versatile experimental setup that enables performing correlative focused ion beam milling, transmission electron microscopy (TEM), and APT under optimized characterization conditions. The setup was designed for high throughput, robustness and practicability. We demonstrate that atom probe tips can be characterized by TEM in the same way as a standard TEM sample. In particular, the use of scanning nanobeam diffraction provides valuable complementary crystallographic information when being performed on atom probe tips. This technique enables the measurement of orientation and phase maps as known from electron backscattering diffraction with a spatial resolution down to one nanometer.

Atomic-scale quantification of grain boundary segregation in nanocrystalline material

Grain boundary segregation leads to nanoscale chemical variations that can alter a material's performance by orders of magnitude (e.g., embrittlement). To understand this phenomenon, a large number of grain boundaries must be characterized in terms of both their five crystallographic interface parameters and their atomic-scale chemical composition. We demonstrate how this can be achieved using an approach that combines the accuracy of structural characterization in transmission electron microscopy with the 3D chemical sensitivity of atom probe tomography. We find a linear trend between carbon segregation and the misorientation angle ω for low-angle grain boundaries in ferrite, which indicates that ω is the most influential crystallographic parameter in this regime. However, there are significant deviations from this linear trend indicating an additional strong influence of other crystallographic parameters (grain boundary plane, rotation axis). For high-angle grain boundaries, no general trend between carbon excess and ω is observed; i.e., the grain boundary plane and rotation axis have an even higher influence on the segregation behavior in this regime. Slight deviations from special grain boundary configurations are shown to lead to unexpectedly high levels of segregation.

Three-dimensional grain mapping by x-ray diffraction contrast tomography and the use of Friedel pairs in diffraction data analysis

X-ray diffraction contrast tomography (DCT) is a technique for mapping grain shape and orientation in plastically undeformed polycrystals. In this paper, we describe a modified DCT data acquisition strategy which permits the incorporation of an innovative Friedel pair method for analyzing diffraction data. Diffraction spots are acquired during a 360 degrees rotation of the sample and are analyzed in terms of the Friedel pairs ((hkl) and (hkl) reflections, observed 180 degrees apart in rotation). The resulting increase in the accuracy with which the diffraction vectors are determined allows the use of improved algorithms for grain indexing (assigning diffraction spots to the grains from which they arise) and reconstruction. The accuracy of the resulting grain maps is quantified with reference to synchrotron microtomography data for a specimen made from a beta titanium system in which a second phase can be precipitated at grain boundaries, thereby revealing the grain shapes. The simple changes introduced to the DCT methodology are equally applicable to other variants of grain mapping.

The relationship between metastatic potential and in vitro mechanical properties of osteosarcoma cells

Osteosarcoma is the most frequent primary tumor of bone and is characterized by its high tendency to metastasize in lungs. Although treatment in cases of early diagnosis results in a 5-yr survival rate of nearly 60%, the prognosis for patients with secondary lesions at diagnosis is poor, and their 5-yr survival rate remains below 30%. In the present work, we have used a number of analytical methods to investigate the impact of increased metastatic potential on the biophysical properties and force generation of osteosarcoma cells. With that aim, we used two paired osteosarcoma cell lines, with each one comprising a parental line with low metastatic potential and its experimentally selected, highly metastatic form. Mechanical characterization was performed by means of atomic force microscopy, tensile biaxial deformation, and real-time deformability, and cell traction was measured using two-dimensional and micropost-based traction force microscopy. Our results reveal that the low metastatic osteosarcoma cells display larger spreading sizes and generate higher forces than the experimentally selected, highly malignant variants. In turn, the outcome of cell stiffness measurements strongly depends on the method used and the state of the probed cell, indicating that only a set of phenotyping methods provides the full picture of cell mechanics.

High-throughput cell mechanical phenotyping for label-free titration assays of cytoskeletal modifications

The mechanical fingerprint of cells is inherently linked to the structure of the cytoskeleton and can serve as a label‐free marker for cell homeostasis or pathologic states. How cytoskeletal composition affects the physical response of cells to external loads has been intensively studied with a spectrum of techniques, yet quantitative and statistically powerful investigations in the form of titration assays are hampered by the low throughput of most available methods. In this study, we employ real‐time deformability cytometry (RT‐DC), a novel microfluidic tool to examine the effects of biochemically modified F‐actin and microtubule stability and nuclear chromatin structure on cell deformation in a human leukemia cell line (HL60). The high throughput of our method facilitates extensive titration assays that allow for significance assessment of the observed effects and extraction of half‐maximal concentrations for most of the applied reagents. We quantitatively show that integrity of the F‐actin cortex and microtubule network dominate cell deformation on millisecond timescales probed with RT‐DC. Drug‐induced alterations in the nuclear chromatin structure were not found to consistently affect cell deformation. The sensitivity of the high‐throughput cell mechanical measurements to the cytoskeletal modifications we present in this study opens up new possibilities for label‐free dose‐response assays of cytoskeletal modifications.

Enlightening discriminative network functional modules behind Principal Component Analysis separation in differential-omic science studies

Omic science is rapidly growing and one of the most employed techniques to explore differential patterns in omic datasets is principal component analysis (PCA). However, a method to enlighten the network of omic features that mostly contribute to the sample separation obtained by PCA is missing. An alternative is to build correlation networks between univariately-selected significant omic features, but this neglects the multivariate unsupervised feature compression responsible for the PCA sample segregation. Biologists and medical researchers often prefer effective methods that offer an immediate interpretation to complicated algorithms that in principle promise an improvement but in practice are difficult to be applied and interpreted. Here we present PC-corr: a simple algorithm that associates to any PCA segregation a discriminative network of features. Such network can be inspected in search of functional modules useful in the definition of combinatorial and multiscale biomarkers from multifaceted omic data in systems and precision biomedicine. We offer proofs of PC-corr efficacy on lipidomic, metagenomic, developmental genomic, population genetic, cancer promoteromic and cancer stem-cell mechanomic data. Finally, PC-corr is a general functional network inference approach that can be easily adopted for big data exploration in computer science and analysis of complex systems in physics.

Mechanical phenotyping of primary human skeletal stem cells in heterogeneous populations by real-time deformability cytometry

Mechanical measurements of skeletal stem cells using RT-DC reveal a distinct sub-population within the human bone marrow.

Statistics for real-time deformability cytometry: Clustering, dimensionality reduction, and significance testing

Real-time deformability (RT-DC) is a method for high-throughput mechanical and morphological phenotyping of cells in suspension. While analysis rates exceeding 1000 cells per second allow for a label-free characterization of complex biological samples, e.g., whole blood, data evaluation has so far been limited to a few geometrical and material parameters such as cell size, deformation, and elastic Young's modulus. But as a microscopy-based technology, RT-DC actually generates and yields multidimensional datasets that require automated and unbiased tools to obtain morphological and rheological cell information. Here, we present a statistical framework to shed light on this complex parameter space and to extract quantitative results under various experimental conditions. As model systems, we apply cell lines as well as primary cells and highlight more than 11 parameters that can be obtained from RT-DC data. These parameters are used to identify sub-populations in heterogeneous samples using Gaussian mixture models, to perform a dimensionality reduction using principal component analysis, and to quantify the statistical significance applying linear mixed models to datasets of multiple replicates.

AIDeveloper: Deep Learning Image Classification in Life Science and Beyond

Artificial intelligence (AI)-based image analysis has increased drastically in recent years. However, all applications use individual solutions, highly specialized for a particular task. Here, an easy-to-use, adaptable, and open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming is presented. AID provides a variety of NN-architectures, allowing to apply trained models on new data, obtain performance metrics, and export final models to different formats. AID is benchmarked on large image datasets (CIFAR-10 and Fashion-MNIST). Furthermore, models are trained to distinguish areas of differentiated stem cells in images of cell culture. A conventional blood cell count and a blood count obtained using an NN are compared, trained on >1.2 million images, and demonstrated how AID can be used for label-free classification of B- and T-cells. All models are generated by non-programmers on generic computers, allowing for an interdisciplinary use.

Deep imaging flow cytometry

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.

The F-actin modifier villin regulates insulin granule dynamics and exocytosis downstream of islet cell autoantigen 512

Objective

Insulin release from pancreatic islet β cells should be tightly controlled to avoid hypoglycemia and insulin resistance. The cortical actin cytoskeleton is a gate for regulated exocytosis of insulin secretory granules (SGs) by restricting their mobility and access to the plasma membrane. Prior studies suggest that SGs interact with F-actin through their transmembrane cargo islet cell autoantigen 512 (Ica512) (also known as islet antigen 2/Ptprn). Here we investigated how Ica512 modulates SG trafficking and exocytosis.

Methods

Transcriptomic changes in Ica512^−/− mouse islets were analyzed. Imaging as well as biophysical and biochemical methods were used to validate if and how the Ica512-regulated gene villin modulates insulin secretion in mouse islets and insulinoma cells.

Results

The F-actin modifier villin was consistently downregulated in Ica512^−/− mouse islets and in Ica512-depleted insulinoma cells. Villin was enriched at the cell cortex of β cells and dispersed villin^−/− islet cells were less round and less deformable. Basal mobility of SGs in villin-depleted cells was enhanced. Moreover, in cells depleted either of villin or Ica512 F-actin cages restraining cortical SGs were enlarged, basal secretion was increased while glucose-stimulated insulin release was blunted. The latter changes were reverted by overexpressing villin in Ica512-depleted cells, but not vice versa.

Conclusion

Our findings show that villin controls the size of the F-actin cages restricting SGs and, thus, regulates their dynamics and availability for exocytosis. Evidence that villin acts downstream of Ica512 also indicates that SGs directly influence the remodeling properties of the cortical actin cytoskeleton for tight control of insulin secretion.

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Ica512-depletion reduces the genetic expression of the F-actin modifier villin.

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Villin-depletion enhances basal insulin granule mobility and exocytosis.

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Villin regulates the size of actin cages restraining insulin granules.

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Villin acts downstream of insulin granule cargo Ica512.

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The Ica512-villin genetic link enables granules to control cytoskeleton plasticity.

Spheroid Culture of Mesenchymal Stromal Cells Results in Morphorheological Properties Appropriate for Improved Microcirculation

Human bone marrow mesenchymal stromal cells (MSCs) are 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, it is hypothesized that traditional 2D plastic-adherent cell expansion fails to result in appropriate morphorheological properties required for successful cell circulation. To address this issue, a method to culture MSCs in nonadherent 3D spheroids (mesenspheres) is adapted. The biological properties of mesensphere-cultured MSCs remain identical to conventional 2D cultures. However, morphorheological analyses reveal a smaller size and lower stiffness of mesensphere-derived MSCs compared to plastic-adherent MSCs, measured using real-time deformability cytometry and atomic force microscopy. These properties result in an increased ability to pass through microconstrictions in an ex vivo microcirculation assay. This ability is confirmed in vivo by comparison of cell accumulation in various organ capillary networks after intravenous injection of both types of MSCs in mouse. The findings generally identify cellular morphorheological properties as attractive targets for improving microcirculation and specifically suggest mesensphere culture as a promising approach for optimized MSC-based therapies.

Depressive disorders are associated with increased peripheral blood cell deformability: a cross-sectional case-control study (Mood-Morph)

Pathophysiological landmarks of depressive disorders are chronic low-grade inflammation and elevated glucocorticoid output. Both can potentially interfere with cytoskeleton organization, cell membrane bending and cell function, suggesting altered cell morpho-rheological properties like cell deformability and other cell mechanical features in depressive disorders. We performed a cross-sectional case-control study using the image-based morpho-rheological characterization of unmanipulated blood samples facilitating real-time deformability cytometry (RT-DC). Sixty-nine pre-screened individuals at high risk for depressive disorders and 70 matched healthy controls were included and clinically evaluated by Composite International Diagnostic Interview leading to lifetime and 12-month diagnoses. Facilitating deep learning on blood cell images, major blood cell types were classified and morpho-rheological parameters such as cell size and cell deformability of every individual cell was quantified. We found peripheral blood cells to be more deformable in patients with depressive disorders compared to controls, while cell size was not affected. Lifetime persistent depressive disorder was associated with increased cell deformability in monocytes and neutrophils, while in 12-month persistent depressive disorder erythrocytes deformed more. Lymphocytes were more deformable in 12-month major depressive disorder, while for lifetime major depressive disorder no differences could be identified. After correction for multiple testing, only associations for lifetime persistent depressive disorder remained significant. This is the first study analyzing morpho-rheological properties of entire blood cells and highlighting depressive disorders and in particular persistent depressive disorders to be associated with increased blood cell deformability. While all major blood cells tend to be more deformable, lymphocytes, monocytes, and neutrophils are mostly affected. This indicates that immune cell mechanical changes occur in depressive disorders, which might be predictive of persistent immune response.

Changes in Blood Cell Deformability in Chorea-Acanthocytosis and Effects of Treatment With Dasatinib or Lithium

Misshaped red blood cells (RBCs), characterized by thorn-like protrusions known as acanthocytes, are a key diagnostic feature in Chorea-Acanthocytosis (ChAc), a rare neurodegenerative disorder. The altered RBC morphology likely influences their biomechanical properties which are crucial for the cells to pass the microvasculature. Here, we investigated blood cell deformability of five ChAc patients compared to healthy controls during up to 1-year individual off-label treatment with the tyrosine kinase inhibitor dasatinib or several weeks with lithium. Measurements with two microfluidic techniques allowed us to assess RBC deformability under different shear stresses. Furthermore, we characterized leukocyte stiffness at high shear stresses. The results showed that blood cell deformability–including both RBCs and leukocytes - in general was altered in ChAc patients compared to healthy donors. Therefore, this study shows for the first time an impairment of leukocyte properties in ChAc. During treatment with dasatinib or lithium, we observed alterations in RBC deformability and a stiffness increase for leukocytes. The hematological phenotype of ChAc patients hinted at a reorganization of the cytoskeleton in blood cells which partly explains the altered mechanical properties observed here. These findings highlight the need for a systematic assessment of the contribution of impaired blood cell mechanics to the clinical manifestation of ChAc.

Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes

Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to rare cell populations and leads to low reproducibility. Moreover, the human eye is not suited to detect shifts of cellular properties of entire populations. Hence, quantitative image analysis could improve the accuracy and reproducibility of MDS diagnosis. We used real-time deformability cytometry (RT-DC) to measure bone marrow biopsy samples of MDS patients and age-matched healthy individuals. RT-DC is a high-throughput (1000 cells/s) imaging flow cytometer capable of recording morphological and mechanical properties of single cells. Properties of single cells were quantified using automated image analysis, and machine learning was employed to discover morpho-mechanical patterns in thousands of individual cells that allow to distinguish healthy vs. MDS samples. We found that distribution properties of cell sizes differ between healthy and MDS, with MDS showing a narrower distribution of cell sizes. Furthermore, we found a strong correlation between the mechanical properties of cells and the number of disease-determining mutations, inaccessible with current diagnostic approaches. Hence, machine-learning assisted RT-DC could be a promising tool to automate sample analysis to assist experts during diagnosis or provide a scalable solution for MDS diagnosis to regions lacking sufficient medical experts.

Image-based cell sorting using focused travelling surface acoustic waves

Sorting cells is an essential primary step in many biological and clinical applications such as high-throughput drug screening, cancer research and cell transplantation. Cell sorting based on their mechanical properties has long been considered as a promising label-free biomarker that could revolutionize the isolation of cells from heterogeneous populations. Recent advances in microfluidic image-based cell analysis combined with subsequent label-free sorting by on-chip actuators demonstrated the possibility of sorting cells based on their physical properties. However, the high purity of sorting is achieved at the expense of a sorting rate that lags behind the analysis throughput. Furthermore, stable and reliable system operation is an important feature in enabling the sorting of small cell fractions from a concentrated heterogeneous population. Here, we present a label-free cell sorting method, based on the use of focused travelling surface acoustic wave (FTSAW) in combination with real-time deformability cytometry (RT-DC). We demonstrate the flexibility and applicability of the method by sorting distinct blood cell types, cell lines and particles based on different physical parameters. Finally, we present a new strategy to sort cells based on their mechanical properties. Our system enables the sorting of up to 400 particles per s. Sorting is therefore possible at high cell concentrations (up to 36 million per ml) while retaining high purity (>92%) for cells with diverse sizes and mechanical properties moving in a highly viscous buffer. Sorting of small cell fraction from a heterogeneous population prepared by processing of small sample volume (10 μl) is also possible and here demonstrated by the 667-fold enrichment of white blood cells (WBCs) from raw diluted whole blood in a continuous 10-hour sorting experiment. The real-time analysis of multiple parameters together with the high sensitivity and high-throughput of our method thus enables new biological and therapeutic applications in the future.