Predicting cancer cell invasion by single-cell physical phenotyping

Cell elasticity measurement and sorting based on microfluidic techniques: Advances and applications

Cell elasticity serves as a crucial physical biomarker that reflects changes in cellular structures and physiological states, providing key insights into cell behaviors. It links mechanical properties to biological function, highlighting its importance for understanding cell health and advancing biomedical research. Microfluidic technologies, with their capabilities for precise manipulation and high-throughput analysis, have significantly advanced the measurement of cell elasticity and elasticity-based cell sorting. This paper presents a comprehensive overview of advanced microsystems for assessing cell elasticity, discussing their advantages and limitations. The biomedical applications of elasticity-based sorting are highlighted, including cell classification, clinical diagnosis, drug screening, and stem cell differentiation prediction. The paper addresses the current challenges in the field, such as limited measurement efficiency and scalability, and explores future research directions, including the development of automated, high-throughput systems and the integration of elasticity measurements into practical biomedical applications. These advancements aim to deepen our understanding of cellular mechanics, improve diagnostic precision, and foster the development of novel therapeutic strategies. Ultimately, this work emphasizes the potential of cell elasticity as a key parameter in advancing disease diagnosis and therapeutic research.

Long-Term Stable and Multifeature Microfluidic Impedance Flow Cytometry Based on a Constricted Channel for Single-Cell Mechanical Phenotyping

The microfluidic impedance flow cytometer (m-IFC) using constricted microchannels is an appealing choice for the high-throughput measurement of single-cell mechanical properties. However, channels smaller than the cells are susceptible to irreversible blockage, extremely affecting the stability of the system and the throughput. Meanwhile, the common practice of extracting a single quantitative index, i.e., total cell passage time, through the constricted part is inadequate to decipher the complex mechanical properties of individual cells. Herein, this study presents a long-term stable and multifeature m-IFC based on a constricted channel for single-cell mechanical phenotyping. The blockage problem is effectively overcome by adding tiny xanthan gum (XG) polymers. The cells can pass through the constricted channel at a flow rate of 500 μL/h without clogging, exhibiting high throughput (∼240 samples per second) and long-term stability (∼2 h). Moreover, six detection regions were implemented to capture the multiple features related to the whole process of a single cell passing through the long-constricted channel, e.g., creep, friction, and relaxation stages. To verify the performance of the multifeature m-IFC, cells treated with perturbations of microtubules and microfilaments within the cytoskeleton were detected, respectively. It suggests that the extracted features provide more comprehensive clues for single-cell analysis in structural and mechanical transformation. Overall, our proposed multifeature m-IFC exhibits the advantages of nonclogging and high throughput, which can be extended to other cell types for nondestructive and real-time mechanical phenotyping in cost-effective applications.

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Glioma Single-Cell Biomechanical Analysis by Cyclic Conical Constricted Microfluidics

Determining the grade of glioma is a critical step in choosing patients' treatment plans in clinical practices. The pathological diagnosis of patient's glioma samples requires extensive staining and imaging procedures, which are expensive and time-consuming. Current advanced uniform-width-constriction-channel-based microfluidics have proven to be effective in distinguishing cancer cells from normal tissues, such as breast cancer, ovarian cancer, prostate cancer, etc. However, the uniform-width-constriction channels can result in low yields on glioma cells with irregular morphologies and high heterogeneity. In this research, we presented an innovative cyclic conical constricted (CCC) microfluidic device to better differentiate glioma cells from normal glial cells. Compared with the widely used uniform-width-constriction microchannels, the new CCC configuration forces single cells to deform gradually and obtains the biophysical attributes from each deformation. The human-derived glioma cell lines U-87 and U-251, as well as the human-derived normal glial astrocyte cell line HA-1800 were selected as the proof of concept. The results showed that CCC channels can effectively obtain the biomechanical characteristics of different 12-25 μm glial cell lines. The patient glioma samples with WHO grades II, III, and IV were tested by CCC channels and compared between Elastic Net (ENet) and Lasso analysis. The results demonstrated that CCC channels and the ENet can successfully select critical biomechanical parameters to differentiate the grades of single-glioma cells. This CCC device can be potentially further applied to the extensive family of brain tumors at the single-cell level.

Altered physical phenotypes of leukemia cells that survive chemotherapy treatment

The recurrence of cancer following chemotherapy treatment is a major cause of death across solid and hematologic cancers. In B-cell acute lymphoblastic leukemia (B-ALL), relapse after initial chemotherapy treatment leads to poor patient outcomes. Here we test the hypothesis that chemotherapy-treated versus control B-ALL cells can be characterized based on cellular physical phenotypes. To quantify physical phenotypes of chemotherapy-treated leukemia cells, we use cells derived from B-ALL patients that are treated for 7 days with a standard multidrug chemotherapy regimen of vincristine, dexamethasone, and L-asparaginase (VDL). We conduct physical phenotyping of VDL-treated versus control cells by tracking the sequential deformations of single cells as they flow through a series of micron-scale constrictions in a microfluidic device; we call this method Quantitative Cyclical Deformability Cytometry. Using automated image analysis, we extract time-dependent features of deforming cells including cell size and transit time (TT) with single-cell resolution. Our findings show that VDL-treated B-ALL cells have faster TTs and transit velocity than control cells, indicating that VDL-treated cells are more deformable. We then test how effectively physical phenotypes can predict the presence of VDL-treated cells in mixed populations of VDL-treated and control cells using machine learning approaches. We find that TT measurements across a series of sequential constrictions can enhance the classification accuracy of VDL-treated cells in mixed populations using a variety of classifiers. Our findings suggest the predictive power of cell physical phenotyping as a complementary prognostic tool to detect the presence of cells that survive chemotherapy treatment. Ultimately such complementary physical phenotyping approaches could guide treatment strategies and therapeutic interventions. Insight box Cancer cells that survive chemotherapy treatment are major contributors to patient relapse, but the ability to predict recurrence remains a challenge. Here we investigate the physical properties of leukemia cells that survive treatment with chemotherapy drugs by deforming individual cells through a series of micron-scale constrictions in a microfluidic channel. Our findings reveal that leukemia cells that survive chemotherapy treatment are more deformable than control cells. We further show that machine learning algorithms applied to physical phenotyping data can predict the presence of cells that survive chemotherapy treatment in a mixed population. Such an integrated approach using physical phenotyping and machine learning could be valuable to guide patient treatments.

Non-contact microfluidic analysis of the stiffness of single large extracellular vesicles from IDH1-mutated glioblastoma cells

In preparation for leveraging extracellular vesicles (EVs) for disease diagnostics and therapeutics, fundamental research is being done to understand EV biological, chemical, and physical properties. Most published studies have investigated nanoscale EVs and focused on EV biochemical content. There is much less understanding of large microscale EV characteristics and EV mechanical properties. We recently introduced a non-contact microfluidic technique that measures the stiffness of large EVs (>1 μm diameter). This pilot study probes the robustness of the microfluidic technique to distinguish between EV populations by comparing stiffness distributions of large EVs derived from glioblastoma cell lines. EVs derived from cells expressing the IDH1 mutation, a common glioblastoma mutation known to disrupt lipid metabolism, were stiffer than those expressed from wild-type cells in a statistical comparison of sample medians. A supporting lipidomics analysis showed that the IDH1 mutation increased the amount of saturated lipids in EVs. Taken together, these data encourage further investigation into the potential of high-throughput microfluidics to distinguish between large EV populations that differ in biomolecular composition. These findings contribute to the understanding of EV biomechanics, in particular for the less studied microscale EVs.

Cancer cell mechanobiology: a new frontier for cancer research

The study of physical and mechanical features of cancer cells, or cancer cell mechanobiology, is a new frontier in cancer research. Such studies may enhance our understanding of the disease process, especially mechanisms associated with cancer cell invasion and metastasis, and may help the effort of developing diagnostic biomarkers and therapeutic drug targets. Cancer cell mechanobiological changes are associated with the complex interplay of activation/inactivation of multiple signaling pathways, which can occur at both the genetic and epigenetic levels, and the interactions with the cancer microenvironment. It has been shown that metastatic tumor cells are more compliant than morphologically similar benign cells in actual human samples. Subsequent studies from us and others further demonstrated that cell mechanical properties are strongly associated with cancer cell invasive and metastatic potential, and thus may serve as a diagnostic marker of detecting cancer cells in human body fluid samples. In this review, we provide a brief narrative of the molecular mechanisms underlying cancer cell mechanobiology, the technological platforms utilized to study cancer cell mechanobiology, the status of cancer cell mechanobiological studies in various cancer types, and the potential clinical applications of cancer cell mechanobiological study in cancer early detection, diagnosis, and treatment.

Pancreatic Ductal Adenocarcinoma Cortical Mechanics and Clinical Implications

Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers due to low therapeutic response rates and poor prognoses. Majority of patients present with symptoms post metastatic spread, which contributes to its overall lethality as the 4th leading cause of cancer-related deaths. Therapeutic approaches thus far target only one or two of the cancer specific hallmarks, such as high proliferation rate, apoptotic evasion, or immune evasion. Recent genomic discoveries reveal that genetic heterogeneity, early micrometastases, and an immunosuppressive tumor microenvironment contribute to the inefficacy of current standard treatments and specific molecular-targeted therapies. To effectively combat cancers like PDAC, we need an innovative approach that can simultaneously impact the multiple hallmarks driving cancer progression. Here, we present the mechanical properties generated by the cell’s cortical cytoskeleton, with a spotlight on PDAC, as an ideal therapeutic target that can concurrently attack multiple systems driving cancer. We start with an introduction to cancer cell mechanics and PDAC followed by a compilation of studies connecting the cortical cytoskeleton and mechanical properties to proliferation, metastasis, immune cell interactions, cancer cell stemness, and/or metabolism. We further elaborate on the implications of these findings in disease progression, therapeutic resistance, and clinical relapse. Manipulation of the cancer cell’s mechanical system has already been shown to prevent metastasis in preclinical models, but it has greater potential for target exploration since it is a foundational property of the cell that regulates various oncogenic behaviors.

Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis

Artificial intelligence (AI) is an emerging technology with great potential, and its robust calculation and analysis capabilities are unmatched by traditional calculation tools. With the promotion of deep learning and open-source platforms, the threshold of AI has also become lower. Combining artificial intelligence with traditional fields to create new fields of high research and application value has become a trend. AI has been involved in many disciplines, such as medicine, materials, energy, and economics. The development of AI requires the support of many kinds of data, and microfluidic systems can often mine object data on a large scale to support AI. Due to the excellent synergy between the two technologies, excellent research results have emerged in many fields. In this review, we briefly review AI and microfluidics and introduce some applications of their combination, mainly in nanomedicine and material synthesis. Finally, we discuss the development trend of the combination of the two technologies.

A primer on machine learning techniques for genomic applications

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous "omic" data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.

A constriction channel analysis of astrocytoma stiffness and disease progression

Studies have demonstrated that cancer cells tend to have reduced stiffness (Young's modulus) compared to their healthy counterparts. The mechanical properties of primary brain cancer cells, however, have remained largely unstudied. To investigate whether the stiffness of primary brain cancer cells decreases as malignancy increases, we used a microfluidic constriction channel device to deform healthy astrocytes and astrocytoma cells of grade II, III, and IV and measured the entry time, transit time, and elongation. Calculating cell stiffness directly from the experimental measurements is not possible. To overcome this challenge, finite element simulations of the cell entry into the constriction channel were used to train a neural network to calculate the stiffness of the analyzed cells based on their experimentally measured diameter, entry time, and elongation in the channel. Our study provides the first calculation of stiffness for grades II and III astrocytoma and is the first to apply a neural network analysis to determine cell mechanical properties from a constriction channel device. Our results suggest that the stiffness of astrocytoma cells is not well-correlated with the cell grade. Furthermore, while other non-central-nervous-system cell types typically show reduced stiffness of malignant cells, we found that most astrocytoma cell lines had increased stiffness compared to healthy astrocytes, with lower-grade astrocytoma having higher stiffness values than grade IV glioblastoma. Differences in nucleus-to-cytoplasm ratio only partly explain differences in stiffness values. Although our study does have limitations, our results do not show a strong correlation of stiffness with cell grade, suggesting that other factors may play important roles in determining the invasive capability of astrocytoma. Future studies are warranted to further elucidate the mechanical properties of astrocytoma across various pathological grades.

Single Cell Mechanotype and Associated Molecular Changes in Urothelial Cell Transformation and Progression

Cancer cell mechanotype changes are newly recognized cancer phenotypic events, whereas metastatic cancer cells show decreased cell stiffness and increased deformability relative to normal cells. To further examine how cell mechanotype changes in early stages of cancer transformation and progression, an in vitro multi-step human urothelial cell carcinogenic model was used to measure cellular Young's modulus, deformability, and transit time using single-cell atomic force microscopy, microfluidic-based deformability cytometry, and quantitative deformability cytometry, respectively. Measurable cell mechanotype changes of stiffness, deformability, and cell transit time occur early in the transformation process. As cells progress from normal, to preinvasive, to invasive cells, Young's modulus of stiffness decreases and deformability increases gradually. These changes were confirmed in three-dimensional cultured microtumor masses and urine exfoliated cells directly from patients. Using gene screening and proteomics approaches, we found that the main molecular pathway implicated in cell mechanotype changes appears to be epithelial to mesenchymal transition.

Fluid shear stress coupled with narrow constrictions induce cell type-dependent morphological and molecular changes in SK-BR-3 and MDA-MB-231 cells

Cancer mortality mainly arises from metastases, due to cells that escape from a primary tumor, circulate in the blood as circulating tumor cells (CTCs), permeate across blood vessels and nest in distant organs. It is still unclear how CTCs overcome the harsh conditions of fluid shear stress and mechanical constraints within the microcirculation. Here, a minimal model of the blood microcirculation was established through the fabrication of microfluidic channels comprising constrictions. Metastatic breast cancer cells of epithelial-like and mesenchymal-like phenotypes were flowed into the microfluidic device. These cells were visualized during circulation and analyzed for their dynamical behavior, revealing long-lived plastic deformations and significant differences in biomechanics between cell types. γ-H2AX staining of cells retrieved post-circulation showed significant increase of DNA damage response in epithelial-like SK-BR-3 cells, while gene expression analysis of key regulators of epithelial-to-mesenchymal transition revealed significant changes upon circulation. This work thus documents first results of the changes at the cellular, subcellular and molecular scales induced by the two main mechanical stimuli arising from circulatory conditions, and suggest a significant role of this still elusive step of the metastatic cascade in cancer cells heterogeneity and aggressiveness.

Cancer Cells Resist Mechanical Destruction in Circulation via RhoA/Actomyosin-Dependent Mechano-Adaptation

During metastasis, cancer cells are exposed to potentially destructive hemodynamic forces including fluid shear stress (FSS) while en route to distant sites. However, prior work indicates that cancer cells are more resistant to brief pulses of high-level FSS in vitro relative to non-transformed epithelial cells. Herein, we identify a mechano-adaptive mechanism of FSS resistance in cancer cells. Our findings demonstrate that cancer cells activate RhoA in response to FSS, which protects them from FSS-induced plasma membrane damage. We show that cancer cells freshly isolated from mouse and human tumors are resistant to FSS, that formin and myosin II activity protects circulating tumor cells (CTCs) from destruction, and that short-term inhibition of myosin II delays metastasis in mouse models. Collectively, our data indicate that viable CTCs actively resist destruction by hemodynamic forces and are likely to be more mechanically robust than is commonly thought.

Differential Contributions of Actin and Myosin to the Physical Phenotypes and Invasion of Pancreatic Cancer Cells

INTRODUCTION

Metastasis is a fundamentally physical process in which cells deform through narrow gaps and generate forces to invade surrounding tissues. While it is commonly thought that increased cell deformability is an advantage for invading cells, we previously found that more invasive pancreatic ductal adenocarcinoma (PDAC) cells are stiffer than less invasive PDAC cells. Here we investigate potential mechanisms of the simultaneous increase in PDAC cell stiffness and invasion, focusing on the contributions of myosin II, Arp2/3, and formins.

METHOD

We measure cell invasion using a 3D scratch wound invasion assay and cell stiffness using atomic force microscopy (AFM). To determine the effects of actin- and myosin-mediated force generation on cell stiffness and invasion, we treat cells with pharmacologic inhibitors of myosin II (blebbistatin), Arp2/3 (CK-666), and formins (SMIFH2).

RESULTS

We find that the activity of myosin II, Arp2/3, and formins all contribute to the stiffness of PDAC cells. Interestingly, we find that the invasion of PDAC cell lines is differentially affected when the activity of myosin II, Arp2/3, or formins is inhibited, suggesting that despite having similar tissue origins, different PDAC cell lines may rely on different mechanisms for invasion.

CONCLUSIONS

These findings deepen our knowledge of the factors that regulate cancer cell mechanotype and invasion, and incite further studies to develop therapeutics that target multiple mechanisms of invasion for improved clinical benefit.

A unified rheological model for cells and cellularised materials

The mechanical response of single cells and tissues exhibits a broad distribution of time-scales that often gives rise to a distinctive power-law rheology. Such complex behaviour cannot be easily captured by traditional rheological approaches, making material characterisation and predictive modelling very challenging. Here, we present a novel model combining conventional viscoelastic elements with fractional calculus that successfully captures the macroscopic relaxation response of epithelial monolayers. The parameters extracted from the fitting of the relaxation modulus allow prediction of the response of the same material to slow stretch and creep, indicating that the model captured intrinsic material properties. Two characteristic times, derived from the model parameters, delimit different regimes in the materials response. We compared the response of tissues with the behaviour of single cells as well as intra and extra-cellular components, and linked the power-law behaviour of the epithelium to the dynamics of the cell cortex. Such a unified model for the mechanical response of biological materials provides a novel and robust mathematical approach to consistently analyse experimental data and uncover similarities and differences in reported behaviour across experimental methods and research groups. It also sets the foundations for more accurate computational models of tissue mechanics.

A unified rheological model for cells and cellularised materials

The mechanical response of single cells and tissues exhibits a broad distribution of time-scales that often gives rise to a distinctive power-law rheology. Such complex behaviour cannot be easily captured by traditional rheological approaches, making material characterisation and predictive modelling very challenging. Here, we present a novel model combining conventional viscoelastic elements with fractional calculus that successfully captures the macroscopic relaxation response of epithelial monolayers. The parameters extracted from the fitting of the relaxation modulus allow prediction of the response of the same material to slow stretch and creep, indicating that the model captured intrinsic material properties. Two characteristic times, derived from the model parameters, delimit different regimes in the materials response. We compared the response of tissues with the behaviour of single cells as well as intra and extra-cellular components, and linked the power-law behaviour of the epithelium to the dynamics of the cell cortex. Such a unified model for the mechanical response of biological materials provides a novel and robust mathematical approach to consistently analyse experimental data and uncover similarities and differences in reported behaviour across experimental methods and research groups. It also sets the foundations for more accurate computational models of tissue mechanics.

Engineering approaches to studying cancer cell migration in three-dimensional environments

Cancer is one of the most devastating diseases of our time, with 17 million new cancer cases and 9.5 million cancer deaths in 2018 worldwide. The mortality associated with cancer results primarily from metastasis, i.e. the spreading of cancer cells from the primary tumour to other organs. The invasion and migration of cells through basement membranes, tight interstitial spaces and endothelial cell layers are key steps in the metastatic cascade. Recent studies demonstrated that cell migration through three-dimensional environments that mimic the in vivo conditions significantly differs from their migration on two-dimensional surfaces. Here, we review recent technological advances made in the field of cancer research that provide more 'true to the source' experimental platforms and measurements for the study of cancer cell invasion and migration in three-dimensional environments. These include microfabrication, three-dimensional bioprinting and intravital imaging tools, along with force and stiffness measurements of cells and their environments. These techniques will enable new studies that better reflect the physiological environment found in vivo, thereby producing more robust results. The knowledge achieved through these studies will aid in the development of new treatment options with the potential to ultimately lighten the devastating cost cancer inflicts on patients and their families. This article is part of a discussion meeting issue 'Forces in cancer: interdisciplinary approaches in tumour mechanobiology'.

DYT1 Dystonia Patient-Derived Fibroblasts Have Increased Deformability and Susceptibility to Damage by Mechanical Forces

DYT1 dystonia is a neurological movement disorder that is caused by a loss-of-function mutation in the DYT1/TOR1A gene, which encodes torsinA, a conserved luminal ATPases-associated with various cellular activities (AAA+) protein. TorsinA is required for the assembly of functional linker of nucleoskeleton and cytoskeleton (LINC) complexes, and consequently the mechanical integration of the nucleus and the cytoskeleton. Despite the potential implications of altered mechanobiology in dystonia pathogenesis, the role of torsinA in regulating cellular mechanical phenotype, or mechanotype, in DYT1 dystonia remains unknown. Here, we define the deformability of mouse fibroblasts lacking functional torsinA as well as human fibroblasts isolated from DYT1 dystonia patients. We find that the deletion of torsinA or the expression of torsinA containing the DYT1 dystonia-causing ΔE302/303 (ΔE) mutation results in more deformable cells. We observe a similar increased deformability of mouse fibroblasts that lack lamina-associated polypeptide 1 (LAP1), which interacts with and stimulates the ATPase activity of torsinA in vitro, as well as with the absence of the LINC complex proteins, Sad1/UNC-84 1 (SUN1) and SUN2, lamin A/C, or lamin B1. Consistent with these findings, we also determine that DYT1 dystonia patient-derived fibroblasts are more compliant than fibroblasts isolated from unafflicted individuals. DYT1 dystonia patient-derived fibroblasts also exhibit increased nuclear strain and decreased viability following mechanical stretch. Taken together, our results establish the foundation for future mechanistic studies of the role of cellular mechanotype and LINC-dependent nuclear-cytoskeletal coupling in regulating cell survival following exposure to mechanical stresses.

Enhanced blebbing as a marker for metastatic prostate cancer

Highly metastatic prostate cancer cells flowing through a microfluidic channel form plasma membrane blebs: they form 27% more than normal cells and have a lower stiffness (about 50%). Hypo-osmotic stress assays (with ∼ 50 % osmolarity) show 22% more blebbing of highly metastatic than moderately metastatic and 30% more than normal cells. Plasma membrane blebbing is known to provide important metastatic capabilities to cancer cells by aiding cell detachment from the primary tumor site and increasing cell deformability to promote cell migration through the extracellular matrix. Increased blebbing was attributed by others to decreased phosphorylated ezrin, radixin, and moesin (ERM) (p-ERM) protein expression-p-ERMs bind the plasma membrane to the actin cortex and reduced p-ERM expression can weaken membrane-cortex attachment. Myosin II also influences blebbing as myosin's natural contraction generates tension in the actin cortex. This increases cellular hydrostatic pressure, causes cortex rupture, cytoplasm flow out of the cortex, and hence blebbing. Highly metastatic cells are surprisingly found to express similar ezrin and myosin II levels but higher moesin levels in comparison with lowly metastatic or normal cells-suggesting that their levels, contrary to the literature [G. Charras and E. Paluch, Nat. Rev. Mol. Cell Biol. 9(9), 730-736 (2008); J.-Y. Tinevez, U. Schulze, G. Salbreux, J. Roensch, J.-F. Joanny, and E. Paluch, Proc. Natl. Acad. Sci. U.S.A. 106(44), 18581-18586 (2009); M. Bergert, S. D. Chandradoss, R. A. Desai, and E. Paluch, Proc. Natl. Acad. Sci. U.S.A. 109(36), 14434-14439 (2012); E. K. Paluch and E. Raz: Curr. Opin. Cell Biol. 25(5), 582-590 (2013)], are not important in metastatic prostate cell blebbing. Our results show that reduced F-actin is primarily responsible for increased blebbing in these metastatic cells. Blebbing can thus serve as a simple prognostic marker for the highly incident and lethal metastatic prostate cancer.

Comment on "Ghost cytometry"

Ota et al (Reports, 15 June 2018, p. 1246) report using pseudo-random optical masks and a spatial-temporal transformation to perform blur-free, high-frame rate imaging of cells in flow with a high signal-to-noise ratio. They also claim sorting at rates of 3000 cells per second, based on imaging data. The experiments conducted and results reported in their study are insufficient to support these conclusions.

Technologies for the Directed Evolution of Cell Therapies

The next generation of therapies is moving beyond the use of small molecules and proteins to using whole cells. Compared with the interactions of small-molecule drugs with biomolecules, which can largely be understood through chemistry, cell therapies act in a chemical and physical world and can actively adapt to that world, amplifying complexity but also the potential for truly breakthrough impact. Although there has been success in introducing targeting proteins into cells to achieve a therapeutic effect, for example, chimeric antigen receptor (CAR) T cells, our ability to engineer cells is generally limited to introducing proteins, but not modulating large-scale traits or structures of cellular "machines," which play critical roles in disease. Example traits include the ability to secrete compounds, deform through tissue, adhere to surrounding cells, apply force to phagocytose targets, or move through extracellular matrix. There is an opportunity to increase the efficacy of cell therapies through the use of quantitative automation tools, to analyze, sort, and select rare cells with beneficial traits. Combined with methods of genetic or epigenetic mutagenesis to create diversity, such approaches can enable the directed cellular evolution of new therapeutically optimal populations of cells and uncover genetic underpinnings of these optimal traits.

A scalable filtration method for high throughput screening based on cell deformability

Cell deformability is a label-free biomarker of cell state in physiological and disease contexts ranging from stem cell differentiation to cancer progression. Harnessing deformability as a phenotype for screening applications requires a method that can simultaneously measure the deformability of hundreds of cell samples and can interface with existing high throughput facilities. Here we present a scalable cell filtration device, which relies on the pressure-driven deformation of cells through a series of pillars that are separated by micron-scale gaps on the timescale of seconds: less deformable cells occlude the gaps more readily than more deformable cells, resulting in decreased filtrate volume which is measured using a plate reader. The key innovation in this method is that we design customized arrays of individual filtration devices in a standard 96-well format using soft lithography, which enables multiwell input samples and filtrate outputs to be processed with higher throughput using automated pipette arrays and plate readers. To validate high throughput filtration to detect changes in cell deformability, we show the differential filtration of human ovarian cancer cells that have acquired cisplatin-resistance, which is corroborated with cell stiffness measurements using quantitative deformability cytometry. We also demonstrate differences in the filtration of human cancer cell lines, including ovarian cancer cells that overexpress transcription factors (Snail, Slug), which are implicated in epithelial-to-mesenchymal transition; breast cancer cells (malignant versus benign); and prostate cancer cells (highly versus weekly metastatic). We additionally show how the filtration of ovarian cancer cells is affected by treatment with drugs known to perturb the cytoskeleton and the nucleus. Our results across multiple cancer cell types with both genetic and pharmacologic manipulations demonstrate the potential of this scalable filtration device to screen cells based on their deformability.

Kernel-Based Microfluidic Constriction Assay for Tumor Sample Identification

A high-throughput multiconstriction microfluidic channels device can distinguish human breast cancer cell lines (MDA-MB-231, HCC-1806, MCF-7) from immortalized breast cells (MCF-10A) with a confidence level of ∼81-85% at a rate of 50-70 cells/min based on velocity increment differences through multiconstriction channels aligned in series. The results are likely related to the deformability differences between nonmalignant and malignant breast cells. The data were analyzed by the methods/algorithms of Ridge, nonnegative garrote on kernel machine (NGK), and Lasso using high-dimensional variables, including the cell sizes, velocities, and velocity increments. In kernel learning based methods, the prediction values of 10-fold cross-validations are used to represent the difference between two groups of data, where a value of 100% indicates the two groups are completely distinct and identifiable. The prediction value is used to represent the difference between two groups using the established algorithm classifier from high-dimensional variables. These methods were applied to heterogeneous cell populations prepared using primary tumor and adjacent normal tissue obtained from two patients. Primary breast cancer cells were distinguished from patient-matched adjacent normal cells with a prediction ratio of 70.07%-75.96% by the NGK method. Thus, this high-throughput multiconstriction microfluidic device together with the kernel learning method can be used to perturb and analyze the biomechanical status of cells obtained from small primary tumor biopsy samples. The resultant biomechanical velocity signatures identify malignancy and provide a new marker for evaluation in risk assessment.