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

Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning

Tumor heterogeneity is a primary factor that contributes to treatment failure. Predictive tools, capable of classifying cancer cells based on their functions, may substantially enhance therapy and extend patient life span. The connection between cell biomechanics and cancer cell functions is used here to classify cells through mechanical measurements, via particle uptake. Machine learning (ML) was used to classify cells based on single-cell patterns of uptake of particles with diverse sizes. Three pairs of human cancer cell subpopulations, varied in their level of drug resistance or malignancy, were studied. Cells were allowed to interact with fluorescently labeled polystyrene particles ranging in size from 0.04 to 3.36 μm and analyzed for their uptake patterns using flow cytometry. ML algorithms accurately classified cancer cell subtypes with accuracy rates exceeding 95%. The uptake data were especially advantageous for morphologically similar cell subpopulations. Moreover, the uptake data were found to serve as a form of "normalization" that could reduce variation in repeated experiments.

"Patchiness" in mechanical stiffness across a tumor as an early-stage marker for malignancy

Mechanical phenotyping of tumors, either at an individual cell level or tumor cell population level is gaining traction as a diagnostic tool. However, the extent of diagnostic and prognostic information that can be gained through these measurements is still unclear. In this work, we focus on the heterogeneity in mechanical properties of cells obtained from a single source such as a tissue or tumor as a potential novel biomarker. We believe that this heterogeneity is a conventionally overlooked source of information in mechanical phenotyping data. We use mechanics-based in-silico models of cell-cell interactions and cell population dynamics within 3D environments to probe how heterogeneity in cell mechanics drives tissue and tumor dynamics. Our simulations show that the initial heterogeneity in the mechanical properties of individual cells and the arrangement of these heterogenous sub-populations within the environment can dictate overall cell population dynamics and cause a shift towards the growth of malignant cell phenotypes within healthy tissue environments. The overall heterogeneity in the cellular mechanotype and their spatial distributions is quantified by a "patchiness" index, which is the ratio of the global to local heterogeneity in cell populations. We observe that there exists a threshold value of the patchiness index beyond which an overall healthy population of cells will show a steady shift towards a more malignant phenotype. Based on these results, we propose that the "patchiness" of a tumor or tissue sample, can be an early indicator for malignant transformation and cancer occurrence in benign tumors or healthy tissues. Additionally, we suggest that tissue patchiness, measured either by biochemical or biophysical markers, can become an important metric in predicting tissue health and disease likelihood just as landscape patchiness is an important metric in ecology.

Recent advances in deformation-assisted microfluidic cell sorting technologies

Cell sorting is an essential prerequisite for cell research and has great value in life science and clinical studies. Among the many microfluidic cell sorting technologies, label-free methods based on the size of different cell types have been widely studied. However, the heterogeneity in size for cells of the same type and the inevitable size overlap between different types of cells would result in performance degradation in size-based sorting. To tackle such challenges, deformation-assisted technologies are receiving more attention recently. Cell deformability is an inherent biophysical marker of cells that reflects the changes in their internal structures and physiological states. It provides additional dimensional information for cell sorting besides size. Therefore, in this review, we summarize the recent advances in deformation-assisted microfluidic cell sorting technologies. According to how the deformability is characterized and the form in which the force acts, the technologies can be divided into two categories: (1) the indirect category including transit-time-based and image-based methods, and (2) the direct category including microstructure-based and hydrodynamics-based methods. Finally, the separation performance and the application scenarios of each method, the existing challenges and future outlook are discussed. Deformation-assisted microfluidic cell sorting technologies are expected to realize greater potential in the label-free analysis of cells.

High extracellular glucose promotes cell motility by modulating cell deformability and contractility via the cAMP-RhoA-ROCK axis in human breast cancer cells

The mechanical properties, or mechanotypes, of cells are largely determined by their deformability and contractility. The ability of cancer cells to deform and generate contractile force is critical in multiple steps of metastasis. Identifying soluble cues that regulate cancer cell mechanotypes and understanding the underlying molecular mechanisms regulating these cellular mechanotypes could provide novel therapeutic targets to prevent metastasis. Although a strong correlation between high glucose level and cancer metastasis has been demonstrated, the causality has not been elucidated, and the underlying molecular mechanisms remain largely unknown. In this study, using novel high-throughput mechanotyping assays, we show that human breast cancer cells become less deformable and more contractile with increased extracellular glucose levels (>5 mM). These altered cell mechanotypes are due to increased F-actin rearrangement and nonmuscle myosin II (NMII) activity. We identify the cAMP-RhoA-ROCK-NMII axis as playing a major role in regulating cell mechanotypes at high extracellular glucose levels, whereas calcium and myosin light-chain kinase (MLCK) are not required. The altered mechanotypes are also associated with increased cell migration and invasion. Our study identifies key components in breast cancer cells that convert high extracellular glucose levels into changes in cellular mechanotype and behavior relevant in cancer metastasis.

Transient nuclear deformation primes epigenetic state and promotes cell reprogramming

Cell reprogramming has wide applications in tissue regeneration, disease modelling and personalized medicine. In addition to biochemical cues, mechanical forces also contribute to the modulation of the epigenetic state and a variety of cell functions through distinct mechanisms that are not fully understood. Here we show that millisecond deformation of the cell nucleus caused by confinement into microfluidic channels results in wrinkling and transient disassembly of the nuclear lamina, local detachment of lamina-associated domains in chromatin and a decrease of histone methylation (histone H3 lysine 9 trimethylation) and DNA methylation. These global changes in chromatin at the early stage of cell reprogramming boost the conversion of fibroblasts into neurons and can be partially reproduced by inhibition of histone H3 lysine 9 and DNA methylation. This mechanopriming approach also triggers macrophage reprogramming into neurons and fibroblast conversion into induced pluripotent stem cells, being thus a promising mechanically based epigenetic state modulation method for cell engineering.

Label-Free Cancer Stem-like Cell Assay Conducted at a Single Cell Level Using Microfluidic Mechanotyping Devices

The mechanical phenotype of cells is an intrinsic property of individual cells. In fact, this property could serve as a label-free, non-destructive, diagnostic marker of the state of cells owing to its remarkable translational potential. A microfluidic device is a strong candidate for meeting the demand of this translational research as it can be used to diagnose a large population of cells at a single cell level in a high-throughput manner, without the need for off-line pretreatment operations. In this study, we investigated the mechanical phenotype of the human colon adenocarcinoma cell, HT29, which is known to be a heterogeneous cell line with both multipotency and self-renewal abilities. This type of cancer stem-like cell (CSC) is believed to be the unique originators of all tumor cells and may serve as the leading cause of cancer metastasis and drug resistance. By combining consecutive constrictions and microchannels with an ionic current sensing system, we found a high heterogeneity of cell deformability in the population of HT29 cells. Moreover, based on the level of aldehyde dehydrogenase (ALDH) activity and the expression level of CD44s, which are biochemical markers that suggest the multipotency of cells, the high heterogeneity of cell deformability was concluded to be a potential mechanical marker of CSCs. The development of label-free and non-destructive identification and collection techniques for CSCs has remarkable potential not only for cancer diagnosis and prognosis but also for the discovery of a new treatment for cancer.

Elongation Index as a Sensitive Measure of Cell Deformation in High-Throughput Microfluidic Systems

One of the promising approaches for high-throughput screening of cell mechanotype is microfluidic deformability cytometry (mDC), in which the apparent deformation index (DI) of the cells stretched by extensional flow at the stagnation point of a cross-slot microchannel is measured. The DI is subject to substantial measurement errors due to cell offset from the flow centerline and velocity fluctuations in inlet channels, leading to artificial widening of DI versus cell size plots. Here, we simulated an mDC experiment using a custom computational algorithm for viscoelastic cell migration. Cell motion and deformation in a cross-slot channel was modeled for fixed or randomized values of cellular mechanical properties (diameter, shear elasticity, cortical tension) and initial cell placement, with or without sinusoidal fluctuations between the inlet velocities. Our numerical simulation indicates that mDC loses sensitivity to changes in shear elasticity when the offset distance exceeds 5 μm, and just 1% velocity fluctuation causes an 11.7% drop in the DI. The obtained relationships between the cell diameter, shear elasticity, and offset distance were used to establish a new measure of cell deformation, referred to as the "elongation index" (EI). In the randomized study, the EI scatter plots were visibly separated for the low- and high-elasticity populations of cells, with a mean of 300 and 3500 Pa, whereas the standard DI output was unable to distinguish between these two groups of cells. The successful suppression of the offset artifacts with a narrower data distribution was shown for the EI output of MCF-7 cells.

The Extracellular, Cellular, and Nuclear Stiffness, a Trinity in the Cancer Resistome-A Review

Alterations in mechano-physiological properties of a tissue instigate cancer burdens in parallel to common genetic and epigenetic alterations. The chronological and mechanistic interrelation between the various extra- and intracellular aspects remains largely elusive. Mechano-physiologically, integrins and other cell adhesion molecules present the main mediators for transferring and distributing forces between cells and the extracellular matrix (ECM). These cues are channeled via focal adhesion proteins, termed the focal adhesomes, to cytoskeleton and nucleus and vice versa thereby affecting the pathophysiology of multicellular cancer tissues. In combination with simultaneous activation of diverse downstream signaling pathways, the phenotypes of cancer cells are created and driven characterized by deregulated transcriptional and biochemical cues that elicit the hallmarks of cancer. It, however, remains unclear how elastostatic modifications, i.e., stiffness, in the extracellular, intracellular, and nuclear compartment contribute and control the resistance of cancer cells to therapy. In this review, we discuss how stiffness of unique tumor components dictates therapy response and what is known about the underlying molecular mechanisms.

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.

Towards the directed evolution of protein materials

Protein-based materials have emerged as a powerful instrument for a new generation of biological materials, with many chemical and mechanical capabilities. Through the manipulation of DNA, researchers can design proteins at the molecular level, engineering a vast array of structural building blocks. However, our capability to rationally design and predict the properties of such materials is limited by the vastness of possible sequence space. Directed evolution has emerged as a powerful tool to improve biological systems through mutation and selection, presenting another avenue to produce novel protein materials. In this prospective review, we discuss the application of directed evolution for protein materials, reviewing current examples and developments that could facilitate the evolution of protein for material applications.