Cell state is often assayed through measurement of biochemical and biophysical markers. Although biochemical markers have been widely used, intrinsic biophysical markers, such as the ability to mechanically deform under a load, are advantageous in that they do not require costly labeling or sample preparation. However, current techniques that assay cell mechanical properties have had limited adoption in clinical and cell biology research applications. Here, we demonstrate an automated microfluidic technology capable of probing single-cell deformability at approximately 2,000 cells∕s. The method uses inertial focusing to uniformly deliver cells to a stretching extensional flow where cells are deformed at high strain rates, imaged with a high-speed camera, and computationally analyzed to extract quantitative parameters. This approach allows us to analyze cells at throughputs orders of magnitude faster than previously reported biophysical flow cytometers and single-cell mechanics tools, while creating easily observable larger strains and limiting user time commitment and bias through automation. Using this approach we rapidly assay the deformability of native populations of leukocytes and malignant cells in pleural effusions and accurately predict disease state in patients with cancer and immune activation with a sensitivity of 91% and a specificity of 86%. As a tool for biological research, we show the deformability we measure is an early biomarker for pluripotent stem cell differentiation and is likely linked to nuclear structural changes. Microfluidic deformability cytometry brings the statistical accuracy of traditional flow cytometric techniques to label-free biophysical biomarkers, enabling applications in clinical diagnostics, stem cell characterization, and single-cell biophysics.flow cytometry | high-throughput | cytology | mechanophenotype

Cell separation and sorting are essential steps in cell biology research and in many diagnostic and therapeutic methods. Recently, there has been interest in methods which avoid the use of biochemical labels; numerous intrinsic biomarkers have been explored to identify cells including size, electrical polarizability, and hydrodynamic properties. This review highlights microfluidic techniques used for label-free discrimination and fractionation of cell populations. Microfluidic systems have been adopted to precisely handle single cells and interface with other tools for biochemical analysis. We analyzed many of these techniques, detailing their mode of separation, while concentrating on recent developments and evaluating their prospects for application. Furthermore, this was done from a perspective where inertial effects are considered important and general performance metrics were proposed which would ease comparison of reported technologies. Lastly, we assess the current state of these technologies and suggest directions which may make them more accessible.FigureA wide range of microfluidic technologies have been developed to separate and sort cells by taking advantage of differences in their intrinsic biophysical properties

Rapid and accurate differentiation of cell types within a heterogeneous solution is a challenging but important task for various applications in biological research and medicine. Flow cytometry is the gold standard in cell analysis and is regularly used for blood analysis (i.e., complete blood counts). Flow cytometry, however, lacks sufficient throughput to analyze rare cells in blood or other dilute solutions in a reasonable time period because it is an inherently serial process. In this study, we exploit inertial effects for label- and sheath-free parallel flow cytometry with extreme throughput. We demonstrate a microfluidic device that consists of 256 high-aspect (W = 16 microm, H = 37 microm) parallel channels yielding a sample rate up to 1 million cells s(-1), only limited by the field-of-view of our high-speed optical interrogation method. The particles or cells flowing through the channels are focused to one uniform z-position (SD = +/-1.81 microm) with uniform downstream velocity (U(ave) = 0.208 +/- 0.004 m s(-1)) to reduce the probability of overlap and out-of-focus blur and provide similar cell signature images for accurate detection and analysis. To demonstrate a proof-of-concept application of our system operating at these throughputs, we conducted automated RBC and leukocyte counts on diluted whole blood and achieved high counting sensitivity and specificity (86-97%) compared to visual inspection of raw images. As no additional external forces are required to create ordered streams of cells, this approach has the potential for future applications in cost-effective hematology or rare-cell analysis platforms with extreme throughput capabilities when integrated with suitable large field-of view imaging or interrogation methods.