High-throughput physical phenotyping of cell differentiation

Sorting differentiated mammalian cells using deterministic lateral displacement microfluidic devices

Separation of differentiated and undifferentiated cells without labeling is required for cell analyses and clinical application of cultured differentiated cells in vitro. To proceed with the passive separation of differentiated cells inside a clean bench, we developed a system of deterministic lateral displacement (DLD) microfluidic devices and applied this system to sort differentiated cells in vitro. The fluid flow is driven by compressed air to the buffer. Priming and sorting can be completed by air pressure control. We use this system to separate C2C12 mononuclear myocytes from multinuclear myotubes. Additionally, using a DLD microfluidic channel of Dc = 20 μm, multinuclear myotubes can be effectively sorted as larger particles. We prepared differentiated adipocytes from mouse embryonic fibroblast (MEF) cells and sorted those containing lipid droplets. The diameters of these sorted adipocytes considered larger particles, exceeded 20 μm, similar to the Dc of the DLD microfluidic channel. Differentiated cell sorting by cell size will contribute to single-cell analyses and in vitro tissue model preparation for drug discovery.

Multiparameter Mechanical Phenotyping for Accurate Cell Identification Using High-Throughput Microfluidic Deformability Cytometry

Mechanical phenotyping has been widely employed for single-cell analysis over recent years. However, most previous works on characterizing the cellular mechanical properties measured only a single parameter from one image. In this paper, the quasi-real-time multiparameter analysis of cell mechanical properties was realized using high-throughput adjustable deformability cytometry. We first extracted 12 deformability parameters from the cell contours. Then, the machine learning for cell identification was performed to preliminarily verify the rationality of multiparameter mechanical phenotyping. The experiments on characterizing cells after cytoskeletal modification verified that multiple parameters extracted from the cell contours contributed to an identification accuracy of over 80%. Through continuous frame analysis of the cell deformation process, we found that temporal variation and an average level of parameters were correlated with cell type. To achieve quasi-real-time and high-precision multiplex-type cell detection, we constructed a back propagation (BP) neural network model to complete the fast identification of four cell lines. The multiparameter detection method based on time series achieved cell detection with an accuracy of over 90%. To solve the challenges of cell rarity and data lacking for clinical samples, based on the developed BP neural network model, the transfer learning method was used for the identification of three different clinical samples, and finally, a high identification accuracy of approximately 95% was achieved.

Elasticity of Carrier Fluid: A Key Factor Affecting Mechanical Phenotyping in Deformability Cytometry

Recently, microfluidics deformability cytometry has emerged as a powerful tool for high-throughput mechanical phenotyping of large populations of cells. These methods characterize cells by their mechanical fingerprints by exerting hydrodynamic forces and monitoring the resulting deformation. These devices have shown great promise for label-free cytometry, yet there is a critical need to improve their accuracy and reconcile any discrepancies with other methods, such as atomic force microscopy. In this study, we employ computational fluid dynamics simulations and uncover how the elasticity of frequently used carrier fluids, such as methylcellulose dissolved in phosphate-buffered saline, is significantly influential to the resulting cellular deformation. We conducted CFD simulations conventionally used within the deformability cytometry field, which neglect fluid elasticity. Subsequently, we incorporated a more comprehensive model that simulates the viscoelastic nature of the carrier fluid. A comparison of the predicted stresses between these two approaches underscores the significance of the emerging elastic stresses in addition to the well-recognized viscous stresses along the channel. Furthermore, we utilize a two-phase flow model to predict the deformation of a promyelocyte (i.e., HL-60 cell type) within a hydrodynamic constriction channel. The obtained results highlight a substantial impact of the elasticity of carrier fluid on cellular deformation and raise questions about the accuracy of mechanical property estimates derived by neglecting elastic stresses.

Passive Focusing of Single Cells Using Microwell Arrays for High-Accuracy Image-Activated Sorting

Sorting single cells from a population was of critical importance in areas such as cell line development and cell therapy. Image-based sorting is becoming a promising technique for the nonlabeling isolation of cells due to the capability of providing the details of cell morphology. This study reported the focusing of cells using microwell arrays and the following automatic size sorting based on the real-time recognition of cells. The simulation first demonstrated the converged streamlines to the symmetrical plane contributed to the focusing effect. Then, the influence of connecting microchannel, flowing length, particle size, and the sample flow rate on the focusing effect was experimentally analyzed. Both microspheres and cells could be aligned in a straight line at the Reynolds number (Re) of 0.027-0.187 and 0.027-0.08, respectively. The connecting channel was proved to drastically improve the focusing performance. Afterward, a tapered microwell array was utilized to focus sphere/cell spreading in a wide channel to a straight line. Finally, a custom algorithm was employed to identify and sort the size of microspheres/K562 cells with a throughput of 1 event/s and an accuracy of 97.8/97.1%. The proposed technique aligned cells to a straight line at low Reynolds numbers and greatly facilitated the image-activated sorting without the need for a high-speed camera or flow control components with high frequency. Therefore, it is of enormous application potential in the field of nonlabeled separation of single cells.

Scaling up stem cell production: harnessing the potential of microfluidic devices

Stem cells are specialised cells characterised by their unique ability to both self-renew and transform into a wide array of specialised cell types. The widespread interest in stem cells for regenerative medicine and cultivated meat has led to a significant demand for these cells in both research and practical applications. Despite the growing need for stem cell manufacturing, the industry faces significant obstacles, including high costs for equipment and maintenance, complicated operation, and low product quality and yield. Microfluidic technology presents a promising solution to the abovementioned challenges. As an innovative approach for manipulating liquids and cells within microchannels, microfluidics offers a plethora of advantages at an industrial scale. These benefits encompass low setup costs, ease of operation and multiplexing, minimal energy consumption, and the added advantage of being labour-free. This review presents a thorough examination of the prominent microfluidic technologies employed in stem cell research and explores their promising applications in the burgeoning stem cell industry. It thoroughly examines how microfluidics can enhance cell harvesting from tissue samples, facilitate mixing and cryopreservation, streamline microcarrier production, and efficiently conduct cell separation, purification, washing, and final cell formulation post-culture.

High-throughput adjustable deformability cytometry utilizing elasto-inertial focusing and virtual fluidic channel

Cell mechanical properties provide a label-free marker for indicating cell states and disease processes. Although microfluidic deformability cytometry has demonstrated great potential and successes in mechanical phenotyping in recent years, its universal applicability for characterizing multiple sizes of cells using a single device has not been realized. Herein, we propose high-throughput adjustable deformability cytometry integrated with three-dimensional (3D) elasto-inertial focusing and a virtual fluidic channel. By properly adjusting the flow ratio of the sample and sheath, the virtual fluidic channel in a wide solid channel can generate a strong shear force in the normal direction of the flow velocity and simultaneously squeeze cells from both sides to induce significant cell deformation. The combination of elasto-inertial focusing and a virtual fluidic channel provides a great hydrodynamic symmetrical force for inducing significant and homogeneous cell deformation. In addition, our deformability cytometry system not only achieves rapid and precise cell deformation, but also allows the adjustable detection of multiple sizes of cells at a high throughput of up to 3000 cells per second. The mini-bilateral segmentation network (mini-BiSeNet) was developed to identify cells and extract features quickly. The classification of different cell populations (A549, MCF-7, MDA-MB-231, and WBCs) was carried out based on the cell size and deformation. By applying deep learning to cell classification, a high accuracy reaching approximately 90% was achieved. We also revealed the potential of our deformability cytometry for characterizing pleural effusions. The flexibility of our deformability cytometry holds promise for the mechanical phenotyping and detection of various biological samples.

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.

Managing the Heterogeneity of Mesenchymal Stem Cells for Cartilage Regenerative Therapy: A Review

Articular cartilage defects commonly result from trauma and are associated with significant morbidity. Since cartilage is an avascular, aneural, and alymphatic tissue with a poor intrinsic healing ability, the regeneration of functional hyaline cartilage remains a difficult clinical problem. Mesenchymal stem cells (MSCs) are multipotent cells with multilineage differentiation potential, including the ability to differentiate into chondrocytes. Due to their availability and ease of ex vivo expansion, clinicians are increasingly applying MSCs in the treatment of cartilage lesions. However, despite encouraging pre-clinical and clinical data, inconsistencies in MSC proliferative and chondrogenic potential depending on donor, tissue source, cell subset, culture conditions, and handling techniques remain a key barrier to widespread clinical application of MSC therapy in cartilage regeneration. In this review, we highlight the strategies to manage the heterogeneity of MSCs ex vivo for more effective cartilage repair, including reducing the MSC culture expansion period, and selecting MSCs with higher chondrogenic potential through specific genetic markers, surface markers, and biophysical attributes. The accomplishment of a less heterogeneous population of culture-expanded MSCs may improve the scalability, reproducibility, and standardisation of MSC therapy for clinical application in cartilage regeneration.

Inertial microfluidics: current status, challenges, and future opportunities

Inertial microfluidics uses the hydrodynamic effects induced at finite Reynolds numbers to achieve passive manipulation of particles, cells, or fluids and offers the advantages of high-throughput processing, simple channel geometry, and label-free and external field-free operation. Since its proposal in 2007, inertial microfluidics has attracted increasing interest and is currently widely employed as an important sample preparation protocol for single-cell detection and analysis. Although great success has been achieved in the inertial microfluidics field, its performance and outcome can be further improved. From this perspective, herein, we reviewed the current status, challenges, and opportunities of inertial microfluidics concerning the underlying physical mechanisms, available simulation tools, channel innovation, multistage, multiplexing, or multifunction integration, rapid prototyping, and commercial instrument development. With an improved understanding of the physical mechanisms and the development of novel channels, integration strategies, and commercial instruments, improved inertial microfluidic platforms may represent a new foundation for advancing biomedical research and disease diagnosis.

Microfluidic technologies for cell deformability cytometry

Microfluidic detection methods for cell deformability cytometry have been regarded as powerful tools for single-cell analysis of cellular mechanical phenotypes, thus having been widely applied in the fields of cell preparation, separation, clinical diagnostics and so on. Featured with traits like easy operations, low cost and high throughput, such methods have shown great potentials on investigating physiological state and pathological changes during cellular deformation. Herein, a review on the advancements of microfluidic-based cell deformation cytometry is presented. We discuss several representative microfluidic-based cell deformability cytometry methods with their frontiers in practical applications. Finally, we analyze the current status and propose the remaining challenges with future perspectives and development directions.

A Systematic Study of Size Correlation and Young's Modulus Sensitivity for Cellular Mechanical Phenotyping by Microfluidic Approaches

Cellular mechanical properties are a class of intrinsic biophysical markers for cell state and health. Microfluidic mechanical phenotyping methods have emerged as promising tools to overcome the challenges of low throughput and high demand for manual skills in conventional approaches. In this work, two types of microfluidic cellular mechanical phenotyping methods, contactless hydro-stretching deformability cytometry (lh-DC) and contact constriction deformability cytometry (cc-DC) are comprehensively studied and compared. Polymerized hydrogel beads with defined sizes are used to characterize a strong negative correlation between size and deformability in cc-DC (r = -0.95), while lh-DC presents a weak positive correlation (r = 0.13). Young's modulus sensitivity in cc-DC is size-dependent while it is a constant in lh-DC. Moreover, the deformability assessment for human breast cell line mixture suggests the lh-DC exhibits better differentiation capability of cells with different size distributions, while cc-DC provides higher sensitivity to identify cellular mechanical changes within a single cell line. This work is the first to present a quantitative study and comparison of size correlation and Young's modulus sensitivity of contactless and contact microfluidic mechanical phenotyping methods, which provides guidance to choose the most suitable cellular mechanical phenotyping platform for specific cell analysis applications.

Molecular determinants of intrinsic cellular stiffness in health and disease

In recent years, the role of intrinsic biophysical features, especially cellular stiffness, in diverse cellular and disease processes is being increasingly recognized. New high throughput techniques for the quantification of cellular stiffness facilitate the study of their roles in health and diseases. In this review, we summarized recent discovery about how cellular stiffness is involved in cell stemness, tumorigenesis, and blood diseases. In addition, we review the molecular mechanisms underlying the gene regulation of cellular stiffness in health and disease progression. Finally, we discussed the current understanding on how the cytoskeleton structure and the regulation of these genes contribute to cellular stiffness, highlighting where the field of cellular stiffness is headed.

The Cell Physiome: What Do We Need in a Computational Physiology Framework for Predicting Single-Cell Biology?

Modern biology and biomedicine are undergoing a big data explosion, needing advanced computational algorithms to extract mechanistic insights on the physiological state of living cells. We present the motivation for the Cell Physiome project: a framework and approach for creating, sharing, and using biophysics-based computational models of single-cell physiology. Using examples in calcium signaling, bioenergetics, and endosomal trafficking, we highlight the need for spatially detailed, biophysics-based computational models to uncover new mechanisms underlying cell biology. We review progress and challenges to date toward creating cell physiome models. We then introduce bond graphs as an efficient way to create cell physiome models that integrate chemical, mechanical, electromagnetic, and thermal processes while maintaining mass and energy balance. Bond graphs enhance modularization and reusability of computational models of cells at scale. We conclude with a look forward at steps that will help fully realize this exciting new field of mechanistic biomedical data science. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Fabricating Silicon Resonators for Analysing Biological Samples

The adaptability of microscale devices allows microtechnologies to be used for a wide range of applications. Biology and medicine are among those fields that, in recent decades, have applied microtechnologies to achieve new and improved functionality. However, despite their ability to achieve assay sensitivities that rival or exceed conventional standards, silicon-based microelectromechanical systems remain underutilised for biological and biomedical applications. Although microelectromechanical resonators and actuators do not always exhibit optimal performance in liquid due to electrical double layer formation and high damping, these issues have been solved with some innovative fabrication processes or alternative experimental approaches. This paper focuses on several examples of silicon-based resonating devices with a brief look at their fundamental sensing elements and key fabrication steps, as well as current and potential biological/biomedical applications.

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.

Inertial microfluidics for high-throughput cell analysis and detection: a review

Since it was first proposed in 2007, inertial microfluidics has been extensively studied in terms of theory, design, fabrication, and application. In recent years, with the rapid development of microfabrication technologies, a variety of channel structures that can focus, concentrate, separate, and capture bioparticles or fluids have been designed and manufactured to extend the range of potential biomedical applications of inertial microfluidics. Due to the advantages of high throughput, simplicity, and low device cost, inertial microfluidics is a promising candidate for rapid sample processing, especially for large-volume samples with low-abundance targets. As an approach to cellular sample pretreatment, inertial microfluidics has been widely employed to ensure downstream cell analysis and detection. In this review, a comprehensive summary of the application of inertial microfluidics for high-throughput cell analysis and detection is presented. According to application areas, the recent advances can be sorted into label-free cell mechanical phenotyping, sheathless flow cytometric counting, electrical impedance cytometer, high-throughput cellular image analysis, and other methods. Finally, the challenges and prospects of inertial microfluidics for cell analysis and detection are summarized.

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.

High-throughput functional profiling of single adherent cells via hydrogel drop-screen

Single-adherent-cell phenotyping on an extracellular matrix (ECM) is essential to determine cellular biological functions, such as morphological adaptations and biomolecule secretions, correlated to medical treatments and metastasis, yet there is no available platform for such high-throughput screening. Here, a novel hydrogel drop-screen device was developed to rapidly measure large-scale single-cell morphologies and multiple secretions on substrates for phenotype profiling. Single cells were first anchored to microfluidically fabricated gelatin particles providing mechanical stimulations similar to those from ECM in vivo. The cellular morphologies were then examined by quantifying the amount of cytoskeleton expressed on the particles. With droplet encapsulation, adherent single-cell multiplexed secretion analysis of a disintegrin and metalloproteinases (ADAMs) and matrix metalloproteinases (MMPs) was conducted at a throughput of ∼102 cells per second, revealing distinct functional heterogeneities associated with extracellular mechanical stimulations. The level of cell heterogeneity increased with increasing substrate stuffiness. Moreover, because of the promising screening capability, a database related to both nontumorigenic and tumorigenic breast cells (MCF10A, MCF-7, and MDA-MB-231) was constructed. The respective cell distributions and heterogeneities based on the morphologies and secreted bioindicators, such as MMP-2, MMP-3, MMP-9, and ADAM-8, were measured and found to correspond to the progress of tumor metastasis.

Optical quantification of intracellular mass density and cell mechanics in 3D mechanical confinement

Biophysical properties of cells such as intracellular mass density and cell mechanics are known to be involved in a wide range of homeostatic functions and pathological alterations. An optical readout that can be used to quantify such properties is the refractive index (RI) distribution. It has been recently reported that the nucleus, initially presumed to be the organelle with the highest dry mass density (ρ) within the cell, has in fact a lower RI and ρ than its surrounding cytoplasm. These studies have either been conducted in suspended cells, or cells adhered on 2D substrates, neither of which reflects the situation in vivo where cells are surrounded by the extracellular matrix (ECM). To better approximate the 3D situation, we encapsulated cells in 3D covalently-crosslinked alginate hydrogels with varying stiffness, and imaged the 3D RI distribution of cells, using a combined optical diffraction tomography (ODT)-epifluorescence microscope. Unexpectedly, the nuclei of cells in 3D displayed a higher ρ than the cytoplasm, in contrast to 2D cultures. Using a Brillouin-epifluorescence microscope we subsequently showed that in addition to higher ρ, the nuclei also had a higher longitudinal modulus (M) and viscosity (η) compared to the cytoplasm. Furthermore, increasing the stiffness of the hydrogel resulted in higher M for both the nuclei and cytoplasm of cells in stiff 3D alginate compared to cells in compliant 3D alginate. The ability to quantify intracellular biophysical properties with non-invasive techniques will improve our understanding of biological processes such as dormancy, apoptosis, cell growth or stem cell differentiation.

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.

Microfluidic-Based Approaches in Targeted Cell/Particle Separation Based on Physical Properties: Fundamentals and Applications

Cell separation is a key step in many biomedical research areas including biotechnology, cancer research, regenerative medicine, and drug discovery. While conventional cell sorting approaches have led to high-efficiency sorting by exploiting the cell's specific properties, microfluidics has shown great promise in cell separation by exploiting different physical principles and using different properties of the cells. In particular, label-free cell separation techniques are highly recommended to minimize cell damage and avoid costly and labor-intensive steps of labeling molecular signatures of cells. In general, microfluidic-based cell sorting approaches can separate cells using "intrinsic" (e.g., fluid dynamic forces) versus "extrinsic" external forces (e.g., magnetic, electric field, etc.) and by using different properties of cells including size, density, deformability, shape, as well as electrical, magnetic, and compressibility/acoustic properties to select target cells from a heterogeneous cell population. In this work, principles and applications of the most commonly used label-free microfluidic-based cell separation methods are described. In particular, applications of microfluidic methods for the separation of circulating tumor cells, blood cells, immune cells, stem cells, and other biological cells are summarized. Computational approaches complementing such microfluidic methods are also explained. Finally, challenges and perspectives to further develop microfluidic-based cell separation methods are discussed.

Acoustic erythrocytometer for mechanically probing cell viscoelasticity

We demonstrate an acoustic device to mechanically probe a population of red blood cells at the single cell level. The device operates by exciting a surface acoustic wave in a microfluidic channel creating a stationary acoustic wave field of nodes and antinodes. Erythrocytes are attracted to the nodes and are deformed. Using a stepwise increasing and periodically oscillating acoustic field we study the static and dynamic deformation of individual red blood cells one by one. We quantify the deformation by the Taylor deformation index D and relaxation times τ₁ and τ₂ that reveal both the viscous and elastic properties of the cells. The precision of the measurement allows us to distinguish between individual cells in the suspension and provides a quantitative viscoelastic fingerprint of the blood sample at single cell resolution. The method overcomes limitations of other techniques that provide averaged values and has the potential for high-throughput.

A comparison of microfluidic methods for high-throughput cell deformability measurements

The mechanical phenotype of a cell is an inherent biophysical marker of its state and function, with many applications in basic and applied biological research. Microfluidics-based methods have enabled single-cell mechanophenotyping at throughputs comparable to those of flow cytometry. Here, we present a standardized cross-laboratory study comparing three microfluidics-based approaches for measuring cell mechanical phenotype: constriction-based deformability cytometry (cDC), shear flow deformability cytometry (sDC) and extensional flow deformability cytometry (xDC). All three methods detect cell deformability changes induced by exposure to altered osmolarity. However, a dose-dependent deformability increase upon latrunculin B-induced actin disassembly was detected only with cDC and sDC, which suggests that when exposing cells to the higher strain rate imposed by xDC, cellular components other than the actin cytoskeleton dominate the response. The direct comparison presented here furthers our understanding of the applicability of the different deformability cytometry methods and provides context for the interpretation of deformability measurements performed using different platforms.

Simulation and practice of particle inertial focusing in 3D-printed serpentine microfluidic chips via commercial 3D-printers

Inertial focusing of particles in serpentine microfluidic chips has been studied over the past decade. Here, a study to investigate the particle inertial focusing in 3D-printed serpentine microfluidic chips was conducted by simulation and practice. A test model was designed and printed using four commercial 3D-printers. Commercial inkjet 3D-printers have shown the best printing channel resolution of up to 0.1 mm. The force analysis of particle inertial focusing in 3D-printed microfluidic chips with large cross-sectional channels was discussed. Important parameters such as the channel curvature and flow velocity were studied by simulation. The optimal channel curvature and flow velocity are 5.9 mm and 480 μL min^-1 (Re: 29.8 and De: 4.49) in the 3D-printed microfluidic chips with 0.2 mm × 0.4 mm cross-sectional channels. Under these optimal conditions, particles were well focused in the middle of the channel. Furthermore, two kinds of cancer cells were focused in these 3D-printed serpentine microfluidic chips under the optimal conditions. We envision that this improved study would provide helpful insights into simulating particle inertial focusing in 3D-printed microfluidic chips and promoting 3D-printed microfluidic chips to commercial production.

Intracellular Nanomaterial Delivery via Spiral Hydroporation

In recent nanobiotechnology developments, a wide variety of functional nanomaterials and engineered biomolecules have been created, and these have numerous applications in cell biology. For these nanomaterials to fulfill their promises completely, they must be able to reach their biological targets at the subcellular level and with a high level of specificity. Traditionally, either nanocarrier- or membrane disruption-based method has been used to deliver nanomaterials inside cells; however, these methods are suboptimal due to their toxicity, inconsistent delivery, and low throughput, and they are also labor intensive and time-consuming, highlighting the need for development of a next-generation, intracellular delivery system. This study reports on the development of an intracellular nanomaterial delivery platform, based on unexpected cell-deformation phenomena via spiral vortex and vortex breakdown exerted in the cross- and T-junctions at moderate Reynolds numbers. These vortex-induced cell deformation and sequential restoration processes open cell membranes transiently, allowing effective and robust intracellular delivery of nanomaterials in a single step without the aid of carriers or external apparatus. By using the platform described here (termed spiral hydroporator), we demonstrate the delivery of different nanomaterials, including gold nanoparticles (200 nm diameter), functional mesoporous silica nanoparticles (150 nm diameter), dextran (hydrodynamic diameters between 2-55 nm), and mRNA, into different cell types. We demonstrate here that the system is highly efficient (up to 96.5%) with high throughput (up to 1 × 10⁶ cells/min) and rapid delivery (∼1 min) while maintaining high levels of cell viability (up to 94%).

From Bivariate to Multivariate Analysis of Cytometric Data: Overview of Computational Methods and Their Application in Vaccination Studies

Flow and mass cytometry are used to quantify the expression of multiple extracellular or intracellular molecules on single cells, allowing the phenotypic and functional characterization of complex cell populations. Multiparametric flow cytometry is particularly suitable for deep analysis of immune responses after vaccination, as it allows to measure the frequency, the phenotype, and the functional features of antigen-specific cells. When many parameters are investigated simultaneously, it is not feasible to analyze all the possible bi-dimensional combinations of marker expression with classical manual analysis and the adoption of advanced automated tools to process and analyze high-dimensional data sets becomes necessary. In recent years, the development of many tools for the automated analysis of multiparametric cytometry data has been reported, with an increasing record of publications starting from 2014. However, the use of these tools has been preferentially restricted to bioinformaticians, while few of them are routinely employed by the biomedical community. Filling the gap between algorithms developers and final users is fundamental for exploiting the advantages of computational tools in the analysis of cytometry data. The potentialities of automated analyses range from the improvement of the data quality in the pre-processing steps up to the unbiased, data-driven examination of complex datasets using a variety of algorithms based on different approaches. In this review, an overview of the automated analysis pipeline is provided, spanning from the pre-processing phase to the automated population analysis. Analysis based on computational tools might overcame both the subjectivity of manual gating and the operator-biased exploration of expected populations. Examples of applications of automated tools that have successfully improved the characterization of different cell populations in vaccination studies are also presented.

Physical Biomarkers of Disease Progression: On-Chip Monitoring of Changes in Mechanobiology of Colorectal Cancer Cells

Disease can induce changes to subcellular components, altering cell phenotype and leading to measurable bulk-material mechanical properties. The mechanical phenotyping of single cells therefore offers many potential diagnostic applications. Cells are viscoelastic and their response to an applied stress is highly dependent on the magnitude and timescale of the actuation. Microfluidics can be used to measure cell deformability over a wide range of flow conditions, operating two distinct flow regimes (shear and inertial) which can expose subtle mechanical properties arising from subcellular components. Here, we investigate the deformability of three colorectal cancer (CRC) cell lines using a range of flow conditions. These cell lines offer a model for CRC metastatic progression; SW480 derived from primary adenocarcinoma, HT29 from a more advanced primary tumor and SW620 from lymph-node metastasis. HL60 (leukemia cells) were also studied as a model circulatory cell, offering a non-epithelial comparison. We demonstrate that microfluidic induced flow deformation can be used to robustly detect mechanical changes associated with CRC progression. We also show that single-cell multivariate analysis, utilising deformation and relaxation dynamics, offers potential to distinguish these different cell types. These results point to the benefit of multiparameter determination for improving detection and accuracy of disease stage diagnosis.

Microfluidic Mechanotyping of a Single Cell with Two Consecutive Constrictions of Different Sizes and an Electrical Detection System

The mechanical properties of a cell, which include parameters such as elasticity, inner pressure, and tensile strength, are extremely important because changes in these properties are indicative of diseases ranging from diabetes to malignant transformation. Considering the heterogeneity within a population of cancer cells, a robust measurement system at the single cell level is required for research and in clinical purposes. In this study, a potential microfluidic device for high-throughput and practical mechanotyping were developed to investigate the deformability and sizes of cells through a single run. This mechanotyping device consisted of two different sizes of consecutive constrictions in a microchannel and measured the size of cells and related deformability during transit. Cell deformability was evaluated based on the transit and on the effects of cytoskeleton-affecting drugs, which were detected within 50 ms. The mechanotyping device was able to also measure a cell cycle without the use of fluorescent or protein tags.

Acoustic deformation for the extraction of mechanical properties of lipid vesicle populations

We use an ultrasonic standing wave to simultaneously trap and deform thousands of soft lipid vesicles immersed in a liquid solution. In our device, acoustic radiation stresses comparable in magnitude to those generated in optical stretching devices are achieved over a spatial extent of more than ten acoustic wavelengths. We solve the acoustic scattering problem in the long-wavelength limit to obtain the radiation stress. The result is then combined with thin-shell elasticity theory to form expressions that relate the deformed geometry to the applied acoustic field intensity. Using observation of the deformed geometry and this model, we rapidly extract mechanical properties, such as the membrane Young's modulus, from populations of lipid vesicles.

Predicting cancer cell invasion by single-cell physical phenotyping

The physical properties of cells are promising biomarkers for cancer diagnosis and prognosis. Here we determine the physical phenotypes that best distinguish human cancer cell lines, and their relationship to cell invasion. We use the high throughput, single-cell microfluidic method, quantitative deformability cytometry (q-DC), to measure six physical phenotypes including elastic modulus, cell fluidity, transit time, entry time, cell size, and maximum strain at rates of 102 cells per second. By training a k-nearest neighbor machine learning algorithm, we demonstrate that multiparameter analysis of physical phenotypes enhances the accuracy of classifying cancer cell lines compared to single parameters alone. We also discover a set of four physical phenotypes that predict invasion; using these four parameters, we generate the physical phenotype model of invasion by training a multiple linear regression model with experimental data from a set of human ovarian cancer cells that overexpress a panel of tumor suppressor microRNAs. We validate the model by predicting invasion based on measured physical phenotypes of breast and ovarian human cancer cell lines that are subject to genetic or pharmacologic perturbations. Taken together, our results highlight how physical phenotypes of single cells provide a biomarker to predict the invasion of cancer cells.

Cells Under Stress: An Inertial-Shear Microfluidic Determination of Cell Behavior

The deformability of a cell is the direct result of a complex interplay between the different constituent elements at the subcellular level, coupling a wide range of mechanical responses at different length scales. Changes to the structure of these components can also alter cell phenotype, which points to the critical importance of cell mechanoresponse for diagnostic applications. The response to mechanical stress depends strongly on the forces experienced by the cell. Here, we use cell deformability in both shear-dominant and inertia-dominant microfluidic flow regimes to probe different aspects of the cell structure. In the inertial regime, we follow cellular response from (visco-)elastic through plastic deformation to cell structural failure and show a significant drop in cell viability for shear stresses >11.8 kN/m2. Comparatively, a shear-dominant regime requires lower applied stresses to achieve higher cell strains. From this regime, deformation traces as a function of time contain a rich source of information including maximal strain, elastic modulus, and cell relaxation times and thus provide a number of markers for distinguishing cell types and potential disease progression. These results emphasize the benefit of multiple parameter determination for improving detection and will ultimately lead to improved accuracy for diagnosis. We present results for leukemia cells (HL60) as a model circulatory cell as well as for a colorectal cancer cell line, SW480, derived from primary adenocarcinoma (Dukes stage B). SW480 were also treated with the actin-disrupting drug latrunculin A to test the sensitivity of flow regimes to the cytoskeleton. We show that the shear regime is more sensitive to cytoskeletal changes and that large strains in the inertial regime cannot resolve changes to the actin cytoskeleton.

Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning

CD8+ T cells constitute an essential compartment of the adaptive immune system. During immune responses, naı̈ve T cells become functional, as they are primed with their cognate determinants by the antigen presenting cells. Current methods of identifying activated CD8+ T cells are laborious, time-consuming and expensive due to the extensive list of required reagents. Here, we demonstrate an optical imaging approach featuring quantitative phase imaging to distinguish activated CD8+ T cells from naı̈ve CD8+ T cells in a rapid and reagent-free manner. We measured the dry mass of live cells and employed transport-based morphometry to better understand their differential morphological attributes. Our results reveal that, upon activation, the dry cell mass of T cells increases significantly in comparison to that of unstimulated cells. By employing deep learning formalism, we are able to accurately predict the population ratios of unknown mixed population based on the acquired quantitative phase images. We envision that, with further refinement, this label-free method of T cell phenotyping will lead to a rapid and cost-effective platform for assaying T cell responses to candidate antigens in the near future.

On-chip refractive index cytometry for whole-cell deformability discrimination

On-chip high-throughput phenotyping of single cells has gained a lot of interest recently due to the discrimination capability of label-free biomarkers such as whole-cell deformability and refractive index. Here we present on-chip refractive index cytometry (RIC) for whole-cell deformability at a high measurement rate. We have further exploited a previously published on-chip optical characterization method which enhances cellular discrimination through the refractive index measurement of single cells. The proposed on-chip RIC can simultaneously probe the cellular refractive index, effective volume and whole-cell deformability while reaching a measurement rate up to 5000 cells per second. Additionally, the relative position of the nucleus inside the cell is reflected by the asymmetry of the measured curve. This particular finding is confirmed by our numerical simulation model and emphasized by a modified cytoskeleton HL-60 cells model. Furthermore, the proposed device discriminated HL-60 derived myeloid cells such as neutrophils, basophils and promyelocytes, which are indistinguishable using flow cytometry. To our knowledge, this is the first integrated device to simultaneously characterize the cellular refractive index and whole-cell deformability, yielding enhanced discrimination of large myeloid cell populations.

Demarcating the membrane damage for the extraction of functional mitochondria

Defective mitochondria have been linked to several critical human diseases such as neurodegenerative disorders, cancers and cardiovascular disease. However, the detailed characterization of mitochondria has remained relatively unexplored, largely due to the lack of effective extraction methods that may sufficiently retain the functionality of mitochondria, particularly when limited amount of sample is considered. In this study, we explore the possibility of modulating hydrodynamic stress through a cross-junction geometry at microscale to selectively disrupt the cellular membrane while mitochondrial membrane is secured. The operational conditions are empirically optimized to effectively shred the cell membranes while keeping mitochondria intact for the model mammalian cell lines, namely human embryonic kidney cells, mouse muscle cells and neuroblastoma cells. Unsurprisingly, the disruption of cell membranes with higher elastic moduli (neuroblastoma) requires elevated stress. This study also presents a comparative analysis of total protein yield and concentrations of extracted functional mitochondria with two commercially available mitochondria extraction approaches, the Dounce Homogenizer and the Qproteome® Mitochondria Isolation Kit, in a range of cell concentrations. Our findings show that the proposed "microscale cell shredder" yields at least 40% more functional mitochondria than the two other approaches and is able to preserve the morphological integrity of extracted mitochondria, particularly at low cell concentrations (5-20 × 104 cells/mL). Characterized by its capability of rapidly processing a limited quantity of samples (200 μL), demarcating the membrane damage through the proposed microscale cell shredder represents a novel strategy to extract subcellular organelles from clinical samples.

Multi-sample deformability cytometry of cancer cells

There is growing recognition that cell deformability can play an important role in cancer metastasis and diagnostics. Advancement of methods to characterize cell deformability in a high throughput manner and the capacity to process numerous samples can impact cancer-related applications ranging from analysis of patient samples to discovery of anti-cancer compounds to screening of oncogenes. In this study, we report a microfluidic technique called multi-sample deformability cytometry (MS-DC) that allows simultaneous measurement of flow-induced deformation of cells in multiple samples at single-cell resolution using a combination of on-chip reservoirs, distributed pressure control, and data analysis system. Cells are introduced at rates of O(100) cells per second with a data processing speed of 10 min per sample. To validate MS-DC, we tested more than 50 cell-samples that include cancer cell lines with different metastatic potential and cells treated with several cytoskeletal-intervention drugs. Results from MS-DC show that (i) the cell deformability correlates with metastatic potential for both breast and prostate cancer cells but not with their molecular histotype, (ii) the strongly metastatic breast cancer cells have higher deformability than the weakly metastatic ones; however, the strongly metastatic prostate cancer cells have lower deformability than the weakly metastatic counterparts, and (iii) drug-induced disruption of the actin network, microtubule network, and actomyosin contractility increased cancer cell deformability, but stabilization of the cytoskeletal proteins does not alter deformability significantly. Our study demonstrates the capacity of MS-DC to mechanically phenotype tumor cells simultaneously in many samples for cancer research.

Optics-Free, Non-Contact Measurements of Fluids, Bubbles, and Particles in Microchannels Using Metallic Nano-Islands on Graphene

Despite the apparent convenience of microfluidic technologies for applications in healthcare, such devices often rely on capital-intensive optics and other peripheral equipment that limit throughput. Here, we monitored the transit of fluids, gases, particles, and cells as they flowed through a microfluidic channel without the use of a camera or laser, i.e., "optics-free" microfluidics. We did this by monitoring the deformation of the side walls caused by the analyte passing through the channel. Critically, the analyte did not have to make contact with the channel walls to induce a deflection. This minute deformation was transduced into a change in electrical resistance using an ultrasensitive piezoresitive film composed of metallic nano-islands on graphene. We related changes in the resistance of the sensor to the theoretical deformation of the channel at varying flow rates. Then, we used air bubbles to induce a perturbation on the elastomeric channel walls and measured the viscoelastic relaxation of the walls of the channel. We obtained a viscoelastic time constant of 11.3 ± 3.5 s-1 for polydimethylsiloxane, which is consistent with values obtained using other techniques. Finally, we flowed silica particles and human mesenchymal stem cells and measured the deformation profiles of the channel. This technique yielded a convenient, continuous, and non-contact measurement of rigid and deformable particles without the use of a laser or camera.

Impact of poloxamer 188 (Pluronic F-68) additive on cell mechanical properties, quantification by real-time deformability cytometry

Advances in cellular therapies have led to the development of new approaches for cell product purification and formulation, e.g., utilizing cell endogenous properties such as size and deformability as a basis for separation from potentially harmful undesirable by-products. However, commonly used additives such as Pluronic F-68 and other poloxamer macromolecules can change the mechanical properties of cells and consequently alter their processing. In this paper, we quantified the short-term effect of Pluronic F-68 on the mechanotype of three different cell types (Jurkat cells, red blood cells, and human embryonic kidney cells) using real-time deformability cytometry. The impact of the additive concentration was assessed in terms of cell size and deformability. We observed that cells respond progressively to the presence of Pluronic F-68 within first 3 h of incubation and become significantly stiffer (p-value < 0.001) in comparison to a serum-free control and a control containing serum. We also observed that the short-term response manifested as cell stiffening is true (p-value < 0.001) for the concentration reaching 1% (w/v) of the poloxamer additive in tested buffers. Additionally, using flow cytometry, we assessed that changes in cell deformability triggered by addition of Pluronic F-68 are not accompanied by size or viability alterations.

Multiparameter cell-tracking intrinsic cytometry for single-cell characterization

An abundance of label-free microfluidic techniques for measuring cell intrinsic markers exists, yet these techniques are seldom combined because of integration complexity such as restricted physical space and incompatible modes of operation. We introduce a multiparameter intrinsic cytometry approach for the characterization of single cells that combines ≥2 label-free measurement techniques onto the same platform and uses cell tracking to associate the measured properties to cells. Our proof-of-concept implementation can measure up to five intrinsic properties including size, deformability, and polarizability at three frequencies. Each measurement module along with the integrated platform were validated and evaluated in the context of chemically induced changes in the actin cytoskeleton of cells. viSNE and machine learning classification were used to determine the orthogonality between and the contribution of the measured intrinsic markers for cell classification.

Measurement of the mechanical properties of single Synechocystis sp. strain PCC6803 cells in different osmotic concentrations using a robot-integrated microfluidic chip

Synechocystis sp. strain PCC6803 (Synechocystis) is a model microorganism and its mechanosensitive (MS) channels play important roles in its osmoadaptation mechanism. When the osmotic concentration of the culture environment changes, the inner pressure of the cell also changes due to the transportation of water through ion channels. Because the tension in the cell membrane relates to the inner pressure, we expect that the response of the MS channels to an osmotic concentration change could be evaluated by measuring their mechanical properties. Here, we propose a system for the measurement of the mechanical properties of a single Synechocystis cell. We developed a robot-integrated microfluidic chip combined with optical tweezers. The chip has an external actuated pushing probe and a force sensor probe. A single cell was located between the tip of both probes using the optical tweezers and was then deformed using the probes. As a result, we could measure the force and deformation and compare the Young's moduli of two groups: a group of wild type cells and a group of mutant (genetically modified) cells with a defect in the MS channels, at three different osmotic concentrations. The results showed that the Young's modulus of each group changed according to the osmotic concentration, while changes in cell size were too small to be detected. These results confirmed that the proposed evaluation method provides an understanding of the physiological function of MS channels for keeping the cell integrity of microorganisms when the cells are exposed to different external osmotic changes.

Characterizing cellular mechanical phenotypes with mechano-node-pore sensing

The mechanical properties of cells change with their differentiation, chronological age, and malignant progression. Consequently, these properties may be useful label-free biomarkers of various functional or clinically relevant cell states. Here, we demonstrate mechano-node-pore sensing (mechano-NPS), a multi-parametric single-cell-analysis method that utilizes a four-terminal measurement of the current across a microfluidic channel to quantify simultaneously cell diameter, resistance to compressive deformation, transverse deformation under constant strain, and recovery time after deformation. We define a new parameter, the whole-cell deformability index (wCDI), which provides a quantitative mechanical metric of the resistance to compressive deformation that can be used to discriminate among different cell types. The wCDI and the transverse deformation under constant strain show malignant MCF-7 and A549 cell lines are mechanically distinct from non-malignant, MCF-10A and BEAS-2B cell lines, and distinguishes between cells treated or untreated with cytoskeleton-perturbing small molecules. We categorize cell recovery time, ΔT_r, as instantaneous (ΔT_r ~ 0 ms), transient (ΔT_r ≤ 40ms), or prolonged (ΔT_r > 40ms), and show that the composition of recovery types, which is a consequence of changes in cytoskeletal organization, correlates with cellular transformation. Through the wCDI and cell-recovery time, mechano-NPS discriminates between sub-lineages of normal primary human mammary epithelial cells with accuracy comparable to flow cytometry, but without antibody labeling. Mechano-NPS identifies mechanical phenotypes that distinguishes lineage, chronological age, and stage of malignant progression in human epithelial cells.

High-throughput assessment of mechanical properties of stem cell derived red blood cells, toward cellular downstream processing

Stem cell products, including manufactured red blood cells, require efficient sorting and purification methods to remove components potentially harmful for clinical application. However, standard approaches for cellular downstream processing rely on the use of specific and expensive labels (e.g. FACS or MACS). Techniques relying on inherent mechanical and physical properties of cells offer high-throughput scalable alternatives but knowledge of the mechanical phenotype is required. Here, we characterized for the first time deformability and size changes in CD34+ cells, and expelled nuclei, during their differentiation process into red blood cells at days 11, 14, 18 and 21, using Real-Time Deformability Cytometry (RT-DC) and Atomic Force Microscopy (AFM). We found significant differences (p < 0.0001; standardised mixed model) between the deformability of nucleated and enucleated cells, while they remain within the same size range. Expelled nuclei are smaller thus could be removed by size-based separation. An average Young's elastic modulus was measured for nucleated cells, enucleated cells and nuclei (day 14) of 1.04 ± 0.47 kPa, 0.53 ± 0.12 kPa and 7.06 ± 4.07 kPa respectively. Our identification and quantification of significant differences (p < 0.0001; ANOVA) in CD34+ cells mechanical properties throughout the differentiation process could enable development of new routes for purification of manufactured red blood cells.