Cell viability assays are essential tools in biomedical research and drug development. Artificial intelligence (AI) offers the potential to simplify these assays by predicting cell viability directly from brightfield microscopy images, but current models lack generalizability across diverse cell types and treatments. Here, we introduce a strategy called "regularized imaging", where single cells are isolated in nanowells to generate standardized image patches that simplify segmentation and improve training data quality. We trained our model using example images of live and dead cells from a single cell line exposed to four cytotoxic conditions (ethanol, andrographolide, daunorubicin, and serum starvation). Despite this narrow training dataset, the resulting model accurately identified live and dead cells after treatments by previously unseen compounds, successfully replicating dose-response curves comparable to fluorescence live/dead assays. Importantly, this model effectively generalized across diverse cell types, including both adherent and suspension cells. Additionally, microscopy-based cell viability analysis is non-destructive, enabling repeated measurements to perform kinetic studies to distinguish between fast- and slow-acting compounds. Our findings highlight how regularized single-cell imaging enables the training of broadly generalizable AI models to recognize biologically relevant cell features for label-free cell screening workflows.

Cell motility is important to many biological processes including cancer, immune response, and tissue repair. Conventional assays measure bulk cell motility, potentially overlooking important heterogeneity and missing important high motility subpopulations. Here, we introduce a high-throughput single-cell motility assay using nanowell-in-microwell plates to precisely track single cell position and analyze their migratory trajectories. By physically confining individual cells in nanowells, we eliminate cell-cell interactions and simplify cell segmentation and tracking. Using this platform, we characterized the motility of single cells across different culture conditions to identify distinct motility phenotypes. Single-cell trajectory analysis revealed pronounced directional persistence, with cells predominantly maintaining their direction of travel and trajectory along nanowell boundaries. Additionally, our approach facilitates the generation of labeled image datasets suitable for AI models to rapidly identify cell motility phenotypes from single-cell images. Together, our platform provides a robust, scalable method to analyze cell motility phenotypes and migration behavior at single-cell resolution.

Cell separation is a critical step in many assays in biomedical research, diagnostics, and drug development. Here, we developed VibroMag, a vibration-assisted magnetic cell separation technology specifically designed for automated processing of standard microwell plates using robotic liquid handling platforms. VibroMag utilizes alternating-polarity magnetic fields to retain magnetically labeled cells in the center of the flat-bottom microwells. Rotational agitation from an integrated plate shaker disperses unlabeled contaminant cells into the supernatant, like gold panning. Efficient supernatant extraction is enabled by pipet aspiration at specific locations. Repeated cycling of this process further enhanced the purity of the resulting cell sample. VibroMag demonstrated superior cell separation performance compared to manual methods and available solutions for robotic liquid handling platforms. Specifically, VibroMag achieved 91% purity with 64% recovery for samples at a 1:5 target-to-background cell ratio and maintained robust performance with 78% purity with 44% recovery at a more challenging 1:30 ratio. Notably, VibroMag excelled at handling precious samples containing low cell numbers, achieving 96% purity with 63% recovery for 10,000-cell samples and 94% purity with 56% recovery for 1000-cell samples. Additionally, the use of flat-bottom microwell plates allows immediate postseparation microscopy analysis without additional pipetting steps. The combination of seamless integration into automated workflows, superior separation performance, and compatibility with downstream analyses highlights the broad utility of VibroMag in high-throughput cellular analysis.

ABSTRACT:Donated red blood cells (RBCs) collected for blood transfusions progressively lose their deformability due to natural aging and cold storage in blood bags. This loss accelerates circulatory clearance via mechanical sensing by the spleen, leading to RBC retention and entrapment. Although reduced deformability is known to shorten RBC circulation time, the extent to which splenic clearance distinguishes and removes RBCs with altered deformability is poorly understood. Here, we show that subpopulations of donor RBCs with a deformability distribution distinct from endogenous recipient's RBCs are selectively and specifically cleared from circulation within 24 hours of infusion in a mouse model, whereas donor RBCs with a deformability distribution similar to endogenous recipient RBCs persist and undergo normal clearance. We performed this study by treating murine donor RBCs with the mild catalase inhibitor aminotriazole to generate donor RBCs with a widened range of deformability. These cells were then fluorescently labeled and infused into syngeneic recipients. Using a microfluidic device capable of deformability-based sorting of RBCs, we concurrently measured the deformability distribution of donor RBCs pretransfusion and posttransfusion, along with endogenous recipient RBCs. Our findings provide direct evidence that RBCs with deformability profiles distinct from endogenous recipient RBCs are selectively and specifically cleared from circulation.

We develop an assay to measure glucose-stimulated insulin secretion from single cells using nanowell-in-microwells. Our results reveal the heterogeneity of single cell function for both stem cell derived β cells and human islet cells.

Microscopic inspection of thin-film blood smears is widely used to identify red blood cell (RBC) pathologies, including malaria parasitism and hemoglobinopathies, such as sickle cell disease and thalassemia. Emerging research indicates that non-pathologic changes in RBCs can also be detected in images, such as deformability and morphological changes resulting from the storage lesion. In transfusion medicine, cell deformability is a potential biomarker for the quality of donated RBCs. However, a major impediment to the clinical translation of this biomarker is the difficulty associated with performing this measurement. To address this challenge, we developed an approach for biophysical profiling of RBCs based on cell images in thin-film blood smears. We hypothesize that subtle cellular changes are evident in blood smear images, but this information is inaccessible to human expert labellers. To test this hypothesis, we developed a deep learning strategy to analyze Giemsa-stained blood smears to assess the subtle morphologies indicative of RBC deformability and storage-based degradation. Specifically, we prepared thin-film blood smears from 27 RBC samples (9 donors evaluated at 3 storage time points) and imaged them using high-resolution microscopy. Using this dataset, we trained a convolutional neural network to evaluate image-based morphological features related to cell deformability. The prediction of donor deformability is strongly correlated to the microfluidic scores and can be used to categorize images into specific deformability groups with high accuracy. We also used this model to evaluate differences in RBC morphology resulting from cold storage. Together, our results demonstrate that deep learning models can detect subtle cellular morphology differences resulting from deformability and cold storage. This result suggests the potential to assess donor blood quality from thin-film blood smears, which can be acquired ubiquitously in clinical workflows.

The release of cellular DNA as neutrophil extracellular traps (NETs) plays a pivotal role in the immune response to pathogens by physically entrapping and killing microbes. NET release occurs at a greater frequency within neutrophil clusters and swarms, indicating a potential for collective behavior. However, little is known about how dense clustering of cells influences the frequency of NET release. Using an image-based assay for NETosis in nanowells, we show that the frequency of NETosis increases with cell density. We then co-incubate NETotic neutrophils with naïve neutrophils and find that NETotic neutrophils can induce secondary NETosis in naïve neutrophils in a cell density-dependent manner. Further mechanistic studies show that secondary NETosis is caused by a combination of DNA and protein factors. Finally, we immobilize NETotic neutrophils in a plaque, and then place the plaque near naïve neutrophils to characterize the spatial propagation of secondary NETosis. We find that secondary NETosis from naïve neutrophils increases over time, but remains spatially restricted to the periphery of the plaque. Together, we show that NETosis is an auto-amplified process, but that the spatial propagation of NET release is strictly regulated.

Red blood cell (RBC) deformability is a vital biophysical property that dictates the ability of these cells to repeatedly squeeze through small capillaries in the microvasculature. This capability is known to differ between individuals and degrades due to natural aging, pathology, and cold storage. There is great interest in measuring RBC deformability because this parameter is a potential biomarker of RBC quality for use in blood transfusions. Measuring this property from microscopy images would greatly reduce the effort required to acquire this information, as well as improve standardization across different centers. This dataset consists of live cell microscopy images of RBC samples from 10 healthy donors. Each RBC sample is sorted into fractions based on deformability using the microfluidic ratchet device. Each deformability fraction is imaged in microwell plates using a Nikon CFI S Plan Fluor ELWD 40 × objective and a Nikon DS-Qi2 CMOS camera on a Nikon Ti-2E inverted microscope. This data could be reused to develop deep learning algorithms to associate live cell images with cell deformability.

Objective: To develop a machine learning algorithm to detect rare human sperm in semen and microsurgical testicular sperm extraction (microTESE) samples using bright-field (BF) microscopy for nonobstructive azoospermia patients. Design: Spermatozoa were collected from fertile men. Testis biopsies were collected from microTESE samples determined to be clinically negative for sperm. A convolutional neural network based on the U-Net architecture was trained using 35,761 BF image patches with fluorescent ground truth image pairs to segment sperm. The algorithm was validated using 7,663 image patches. The algorithm was tested using 7,663 image patches containing abundant sperm, as well as 7,985 image patches containing rare sperm. Setting: In vitro fertilization center and university laboratories. Patient(s): Normospermic and nonobstructive azoospermia patients. Intervention(s): None. Main Outcome Measure(s): Precision (positive predictive value [PPV]), recall (sensitivity), and F1-score of detected sperm locations. Result(s): For sperm-only samples, our algorithm achieved 91% PPV, 95.8% sensitivity, and 93.3% F1-score at x10 magnification. For dissociated microTESE samples doped with an abundant quantity of sperm, our algorithm achieved 84.0% PPV, 72.7% sensitivity, and 77.9% F1-score. For dissociated microTESE samples doped with rare sperm, our algorithm achieved 84.4% PPV, 86.1% sensitivity, and 85.2% F1-score. Conclusion(s): Rare sperm can be detected in patients' testis biopsy samples for potential subsequent use in in vitro fertilization-intracytoplasmic sperm injection. A machine learning algorithm can use BF images at x10 magnification to accurately detect sperm locations using automated imaging. ((C) 2022 by American Society for Reproductive Medicine.)

Single cell RNA sequencing has the potential to elucidate transcriptional programs underlying key cellular phenotypes and behaviors. However, many cell phenotypes are incompatible with indiscriminate single cell sequencing because they are rare, transient, or can only be identified by imaging. Existing methods for isolating cells based on imaging for single cell sequencing are technically challenging, time-consuming, and prone to loss because of the need to physically transport single cells. Here, we developed See-N-Seq, a method to rapidly screen cells in microwell plates in order to isolate RNA from specific single cells without needing to physically extract each cell. Our approach involves encapsulating the cell sample in a micropatterned hydrogel with spatially varying porosity to selectively expose specific cells for targeted RNA extraction. Extracted RNA can then be captured, barcoded, reverse transcribed, amplified, and sequenced at high-depth. We used See-N-Seq to isolate and sequence RNA from cell-cell conjugates forming an immunological synapse between T-cells and antigen presenting cells. In the hours after synapsing, we found time-dependent bifurcation of single cell transcriptomic profiles towards Type 1 and Type 2 helper T-cells lineages. Our results demonstrate how See-N-Seq can be used to associate transcriptomic data with specific functions and behaviors in single cells.

Human red blood cells (RBCs) are approximately 8 μm in diameter, but must repeatedly deform through capillaries as small as 2 μm in order to deliver oxygen to all parts of the body. The loss of this capability is associated with the pathology of many diseases, and is therefore a potential biomarker for disease status and treatment efficacy. Measuring RBC deformability is a difficult problem because of the minute forces (∼pN) that must be exerted on these cells, as well as the requirements for throughput and multiplexing. The development of technologies for measuring RBC deformability date back to the 1960s with the development of micropipette aspiration, ektacytometry, and the cell transit analyzer. In the past 10 years, significant progress has been made using microfluidics by leveraging the ability to precisely control fluid flow through microstructures at the size scale of individual RBCs. These technologies have now surpassed traditional methods in terms of sensitivity, throughput, consistency, and ease of use. As a result, these efforts are beginning to move beyond feasibility studies and into applications to enable biomedical discoveries. In this review, we provide an overview of both traditional and microfluidic techniques for measuring RBC deformability. We discuss the capabilities of each technique and compare their sensitivity, throughput, and robustness in measuring bulk and single-cell RBC deformability. Finally, we discuss how these tools could be used to measure changes in RBC deformability in the context of various applications including pathologies caused by malaria and hemoglobinopathies, as well as degradation during storage in blood bags prior to blood transfusions.

ABSTRACTBACKGROUNDThe biophysical properties of red blood cells (RBCs) provide potential biomarkers for the quality of donated blood. Blood unit segments provide a simple and nondestructive way to sample RBCs in clinical studies of transfusion efficacy, but it is not known whether RBCs sampled from segments accurately represent the biophysical properties of RBCs in blood bags.STUDY DESIGN AND METHODSRBCs were sampled from blood bags and segments every two weeks during 8 weeks of storage at 4 °C. RBC deformability was measured by deformabilitybased sorting using the microfluidic ratchet device in order to derive a rigidity score. Standard hematological parameters, including mean corpuscular volume (MCV), red cell distribution width (RDW), mean cell hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and hemolysis were measured at the same time points.RESULTSDeformability of RBCs stored in blood bags was retained over 4 weeks storage but a progressive loss of deformability was observed at weeks 6 and 8. This trend was mirrored in blood unit segments with a strong correlation to the blood bag data. Strong correlations were also observed between blood bag and segment for MCV, MCHC and MCH, but not for hemolysis.CONCLUSIONRBCs sampled from blood unit segments accurately represents the biophysical properties of RBCs in blood bags, but not hemolysis. Blood unit segments provide a simple and non-destructive sample for measuring RBC biophysical properties in clinical studies.

ABSTRACT Non-obstructive azoospermia (NOA), the most severe form of male infertility, is currently treated using microsurgical sperm extraction (microTESE) to retrieve sperm cells for in vitro fertilization via intracytoplasmic sperm injection (IVF-ICSI). The success rate of this procedure for NOA patients is currently limited by the ability of andrologists to identify a few rare sperm cells among millions of background testis cells. To improve this success rate, we developed a convolution neural network (CNN) to detect rare sperm from low-resolution microscopy images of microTESE samples. Our CNN uses the U-Net architecture to perform pixel-based classification on image patches from brightfield microscopy, which is followed by morphological analysis to detect individual sperm instances. This CNN is trained using microscopy images of fluorescently labeled sperm, which is fixed to eliminate their motility, and doped into testis biopsies obtained from NOA patients. We initially tested this algorithm using purified sperm samples at different imaging magnifications in order to determine the upper bounds of performance. We then tested this algorithm by doping rare sperm cells into testis biopsy samples from NOA patients and found a sperm detection F1 score of 85.2%. These results demonstrate the potential to use automated microscopy to dramatically increase the amount of testis biopsy tissue that could be comprehensively examined, which greatly increases the chance of finding rare viable sperm, and thereby increases the success rates of IVF-ICSI for couples with NOA.

Red blood cells (RBCs) stored in blood bags develop a storage lesion that include structural, metabolic, and morphologic transformations resulting in a progressive loss of RBC deformability. The speed of RBC deformability loss is donor-dependent, which if properly characterized, could be used as a biomarker to select high-quality RBC units for sensitive recipients or to provide customized storage timelines depending on the donor. We used the microfluidic ratchet device to measure the deformability of red blood cells stored in blood bags every 14 days over a span of 56 days. We observed that storage in blood bags generally prevented RBC deformability loss over the current standard 42-day storage window. However, between 42 and 56 days, the deformability loss profile varied dramatically between donors. In particular, we observed accelerated RBC deformability loss for a majority of male donors, but for none of the female donors. Together, our results suggest that RBC deformability loss could be used to screen for donors who can provide stable RBCs for sensitive transfusion recipients or to identify donors capable of providing RBCs that could be stored for longer than the current 42-day expiration window.

The ability to selectively propagate specific cells is fundamentally important to the development of clonal cell populations. Current methods rely on techniques such as limiting dilution, colony picking, and flow cytometry to transfer single cells into single wells, resulting in workflows that are low-throughput, slowed by propagation kinetics, and susceptible to contamination. Here, we developed a method, called selective laser gelation (SLG), to micropattern hydrogels in cell culture media in order to encapsulate specific cells to selectively arrest their growth. This process relies on the inverse gelation of methylcellulose, which forms a hydrogel when heated rather than cooled. Local heating using an infrared laser enables hydrogel micropatterning, while phase transition hysteresis retains the hydrogel after laser excitation. As a demonstration, we used this approach to selectively propagate transgenic CHO cells with increased antibody productivity. More generally, hydrogel micropatterning provides a simple and non-contact method to selective propagation of cells based on features identified by imaging. One Sentence Summary Inverse gelation of methylcellulose enables hydrogel micropatterning to selectively propagate cells identified by microscopy.

The ability to phenotype cells is fundamentally important in biological research and medicine. Current methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is currently unclear if this approach can reliably phenotype cells that are difficult to distinguish by the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cytoskeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an average F1-score of 95.3%, tested using separately acquired images. Our results demonstrate the potential to develop an “electronic eye” to phenotype cells directly from microscopy images.

A fundamental challenge to multiplexing microfluidic chemotaxis assays at scale is the requirement for time-lapse imaging to continuously track migrating cells. Drug testing and drug screening applications require the ability to perform hundreds of experiments in parallel, which is not feasible for assays that require continuous imaging. To address this limitation, end-point chemotaxis assays have been developed using fluid flow to align cells in traps or sieves prior to cell migration. However, these methods require precisely controlled fluid flow to transport cells to the correct location without undesirable mechanical stress, which introduce significant set up time and design complexity. Here, we describe a microfluidic device that eliminates the need for precise flow control by using centrifugation to align cells at a common starting point. A chemoattractant gradient is then formed using passive diffusion prior to chemotaxis in an incubated environment. This approach provides a simple and scalable approach to multiplexed chemotaxis assays. Centrifugal alignment is also insensitive to cell geometry, enabling this approach to be compatible with primary cell samples that are often heterogeneous. We demonstrate the capability of this approach by assessing chemotaxis of primary neutrophils in response to an fMLP (N-formyl-met-leu-phe) gradient. Our results show that cell alignment by centrifugation offers a potential avenue to develop scalable end-point multiplexed microfluidic chemotaxis assays.

Immunocytochemistry (ICC), or immunofluorescence microscopy, is an essential biological technique for phenotyping cells in both research and diagnostic applications. Standard ICC methods often do not work well when the cell sample contains a small number of cells (<10,000) because of the significant cell loss that occurs during washing, staining, and centrifugation steps. Cell loss is particularly relevant when working with rare cells, such as circulating tumor cells, where such losses could significantly bias experimental outcomes. In order to eliminate cell loss in ICC protocols, we present a method to encapsulate the cell sample in a photo-polymerized hydrogel thin-film. The hydrogel thin-film is permeable to antibodies and other ICC reagents, thereby allowing the use of standard ICC protocols without modification. The cell sample is physically constrained by the hydrogel at the bottom surface of a standard (unmodified) imaging microtiter plate, thereby enabling the acquisition of high-quality micrographs regardless of the properties of the cell sample or staining reagents. Furthermore, while standard ICC requires several centrifugation steps during staining and washing, our hydrogel encapsulation method requires only a single centrifugation step. This property greatly reduces the time required to perform ICC protocols and is more compatible with robotic platforms. In this study, we show that standard ICC and Cytospin protocols are extremely lossy (>70% loss) when the sample contains less than 10,000 cells, while encapsulating the cells using a permeable hydrogel thin-film results in a lossless ICC process.

Cytokine secretion is a form of cellular communication that regulates a wide range of biological processes. A common approach for measuring cytokine secretion from single cells is to confine individual cells in arrays of nanoliter wells (nanowells) fabricated using polydimethylsiloxane. However, this approach cannot be easily integrated in standard microwell plates in order to take advantage of high-throughput infrastructure for automated and multiplexed analysis. Here, we used laser micropatterning to fabricate monolithic hydrogel nanowells inside wells in a microwell plate (microwells) using polyethylene glycol diacrylate (PEGDA). This approach produces high-aspect ratio nanowells that retain cells and beads during reagent exchange, enabling simultaneous profiling of single cell secretion and phenotyping via immunostaining. To limit contamination between nanowells, we used methylcellulose as a media additive to reduce diffusion distance. Patterning nanowells monolithically in microwells also dramatically increases density, providing ∼1200 nanowells per microwell in a microwell plate. Using this approach, we profiled IL-8 secretion from single MDA-MB-231 cells, which showed significant heterogeneity. We further profiled the polarization of THP-1 cells into M1 and M2 macrophages, along with their associated IL-1β and CCL-22 secretion profiles. These results demonstrate the potential to use this approach for high-throughput secretion and phenotype analysis on single cells.

Circulating tumor cells (CTCs) are malignant cells released into the bloodstream with the potential to form metastases in secondary sites. These cells, acquired non-invasively, represent a sample of highly relevant tumor tissue that is an alternative to difficult and low-yield tumor biopsies. In recent years, there has been growing interest in genomic profiling of CTCs to enable longitudinal monitoring of the tumor's adaptive response to therapy. However, due to their extreme rarity, genotyping CTCs has proved challenging. Relevant mutations can be masked by leukocyte contamination in isolates. Heterogeneity between subpopulations of tumor cells poses an additional obstacle. Recent advances in single-cell sequencing can overcome these limitations but isolation of single CTCs is prone to cell loss and is prohibitively difficult and time consuming. To address these limitations, we developed a single cell sample preparation and genome sequencing pipeline that combines biophysical enrichment and single cell isolation using laser capture microdissection (LCM). A key component of this process is the encapsulation of enriched CTC sample in a hydrogel matrix, which enhances the efficiency of single-cell isolation by LCM, and is compatible with downstream sequencing. We validated this process by sequencing of single CTCs and cell free DNA (cfDNA) from a single patient with castration resistant prostate cancer. Identical mutations were observed in prostate cancer driver genes (TP53, PTEN, FOXA1) in both single CTCs and cfDNA. However, two independently isolated CTCs also had identical missense mutations in the genes for ATR serine/threonine kinase, KMT2C histone methyltransferase, and FANCC DNA damage repair gene. These mutations may be missed by bulk sequencing libraries, whereas single cell sequencing could potentially enable the characterization of key CTC subpopulations that arise during metastasis.