Label-free cell cycle analysis for high-throughput imaging flow cytometry

Classification of T-cell activation via autofluorescence lifetime imaging

The function of a T cell depends on its subtype and activation state. Here, we show that the imaging of autofluorescence-lifetime signals from quiescent and activated T cells can be used to classify the cells. T cells isolated from human peripheral blood and activated in culture via a tetrameric antibody against the surface ligands CD2, CD3 and CD28 showed specific activation-state-dependent patterns of autofluorescence lifetime. Logistic-regression models and random-forest models classified T cells according to activation state with 97–99% accuracy, and according to activation state (quiescent or activated) and subtype (CD3⁺ CD8⁺ or CD3⁺ CD4⁺) with 97% accuracy. Autofluorescence-lifetime imaging could be used to non-destructively determine T-cell function.

In vivo identification of apoptotic and extracellular vesicle-bound live cells using image-based deep learning

The in vivo detection of dead cells remains a major challenge due to technical hurdles. Here, we present a novel method, where injection of fluorescent milk fat globule-EGF factor 8 protein (MFG-E8) in vivo combined with imaging flow cytometry and deep learning allows the identification of dead cells based on their surface exposure of phosphatidylserine (PS) and other image parameters. A convolutional autoencoder (CAE) was trained on defined pictures and successfully used to identify apoptotic cells in vivo. However, unexpectedly, these analyses also revealed that the great majority of PS⁺ cells were not apoptotic, but rather live cells associated with PS⁺ extracellular vesicles (EVs). During acute viral infection apoptotic cells increased slightly, while up to 30% of lymphocytes were decorated with PS⁺ EVs of antigen-presenting cell (APC) exosomal origin. The combination of recombinant fluorescent MFG-E8 and the CAE-method will greatly facilitate analyses of cell death and EVs in vivo.

Raman image-activated cell sorting

The advent of image-activated cell sorting and imaging-based cell picking has advanced our knowledge and exploitation of biological systems in the last decade. Unfortunately, they generally rely on fluorescent labeling for cellular phenotyping, an indirect measure of the molecular landscape in the cell, which has critical limitations. Here we demonstrate Raman image-activated cell sorting by directly probing chemically specific intracellular molecular vibrations via ultrafast multicolor stimulated Raman scattering (SRS) microscopy for cellular phenotyping. Specifically, the technology enables real-time SRS-image-based sorting of single live cells with a throughput of up to ~100 events per second without the need for fluorescent labeling. To show the broad utility of the technology, we show its applicability to diverse cell types and sizes. The technology is highly versatile and holds promise for numerous applications that are previously difficult or undesirable with fluorescence-based technologies.

Most current cell sorting methods are based on fluorescence detection with no imaging capability. Here the authors generate and use Raman image-activated cell sorting with a throughput of around 100 events per second, providing molecular images with no need for labeling.

Interphase Cell Cycle Staging using Deep Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020;2020:1432-1435. [PMID: 33018259 DOI: 10.1109/embc44109.2020.9175583]

The progression of cells through the cell cycle is a tightly regulated process and is known to be key in maintaining normal tissue architecture and function. Disruption of these orchestrated phases will result in alterations that can lead to many diseases including cancer. Regrettably, reliable automatic tools to evaluate the cell cycle stage of individual cells are still lacking, in particular at interphase. Therefore, the development of new tools for a proper classification are urgently needed and will be of critical importance for cancer prognosis and predictive therapeutic purposes. Thus, in this work, we aimed to investigate three deep learning approaches for interphase cell cycle staging in microscopy images: 1) joint detection and cell cycle classification of nuclei patches; 2) detection of cell nuclei patches followed by classification of the cycle stage; 3) detection and segmentation of cell nuclei followed by classification of cell cycle staging. Our methods were applied to a dataset of microscopy images of nuclei stained with DAPI. The best results (0.908 F1-Score) were obtained with approach 3 in which the segmentation step allows for an intensity normalization that takes into account the intensities of all nuclei in a given image. These results show that for a correct cell cycle staging it is important to consider the relative intensities of the nuclei. Herein, we have developed a new deep learning method for interphase cell cycle staging at single cell level with potential implications in cancer prognosis and therapeutic strategies.

Large-Scale Image Analysis for Investigating Spatio-Temporal Changes in Nuclear DNA Damage Caused by Nitrogen Atmospheric Pressure Plasma Jets

The effective clinical application of atmospheric pressure plasma jet (APPJ) treatments requires a well-founded methodology that can describe the interactions between the plasma jet and a treated sample and the temporal and spatial changes that result from the treatment. In this study, we developed a large-scale image analysis method to identify the cell-cycle stage and quantify damage to nuclear DNA in single cells. The method was then tested and used to examine spatio-temporal distributions of nuclear DNA damage in two cell lines from the same anatomic location, namely the oral cavity, after treatment with a nitrogen APPJ. One cell line was malignant, and the other, nonmalignant. The results showed that DNA damage in cancer cells was maximized at the plasma jet treatment region, where the APPJ directly contacted the sample, and declined radially outward. As incubation continued, DNA damage in cancer cells decreased slightly over the first 4 h before rapidly decreasing by approximately 60% at 8 h post-treatment. In nonmalignant cells, no damage was observed within 1 h after treatment, but damage was detected 2 h after treatment. Notably, the damage was 5-fold less than that detected in irradiated cancer cells. Moreover, examining damage with respect to the cell cycle showed that S phase cells were more susceptible to DNA damage than either G1 or G2 phase cells. The proposed methodology for large-scale image analysis is not limited to APPJ post-treatment applications and can be utilized to evaluate biological samples affected by any type of radiation, and, more so, the cell-cycle classification can be used on any cell type with any nuclear DNA staining.

Methodological considerations for examining the relationship between sperm morphology and motility

Sperm cells of all taxa share a common goal to reach and fertilize an ovum, yet sperm are one of the most diverse cell types in nature. While the structural diversity of these cells is well recognized, the functional significance of variation in sperm design remains elusive. An important function of spermatozoa is a need to migrate toward the ova, often over long distances in a foreign environment, which may include a complex and hostile female reproductive tract. Several comparative and experimental studies have attempted to address the link between sperm morphology and motility, yet the conclusions drawn from these studies are often inconsistent, even within the same taxa. Much of what we know about the functional significance of sperm design in internally fertilizing species has been gleaned from in vitro studies, for which experimental parameters often vary among studies. We propose that discordant results from these studies are in part due to a lack of consistency of methods, conditions that do not replicate those of the female reproductive tract, and the overuse of simple linear measures of sperm shape. Within this review, we provide a toolkit for imaging, quantifying, and analyzing sperm morphology and movement patterns for in vitro studies and discuss emerging approaches. Results from studies linking morphology to motility enhance our understanding of the evolution of adaptive sperm traits and the mechanisms that regulate fertility, thus offering new insights into methods used in assisted reproductive technologies in animal science, conservation and public health.

The palette of techniques for cell cycle analysis

The cell division cycle is the generational period of cellular growth and propagation. Cell cycle progression needs to be highly regulated to preserve genomic fidelity while increasing cell number. In multicellular organisms, the cell cycle must also coordinate with cell fate specification during development and tissue homeostasis. Altered cell cycle dynamics play a central role also in a number of pathophysiological processes. Thus, extensive effort has been made to define the biochemical machineries that execute the cell cycle and their regulation, as well as implementing more sensitive and accurate cell cycle measurements. Here, we review the available techniques for cell cycle analysis, revisiting the assumptions behind conventional population-based measurements and discussing new tools to better address cell cycle heterogeneity in the single-cell era. We weigh the strengths, weaknesses, and trade-offs of methods designed to measure temporal aspects of the cell cycle. Finally, we discuss emerging techniques for capturing cell cycle speed at single-cell resolution in live animals.

Early Prediction of Single-Cell Derived Sphere Formation Rate Using Convolutional Neural Network Image Analysis

Functional identification of cancer stem-like cells (CSCs) is an established method to identify and study this cancer subpopulation critical for cancer progression and metastasis. The method is based on the unique capability of single CSCs to survive and grow to tumorspheres in harsh suspension culture environment. Recent advances in microfluidic technology have enabled isolating and culturing thousands of single cells on a chip. However, tumorsphere assay takes a relatively long period of time, limiting the throughput of this assay. In this work, we incorporated machine learning with single-cell analysis to expedite tumorsphere assay. We collected 1,710 single-cell events as the database and trained a convolutional neural network model that predicts whether a single cell could grow to a tumorsphere on Day 14 based on its Day 4 image. With this future-telling model, we precisely estimated the sphere formation rate of SUM159 breast cancer cells to be 17.8% based on Day 4 images. The estimation was close to the ground truth of 17.6% on Day 14. The preliminary work demonstrates not only the feasibility to significantly accelerate tumorsphere assay but also a synergistic combination between single-cell analysis with machine learning, which can be applied to many other biomedical applications.

High-throughput cell and spheroid mechanics in virtual fluidic channels

Microfluidics by soft lithography has proven to be of key importance for biophysics and life science research. While being based on replicating structures of a master mold using benchtop devices, design modifications are time consuming and require sophisticated cleanroom equipment. Here, we introduce virtual fluidic channels as a flexible and robust alternative to microfluidic devices made by soft lithography. Virtual channels are liquid-bound fluidic systems that can be created in glass cuvettes and tailored in three dimensions within seconds for rheological studies on a wide size range of biological samples. We demonstrate that the liquid-liquid interface imposes a hydrodynamic stress on confined samples, and the resulting strain can be used to calculate rheological parameters from simple linear models. In proof-of-principle experiments, we perform high-throughput rheology inside a flow cytometer cuvette and show the Young's modulus of isolated cells exceeds the one of the corresponding tissue by one order of magnitude.

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

MOTIVATION

New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity.

RESULTS

We introduce a highly scalable graph-based clustering algorithm PARC-Phenotyping by Accelerated Refined Community-partitioning-for large-scale, high-dimensional single-cell data (>1 million cells). Using large single-cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without subsampling of cells, including Phenograph, FlowSOM and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single-cell dataset of 1.1 million cells within 13 min, compared with >2 h for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ShobiStassen/PARC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The TIPE Molecular Pilot That Directs Lymphocyte Migration in Health and Inflammation

Lymphocytes are some of the most motile cells of vertebrates, constantly navigating through various organ systems. Their specific positioning in the body is delicately controlled by site-specific directional cues such as chemokines. While it has long been suspected that an intrinsic molecular pilot, akin to a ship's pilot, guides lymphocyte navigation, the nature of this pilot is unknown. Here we show that the TIPE (TNF-α-induced protein 8-like) family of proteins pilot lymphocytes by steering them toward chemokines. TIPE proteins are carriers of lipid second messengers. They mediate chemokine-induced local generation of phosphoinositide second messengers, but inhibit global activation of the small GTPase Rac. TIPE-deficient T lymphocytes are completely pilot-less: they are unable to migrate toward chemokines despite their normal ability to move randomly. As a consequence, TIPE-deficient mice have a marked defect in positioning their T lymphocytes to various tissues, both at the steady-state and during inflammation. Thus, TIPE proteins pilot lymphocytes during migration and may be targeted for the treatment of lymphocyte-related disorders.

Controlling Neuronal Cell Growth through Composite Laminin Supramolecular Hydrogels

Designing an extracellular matrix mimic by biofunctionalization of polymeric scaffolds is a popular strategy and extremely crucial for facilitating the interactions between cells and the matrix. To this direction, supramolecular gels are gaining exponential attention over the last few years, owing to their potential biocompatibility and biodegradability. In spite of diverse biological roles of native laminin, the bioactivities of self-assembling laminin-derived short peptides were less explored. In this work, we have explored the minimalist design to develop hydrogel scaffolds based on IKVAV and YIGSR peptides individually and their composite matrix, which can provide structurally and functionally relevant materials for tissue engineering. Till date, composite supramolecular gels solely made up of self-assembling IKVAV and YIGSR peptides have never been reported. Such composite gels can be a closer mimic of natural laminin protein, which could mimic the essential functions of the short peptide fragments present on different chains of the extracellular matrix protein, laminin. Interestingly, we used a unique strategy of simple mixing of the two laminin mimetic peptides, which tend to induce coassembly with a self-sorted nanofibrous network with relatively enhanced mechanical strength. The physicochemical properties of the biofunctional hydrogels were studied using different microscopic, spectroscopic, and rheology techniques. To assess the bioactivity of laminin-derived scaffolds in controlling neuronal cell growth, its biocompatibility, cellular growth, and proliferation were quantified using C6 glial cells and SHSY5Y neuroblastoma cells. The live/dead staining further confirmed the adhesion and proliferation of the cells. A significant increase in neurite length provides clear evidence on mimicking the neurite extension function of native laminin protein by its short derivatives. Interestingly, similar β-III tubulin expression and cell cycle phases were observed, in comparison to control, which indicated normal cellular functioning of the cells cultured over short laminin hydrogel scaffolds. All bioassays suggested that Fmoc YIGSR promotes growth of neural cells to a greater extent and maintains healthier morphology, in comparison to hydrophobic Fmoc IKVAV, owing to the entangled longer fibrous network formed by YIGSR peptide. It is expected that thinner long fibers provide a more uniform surface and are more supportive for cell adhesion in comparison to hydrophobic, shorter fibers IKVAV peptide. However, in composite gels, the detrimental effect of hydrophobic IKVAV peptide could be reduced and better adhesion and proliferation could be achieved along with enhanced cell survival. These observations demonstrate the high potential of the laminin-derived hydrogels in tissue engineering and neuronal stem cell differentiation in future.

Label-free imaging flow cytometer for analyzing large cell populations by line-field quantitative phase microscopy with digital refocusing

We propose a line-field quantitative phase-imaging flow cytometer for analyzing large populations of label-free cells. Hydrodynamical focusing brings cells into the focus plane of an optical system while diluting the cell suspension, resulting in decreased throughput rate. To overcome the trade-off between throughput rate and in-focus imaging, our cytometer involves digitally extending the depth-of-focus on loosely hydrodynamically focusing cell suspensions. The cells outside the depth-of-focus range in the 70-µm diameter of the core flow were automatically digitally refocused after image acquisition. We verified that refocusing was successful with our cytometer through statistical analysis of image quality before and after digital refocusing.

Multiplex Stimulated Raman Scattering Imaging Cytometry Reveals Lipid-Rich Protrusions in Cancer Cells under Stress Condition

In situ measurement of cellular metabolites is still a challenge in biology. Conventional methods, such as mass spectrometry or fluorescence microscopy, would either destroy the sample or introduce strong perturbations to target molecules. Here, we present multiplex stimulated Raman scattering (SRS) imaging cytometry as a label-free single-cell analysis platform with chemical specificity and high-throughput capabilities. Using SRS imaging cytometry, we studied the metabolic responses of human pancreatic cancer cells under stress by starvation and chemotherapeutic drug treatments. We unveiled protrusions containing lipid droplets as a metabolic marker for stress-resistant cancer cells. Furthermore, by spectroscopic SRS mapping, we unveiled that triglyceride in lipid droplets are used for local energy production through lipolysis, autophagy, and β-oxidation. Our findings demonstrate the potential of targeting lipid metabolism for selective treatment of stress-resistant cancers. Collectively, these results highlight SRS imaging cytometry as a powerful label-free tool for biological discoveries with a high-throughput, high-content capacity.

Virtual-freezing fluorescence imaging flow cytometry

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s⁻¹ without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

High throughput imaging flow cytometry suffers from trade-offs between throughput, sensitivity and spatial resolution. Here the authors introduce a method to virtually freeze cells in the image acquisition window to enable 1000 times longer signal integration time and improve signal-to-noise ratio.

Implementing machine learning methods for imaging flow cytometry

In this review, we focus on the applications of machine learning methods for analyzing image data acquired in imaging flow cytometry technologies. We propose that the analysis approaches can be categorized into two groups based on the type of data, raw imaging signals or features explicitly extracted from images, being analyzed by a trained model. We hope that this categorization is helpful for understanding uniqueness, differences and opportunities when the machine learning-based analysis is implemented in recently developed ‘imaging’ cell sorters.

Diffusion Mapping of Eosinophil-Activation State

Eosinophils are granular leukocytes that play a role in mediating inflammatory responses linked to infection and allergic disease. Their activation during an immune response triggers spatial reorganization and eventual cargo release from intracellular granules. Understanding this process is important in diagnosing eosinophilic disorders and in assessing treatment efficacy; however, current protocols are limited to simply quantifying the number of eosinophils within a blood sample. Given that high optical absorption and scattering by the granular structure of these cells lead to marked image features, the physical changes that occur during activation should be trackable using image analysis. Here, we present a study in which imaging flow cytometry is used to quantify eosinophil activation state, based on the extraction of 85 distinct spatial features from dark-field images formed by light scattered orthogonally to the illuminating beam. We apply diffusion mapping, a time inference method that orders cells on a trajectory based on similar image features. Analysis of exogenous cell activation using eotaxin and endogenous activation in donor samples with elevated eosinophil counts shows that cell position along the diffusion-path line correlates with activation level (99% confidence level). Thus, the diffusion mapping provides an activation metric for each cell. Assessment of activated and control populations using both this spatial image-based, activation score and the integrated side-scatter intensity shows an improved Fisher discriminant ratio rd = 0.7 for the multivariate technique compared with an rd = 0.47 for the traditional whole-cell scatter metric. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Label-Free Leukemia Monitoring by Computer Vision

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well‐recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemotherapy (Campana and Pui: Blood 129 (2017) 1913–1918). Given the potential for machine learning to improve precision medicine, we tested its capacity to monitor disease in children undergoing ALL treatment. Diagnostic and on‐treatment bone marrow samples were labeled with an ALL‐discriminating antibody combination and analyzed by imaging flow cytometry. Ignoring the fluorescent markers and using only features extracted from bright‐field and dark‐field cell images, a deep learning model was able to identify ALL cells at an accuracy of >88%. This antibody‐free, single cell method is cheap, quick, and could be adapted to a simple, laser‐free cytometer to allow automated, point‐of‐care testing to detect slow early responders. Adaptation to other types of leukemia is feasible, which would revolutionize residual disease monitoring. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

High-Content Image-Based Single-Cell Phenotypic Analysis for the Testicular Toxicity Prediction Induced by Bisphenol A and Its Analogs Bisphenol S, Bisphenol AF, and Tetrabromobisphenol A in a Three-Dimensional Testicular Cell Co-culture Model

Emerging data indicate that structural analogs of bisphenol A (BPA) such as bisphenol S (BPS), tetrabromobisphenol A (TBBPA), and bisphenol AF (BPAF) have been introduced into the market as substitutes for BPA. Our previous study compared in vitro testicular toxicity using murine C18-4 spermatogonial cells and found that BPAF and TBBPA exhibited higher spermatogonial toxicities as compared with BPA and BPS. Recently, we developed a novel in vitro three-dimensional (3D) testicular cell co-culture model, enabling the classification of reproductive toxic substances. In this study, we applied the testicular cell co-culture model and employed a high-content image (HCA)-based single-cell analysis to further compare the testicular toxicities of BPA and its analogs. We also developed a machine learning (ML)-based HCA pipeline to examine the complex phenotypic changes associated with testicular toxicities. We found dose- and time-dependent changes in a wide spectrum of adverse endpoints, including nuclear morphology, DNA synthesis, DNA damage, and cytoskeletal structure in a single-cell-based analysis. The co-cultured testicular cells were more sensitive than the C18 spermatogonial cells in response to BPA and its analogs. Unlike conventional population-averaged assays, single-cell-based assays not only showed the levels of the averaged population, but also revealed changes in the sub-population. Machine learning-based phenotypic analysis revealed that treatment of BPA and its analogs resulted in the loss of spatial cytoskeletal structure, and an accumulation of M phase cells in a dose- and time-dependent manner. Furthermore, treatment of BPAF-induced multinucleated cells, which were associated with altered DNA damage response and impaired cellular F-actin filaments. Overall, we demonstrated a new and effective means to evaluate multiple toxic endpoints in the testicular co-culture model through the combination of ML and high-content image-based single-cell analysis. This approach provided an in-depth analysis of the multi-dimensional HCA data and provided an unbiased quantitative analysis of the phenotypes of interest.

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

SIGNIFICANCE

We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies.

AIM

Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies.

APPROACH

Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning.

RESULTS

Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line.

CONCLUSIONS

The proposed epithelial-mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.

Single-cell dispensing and 'real-time' cell classification using convolutional neural networks for higher efficiency in single-cell cloning

Single-cell dispensing for automated cell isolation of individual cells has gained increased attention in the biopharmaceutical industry, mainly for production of clonal cell lines. Here, machine learning for classification of cell images is applied for ‘real-time’ cell viability sorting on a single-cell printer. We show that an extremely shallow convolutional neural network (CNN) for classification of low-complexity cell images outperforms more complex architectures. Datasets with hundreds of cell images from four different samples were used for training and validation of the CNNs. The clone recovery, i.e. the fraction of single-cells that grow to clonal colonies, is predicted to increase for all the samples investigated. Finally, a trained CNN was deployed on a c.sight single-cell printer for ‘real-time’ sorting of a CHO-K1 cells. On a sample with artificially damaged cells the clone recovery could be increased from 27% to 73%, thereby resulting in a significantly faster and more efficient cloning. Depending on the classification threshold, the frequency at which viable cells are dispensed could be increased by up to 65%. This technology for image-based cell sorting is highly versatile and can be expected to enable cell sorting by computer vision with respect to different criteria in the future.

Deep Learning-Based Single-Cell Optical Image Studies

Optical imaging technology that has the advantages of high sensitivity and cost-effectiveness greatly promotes the progress of nondestructive single-cell studies. Complex cellular image analysis tasks such as three-dimensional reconstruction call for machine-learning technology in cell optical image research. With the rapid developments of high-throughput imaging flow cytometry, big data cell optical images are always obtained that may require machine learning for data analysis. In recent years, deep learning has been prevalent in the field of machine learning for large-scale image processing and analysis, which brings a new dawn for single-cell optical image studies with an explosive growth of data availability. Popular deep learning techniques offer new ideas for multimodal and multitask single-cell optical image research. This article provides an overview of the basic knowledge of deep learning and its applications in single-cell optical image studies. We explore the feasibility of applying deep learning techniques to single-cell optical image analysis, where popular techniques such as transfer learning, multimodal learning, multitask learning, and end-to-end learning have been reviewed. Image preprocessing and deep learning model training methods are then summarized. Applications based on deep learning techniques in the field of single-cell optical image studies are reviewed, which include image segmentation, super-resolution image reconstruction, cell tracking, cell counting, cross-modal image reconstruction, and design and control of cell imaging systems. In addition, deep learning in popular single-cell optical imaging techniques such as label-free cell optical imaging, high-content screening, and high-throughput optical imaging cytometry are also mentioned. Finally, the perspectives of deep learning technology for single-cell optical image analysis are discussed. © 2020 International Society for Advancement of Cytometry.

Mechano-Optical Analysis of Single Cells with Transparent Microcapillary Resonators

The study of biophysical properties of single cells is becoming increasingly relevant in cell biology and pathology. The measurement and tracking of magnitudes such as cell stiffness, morphology, and mass or refractive index have brought otherwise inaccessible knowledge about cell physiology, as well as innovative methods for high-throughput label-free cell classification. In this work, we present hollow resonator devices based on suspended glass microcapillaries for the simultaneous measurement of single-cell buoyant mass and reflectivity with a throughput of 300 cells/minute. In the experimental methodology presented here, both magnitudes are extracted from the devices' response to a single probe, a focused laser beam that enables simultaneous readout of changes in resonance frequency and reflected optical power of the devices as cells flow within them. Through its application to MCF-7 human breast adenocarcinoma cells and MCF-10A nontumorigenic cells, we demonstrate that this mechano-optical technique can successfully discriminate pathological from healthy cells of the same tissue type.

Precise Quantitative Analysis of Cell Targeting by Particle-Based Agents Using Imaging Flow Cytometry and Convolutional Neural Network

Understanding the intricacies of particle-cell interactions is essential for many applications such as imaging, phototherapy, and drug/gene delivery, because it is the key to accurate control of the particle properties for the improvement of their therapeutic and diagnostic efficiency. Recently, high-throughput methods have emerged for the detailed investigation of these interactions. For example, imaging flow cytometry (IFC) collects up to 60,000 images of cells per second (in 12 optical channels) and provides information about morphology and organelle localization in combination with fluorescence and side scatter intensity data. However, analysis of IFC data is extremely difficult to perform using conventional methods that calculate integral parameters or use mask-based object recognition. Here, we show application of a convolutional neural network (CNN) for precise quantitative analysis of particle targeting of cells using IFC data. CNN provides high-throughput object detection with almost human precision but avoids the subjective choice of image processing parameters that often leads to incorrect data interpretation. The method allows accurate counting of cell-bound particles with reliable discrimination from the nonbound particles in the field of view. The proposed method expands capabilities of spot counting applications (such as organelle counting, quantification of cell-cell and cell-bacteria interactions) and is going to be useful not only for high-throughput analysis of IFC data but also for other imaging techniques. © 2019 International Society for Advancement of Cytometry.

Live and Let Dye: Visualizing the Cellular Compartments of the Malaria Parasite Plasmodium falciparum

Malaria remains one of the deadliest diseases worldwide and it is caused by the protozoan parasite Plasmodium spp. Parasite visualization is an important tool for the correct detection of malarial cases but also to understand its biology. Advances in visualization techniques promote new insights into the complex life cycle and biology of Plasmodium parasites. Live cell imaging by fluorescence microscopy or flow cytometry are the foundation of the visualization technique for malaria research. In this review, we present an overview of possibilities in live cell imaging of the malaria parasite. We discuss some of the state-of-the-art techniques to visualize organelles and processes of the parasite and discuss limitation and advantages of each technique. © 2019 International Society for Advancement of Cytometry.

Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition)

These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.

Implementation of novel statistical procedures and other advanced approaches to improve analysis of CASA data

Computer-aided sperm analysis (CASA) produces a wealth of data that is frequently ignored. The use of multiparametric statistical methods can help explore these datasets, unveiling the subpopulation structure of sperm samples. In this review we analyse the significance of the internal heterogeneity of sperm samples and its relevance. We also provide a brief description of the statistical tools used for extracting sperm subpopulations from the datasets, namely unsupervised clustering (with non-hierarchical, hierarchical and two-step methods) and the most advanced supervised methods, based on machine learning. The former method has allowed exploration of subpopulation patterns in many species, whereas the latter offering further possibilities, especially considering functional studies and the practical use of subpopulation analysis. We also consider novel approaches, such as the use of geometric morphometrics or imaging flow cytometry. Finally, although the data provided by CASA systems provides valuable information on sperm samples by applying clustering analyses, there are several caveats. Protocols for capturing and analysing motility or morphometry should be standardised and adapted to each experiment, and the algorithms should be open in order to allow comparison of results between laboratories. Moreover, we must be aware of new technology that could change the paradigm for studying sperm motility and morphology.

Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia

Drug susceptibility (also called chemosensitivity) is an important criterion for developing a therapeutic strategy for various cancer types such as breast cancer and leukemia. Recently, functional assays such as high-content screening together with genomic analysis have been shown to be effective for predicting drug susceptibility, but their clinical applicability is poor since they are time-consuming (several days long), labor-intensive, and costly. Here we present a highly simple, rapid, and cost-effective liquid biopsy for ex vivo drug-susceptibility testing of leukemia. The method is based on an extreme-throughput (>1 million cells per second), label-free, whole-blood imaging flow cytometer with a deep convolutional autoencoder, enabling image-based identification of the drug susceptibility of every single white blood cell in whole blood within 24 hours by simply flowing a drug-treated whole blood sample as little as 500 μL into the imaging flow cytometer without labeling. Our results show that the method accurately evaluates the drug susceptibility of white blood cells from untreated patients with acute lymphoblastic leukemia. Our method holds promise for affordable precision medicine.

Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation

Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method’s accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering

Imaging flow cytometry is a powerful tool for analyzing every single cell in a large heterogeneous population but relies on fluorescent labeling, which comes with cytotoxicity, nonspecific binding, and interference with natural cellular functions. This paper presents label-free multicolor chemical imaging flow cytometry based on stimulated Raman scattering (SRS), a highly sensitive method of molecular vibrational spectroscopy. With the help of deep learning, it demonstrates high-precision characterization and classification of microalgal cells and cancer cells without the need for fluorescent labeling.

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

Quantitative Analysis of Different Cell Entry Routes of Actively Targeted Nanomedicines Using Imaging Flow Cytometry

A rapid, high-throughput, and quantitative method for cell entry route characterization is still lacking in nanomedicine research. Here, we report the application of imaging flow cytometry for quantitatively analyzing cell entry routes of actively targeted nanomedicines. We first engineered ICAM1 antibody-directed fusogenic nanoliposomes (ICAM1-FusoNLPs) and ICAM1 antibody-directed endocytic nanolipogels (ICAM1-EndoNLGs) featuring highly similar surface properties but different cell entry routes: receptor-mediated membrane fusion and receptor-mediated endocytosis, respectively. By using imaging flow cytometry, we characterized their intracellular delivery into human breast cancer MDA-MB-231 cells. We found that ICAM1-FusoNLPs mediated a 2.8-fold increased cell uptake of fluorescent payload, FITC-dextran, with a 2.4-fold increased intracellular distribution area in comparison with ICAM1-EndoNLGs. We also investigated the effects of incubation time and endocytic inhibitors on the cell entry routes of ICAM1-FusoNLP and ICAM1-EndoNLG. Our results indicate that receptor-mediated membrane fusion is a faster and more efficient cell entry route than receptor-mediated endocytosis, bringing with it a significant therapeutic benefit in a proof-of-principle nanomedicine-mediated siRNA transfection experiment. Our studies suggest that cell entry route may be an important design parameter to be considered in the development of next-generation nanomedicines. © 2019 International Society for Advancement of Cytometry.

Deep learning-based selection of human sperm with high DNA integrity

Despite the importance of sperm DNA to human reproduction, currently no method exists to assess individual sperm DNA quality prior to clinical selection. Traditionally, skilled clinicians select sperm based on a variety of morphological and motility criteria, but without direct knowledge of their DNA cargo. Here, we show how a deep convolutional neural network can be trained on a collection of ~1000 sperm cells of known DNA quality, to predict DNA quality from brightfield images alone. Our results demonstrate moderate correlation (bivariate correlation ~0.43) between a sperm cell image and DNA quality and the ability to identify higher DNA integrity cells relative to the median. This deep learning selection process is directly compatible with current, manual microscopy-based sperm selection and could assist clinicians, by providing rapid DNA quality predictions (under 10 ms per cell) and sperm selection within the 86th percentile from a given sample.

Cell Cytometry: Review and Perspective on Biotechnological Advances

Cell identification and enumeration are essential procedures within clinical and research laboratories. For over 150 years, quantitative investigation of body fluids such as counts of various blood cells has been an important tool for diagnostic analysis. With the current evolution of point-of-care diagnostics and precision medicine, cheap and precise cell counting technologies are in demand. This article reviews the timeline and recent notable advancements in cell counting that have occurred as a result of improvements in sensing including optical and electrical technology, enhancements in image processing capabilities, and contributions of micro and nanotechnologies. Cell enumeration methods have evolved from the use of manual counting using a hemocytometer to automated cell counters capable of providing reliable counts with high precision and throughput. These developments have been enabled by the use of precision engineering, micro and nanotechnology approaches, automation and multivariate data analysis. Commercially available automated cell counters can be broadly classified into three categories based on the principle of detection namely, electrical impedance, optical analysis and image analysis. These technologies have many common scientific uses, such as hematological analysis, urine analysis and bacterial enumeration. In addition to commercially available technologies, future technological trends using lab-on-a-chip devices have been discussed in detail. Lab-on-a-chip platforms utilize the existing three detection technologies with innovative design changes utilizing advanced nano/microfabrication to produce customized devices suited to specific applications.

Label-Free Identification of White Blood Cells Using Machine Learning

White blood cell (WBC) differential counting is an established clinical routine to assess patient immune system status. Fluorescent markers and a flow cytometer are required for the current state‐of‐the‐art method for determining WBC differential counts. However, this process requires several sample preparation steps and may adversely disturb the cells. We present a novel label‐free approach using an imaging flow cytometer and machine learning algorithms, where live, unstained WBCs were classified. It achieved an average F1‐score of 97% and two subtypes of WBCs, B and T lymphocytes, were distinguished from each other with an average F1‐score of 78%, a task previously considered impossible for unlabeled samples. We provide an open‐source workflow to carry out the procedure. We validated the WBC analysis with unstained samples from 85 donors. The presented method enables robust and highly accurate identification of WBCs, minimizing the disturbance to the cells and leaving marker channels free to answer other biological questions. It also opens the door to employing machine learning for liquid biopsy, here, using the rich information in cell morphology for a wide range of diagnostics of primary blood. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Machine Learning Based Real-Time Image-Guided Cell Sorting and Classification

Cell classification based on phenotypical, spatial, and genetic information greatly advances our understanding of the physiology and pathology of biological systems. Technologies derived from next generation sequencing and fluorescent activated cell sorting are cornerstones for cell- and genomic-based assays supporting cell classification and mapping. However, there exists a deficiency in technology space to rapidly isolate cells based on high content image information. Fluorescence-activated cell sorting can only resolve cell-to-cell variation in fluorescence and optical scattering. Utilizing microfluidics, photonics, computation microscopy, real-time image processing and machine learning, we demonstrate an image-guided cell sorting and classification system possessing the high throughput of flow cytometer and high information content of microscopy. We demonstrate the utility of this technology in cell sorting based on (1) nuclear localization of glucocorticoid receptors, (2) particle binding to the cell membrane, and (3) DNA damage induced γ-H2AX foci. © 2019 International Society for Advancement of Cytometry.

Near real-time enumeration of live and dead bacteria using a fibre-based spectroscopic device

A rapid, cost-effective and easy method that allows on-site determination of the concentration of live and dead bacterial cells using a fibre-based spectroscopic device (the optrode system) is proposed and demonstrated. Identification of live and dead bacteria was achieved by using the commercially available dyes SYTO 9 and propidium iodide, and fluorescence spectra were measured by the optrode. Three spectral processing methods were evaluated for their effectiveness in predicting the original bacterial concentration in the samples: principal components regression (PCR), partial least squares regression (PLSR) and support vector regression (SVR). Without any sample pre-concentration, PCR achieved the most reliable results. It was able to quantify live bacteria from 108 down to 106.2 bacteria/mL and showed the potential to detect as low as 105.7 bacteria/mL. Meanwhile, enumeration of dead bacteria using PCR was achieved between 108 and 107 bacteria/mL. The general procedures described in this article can be applied or modified for the enumeration of bacteria within populations stained with fluorescent dyes. The optrode is a promising device for the enumeration of live and dead bacterial populations particularly where rapid, on-site measurement and analysis is required.

Label-Free Multi Parameter Optical Interrogation of Endothelial Activation in Single Cells using a Lab on a Disc Platform

Cellular activation and inflammation leading to endothelial dysfunction is associated with cardiovascular disease (CVD). We investigated whether a single cell label-free multi parameter optical interrogation system can detect endothelial cell and endothelial progenitor cell (EPC) activation in vitro and ex vivo, respectively. Cultured human endothelial cells were exposed to increasing concentrations of tumour necrosis factor alpha (TNF-α) or lipopolysaccharide (LPS) before endothelial activation was validated using fluorescence-activated cell sorting (FACS) analysis of inflammatory marker expression (PECAM-1, E-selectin and ICAM-1). A centrifugal microfluidic system and V-cup array was used to capture individual cells before optical measurement of light scattering, immunocytofluorescence, auto-fluorescence (AF) and cell morphology was determined. In vitro, TNF-α promoted specific changes to the refractive index and cell morphology of individual cells concomitant with enhanced photon activity of fluorescently labelled inflammatory markers and increased auto-fluorescence (AF) intensity at three different wavelengths, an effect blocked by inhibition of downstream signalling with Iκβ. Ex vivo, there was a significant increase in EPC number and AF intensity of individual EPCs from CVD patients concomitant with enhanced PECAM-1 expression when compared to normal controls. This novel label-free 'lab on a disc' (LoaD) platform can successfully detect endothelial activation in response to inflammatory stimuli in vitro and ex vivo.

Label-free classification of cells based on supervised machine learning of subcellular structures

It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.

Microfluidic droplets content classification and analysis through convolutional neural networks in a liquid biopsy workflow

In a recent paper we presented an innovative method of liquid biopsy, for the detection of circulating tumor cells (CTC) in the peripheral blood. Using microfluidics, CTC are individually encapsulated in water-in-oil droplets and selected by their increased rate of extracellular acidification (ECAR). During the analysis, empty or debris-containing droplets are discarded manually by screening images of positive droplets, increasing the operator-dependency and time-consumption of the assay. In this work, we addressed the limitations of the current method integrating computer vision techniques in the analysis. We implemented an automatic classification of droplets using convolutional neural networks, correctly classifying more than 96% of droplets. A second limitation of the technique is that ECAR is computed using an average droplet volume, without considering small variations in extracellular volume which can occur due to the normal variability in the size of the droplets or cells. Here, with the use of neural networks for object detection, we segmented the images of droplets and cells to measure their relative volumes, correcting over- or under-estimation of ECAR, which was present up to 20%. Finally, we evaluated whether droplet images contained additional information. We preliminarily gave a proof-of-concept demonstration showing that white blood cells expression of CD45 can be predicted with 82.9% accuracy, based on bright-field cell images alone. Then, we applied the method to classify acid droplets as coming from metastatic breast cancer patients or healthy donors, obtaining an accuracy of 90.2%.