A practical guide to intelligent image-activated cell sorting

High-throughput fluorescence lifetime imaging flow cytometry

Flow cytometry is a vital tool in biomedical research and laboratory medicine. However, its accuracy is often compromised by undesired fluctuations in fluorescence intensity. While fluorescence lifetime imaging microscopy (FLIM) bypasses this challenge as fluorescence lifetime remains unaffected by such fluctuations, the full integration of FLIM into flow cytometry has yet to be demonstrated due to speed limitations. Here we overcome the speed limitations in FLIM, thereby enabling high-throughput FLIM flow cytometry at a high rate of over 10,000 cells per second. This is made possible by using dual intensity-modulated continuous-wave beam arrays with complementary modulation frequency pairs for fluorophore excitation and acquiring fluorescence lifetime images of rapidly flowing cells. Moreover, our FLIM system distinguishes subpopulations in male rat glioma and captures dynamic changes in the cell nucleus induced by an anti-cancer drug. FLIM flow cytometry significantly enhances cellular analysis capabilities, providing detailed insights into cellular functions, interactions, and environments.

Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity

Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD  also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.

Accessible high-speed image-activated cell sorting

Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.

The use of droplet-based microfluidic technologies for accelerated selection of Yarrowia lipolytica and Phaffia rhodozyma yeast mutants

Microorganisms are widely used for the industrial production of various valuable products, such as pharmaceuticals, food and beverages, biofuels, enzymes, amino acids, vaccines, etc. Research is constantly carried out to improve their properties, mainly to increase their productivity and efficiency and reduce the cost of the processes. The selection of microorganisms with improved qualities takes a lot of time and resources (both human and material); therefore, this process itself needs optimization. In the last two decades, microfluidics technology appeared in bioengineering, which allows for manipulating small particles (from tens of microns to nanometre scale) in the flow of liquid in microchannels. The technology is based on small-volume objects (microdroplets from nano to femtolitres), which are manipulated using a microchip. The chip is made of an optically transparent inert to liquid medium material and contains a series of channels of small size (<1 mm) of certain geometry. Based on the physical and chemical properties of microparticles (like size, weight, optical density, dielectric constant, etc.), they are separated using microsensors. The idea of accelerated selection of microorganisms is the application of microfluidic technologies to separate mutants with improved qualities after mutagenesis. This article discusses the possible application and practical implementation of microfluidic separation of mutants, including yeasts like Yarrowia lipolytica and Phaffia rhodozyma after chemical mutagenesis will be discussed.

Optofluidic time-stretch imaging flow cytometry with a real-time storage rate beyond 5.9 GB/s

Optofluidic time-stretch imaging flow cytometry (OTS-IFC) provides a suitable solution for high-precision cell analysis and high-sensitivity detection of rare cells due to its high-throughput and continuous image acquisition. However, transferring and storing continuous big data streams remains a challenge. In this study, we designed a high-speed streaming storage strategy to store OTS-IFC data in real-time, overcoming the imbalance between the fast generation speed in the data acquisition and processing subsystem and the comparatively slower storage speed in the transmission and storage subsystem. This strategy, utilizing an asynchronous buffer structure built on the producer-consumer model, optimizes memory usage for enhanced data throughput and stability. We evaluated the storage performance of the high-speed streaming storage strategy in ultra-large-scale blood cell imaging on a common commercial device. The experimental results show that it can provide a continuous data throughput of up to 5891 MB/s.

Label-free cell classification in holographic flow cytometry through an unbiased learning strategy

Nowadays, label-free imaging flow cytometry at the single-cell level is considered the stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology, life sciences and healthcare. In this framework, digital holography in microscopy promises to be a powerful imaging modality thanks to its multi-refocusing and label-free quantitative phase imaging capabilities, along with the encoding of the highest information content within the imaged samples. Moreover, the recent achievements of new data analysis tools for cell classification based on deep/machine learning, combined with holographic imaging, are urging these systems toward the effective implementation of point of care devices. However, the generalization capabilities of learning-based models may be limited from biases caused by data obtained from other holographic imaging settings and/or different processing approaches. In this paper, we propose a combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder, used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features. We demonstrate the proposed approach in the challenging classification task related to the identification of drug-resistant endometrial cancer cells.

High-throughput single-cell sorting by stimulated Raman-activated cell ejection

Single-cell sorting is essential to explore cellular heterogeneity in biology and medicine. Recently developed Raman-activated cell sorting (RACS) circumvents the limitations of fluorescence-activated cell sorting, such as the cytotoxicity of labels. However, the sorting throughputs of all forms of RACS are limited by the intrinsically small cross-section of spontaneous Raman scattering. Here, we report a stimulated Raman-activated cell ejection (S-RACE) platform that enables high-throughput single-cell sorting based on high-resolution multi-channel stimulated Raman chemical imaging, in situ image decomposition, and laser-induced cell ejection. The performance of this platform was illustrated by sorting a mixture of 1 μm polymer beads, where 95% yield, 98% purity, and 14 events per second throughput were achieved. Notably, our platform allows live cell ejection, allowing for the growth of single colonies of bacteria and fungi after sorting. To further illustrate the chemical selectivity, lipid-rich Rhodotorula glutinis cells were successfully sorted from a mixture with Saccharomyces cerevisiae, confirmed by downstream quantitative PCR. Furthermore, by integrating a closed-loop feedback control circuit into the system, we realized real-time single-cell imaging and sorting, and applied this method to precisely eject regions of interest from a rat brain tissue section. The reported S-RACE platform opens exciting opportunities for a wide range of single-cell applications in biology and medicine.

High-throughput microbial culturomics using automation and machine learning

Pure bacterial cultures remain essential for detailed experimental and mechanistic studies in microbiome research, and traditional methods to isolate individual bacteria from complex microbial ecosystems are labor-intensive, difficult-to-scale and lack phenotype-genotype integration. Here we describe an open-source high-throughput robotic strain isolation platform for the rapid generation of isolates on demand. We develop a machine learning approach that leverages colony morphology and genomic data to maximize the diversity of microbes isolated and enable targeted picking of specific genera. Application of this platform on fecal samples from 20 humans yields personalized gut microbiome biobanks totaling 26,997 isolates that represented >80% of all abundant taxa. Spatial analysis on >100,000 visually captured colonies reveals cogrowth patterns between Ruminococcaceae, Bacteroidaceae, Coriobacteriaceae and Bifidobacteriaceae families that suggest important microbial interactions. Comparative analysis of 1,197 high-quality genomes from these biobanks shows interesting intra- and interpersonal strain evolution, selection and horizontal gene transfer. This culturomics framework should empower new research efforts to systematize the collection and quantitative analysis of imaging-based phenotypes with high-resolution genomics data for many emerging microbiome studies.

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.

Computer vision meets microfluidics: a label-free method for high-throughput cell analysis

In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

Bio-inspired microfluidics: A review

Biomicrofluidics, a subdomain of microfluidics, has been inspired by several ideas from nature. However, while the basic inspiration for the same may be drawn from the living world, the translation of all relevant essential functionalities to an artificially engineered framework does not remain trivial. Here, we review the recent progress in bio-inspired microfluidic systems via harnessing the integration of experimental and simulation tools delving into the interface of engineering and biology. Development of "on-chip" technologies as well as their multifarious applications is subsequently discussed, accompanying the relevant advancements in materials and fabrication technology. Pointers toward new directions in research, including an amalgamated fusion of data-driven modeling (such as artificial intelligence and machine learning) and physics-based paradigm, to come up with a human physiological replica on a synthetic bio-chip with due accounting of personalized features, are suggested. These are likely to facilitate physiologically replicating disease modeling on an artificially engineered biochip as well as advance drug development and screening in an expedited route with the minimization of animal and human trials.

Protocol for bacterial typing using Fourier transform infrared spectroscopy

The Fourier transform infrared (FT-IR) signals obtained from bacterial samples are specific and reproducible, making FT-IR an efficient tool for bacterial typing at a subspecies level. However, the typing accuracy could be affected by many factors, including sample preparation and spectral acquisition. Here, we present a unified protocol for bacterial typing based on FT-IR spectroscopy. We describe sample preparation from bacterial culture and FT-IR spectrum collection. We then detail FT-IR spectrum preprocessing and multivariate analysis of spectral data for bacterial typing.

Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry

Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells' refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method.

Virtual-freezing fluorescence imaging flow cytometry with 5-aminolevulinic acid stimulation and antibody labeling for detecting all forms of circulating tumor cells

Circulating tumor cells (CTCs) are precursors to cancer metastasis. In blood circulation, they take various forms such as single CTCs, CTC clusters, and CTC-leukocyte clusters, all of which have unique characteristics in terms of physiological function and have been a subject of extensive research in the last several years. Unfortunately, conventional methods are limited in accurately analysing the highly heterogeneous nature of CTCs. Here we present an effective strategy for simultaneously analysing all forms of CTCs in blood by virtual-freezing fluorescence imaging (VIFFI) flow cytometry with 5-aminolevulinic acid (5-ALA) stimulation and antibody labeling. VIFFI is an optomechanical imaging method that virtually freezes the motion of fast-flowing cells on an image sensor to enable high-throughput yet sensitive imaging of every single event. 5-ALA stimulates cancer cells to induce the accumulation of protoporphyrin (PpIX), a red fluorescent substance, making it possible to detect all cancer cells even if they show no expression of the epithelial cell adhesion molecule, a typical CTC biomarker. Although PpIX signals are generally weak, VIFFI flow cytometry can detect them by virtue of its high sensitivity. As a proof-of-principle demonstration of the strategy, we applied cancer cells spiked in blood to the strategy to demonstrate image-based detection and accurate classification of single cancer cells, clusters of cancer cells, and clusters of a cancer cell(s) and a leukocyte(s). To show the clinical utility of our method, we used it to evaluate blood samples of four breast cancer patients and four healthy donors and identified EpCAM-positive PpIX-positive cells in one of the patient samples. Our work paves the way toward the determination of cancer prognosis, the guidance and monitoring of treatment, and the design of antitumor strategies for cancer patients.

On monocytes and lymphocytes biolens clustering by in flow holographic microscopy

Live cells act as biological lenses and can be employed as real-world optical components in bio-hybrid systems. Imaging at nanoscale, optical tweezers, lithography and also photonic waveguiding are some of the already proven functionalities, boosted by the advantage that cells are fully biocompatible for intra-body applications. So far, various cell types have been studied for this purpose, such as red blood cells, bacterial cells, stem cells and yeast cells. White Blood Cells (WBCs) play a very important role in the regulation of the human body activities and are usually monitored for assessing its health. WBCs can be considered bio-lenses but, to the best of our knowledge, characterization of their optical properties have not been investigated yet. Here, we report for the first time an accurate study of two model classes of WBCs (i.e., monocytes and lymphocytes) by means of a digital holographic microscope coupled with a microfluidic system, assuming WBCs bio-lens characteristics. Thus, quantitative phase maps for many WBCs have been retrieved in flow-cytometry (FC) by achieving a significant statistical analysis to prove the enhancement in differentiation among sphere-like bio-lenses according to their sizes (i.e., diameter d) exploiting intensity parameters of the modulated light in proximity of the cell optical axis. We show that the measure of the low intensity area (S: I z < I th z ) in a fixed plane, is a feasible parameter for cell clustering, while achieving robustness against experimental misalignments and allowing to adjust the measurement sensitivity in post-processing. 2D scatterplots of the identified parameters (d-S) show better differentiation respect to the 1D case. The results show that the optical focusing properties of WBCs allow the clustering of the two populations by means of a mere morphological analysis, thus leading to the new concept of cell-optical-fingerprint avoiding fluorescent dyes. This perspective can open new routes in biomedical sciences, such as the chance to find optical-biomarkers at single cell level for label-free diagnosis.

Microalgae identification: Future of image processing and digital algorithm

The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.

Intelligent sort-timing prediction for image-activated cell sorting

Intelligent image-activated cell sorting (iIACS) has enabled high-throughput image-based sorting of single cells with artificial intelligence (AI) algorithms. This AI-on-a-chip technology combines fluorescence microscopy, AI-based image processing, sort-timing prediction, and cell sorting. Sort-timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed. The long latency amplifies the effects of the fluctuations in the flow speed of cells, leading to fluctuation and uncertainty in the arrival time of cells at the sort point on the microfluidic chip. To compensate for this fluctuation, iIACS measures the flow speed of each cell upstream, predicts the arrival timing of the cell at the sort point, and activates the actuation of the cell sorter appropriately. Here, we propose and demonstrate a machine learning technique to increase the accuracy of the sort-timing prediction that would allow for the improvement of sort event rate, yield, and purity. Specifically, we trained an algorithm to predict the sort timing for morphologically heterogeneous budding yeast cells. The algorithm we developed used cell morphology, position, and flow speed as inputs for prediction and achieved 41.5% lower prediction error compared to the previously employed method based solely on flow speed. As a result, our technique would allow for an increase in the sort event rate of iIACS by a factor of ~2.

NF-κB dynamics in the language of immune cells

Biological discovery has been driven by advances in throughput and resolution of analysis technologies. They have also created an indelible bias for snapshot-based knowledge. Even though recent methods such as multi-omics single-cell assays have empowered immunological investigations, they still provide snapshots of cellular behaviors and thus, have inherent limitations in reconstructing unsynchronized dynamic events across individual cells. Here, we present a rationale for how NF-κB may convey specificity of contextual information through subtle quantitative features of its signaling dynamics. The next frontier of predictive understanding should involve functional characterization of NF-κB signaling dynamics and their immunological implications. This may help solve the apparent paradox that a ubiquitously activated transcription factor can shape accurate responses to different immune challenges.

Intelligent image-activated sorting of Chlamydomonas reinhardtii by mitochondrial localization

Organelle positioning in cells is associated with various metabolic functions and signaling in unicellular organisms. Specifically, the microalga Chlamydomonas reinhardtii repositions its mitochondria, depending on the levels of inorganic carbon. Mitochondria are typically randomly distributed in the Chlamydomonas cytoplasm, but relocate toward the cell periphery at low inorganic carbon levels. This mitochondrial relocation is linked with the carbon-concentrating mechanism, but its significance is not yet thoroughly understood. A genotypic understanding of this relocation would require a high-throughput method to isolate rare mutant cells not exhibiting this relocation. However, this task is technically challenging due to the complex intracellular morphological difference between mutant and wild-type cells, rendering conventional non-image-based high-event-rate methods unsuitable. Here, we report our demonstration of intelligent image-activated cell sorting by mitochondrial localization. Specifically, we applied an intelligent image-activated cell sorting system to sort for C. reinhardtii cells displaying no mitochondrial relocation. We trained a convolutional neural network (CNN) to distinguish the cell types based on the complex morphology of their mitochondria. The CNN was employed to perform image-activated sorting for the mutant cell type at 180 events per second, which is 1-2 orders of magnitude faster than automated microscopy with robotic pipetting, resulting in an enhancement of the concentration from 5% to 56.5% corresponding to an enrichment factor of 11.3. These results show the potential of image-activated cell sorting for connecting genotype-phenotype relations for rare-cell populations, which require a high throughput and could lead to a better understanding of metabolic functions in cells.

Targeting biophysical cues to address platelet storage lesions

Platelets play vital roles in vascular repair, especially in primary hemostasis, and have been widely used in transfusion to prevent bleeding or manage active bleeding. Recently, platelets have been used in tissue repair (e.g., bone, skin, and dental alveolar tissue) and cell engineering as drug delivery carriers. However, the biomedical applications of platelets have been associated with platelet storage lesions (PSLs), resulting in poor clinical outcomes with reduced recovery, survival, and hemostatic function after transfusion. Accumulating evidence has shown that biophysical cues play important roles in platelet lesions, such as granule secretion caused by shear stress, adhesion affected by substrate stiffness, and apoptosis caused by low temperature. This review summarizes four major biophysical cues (i.e., shear stress, substrate stiffness, hydrostatic pressure, and thermal microenvironment) involved in the platelet preparation and storage processes, and discusses how they may synergistically induce PSLs such as platelet shape change, activation, apoptosis and clearance. We also review emerging methods for studying these biophysical cues in vitro and existing strategies targeting biophysical cues for mitigating PSLs. We conclude with a perspective on the future direction of biophysics-based strategies for inhibiting PSLs. STATEMENT OF SIGNIFICANCE: Platelet storage lesions (PSLs) involve a series of structural and functional changes. It has long been accepted that PSLs are initiated by biochemical cues. Our manuscript is the first to propose four major biophysical cues (shear stress, substrate stiffness, hydrostatic pressure, and thermal microenvironment) that platelets experience in each operation step during platelet preparation and storage processes in vitro, which may synergistically contribute to PSLs. We first clarify these biophysical cues and how they induce PSLs. Strategies targeting each biophysical cue to improve PSLs are also summarized. Our review is designed to draw the attention from a broad range of audience, including clinical doctors, biologists, physical scientists, engineers and materials scientists, and immunologist, who study on platelets physiology and pathology.

Simple Detection of Unstained Live Senescent Cells with Imaging Flow Cytometry

Cellular senescence is a hallmark of aging and a promising target for therapeutic approaches. The identification of senescent cells requires multiple biomarkers and complex experimental procedures, resulting in increased variability and reduced sensitivity. Here, we propose a simple and broadly applicable imaging flow cytometry (IFC) method. This method is based on measuring autofluorescence and morphological parameters and on applying recent artificial intelligence (AI) and machine learning (ML) tools. We show that the results of this method are superior to those obtained measuring the classical senescence marker, senescence-associated beta-galactosidase (SA-β-Gal). We provide evidence that this method has the potential for diagnostic or prognostic applications as it was able to detect senescence in cardiac pericytes isolated from the hearts of patients affected by end-stage heart failure. We additionally demonstrate that it can be used to quantify senescence “in vivo” and can be used to evaluate the effects of senolytic compounds. We conclude that this method can be used as a simple and fast senescence assay independently of the origin of the cells and the procedure to induce senescence.

High-Resolution Micro-object Separation by Rotating Magnetic Chromatography

The development of new technologies for the separation, selection, and isolation of microparticles such as rare target cells, circulating tumor cells, cancer stem cells, and immune cells has become increasingly important in the last few years. Microparticle separation technologies are usually applied to the analysis of disease-associated cells, but these procedures often face a cell separation problem that is often insufficient for single specific cell analyses. To overcome these limitations, a highly accurate size-based microparticle separation technique, herein called "rotating magnetic chromatography", is proposed in this work. Magnetic nanoparticles, placed in a microfluidic separation channel, are forced to move in well-defined trajectories by an external magnetic field, colliding with microparticles that are in this way separated on the basis of their dimensions with high accuracy and reproducibility. The method was optimized by using fluorescein isothiocyanate-modified polystyrene particles (chosen as a reference standard) and then applied to the analysis of cancer cells like Hep-3B and SK-Hep-1, allowing their fast and high-resolution chromatographic separation as a function of their dimensions. Due to its unmatched sub-micrometer cell separation capabilities, RMC can be considered a break-through technique that can unlock new perspectives in different scientific fields, that is, in medical oncology.

Machine learning for microfluidic design and control

Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.

Deciphering impedance cytometry signals with neural networks

Microfluidic impedance cytometry is a label-free technique for high-throughput single-cell analysis. Multi-frequency impedance measurements provide data that allows full characterisation of cells, linking electrical phenotype to individual biophysical properties. To efficiently extract the information embedded in the electrical signals, potentially in real-time, tailored signal processing is needed. Artificial intelligence approaches provide a promising new direction. Here we demonstrate the ability of neural networks to decipher impedance cytometry signals in two challenging scenarios: (i) to determine the intrinsic dielectric properties of single cells directly from raw impedance data streams, (ii) to capture single-cell signals that are hidden in the measured signals of coincident cells. The accuracy of the results and the high processing speed (fractions of ms per cell) demonstrate that neural networks can have an important role in impedance-based single-cell analysis.

Deterministic Lateral Displacement (DLD) Analysis Tool Utilizing Machine Learning towards High-Throughput Separation

Deterministic lateral displacement (DLD) is a microfluidic method for the continuous separation of particles based on their size. There is growing interest in using DLD for harvesting circulating tumor cells from blood for further assays due to its low cost and robustness. While DLD is a powerful tool and development of high-throughput DLD separation devices holds great promise in cancer diagnostics and therapeutics, much of the experimental data analysis in DLD research still relies on error-prone and time-consuming manual processes. There is a strong need to automate data analysis in microfluidic devices to reduce human errors and the manual processing time. In this work, a reliable particle detection method is developed as the basis for the DLD separation analysis. Python and its available packages are used for machine vision techniques, along with existing identification methods and machine learning models. Three machine learning techniques are implemented and compared in the determination of the DLD separation mode. The program provides a significant reduction in video analysis time in DLD separation, achieving an overall particle detection accuracy of 97.86% with an average computation time of 25.274 s.

Deep learning-based image processing in optical microscopy

Optical microscopy has emerged as a key driver of fundamental research since it provides the ability to probe into imperceptible structures in the biomedical world. For the detailed investigation of samples, a high-resolution image with enhanced contrast and minimal damage is preferred. To achieve this, an automated image analysis method is preferable over manual analysis in terms of both speed of acquisition and reduced error accumulation. In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in various problems related to image classification and segmentation. It has also performed well in enhancing image resolution in smartphone-based microscopy, which in turn enablse crucial medical assistance in remote places.

Graphical abstract

A guide to molecular and functional investigations of platelets to bridge basic and clinical sciences

Platelets have been shown to be associated with pathophysiological process beyond thrombosis, demonstrating critical additional roles in homeostatic processes, such as immune regulation, and vascular remodeling. Platelets themselves can have multiple functional states and can communicate and regulate other cells including immune cells and vascular smooth muscle cells, to serve such diverse functions. Although traditional platelet functional assays are informative and reliable, they are limited in their ability to unravel platelet phenotypic heterogeneity and interactions. Developments in methods such as electron microscopy, flow cytometry, mass spectrometry, and 'omics' studies, have led to new insights. In this Review, we focus on advances in platelet biology and function, with an emphasis on current and promising methodologies. We also discuss technical and biological challenges in platelet investigations. Using coronavirus disease 2019 (COVID-19) as an example, we further describe the translational relevance of these approaches and the possible 'bench-to-bedside' utility in patient diagnosis and care.

Deep imaging flow cytometry

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.

Single-cell genotyping of phytoplankton from ocean water by gel-based cell manipulation

A comprehensive understanding of phytoplankton diversity is valuable for assessing an environment of interest as phytoplankton are primary producers in various aquatic food webs. Microscopic analyses are useful for diversity assessment based on characteristic cell morphologies. However, phylogenetic classification based solely on morphology requires an extremely high level of expertise. The genetic approach is another option for evaluating phytoplankton diversity; however, it cannot reveal morphological information. To integrate these two approaches, we developed an original technology that is referred to as microcavity array (MCA)/gel-based cell manipulation (GCM). The model experiments using monocultures of various phytoplankton indicated that the efficiencies of cell recovery and isolation of single-cell plankton were dependent on cell size and shape. Cells with widths larger than the cavity width showed high level of recovery and isolation efficiency. Subsequent whole-genome amplification (WGA) of isolated single-cell plankton provided a sufficient amount (approximately 30 μg) of WGA products for genetic analyses. Furthermore, we showed that MCA/GCM could directly analyze phytoplankton in ocean water obtained from Suruga Bay, Japan, without any cumbersome pretreatment. These results indicate that MCA/GCM technology is a powerful tool for elucidating the phytoplankton diversity in marine environment. This article is protected by copyright. All rights reserved.

The role of machine learning to boost the bioenergy and biofuels conversion

The development and application of bioenergy and biofuels conversion technology can play a significant role for the production of renewable and sustainable energy sources in the future. However, the complexity of bioenergy systems and the limitations of human understanding make it difficult to build models based on experience or theory for accurate predictions. Recent developments in data science and machine learning (ML), can provide new opportunities. Accordingly, this critical review provides a deep insight into the application of ML in the bioenergy context. The latest advances in ML assisted bioenergy technology, including energy utilization of lignocellulosic biomass, microalgae cultivation, biofuels conversion and application, are reviewed in detail. The strengths and limitations of ML in bioenergy systems are comprehensively analysed. Moreover, we highlight the capabilities and potential of advanced ML methods when encountering multifarious tasks in the future prospects to advance a new generation of bioenergy and biofuels conversion technologies.

Versatile acoustic manipulation of micro-objects using mode-switchable oscillating bubbles: transportation, trapping, rotation, and revolution

Controllable on-chip multimodal manipulation of micro-objects in microfluidic devices is urgently required for enhancing the efficiency of potential biomedical applications. However, fixed design and driving models make it difficult to achieve switchable multifunction efficiently in a single device. In this study, a versatile bubble-based acoustofluidic device is proposed for multimodal manipulation of micro-objects in a biocompatible manner. Identical bubbles trapped over the bottom microcavities are made to flexibly switch between four different oscillatory motions by varying the applied frequency to generate corresponding modes of streaming patterns in the microchannel. Such regular modes enable stable transportation, trapping, 3D rotation, and circular revolution of the micro-objects, which were experimentally and numerically verified. The mode-switchable manipulations can be noninvasively applied to particles, cells, and organisms with different sizes, shapes, and quantities and can be controlled by key driving parameters. Moreover, 3D cell reconstruction is developed by applying the out-of-plane rotational mode and analyzed for illustration of cell surface morphology while quantifying reliably basic cell properties. Finally, a simple platform is established to integrate user-friendly function control and reconstruction analysis. The mode-switchable acoustofluidic device features a versatile, controllable, and contactless micro-object manipulation method, which provides an efficient solution for biomedical applications.

What is the future of electrical impedance spectroscopy in flow cytometry?

More than 20 years ago, electrical impedance spectroscopy (EIS) was proposed as a potential characterization method for flow cytometry. As the setup is comparably simple and the method is label-free, EIS has attracted considerable interest from the research community as a potential alternative to standard optical methods, such as fluorescence-activated cell sorting (FACS). However, until today, FACS remains by and large the laboratory standard with highly developed capabilities and broad use in research and clinical settings. Nevertheless, can EIS still provide a complement or alternative to FACS in specific applications? In this Perspective, we will give an overview of the current state of the art of EIS in terms of technologies and capabilities. We will then describe recent advances in EIS-based flow cytometry, compare the performance to that of FACS methods, and discuss potential prospects of EIS in flow cytometry.

High-throughput sorting of nanoliter droplets enabled by a sequentially addressable dielectrophoretic array

Droplet microfluidics has emerged as a powerful tool for a diverse range of biomedical and industrial applications such as single-cell analysis, directed evolution, and metabolic engineering. In these applications, droplet sorting has been effective for isolating small droplets encapsulating molecules, cells, or crystals of interest. Recently, there is an increased interest in extending the applicability of droplet sorting to larger droplets to utilize their size advantage. However, sorting throughputs of large droplets have been limited, hampering their wide adoption. Here, we report our demonstration of high-throughput fluorescence-activated droplet sorting of 1 nL droplets using an upgraded version of the sequentially addressable dielectrophoretic array (SADA), which we reported previously. The SADA is an array of electrodes that are individually and sequentially activated/deactivated according to the speed and position of a droplet passing nearby the array. We upgraded the SADA by increasing the number of driving electrodes constituting the SADA and incorporating a slanted microchannel. By using a ten-electrode SADA with the slanted microchannel, we achieved fluorescence-activated droplet sorting of 1 nL droplets at a record high throughput of 1752 droplets/s, twice as high as the previously reported maximum sorting throughput of 1 nL droplets.

Morphological Indicator for Directed Evolution of Euglena gracilis with a High Heavy Metal Removal Efficiency

In the past few decades, microalgae-based bioremediation methods for treating heavy metal (HM)-polluted wastewater have attracted much attention by virtue of their environment friendliness, cost efficiency, and sustainability. However, their HM removal efficiency is far from practical use. Directed evolution is expected to be effective for developing microalgae with a much higher HM removal efficiency, but there is no non-invasive or label-free indicator to identify them. Here, we present an intelligent cellular morphological indicator for identifying the HM removal efficiency of Euglena gracilis in a non-invasive and label-free manner. Specifically, we show a strong monotonic correlation (Spearman's ρ = -0.82, P = 2.1 × 10^-5) between a morphological meta-feature recognized via our machine learning algorithms and the Cu²⁺ removal efficiency of 19 E. gracilis clones. Our findings firmly suggest that the morphology of E. gracilis cells can serve as an effective HM removal efficiency indicator and hence have great potential, when combined with a high-throughput image-activated cell sorter, for directed-evolution-based development of E. gracilis with an extremely high HM removal efficiency for practical wastewater treatment worldwide.

Droplet flow cytometry for single-cell analysis

The interrogation of single cells has revolutionised biology and medicine by providing crucial unparalleled insights into cell-to-cell heterogeneity. Flow cytometry (including fluorescence-activated cell sorting) is one of the most versatile and high-throughput approaches for single-cell analysis by detecting multiple fluorescence parameters of individual cells in aqueous suspension as they flow past through a focus of excitation lasers. However, this approach relies on the expression of cell surface and intracellular biomarkers, which inevitably lacks spatial and temporal phenotypes and activities of cells, such as secreted proteins, extracellular metabolite production, and proliferation. Droplet microfluidics has recently emerged as a powerful tool for the encapsulation and manipulation of thousands to millions of individual cells within pico-litre microdroplets. Integrating flow cytometry with microdroplet architectures surrounded by aqueous solutions (e.g., water-in-oil-in-water (W/O/W) double emulsion and hydrogel droplets) opens avenues for new cellular assays linking cell phenotypes to genotypes at the single-cell level. In this review, we discuss the capabilities and applications of droplet flow cytometry (DFC). This unique technique uses standard commercially available flow cytometry instruments to characterise or select individual microdroplets containing single cells of interest. We explore current challenges associated with DFC and present our visions for future development.

Plasmonic Optical Tweezers for Particle Manipulation: Principles, Methods, and Applications

Inspired by the idea of combining conventional optical tweezers with plasmonic nanostructures, a technique named plasmonic optical tweezers (POT) has been widely explored from fundamental principles to applications. With the ability to break the diffraction barrier and enhance the localized electromagnetic field, POT techniques are especially effective for high spatial-resolution manipulation of nanoscale or even subnanoscale objects, from small bioparticles to atoms. In addition, POT can be easily integrated with other techniques such as lab-on-chip devices, which results in a very promising alternative technique for high-throughput single-bioparticle sensing or imaging. Despite its label-free, high-precision, and high-spatial-resolution nature, it also suffers from some limitations. One of the main obstacles is that the plasmonic nanostructures are located over the surfaces of a substrate, which makes the manipulation of bioparticles turn from a three-dimensional problem to a nearly two-dimensional problem. Meanwhile, the operation zone is limited to a predefined area. Therefore, the target objects must be delivered to the operation zone near the plasmonic structures. This review summarizes the state-of-the-art target delivery methods for the POT-based particle manipulating technique, along with its applications in single-bioparticle analysis/imaging, high-throughput bioparticle purifying, and single-atom manipulation. Future developmental perspectives of POT techniques are also discussed.

Breakthrough in purification of fossil pollen for dating of sediments by a new large-particle on-chip sorter

Particle sorting is a fundamental method in various fields of medical and biological research. However, existing sorting applications are not capable for high-throughput sorting of large-size (>100 micrometers) particles. Here, we present a novel on-chip sorting method using traveling vortices generated by on-demand microjet flows, which locally exceed laminar flow condition, allowing for high-throughput sorting (5 kilohertz) with a record-wide sorting area of 520 micrometers. Using an activation system based on fluorescence detection, the method successfully sorted 160-micrometer microbeads and purified fossil pollen (maximum dimension around 170 micrometers) from lake sediments. Radiocarbon dates of sorting-derived fossil pollen concentrates proved accurate, demonstrating the method's ability to enhance building chronologies for paleoenvironmental records from sedimentary archives. The method is capable to cover urgent needs for high-throughput large-particle sorting in genomics, metabolomics, and regenerative medicine and opens up new opportunities for the use of pollen and other microfossils in geochronology, paleoecology, and paleoclimatology.

Optical Detection Methods for High-Throughput Fluorescent Droplet Microflow Cytometry

High-throughput microflow cytometry has become a focal point of research in recent years. In particular, droplet microflow cytometry (DMFC) enables the analysis of cells reacting to different stimuli in chemical isolation due to each droplet acting as an isolated microreactor. Furthermore, at high flow rates, the droplets allow massive parallelization, further increasing the throughput of droplets. However, this novel methodology poses unique challenges related to commonly used fluorometry and fluorescent microscopy techniques. We review the optical sensor technology and light sources applicable to DMFC, as well as analyze the challenges and advantages of each option, primarily focusing on electronics. An analysis of low-cost and/or sufficiently compact systems that can be incorporated into portable devices is also presented.

Deep Learning for Biospectroscopy and Biospectral Imaging: State-of-the-Art and Perspectives

With the advances in instrumentation and sampling techniques, there is an explosive growth of data from molecular and cellular samples. The call to extract more information from the large data sets has greatly challenged the conventional chemometrics method. Deep learning, which utilizes very large data sets for finding hidden features therein and for making accurate predictions for a wide range of applications, has been applied in an unbelievable pace in biospectroscopy and biospectral imaging in the recent 3 years. In this Feature, we first introduce the background and basic knowledge of deep learning. We then focus on the emerging applications of deep learning in the data preprocessing, feature detection, and modeling of the biological samples for spectral analysis and spectroscopic imaging. Finally, we highlight the challenges and limitations in deep learning and the outlook for future directions.

Intelligent Platelet Morphometry

Technological advances in image-based platelet analysis or platelet morphometry are critical for a better understanding of the structure and function of platelets in biological research as well as for the development of better clinical strategies in medical practice. Recently, the advent of high-throughput optical imaging and deep learning has boosted platelet morphometry to the next level by providing a new set of capabilities beyond what is achievable with traditional platelet morphometry, shedding light on the unexplored domain of platelet analysis. This Opinion article introduces emerging opportunities in 'intelligent' platelet morphometry, which are expected to pave the way for a new class of diagnostics, pharmacometrics, and therapeutics.

The importance of advanced cytometry in defining new immune cell types and functions relevant for the immunopathogenesis of HIV infection

: In the last years, novel, exciting immunological findings of interest for HIV research and treatment were identified thanks to different cytometric approaches. The analysis of the phenotypes and functionality of cells belonging to the immune system could clarify their role in the immunopathogenesis of HIV infection, and to elaborate key concepts, relevant in the treatment of this disease. Important discoveries have been made concerning cells that are important for protective immunity like lymphocytes that display polyfunctionality, resident memory T cells, innate lymphoid cells, to mention a few. The complex phenotype of myeloid-derived suppressor cells has been investigated, and relevant changes have been reported during chronic and primary HIV infection, in correlation with changes in CD4 T-cell number, T-cell activation, and with advanced disease stage. The search for markers of HIV persistence present in latently infected cells, namely those molecules that are important for a functional or sterilizing cure, evidenced the role of follicular helper T cells, and opened a discussion on the meaning and use of different surface molecules not only in identifying such cells, but also in designing new strategies. Finally, advanced technologies based upon the simultaneous detection of HIV-RNA and proteins at the single cell level, as well as those based upon spectral cytometry or mass cytometry are now finding new actors and depicting a new scenario in the immunopathogenesis of the infection, that will allow to better design innovative therapies based upon novel drugs and vaccines.

Plant Single-Cell Metabolomics-Challenges and Perspectives

Omics approaches for investigating biological systems were introduced in the mid-1990s and quickly consolidated to become a fundamental pillar of modern biology. The idea of measuring the whole complement of genes, transcripts, proteins, and metabolites has since become widespread and routinely adopted in the pursuit of an infinity of scientific questions. Incremental improvements over technical aspects such as sampling, sensitivity, cost, and throughput pushed even further the boundaries of what these techniques can achieve. In this context, single-cell genomics and transcriptomics quickly became a well-established tool to answer fundamental questions challenging to assess at a whole tissue level. Following a similar trend as the original development of these techniques, proteomics alternatives for single-cell exploration have become more accessible and reliable, whilst metabolomics lag behind the rest. This review summarizes state-of-the-art technologies for spatially resolved metabolomics analysis, as well as the challenges hindering the achievement of sensu stricto metabolome coverage at the single-cell level. Furthermore, we discuss several essential contributions to understanding plant single-cell metabolism, finishing with our opinion on near-future developments and relevant scientific questions that will hopefully be tackled by incorporating these new exciting technologies.

Image-Based Live Cell Sorting

Technologies capable of cell separation based on cell images provide powerful tools enabling cell selection criteria that rely on spatially or temporally varying properties. Image-based cell sorting (IBCS) systems utilize microfluidic or microarray platforms, each having unique characteristics and applications. The advent of IBCS marks a new paradigm in which cell phenotype and behavior can be explored with high resolution and tied to cellular physiological and omics data, providing a deeper understanding of single-cell physiology and the creation of cell lines with unique properties. Cell sorting guided by high-content image information has far-reaching implications in biomedical research, clinical medicine, and pharmaceutical development.

Guidelines for establishing a 3-D printing biofabrication laboratory

Advanced manufacturing and 3D printing are transformative technologies currently undergoing rapid adoption in healthcare, a traditionally non-manufacturing sector. Recent development in this field, largely enabled by merging different disciplines, has led to important clinical applications from anatomical models to regenerative bioscaffolding and devices. Although much research to-date has focussed on materials, designs, processes, and products, little attention has been given to the design and requirements of facilities for enabling clinically relevant biofabrication solutions. These facilities are critical to overcoming the major hurdles to clinical translation, including solving important issues such as reproducibility, quality control, regulations, and commercialization. To improve process uniformity and ensure consistent development and production, large-scale manufacturing of engineered tissues and organs will require standardized facilities, equipment, qualification processes, automation, and information systems. This review presents current and forward-thinking guidelines to help design biofabrication laboratories engaged in engineering model and tissue constructs for therapeutic and non-therapeutic applications.

AI on a chip

Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning. Despite its prominent importance, the very first process of the AI development, namely data collection and data preparation, is typically the most laborious task and is often a limiting factor of constructing functional AI algorithms. Lab-on-a-chip technology, in particular microfluidics, is a powerful platform for both the construction and implementation of AI in a large-scale, cost-effective, high-throughput, automated, and multiplexed manner, thereby overcoming the above bottleneck. On this platform, high-throughput imaging is a critical tool as it can generate high-content information (e.g., size, shape, structure, composition, interaction) of objects on a large scale. High-throughput imaging can also be paired with sorting and DNA/RNA sequencing to conduct a massive survey of phenotype-genotype relations whose data is too complex to analyze with traditional computational tools, but is analyzable with the power of AI. In addition to its function as a data provider, lab-on-a-chip technology can also be employed to implement the developed AI for accurate identification, characterization, classification, and prediction of objects in mixed, heterogeneous, or unknown samples. In this review article, motivated by the excellent synergy between AI and lab-on-a-chip technology, we outline fundamental elements, recent advances, future challenges, and emerging opportunities of AI with lab-on-a-chip technology or "AI on a chip" for short.

Rapid counting and spectral sorting of live coral larvae using large-particle flow cytometry

Research with coral embryos and larvae often requires laborious manual counting and sorting of individual specimens, usually via microscopy. Because many coral species spawn only once per year during a narrow temporal window, sample processing is a time-limiting step for research on the early life-history stages of corals. Flow cytometry, an automated technique for measuring and sorting particles, cells, and cell-clusters, is a potential solution to this bottleneck. Yet most flow cytometers do not accommodate live organisms of the size of most coral embryos (> 250 µm), and sample processing is often destructive. Here we tested the ability of a large-particle flow cytometer with a gentle pneumatic sorting mechanism to process and spectrally sort live and preserved Montipora capitata coral embryos and larvae. Average survival rates of mechanically-sorted larvae were over 90% and were comparable to those achieved by careful hand-sorting. Preserved eggs and embryos remained intact throughout the sorting process and were successfully sorted based on real-time size and fluorescence detection. In-line bright-field microscopy images were captured for each sample object as it passed through the flow-cell, enabling the identification of early-stage embryos (2-cell to morula stage). Samples were counted and sorted at an average rate of 4 s larva⁻¹ and as high as 0.2 s larva⁻¹ for high-density samples. Results presented here suggest that large-particle flow cytometry has the potential to significantly increase efficiency and accuracy of data collection and sample processing during time-limited coral spawning events, facilitating larger-scale and higher-replication studies with an expanded number of species.

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.