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Key Points Standardized immunophenotyping assays are a requisite for accomplishing the proposed Human Immunology Project, which involves the comprehensive elucidation of the metrics of healthy versus diseased or perturbed human immune systems. The variables inherent in flow cytometry immunophenotyping are largely known, and include reagent choice, sample handling, instrument setup and data analysis; strategies to mitigate each of these variables are available. Several groups, including the Human Immunophenotyping Consortium, are standardizing reagent panels for flow cytometry. Together with the adoption of such standard panels, an infrastructure for aggregating and mining results will be needed. Availability of such panels and the data-mining infrastructure should result in more rapid biomarker discovery for immunologically relevant diseases. The authors use flow cytometry of peripheral blood mononuclear cells as an example to outline the approaches to assay standardization that will be required to realize the full potential of immunophenotyping as a research tool and in the clinic. The heterogeneity in the healthy human immune system, and the immunological changes that portend various diseases, have been only partially described. Their comprehensive elucidation has been termed the 'Human Immunology Project'. The accurate measurement of variations in the human immune system requires precise and standardized assays to distinguish true biological changes from technical artefacts. Thus, to be successful, the Human Immunology Project will require standardized assays for immunophenotyping humans in health and disease. A major tool in this effort is flow cytometry, which remains highly variable with regard to sample handling, reagents, instrument setup and data analysis. In this Review, we outline the current state of standardization of flow cytometry assays and summarize the steps that are required to enable the Human Immunology Project.

Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition. However, existing IACS frameworks suffer from the challenges of 3D information loss and processing latency dilemma in real-time sorting operation. Herein, we establish a neuromorphic-enabled video-activated cell sorter (NEVACS) framework, designed to achieve high-dimensional spatiotemporal characterization content alongside high-throughput sorting of particles in wide field of view. NEVACS adopts event camera, CPU, spiking neural networks deployed on a neuromorphic chip, and achieves sorting throughput of 1000 cells/s with relatively economic hybrid hardware solution (~$10 K for control) and simple-to-make-and-use microfluidic infrastructures. Particularly, the application of NEVACS in classifying regular red blood cells and blood-disease-relevant spherocytes highlights the accuracy of using video over a single frame (i.e., average error of 0.99% vs 19.93%), indicating NEVACS’ potential in cell morphology screening and disease diagnosis. Existing image-activated cell sorting tools suffer from the challenges of 3D information loss and processing latency in real-time sorting operations. Here, the authors propose a neuromorphic-enabled video-activated cell sorter (NEVACS) framework, which achieves high-dimensional spatiotemporal characterization content and high-throughput sorting of particles.

Flow cytometry has become a highly valuable method to monitor minimal residual disease (MRD) and evaluate the depth of complete response (CR) in bone marrow (BM) of multiple myeloma (MM) after therapy. However, current flow-MRD has lower sensitivity than molecular methods and lacks standardization. Here we report on a novel next generation flow (NGF) approach for highly sensitive and standardized MRD detection in MM. An optimized 2-tube 8-color antibody panel was constructed in five cycles of design-evaluation-redesign. In addition, a bulk-lysis procedure was established for acquisition of ⩾10 7 cells/sample, and novel software tools were constructed for automatic plasma cell gating. Multicenter evaluation of 110 follow-up BM from MM patients in very good partial response (VGPR) or CR showed a higher sensitivity for NGF-MRD vs conventional 8-color flow-MRD -MRD-positive rate of 47 vs 34% ( P =0.003)-. Thus, 25% of patients classified as MRD-negative by conventional 8-color flow were MRD-positive by NGF, translating into a significantly longer progression-free survival for MRD-negative vs MRD-positive CR patients by NGF (75% progression-free survival not reached vs 7 months; P =0.02). This study establishes EuroFlow-based NGF as a highly sensitive, fully standardized approach for MRD detection in MM which overcomes the major limitations of conventional flow-MRD methods and is ready for implementation in routine diagnostics.

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.

Imaging flow cytometry (IFC) captures multichannel images of hundreds of thousands of single cells within minutes. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. We predict a wealth of applications with potential translation into clinical practice.

Real-time deformability cytometry allows the continuous mechanical characterization of cells with high throughput and is applied to distinguish cell-cycle phases, track differentiated cells and profile cell populations in whole blood. We introduce real-time deformability cytometry (RT-DC) for continuous cell mechanical characterization of large populations (>100,000 cells) with analysis rates greater than 100 cells/s. RT-DC is sensitive to cytoskeletal alterations and can distinguish cell-cycle phases, track stem cell differentiation into distinct lineages and identify cell populations in whole blood by their mechanical fingerprints. This technique adds a new marker-free dimension to flow cytometry with diverse applications in biology, biotechnology and medicine.

The EU-supported EuroFlow Consortium aimed at innovation and standardization of immunophenotyping for diagnosis and classification of hematological malignancies by introducing 8-color flow cytometry with fully standardized laboratory procedures and antibody panels in order to achieve maximally comparable results among different laboratories. This required the selection of optimal combinations of compatible fluorochromes and the design and evaluation of adequate standard operating procedures (SOPs) for instrument setup, fluorescence compensation and sample preparation. Additionally, we developed software tools for the evaluation of individual antibody reagents and antibody panels. Each section describes what has been evaluated experimentally versus adopted based on existing data and experience. Multicentric evaluation demonstrated high levels of reproducibility based on strict implementation of the EuroFlow SOPs and antibody panels. Overall, the 6 years of extensive collaborative experiments and the analysis of hundreds of cell samples of patients and healthy controls in the EuroFlow centers have provided for the first time laboratory protocols and software tools for fully standardized 8-color flow cytometric immunophenotyping of normal and malignant leukocytes in bone marrow and blood; this has yielded highly comparable data sets, which can be integrated in a single database.

In this critical review, we provide a comprehensive overview of immunochemical food allergen assays and detectors in the context of their user-friendliness, through their connection to smartphones. Smartphone-based analysis is centered around citizen science, putting analysis into the hands of the consumer. Food allergies represent a significant worldwide health concern and consumers should be able to analyze their foods, whenever and wherever they are, for allergen presence. Owing to the need for a scientific background, traditional laboratory-based detection methods are generally unsuitable for the consumer. Therefore, it is important to develop simple, safe, and rapid assays that can be linked with smartphones as detectors to improve user accessibility. Smartphones make excellent detection systems because of their cameras, embedded flash functions, portability, connectivity, and affordability. Therefore, this review has summarized traditional laboratory-based methods for food allergen detection such as enzyme-linked-immunosorbent assay, flow cytometry, and surface plasmon resonance, and the potential to modernize these methods by interfacing them with a smartphone readout system, based on the aforementioned smartphone characteristics. This is the first review focusing on smartphone-based food-allergen detection methods designed with the intention of being consumer-friendly.

The ability to rapidly assay morphological and intracellular molecular variations within large heterogeneous populations of cells is essential for understanding and exploiting cellular heterogeneity. Optofluidic time-stretch microscopy is a powerful method for meeting this goal, as it enables high-throughput imaging flow cytometry for large-scale single-cell analysis of various cell types ranging from human blood to algae, enabling a unique class of biological, medical, pharmaceutical, and green energy applications. Here, we describe how to perform high-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Specifically, this protocol provides step-by-step instructions on how to build an optical time-stretch microscope and a cell-focusing microfluidic device for optofluidic time-stretch microscopy, use it for high-throughput single-cell image acquisition with sub-micrometer resolution at >10,000 cells per s, conduct image construction and enhancement, perform image analysis for large-scale single-cell analysis, and use computational tools such as compressive sensing and machine learning for handling the cellular ‘big data’. Assuming all components are readily available, a research team of three to four members with an intermediate level of experience with optics, electronics, microfluidics, digital signal processing, and sample preparation can complete this protocol in a time frame of 1 month.

Background Recent developments in droplet-based microfluidics allow the transcriptional profiling of thousands of individual cells in a quantitative, highly parallel and cost-effective way. A critical, often limiting step is the preparation of cells in an unperturbed state, not altered by stress or ageing. Other challenges are rare cells that need to be collected over several days or samples prepared at different times or locations. Methods Here, we used chemical fixation to address these problems. Methanol fixation allowed us to stabilise and preserve dissociated cells for weeks without compromising single-cell RNA sequencing data. Results By using mixtures of fixed, cultured human and mouse cells, we first showed that individual transcriptomes could be confidently assigned to one of the two species. Single-cell gene expression from live and fixed samples correlated well with bulk mRNA-seq data. We then applied methanol fixation to transcriptionally profile primary cells from dissociated, complex tissues. Low RNA content cells from Drosophila embryos, as well as mouse hindbrain and cerebellum cells prepared by fluorescence-activated cell sorting, were successfully analysed after fixation, storage and single-cell droplet RNA-seq. We were able to identify diverse cell populations, including neuronal subtypes. As an additional resource, we provide 'dropbead', an R package for exploratory data analysis, visualization and filtering of Drop-seq data. Conclusions We expect that the availability of a simple cell fixation method will open up many new opportunities in diverse biological contexts to analyse transcriptional dynamics at single-cell resolution.