Explore the vast range of titles available.

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.

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.

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.

In this analysis, the authors directly compared the performance of flow cytometry data processing algorithms to manual gating approaches. The results offer information of practical utility about the performance of the algorithms as applied to different data sets and challenges. Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

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.

Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection of rare pathogenic objects in patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer development, various microorganisms and parasites in the blood during acute blood infections. All of these rare diagnostic objects can be detected and identified very rapidly to save a patient’s life. This review outlines the main techniques of visualization of rare objects in the blood flow, methods for extraction of such objects from the blood flow for further investigations and new approaches to identify the objects automatically with the modern deep learning methods.

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.

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.

In fluorescence-activated cell sorting, characteristic target features are labeled with a specific fluorophore, and cells displaying different fluorophores are sorted. Ota et al. describe a technique called ghost cytometry that allows cell sorting based on the morphology of the cytoplasm, labeled with a single-color fluorophore. The motion of cells relative to a patterned optical structure provides spatial information that is compressed into temporal signals, which are sequentially measured by a single-pixel detector. Images can be reconstructed from this spatial and temporal information, but this is computationally costly. Instead, using machine learning, cells are classified directly from the compressed signals, without reconstructing an image. The method was able to separate morphologically similar cell types in an ultrahigh-speed fluorescence imaging–activated cell sorter. Science , this issue p. 1246 Morphology-based cell classification and sorting is achieved at high accuracy and throughput without obtaining images. Ghost imaging is a technique used to produce an object’s image without using a spatially resolving detector. Here we develop a technique we term “ghost cytometry,” an image-free ultrafast fluorescence “imaging” cytometry based on a single-pixel detector. Spatial information obtained from the motion of cells relative to a static randomly patterned optical structure is compressively converted into signals that arrive sequentially at a single-pixel detector. Combinatorial use of the temporal waveform with the intensity distribution of the random pattern allows us to computationally reconstruct cell morphology. More importantly, we show that applying machine-learning methods directly on the compressed waveforms without image reconstruction enables efficient image-free morphology-based cytometry. Despite a compact and inexpensive instrumentation, image-free ghost cytometry achieves accurate and high-throughput cell classification and selective sorting on the basis of cell morphology without a specific biomarker, both of which have been challenging to accomplish using conventional flow cytometers.

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.