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A historical description of Wallace H. Coulter's invention and development of his companies' instrumentation for analysis of blood cells or particles has never been available, and too often, this void has been addressed by conjectural or promotional publications. A history thesis, based on the author's access to Coulter's personal papers and to files kept by his father, Joseph R. Coulter, Sr., has recently become available; for the first time Coulter's developmental process is factually detailed against the historical context in which it occurred. This paper is an introductory overview of that developmental process as discussed therein. It should be of interest to not only the ISAC membership, but to Cytometry's broader readership as well. [ABSTRACT FROM AUTHOR]

This paper reported a micro flow cytometer capable of high‐throughput characterization of single‐cell electrical and structural features based on constrictional microchannels and deep neural networks. When single cells traveled through microchannels with constricted cross‐sectional areas, they effectively blocked concentrated electric field lines, producing large impedance variations. Meanwhile, the traveling cells were confined within the cross‐sectional areas of the constrictional microchannels, enabling the capture of high‐quality images without losing focuses. Then single‐cell features from impedance profiles and optical images were extracted from customized recurrent and convolution networks (RNN and CNN), which were further fused for cell‐type classification based on support vector machines (SVM). As a demonstration, two leukemia cell lines (e.g., HL60 vs. Jurkat) were analyzed, producing high‐classification accuracies of 99.3% based on electrical features extracted from Long Short‐Term Memory (LSTM) of RNN, 96.7% based on structural features extracted from Resnet18 of CNN and 100.0% based on combined features enabled by SVM. The microfluidic flow cytometry developed in this study may provide a new perspective for the field of single‐cell analysis. [ABSTRACT FROM AUTHOR]

This paper developed an electrical micro flow cytometry to realize leukocyte differentials leveraging a constrictional microchannel and a deep neural network. Firstly, purified granulocytes, lymphocytes or monocytes traveled through the constrictional microchannel with a cross‐sectional area marginally larger than individual cells and produced large impedance variations by blocking focused electric field lines. By optimizing key elements (e.g., normalization, learning rate, batch size and neuron number) of the recurrent neural network (RNN), electrical results of purified leukocytes were analyzed to establish a leukocyte differential system with a classification accuracy of 95.2%. Then the leukocyte mixtures were forced to travel through the same constrictional microchannel, producing mixed impedance profiles which were classified into granulocytes, lymphocytes and monocytes based on the aforementioned differential system. As to the classification results, two leukocyte mixtures from the same donor were processed, producing comparable classification results, which were 57% versus 59% of granulocytes, 37% versus 34% of lymphocytes and 6% versus 7% of monocytes. These results validated the established classification system based on the constrictional microchannel and the recurrent neural network, providing a new perspective of differentiating white blood cells by electrical flow cytometry. [ABSTRACT FROM AUTHOR]

Flow cytometry (FCM) is currently the most widely‐used method to establish nuclear DNA content in plants. Since simple, 1‐3‐parameter, flow cytometers, which are sufficient for most plant applications, are commercially available at a reasonable price, the number of laboratories equipped with these instruments, and consequently new FCM users, has greatly increased over the last decade. This paper meets an urgent need for comprehensive recommendations for best practices in FCM for different plant science applications. We discuss advantages and limitations of establishing plant ploidy, genome size, DNA base composition, cell cycle activity, and level of endoreduplication. Applications of such measurements in plant systematics, ecology, molecular biology research, reproduction biology, tissue cultures, plant breeding, and seed sciences are described. Advice is included on how to obtain accurate and reliable results, as well as how to manage troubleshooting that may occur during sample preparation, cytometric measurements, and data handling. Each section is followed by best practice recommendations; tips as to what specific information should be provided in FCM papers are also provided. [ABSTRACT FROM AUTHOR]

Briefly depicted are the publications in CYTOMETRY that received the highest frequency of citations. Among them are seminal papers describing application of metachromatic fluorochrome acridine orange to differentially stain DNA versus RNA or to analyze susceptibility of DNA in situ to denaturation; both features being markers of different sections of the cell cycle including identification of noncycling quiescent cells. The papers reviewing detection of cyclins D1, E, A or B1, each in relation to cell cycle phase, were also among the highly cited ones. The highest citation rates received publications describing development of the TUNEL methodology to detect apoptotic DNA fragmentation, and more recently expression of ϒH2AX to reveal DNA damage. © 2020 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Essential thrombocythemia (ET) is an uncommon situation in which the body produces too many platelets. This can cause blood clots anywhere in the body and results in various symptoms and even strokes or heart attacks. Removing excessive platelets using acoustofluidic methods receives extensive attention due to their high efficiency and high yield. While the damage to the remaining cells, such as erythrocytes and leukocytes is yet evaluated. Existing cell damage evaluation methods usually require cell staining, which are time‐consuming and labor‐intensive. In this paper, we investigate cell damage by optical time‐stretch (OTS) imaging flow cytometry with high throughput and in a label‐free manner. Specifically, we first image the erythrocytes and leukocytes sorted by acoustofluidic sorting chip with different acoustic wave powers and flowing speed using OTS imaging flow cytometry at a flowing speed up to 1 m/s. Then, we employ machine learning algorithms to extract biophysical phenotypic features from the cellular images, as well as to cluster and identify images. The results show that both the errors of the biophysical phenotypic features and the proportion of abnormal cells are within 10% in the undamaged cell groups, while the errors are much greater than 10% in the damaged cell groups, indicating that acoustofluidic sorting causes little damage to the cells within the appropriate acoustic power, agreeing well with clinical assays. Our method provides a novel approach for high‐throughput and label‐free cell damage evaluation in scientific research and clinical settings. [ABSTRACT FROM AUTHOR]

In biomedicine, the automatic processing of medical microscope images plays a key role in the subsequent analysis and diagnosis. Cell or nucleus segmentation is one of the most challenging tasks for microscope image processing. Due to the frequently occurred overlapping, few segmentation methods can achieve satisfactory segmentation accuracy yet. In this paper, we propose an approach to separate the overlapped cells or nuclei based on the outer Canny edges and morphological erosion. The threshold selection is first used to segment the foreground and background of cell or nucleus images. For each binary connected domain in the segmentation image, an intersection based edge selection method is proposed to choose the outer Canny edges of the overlapped cells or nuclei. The outer Canny edges are used to generate a binary cell or nucleus image that is then used to compute the cell or nucleus seeds by the proposed morphological erosion method. The nuclei of the Human U2OS cells, the mouse NIH3T3 cells and the synthetic cells are used for evaluating our proposed approach. The quantitative quantification accuracy is computed by the Dice score and 95.53% is achieved by the proposed approach. Both the quantitative and the qualitative comparisons show that the accuracy of the proposed approach is better than those of the area constrained morphological erosion (ACME) method, the iterative erosion (IE) method, the morphology and watershed (MW) method, the Generalized Laplacian of Gaussian filters (GLGF) method and ellipse fitting (EF) method in separating the cells or nuclei in three publicly available datasets. [ABSTRACT FROM AUTHOR]

The development of mouse spermatozoa is a continuous process from spermatogonia, spermatocytes, spermatids to mature sperm. Those developing germ cells (spermatogonia, spermatocyte, and spermatids) together with supporting sertoli cells are all enclosed inside seminiferous tubules of the testis, their identification is key to testis histology and pathology analysis. Automated segmentation of all these cells is a challenging task because of their dynamical changes in different stages. The accurate segmentation of testicular cells is critical in developing computerized spermatogenesis staging. In this paper, we present a novel segmentation model, SED‐Net, which incorporates a squeeze‐and‐excitation (SE) module and a dense unit. The SE module optimizes and obtains features from different channels, whereas the dense unit uses fewer parameters to enhance the use of features. A human‐in‐the‐loop strategy, named deep interactive learning, is developed to achieve better segmentation performance while reducing the workload of manual annotation and time consumption. Across a cohort of 274 seminiferous tubules from stages VI to VIII, the SED‐Net achieved a pixel accuracy of 0.930, a mean pixel accuracy of 0.866, a mean intersection over union of 0.710, and a frequency weighted intersection over union of 0.878, respectively, in terms of four types of testicular cell segmentation. There is no significant difference between manual annotated tubules and segmentation results by SED‐Net in cell composition analysis for tubules from stages VI to VIII. In addition, we performed cell composition analysis on 2346 segmented seminiferous tubule images from 12 segmented testicular section results. The results provided quantitation of cells of various testicular cell types across 12 stages. The rule reflects the cell variation tendency across 12 stages during development of mouse spermatozoa. The method could enable us to not only analyze cell morphology and staging during the development of mouse spermatozoa but also potentially could be applied to the study of reproductive diseases such as infertility. [ABSTRACT FROM AUTHOR]

This paper reported a microfluidic platform which realized the characterization of inherent single‐cell biomechanical and bioelectrical parameters simultaneously. Individual cells traveled through a constriction channel with deformation images and impedance variations captured and processed into cortical tension Tc, specific membrane capacitance Csm, and cytoplasmic conductivity σcy based on an equivalent biophysical model. These properties of thousands of individual cells of K562, Jurkat, HL‐60, HL‐60 treated with paraformaldehyde (PA)/cytochalasin D (CD)/concanavalin A (ConA), granulocytes of Donor 1, Donor 2, and Donor 3 were quantified for the first time. Leveraging Tc, Csm, and σcy, (1) high accuracies of classifying wild‐type and processed HL‐60 cells (e.g., 93.5% of PA treated vs. CD treated HL‐60 cells) were realized, revealing the effectiveness of using these three biophysical parameters in cell‐type classification; (2) low accuracies of classifying normal granulocytes from three donors (e.g., 56.4% of Donor 1 vs. 2), indicating comparable parameters for normal granulocytes. In conclusion, this platform can characterize single‐cell Tc, Csm, and σcy concurrently and quantify multiple parameters in single‐cell analysis. [ABSTRACT FROM AUTHOR]

Förster resonance energy transfer (FRET) is the direct energy exchange between two‐component fluorescent molecules. FRET methods utilize chemically linked molecules or unlinked fluorescent molecules such as fluoresscent protein–protein interactions. FRET is therefore a powerful indicator of molecular proximity, but standardized determination of FRET efficiency is challenged when investigating natural (chemically unlinked) interactions. In this paper, we have examined the interactions of tumor necrosis factor receptor‐1 (TNFR1) molecules expressed as recombinant C‐terminal fusion proteins of cyan, yellow, or red fluorescent protein (‐CFP, ‐YFP, or ‐RFP) to evaluate two‐molecule chemically unlinked FRET by flow cytometry. We demonstrate three independent FRET pairs of TNFR1 CFP→YFP (FRET‐1), YFP→RFP (FRET‐2) and CFP→RFP (FRET‐3), by comparing TNFR1+TNFR1 with non‐interacting TNFR1+CD27 proteins, on both LSR‐II and Fortessa X‐20 cytometers. We describe genuine FRET activities reflecting TNFR1 homotypic interactions. The FRET events can be visualized during sample acquisition via the use of "spiked" FRET donor cells, together with TNFR1+TNFR1 co‐transfected cells, as FRET channel mean fluorescence intensity (MFI) overlays. FRET events can also be indicated by comparing concatenated files of cells expressing either FRET positive events (TNFR1+TNFR1) or FRET negative events (TNFR1+CD27) to generate single‐cell scatter plots showing loss of FRET donor brightness. Robust determination of FRET efficiency is then confirmed at the single‐cell level by applying matrix calculations based on the measurements of FRET, using donor, acceptor, and FRET fluorescent intensities (I), detector channel emission coefficient (S), fluorescent protein extinction coefficients (ε) and the α factor. In this TNFR1‐based system the mean CFP→YFP FRET‐1 efficiency is 0.43 (LSR‐II) and 0.41 (Fortessa X‐20), the mean YFP→RFP FRET‐2 efficiency is 0.30 (LSR‐II) and 0.29 (Fortessa X‐20), and the mean CFP→RFP FRET‐3 efficiency is 0.56 (LSR‐II) and 0.54 (Fortessa X‐20). This study also embraces multi‐dimensional clustering using t‐SNE, Fit‐SNE, UMAP, Tri‐Map and PaCMAP to further demonstrate FRET. These approaches establish a robust system for standardized detection of chemically unlinked TNFR1 homotypic interactions with three individual FRET pairs. [ABSTRACT FROM AUTHOR]

This month's cover is the artistic rendering the Original Article by Mestrum and colleagues on "Proliferative and anti‐apoptotic fractions in maturing hematopoietic cell lineages and their role in homeostasis of normal bone marrow." This research paper is the Editor's Choice for July 2022 (Read the full article on Page 552). Cover design by Bärbel Beran, http://www.beran‐design.de/)." [ABSTRACT FROM AUTHOR]

Abstract: The in vitro micronucleus (MN) assay is a well‐established test for evaluating genotoxicity and cytotoxicity. The use of manual microscopy to perform the assay can be laborious and often suffers from user subjectivity and interscorer variability. Automated methods including slide‐scanning microscopy and conventional flow cytometry have been developed to eliminate scorer bias and improve throughput. However, these methods possess several limitations such as lack of cytoplasmic visualization using slide‐scanning microscopy and the inability to visually confirm the legitimacy of MN or storage of image data for re‐evaluation using flow cytometry. The ImageStreamX® MK II (ISX) imaging flow cytometer has been demonstrated to overcome all of these limitations. The ISX combines the speed, statistical robustness, and rare event capture capability of conventional flow cytometry with high resolution fluorescent imagery of microscopy and possesses the ability to store all collected image data. This paper details the methodology developed to perform the in vitro MN assay in human lymphoblastoid TK6 cells on the ISX. High resolution images of micronucleated mono‐ and bi‐nucleated cells as well as polynucleated cells can be acquired at a high rate of capture. All images can then be automatically identified, categorized and enumerated in the data analysis software that accompanies the ImageStream, allowing for the scoring of both genotoxicity and cytotoxicity. The results demonstrate that statistically significant increases in MN frequency when compared with solvent controls can be detected at varying levels of cytotoxicity following exposure to well‐known aneugens and clastogens. This work demonstrates a fully automated method for performing the in vitro micronucleus assay on the ISX imaging flow cytometry platform. © 2018 The Author. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC. [ABSTRACT FROM AUTHOR]

Multicolor approaches are challenging for microbial flow cytometry; as flow cytometers are mainly developed for biomedical applications, modern instruments contain more detectors than needed. Some of these additional fluorescence detectors measure biological information due to spectral overlap, yet the extent to which this information is relevant for the identification of bacterial populations is ambiguous. In this paper we characterize the usefulness of these additional detectors. We propose a data-driven detector selection method to select the smallest subset of detectors that will optimally discriminate between bacterial populations. Using a detector elimination strategy, we show that one or more detectors can be removed without loss of resolving power. A number of additional detectors are included in the final subset, which help to improve the identification of bacterial populations. Experimental data were retrieved from two types of modern cytometers with different configurations. The method reveals a clear ordering of detector importances, which depends on the instrument from which the data were retrieved. In addition, we were able to pinpoint unexpected behavior of SYBR Green I in the red spectrum. As the field of microbial flow cytometry is maturing, these results motivate the construction of a different kind of cytometric instruments for microbiologists, for which the number of detectors is reduced, but tailored toward the characteristics of microbial experiments. © 2017 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Neoadjuvant treatment (NAT) of breast cancer (BCa) is an option for patients with the locally advanced disease. It has been compared with standard adjuvant therapy with the aim of improving prognosis and surgical outcome. Moreover, the response of the tumor to the therapy provides useful information for patient management. The pathological examination of the tissue sections after surgery is the gold-standard to estimate the residual tumor and the assessment of cellularity is an important component of tumor burden assessment. In the current clinical practice, tumor cellularity is manually estimated by pathologists on hematoxylin and eosin (H&E) stained slides, the quality, and reliability of which might be impaired by inter-observer variability which potentially affects prognostic power assessment in NAT trials. This procedure is also qualitative and time-consuming. In this paper, we describe a method of automatically assessing cellularity. A pipeline to automatically segment nuclei figures and estimate residual cancer cellularity from within patches and whole slide images (WSIs) of BCa was developed. We have compared the performance of our proposed pipeline in estimating residual cancer cellularity with that of two expert pathologists. We found an intra-class agreement coefficient (ICC) of 0.89 (95% CI of [0.70, 0.95]) between pathologists, 0.74 (95% CI of [0.70, 0.77]) between pathologist #1 and proposed method, and 0.75 (95% CI of [0.71, 0.79]) between pathologist #2 and proposed method. We have also successfully applied our proposed technique on a WSI to locate areas with high concentration of residual cancer. The main advantage of our approach is that it is fully automatic and can be used to find areas with high cellularity in WSIs. This provides a first step in developing an automatic technique for post-NAT tumor response assessment from pathology slides. © 2017 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

We present cytometric classification of live healthy and cancerous cells by using the spatial morphological and textural information found in the label-free quantitative phase images of the cells. We compare both healthy cells to primary tumor cells and primary tumor cells to metastatic cancer cells, where tumor biopsies and normal tissues were isolated from the same individuals. To mimic analysis of liquid biopsies by flow cytometry, the cells were imaged while unattached to the substrate. We used low-coherence off-axis interferometric phase microscopy setup, which allows a single-exposure acquisition mode, and thus is suitable for quantitative imaging of dynamic cells during flow. After acquisition, the optical path delay maps of the cells were extracted and then used to calculate 15 parameters derived from the cellular 3D morphology and texture. Upon analyzing tens of cells in each group, we found high statistical significance in the difference between the groups in most of the parameters calculated, with the same trends for all statistically significant parameters. Furthermore, a specially designed machine learning algorithm, implemented on the phase map extracted features, classified the correct cell type (healthy/cancer/metastatic) with 81-93% sensitivity and 81-99% specificity. The quantitative phase imaging approach for liquid biopsies presented in this paper could be the basis for advanced techniques of staging freshly isolated live cancer cells in imaging flow cytometers. © 2017 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Islet cell quantification and function is important for developing novel therapeutic interventions for diabetes. Existing methods of pancreatic islet segmentation in histopathological images depend strongly on cell/nuclei detection, and thus are limited due to a wide variance in the appearance of pancreatic islets. In this paper, we propose a supervised learning pipeline to segment pancreatic islets in histopathological images, which does not require cell detection. The proposed framework firstly partitions images into superpixels, and then extracts multi-scale color-texture features from each superpixel and processes these features using rolling guidance filters, in order to simultaneously reduce inter-class ambiguity and intra-class variation. Finally, a linear support vector machine (SVM) is trained and applied to segment the testing images. A total of 23 hematoxylin-and-eosin-stained histopathological images with pancreatic islets are used for verifying the framework. With an average accuracy of 95%, training time of 20 min and testing time of 1 min per image, the proposed framework outperforms existing approaches with better segmentation performance and lower computational cost. © 2016 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Precision in flow cytometry depends on many factors, the first of which is accurate and stable positioning of the hydrodynamically focused cells. However, no method exists to evaluate the stability of laminar flow and single-cell flow in the flow chamber of the flow cytometer directly because of the small size of the rectangular channel of the flow chamber. In this paper, a method of high-speed particle image velocimetry is proposed to solve this problem. The velocity stability of the particles in the flow chamber is used to evaluate the flow stability of the fluid path of the flow cytometer. The side scattering images of particles are obtained by a high-speed camera. Upon exposure, cells were imaged at random positions in the flow cell, resulting in four different types of the images: blank, inadequate, normal, or overlapped. Normal images were identified utilizing a grey cluster analysis algorithm based on trapezoid whitenization weight functions. A mid-point method is applied to determine the length of the particle track at a fixed exposure time. The variation of the trajectory lengths of the normal images are used to evaluate the stability of the liquid path. Experiments are carried out to verify the feasibility of our method in which different diameter microspheres at different flow rates. The results indicate that the standard deviation and relative standard deviation of the trajectory lengths can be used as the evaluation indices of the liquid path stability of the flow cytometer. © 2016 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

Formamide has long been one of the most widely used reagents in the study of nucleic acids. However, the use of formamide for treating cells to be analyzed by flow cytometry is a recent development and is restricted to measuring telomere lengths by flow-FISH. In this field, we have published several papers in order to observe the effects of formamide treatment on cells at room temperature. We therefore discovered that, with suitable modifications, a short and simple incubation in this ionizing solvent facilitates cell cycle analysis by flow cytometry, equivalent or superior to that obtained with treatments in alcohol, acetone or detergent in hypotonic solution. Even using a bulky and problematic stain (low quantum efficiency and G-C base preference), such as 7-aminoactinomycin D (7-AAD) which, on the other hand, has the advantage of being excited at 488 nm and does not bind to the RNA, it is possible to obtain excellent coefficients of variation and (G2-M) mode/(G0-G1) mode ratios. These parameters, especially if stained cells are washed before acquisition, arrive at optimal values. It is noteworthy that the ability to wash the cells stained for DNA content analysis without affecting the stoichiometry of the staining has not been described elsewhere in the literature. With formamide treatment the doublets are practically absent, sample recovery is efficient, as well as the preservation of physical parameters, and the stained cells can be stored for at least 10 days at room temperature before acquisition. © 2016 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

We present an algorithm for modeling flow cytometry data in the presence of large inter-sample variation. Large-scale cytometry datasets often exhibit some within-class variation due to technical effects such as instrumental differences and variations in data acquisition, as well as subtle biological heterogeneity within the class of samples. Failure to account for such variations in the model may lead to inaccurate matching of populations across a batch of samples and poor performance in classification of unlabeled samples. In this paper, we describe the Joint Clustering and Matching (JCM) procedure for simultaneous segmentation and alignment of cell populations across multiple samples. Under the JCM framework, a multivariate mixture distribution is used to model the distribution of the expressions of a fixed set of markers for each cell in a sample such that the components in the mixture model may correspond to the various populations of cells, which have similar expressions of markers (that is, clusters), in the composition of the sample. For each class of samples, an overall class template is formed by the adoption of random-effects terms to model the inter-sample variation within a class. The construction of a parametric template for each class allows for direct quantification of the differences between the template and each sample, and also between each pair of samples, both within or between classes. The classification of a new unclassified sample is then undertaken by assigning the unclassified sample to the class that minimizes the distance between its fitted mixture density and each class density as provided by the class templates. For illustration, we use a symmetric form of the Kullback-Leibler divergence as a distance measure between two densities, but other distance measures can also be applied. We show and demonstrate on four real datasets how the JCM procedure can be used to carry out the tasks of automated clustering and alignment of cell populations, and supervised classification of samples. © 2015 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

As the technology of cytometry matures, there is mounting pressure to address two major issues with data analyses. The first issue is to develop new analysis methods for high-dimensional data that can directly reveal and quantify important characteristics associated with complex cellular biology. The other issue is to replace subjective and inaccurate gating with automated methods that objectively define subpopulations and account for population overlap due to measurement uncertainty. Probability state modeling (PSM) is a technique that addresses both of these issues. The theory and important algorithms associated with PSM are presented along with simple examples and general strategies for autonomous analyses. PSM is leveraged to better understand B-cell ontogeny in bone marrow in a companion Cytometry Part B manuscript. Three short relevant videos are available in the online supporting information for both of these papers. PSM avoids the dimensionality barrier normally associated with high-dimensionality modeling by using broadened quantile functions instead of frequency functions to represent the modulation of cellular epitopes as cells differentiate. Since modeling programs ultimately minimize or maximize one or more objective functions, they are particularly amenable to automation and, therefore, represent a viable alternative to subjective and inaccurate gating approaches. © 2015 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

In March 2020, with lockdown due to the coronavirus pandemic underway, the Francis Crick Institute (the Crick) regeared its research laboratories into clinical testing facilities. Two pipelines were established, one for polymerase chain reaction and the other for Serology. This article discusses the Cricks Flow Cytometry Science Technology Platform (Flow STP) role in setting up the Serology pipeline. Pipeline here referring to the overarching processes in place to facilitate the receipt of human sera through to a SARs‐CoV‐2 enzyme‐linked immunosorbent assay result. We examine the challenges that had to be overcome by a research laboratory to incorporate clinical diagnostics and the processes by which this was achieved. It describes the governance required to run the service, the design of the standard operating procedures (SOPs) and pipeline, the setting up of the assay, the validation required to show the robustness of the pipeline and reporting the results of the assay. Finally, as the lockdown started to ease in June 2020, it examines how this new service affects the daily running of the Flow STP. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC on behalf of International Society for Advancement of Cytometry. [ABSTRACT FROM AUTHOR]

We present a model-based clustering method, SWIFT (Scalable Weighted Iterative Flow-clustering Technique), for digesting high-dimensional large-sized datasets obtained via modern flow cytometry into more compact representations that are well-suited for further automated or manual analysis. Key attributes of the method include the following: (a) the analysis is conducted in the multidimensional space retaining the semantics of the data, (b) an iterative weighted sampling procedure is utilized to maintain modest computational complexity and to retain discrimination of extremely small subpopulations (hundreds of cells from datasets containing tens of millions), and (c) a splitting and merging procedure is incorporated in the algorithm to preserve distinguishability between biologically distinct populations, while still providing a significant compaction relative to the original data. This article presents a detailed algorithmic description of SWIFT, outlining the application-driven motivations for the different design choices, a discussion of computational complexity of the different steps, and results obtained with SWIFT for synthetic data and relatively simple experimental data that allow validation of the desirable attributes. A companion paper (Part 2) highlights the use of SWIFT, in combination with additional computational tools, for more challenging biological problems. © 2014 The Authors. Published by Wiley Periodicals Inc. [ABSTRACT FROM AUTHOR]

Flow cytometry, in conjunction with immunoprecipitation (IP-FCM), is suggested to have some advantages to conventional IP-western blot technology in analyzing protein complexes. In this paper, to further examine its practicability, we test the use of IP-FCM in detecting the HSP90 complex, which has gained importance in drug research and development and involves more than a dozen components. We found that IP-FCM could effectively detect HSP70, p23, Cdc37, and Cdk6 components in the HSP90 complex naturally formed in U937 cells when this complex was captured by anti-HSP90 antibody-coated polystyrene microspheres. IP-FCM could also detect alteration in components caused by treating cells with HSP90 inhibitors. In a cell-free environment, IP-FCM could detect the direct effects of ATP and/or HSP90 inhibitors (17-N-allylamino-17-demethoxygeldanamycin or celastrol) in causing component dissociation and the time- and dose-effects of inhibitor-caused dissociation. IP-FCM is a practical and powerful platform for analyzing HSP90 complex components, and is thus a useful tool in studying HSP90 complex function and screening inhibitors. © 2013 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

The objective evaluation of the color and shade in stained images remains unsolved and is frequently and extensively encountered in biomedical studies. Most of the evaluations on the color and shade in the stained images are currently performed by subjective grading, which is prone to be affected by inter-reader variation. This paper introduces a novel approach to automatically quantify the color and shade in the stained histological image based on its similarity map in the CIELAB color space with respect to a user specified reference color. The proposed algorithm was applied on three datasets, i.e., a phantom image, the Prussian blue staining of human osteosarcoma cell culture, and histological sections of the Prussian blue stained rat kidney, liver and spleen. The result shows that our method is able to represent the color and shade as a numerical value that correlated well with human perception. © 2011 International Society for Advancement of Cytometry [ABSTRACT FROM AUTHOR]

The purpose of this document is to provide guidance for establishing and maintaining growth and development of flow cytometry shared resource laboratories. While the best practices offered in this manuscript are not intended to be universal or exhaustive, they do outline key goals that should be prioritized to achieve operational excellence and meet the needs of the scientific community. Additionally, this document provides information on available technologies and software relevant to shared resource laboratories. This manuscript builds on the work of Barsky et al. 2016 published in Cytometry Part A and incorporates recent advancements in cytometric technology. A flow cytometer is a specialized piece of technology that require special care and consideration in its housing and operations. As with any scientific equipment, a thorough evaluation of the location, space requirements, auxiliary resources, and support is crucial for successful operation. This comprehensive resource has been written by past and present members of the International Society for Advancement of Cytometry (ISAC) Shared Resource Laboratory (SRL) Emerging Leaders Program https://isac-net.org/general/custom.asp?page=SRL-Emerging-Leaders with extensive expertise in managing flow cytometry SRLs from around the world in different settings including academia and industry. It is intended to assist in establishing a new flow cytometry SRL, re‐purposing an existing space into such a facility, or adding a flow cytometer to an individual lab in academia or industry. This resource reviews the available cytometry technologies, the operational requirements, and best practices in SRL staffing and management. [ABSTRACT FROM AUTHOR]

Hematopoietic stem cells are key players in hematopoiesis as the body maintains a physiologic steady state, and the signaling pathways and control mechanisms of these dynamic cells are implicated in processes from inflammation to cancer. Although the bone marrow is commonly regarded as the site of hematopoiesis and hematopoietic stem cell residence, these cells also circulate in the blood and reside in extramedullary tissues, including the lungs. Flow cytometry is an invaluable tool in evaluating hematopoietic stem cells, revealing their phenotypes and relative abundances in both healthy and diseased states. This review outlines current protocols and cell markers used in flow cytometric analysis of hematopoietic stem and progenitor cell populations. Specific niches within the bone marrow are discussed, as are metabolic processes that contribute to stem cell self‐renewal and differentiation, as well as the role of hematopoietic stem cells outside of the bone marrow at physiologic steady state. Finally, pulmonary extramedullary hematopoiesis and its associated disease states are outlined. Hematopoiesis in the lungs is a new and emerging concept, and discovering ways in which the study of lung‐resident hematopoietic stem cells can be translated from murine models to patients will impact clinical treatment. [ABSTRACT FROM AUTHOR]

Peptide presentation by MHC class I and MHC class II molecules plays important roles in the regulation of the immune response. One factor in these displays is the density of antigen, which must exceed a critical threshold for the effective activation of T cells. Nonrandom distribution of MHC class I and class II has already been detected at the nanometer level and at higher hierarchical levels. It is not clear how the absence and reappearance of some protein molecules can influence the nonrandom distribution. Therefore, we performed experiments on HLA II‐deficient bare lymphocyte syndrome (BLS1) cells: we created a stable transfected cell line, tDQ6‐BLS‐1, and were able to detect the effect of the appearance of HLA‐DQ6 molecules on the homo and heteroassociation of different cell surface molecules by comparing Förster resonance energy transfer (FRET) efficiency on transfected cells to that on nontransfected BLS‐1 and JY human B‐cell lines. Our FRET results show a decrease in homoassociation FRET between HLA I chains in HLA‐DQ6‐transfected tDQ6‐BLS‐1 cells compared with the parent BLS‐1 cell line and an increase in heteroassociation FRET between HLA I and HLA II (compared with JY cells), suggesting a similar pattern of antigen presentation by the HLA‐DQ6 allele. Transmission electron microscopy (TEM) revealed that both HLA class I and class II molecules formed clusters at higher hierarchical levels on the tDQ6‐BLS‐1 cells, and the de novo synthesized HLA DQ molecules did not intersperse with HLA class I islands. These observations could be important in understanding the fine tuning of the immune response. [ABSTRACT FROM AUTHOR]

The analysis of large amounts of data is important for the development of machine learning (ML) models. flowSim is the first algorithm designed to visualize, detect and remove highly redundant information in flow cytometry (FCM) training sets to decrease the computational time for training and increase the performance of ML algorithms by reducing overfitting. flowSim performs near duplicate image detection by combining community detection algorithms with the density analysis of the marker expression values. flowSim clustering compared to consensus manual clustering on a dataset composed of 160 images of bivariate FCM data had a mean Adjusted Rand Index of 0.90, demonstrating its efficiency in identifying similar patterns. flowSim selectively discarded near duplicate files in datasets constructed with known redundancy, and removed 92.6% of FCM images in a dataset of over 500,000 drawn from public repositories. [ABSTRACT FROM AUTHOR]

Cell identification and analysis play a crucial role in many biology‐ and health‐related applications. The internal and surface structures of a cell are complex and many of the features are sub‐micron in scale. Well‐resolved images of these features cannot be obtained using optical microscopy. Previous studies have reported that the single‐cell angular laser‐light scattering patterns (ALSP) can be used for label‐free cell identification and analysis. The ALSP can be affected by cell properties and the wavelength of the probing laser. Two cell properties, cell surface roughness and the number of mitochondria, are investigated in this study. The effects of probing laser wavelengths (blue, green, and red) and the directions of scattered light collection (forward, side, and backward) are studied to determine the optimum conditions for distinguishing the two cell properties. Machine learning (ML) analysis has been applied to ALSP obtained from numerical simulations. The results of ML analysis show that the backward scattering is the best direction for characterizing the surface roughness, while the forward scattering is the best direction for differentiating the number of mitochondria. The laser light having red or green wavelength is found to perform better than that having the blue wavelength in differentiating the surface roughness and the number of mitochondria. This study provides important insights into the effects of probing laser wavelength on gaining information about cells from their ALSP. [ABSTRACT FROM AUTHOR]

Very small embryonic like stem cells (VSELs) are a dormant population of stem cells that, as proposed, are deposited during embryogenesis in various tissues, including bone marrow (BM). These cells are released under steady state conditions from their tissue locations and circulate at a low level in peripheral blood (PB). Their number increases in response to stressors as well as tissue/organ damage. This increase is evident during neonatal delivery, as delivery stress prompts enrichment of umbilical cord blood (UCB) with VSELs. These cells could be purified from BM, PB, and UCB by multiparameter sorting as a population of very small CXCR4+Lin−CD45− cells that express the CD34 or CD133 antigen. In this report, we evaluated a number of CD34+Lin−CD45− and CD133+Lin−CD45− UCB‐derived VSELs. We also performed initial molecular characterization of both cell populations for expression of selected pluripotency markers and compared these cells at the proteomic level. We noticed that CD133+Lin−CD45− population is more rare and express, at a higher level, mRNA for pluripotency markers Oct‐4 and Nanog as well as the stromal‐derived factor‐1 (SDF‐1) CXCR4 receptor that regulates trafficking of these cells, however both cells population did not significantly differ in the expression of proteins assigned to main biological processes. [ABSTRACT FROM AUTHOR]

Ultraviolet lasers are commonly used in flow cytometry to excite fluorochrome molecules with subsequent measurement of the specific fluorescence of individual cells. In this study, the performance of the ultraviolet light scattering (UVLS) in the analysis of individual particles with flow cytometry has been demonstrated for the first time. The main advantage of the UVLS relates to the improvement of the analysis of submicron particles due to the strong dependence of the scattering efficiency on the wavelength of the incident light. In this work, submicron particles were analyzed using a scanning flow cytometer (SFC) that allows measurements of light scattering in an angle‐resolved regime. The measured light‐scattering profiles of individual particles were utilized in solution of the inverse light‐scattering problem to retrieve the particle characteristics using a global optimization. The standard polystyrene microspheres were successfully characterized from the analysis of UVLS which provided the size and refractive index (RI) of individual beads. We believe that the main application of UVLS relates to the analysis of microparticles in a serum, in particular in the analysis of chylomicrons (CMs). We have demonstrated the performance of the UVLS SFC in the analysis of CMs of a donor. The RI versus size scatterplot of CMs was successfully retrieved from the analysis. The current set‐up of the SFC has allowed us to characterize individual CMs starting from the size of 160 nm that provides determination of the CM concentration in a serum with flow cytometry. This feature of the UVLS should help with the analysis of lipid metabolism measuring RI and size map evolution after lipase action. [ABSTRACT FROM AUTHOR]