Computational methods for identification of cell populations from polychromatic flow cytometry data are changing the paradigm of cytometry bioinformatics. Data clustering is the most common computational approach to unsupervised identification of cell populations from multidimensional cytometry data. However, interpretation of the identified data clusters is labor-intensive. Certain types of user-defined cell populations are also difficult to identify by fully automated data clustering analysis. Both are roadblocks before a cytometry lab can adopt the data clustering approach for cell population identification in routine use. We found that combining recursive data filtering and clustering with constraints converted from the user manual gating strategy can effectively address these two issues. We named this new approach DAFi: Directed Automated Filtering and Identification of cell populations. Design of DAFi preserves the data-driven characteristics of unsupervised clustering for identifying novel cell subsets, but also makes the results interpretable to experimental scientists through mapping and merging the multidimensional data clusters into the user-defined two-dimensional gating hierarchy. The recursive data filtering process in DAFi helped identify small data clusters which are otherwise difficult to resolve by a single run of the data clustering method due to the statistical interference of the irrelevant major clusters. Our experiment results showed that the proportions of the cell populations identified by DAFi, while being consistent with those by expert centralized manual gating, have smaller technical variances across samples than those from individual manual gating analysis and the nonrecursive data clustering analysis. Compared with manual gating segregation, DAFi-identified cell populations avoided the abrupt cut-offs on the boundaries. DAFi has been implemented to be used with multiple data clustering methods including K-means, FLOCK, FlowSOM, and the ClusterR package. For cell population identification, DAFi supports multiple options including clustering, bisecting, slope-based gating, and reversed filtering to meet various autogating needs from different scientific use cases. © 2018 International Society for Advancement of Cytometry.

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Neutrophils are the most abundant white blood cells in the human body responsible for fighting viral, bacterial and fungi infections. Out of the 100 billion neutrophils produced daily, it is estimated that 10 % of these cells end up in oral biofluids. Because saliva is a fluid accessible through non-invasive techniques, it is an optimal source of cells and molecule surveillance in health and disease. While neutrophils are abundant in saliva, scientific advancements in neutrophil biology have been hampered likely due to their short life span, inability to divide once terminally differentiated, sensitivity to physical stress, and low RNA content. Here, we devise a protocol aiming to understand neutrophil heterogeneity by improving isolation methods, single-cell RNA extraction, sequencing and bioinformatic pipelines. Advanced flow cytometry 3D analysis, and machine learning validated our gating system model, by including positive neutrophil markers and excluding other immune cells and uncovered neutrophil heterogeneity. Considering specific cell markers, unique mitochondrial content, stringent and less stringent filtering strategies, our transcriptome single cell findings unraveled novel neutrophil subpopulations. Collectively, this methodology accelerates the discovery of salivary immune landscapes, with the promise of improving the understanding of diversification mechanisms, clinical diagnostics in health and disease, and guide targeted therapies.

High‐throughput single‐cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally‐optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Abstract Neutrophils are the most abundant white blood cells in the human body responsible for fighting viral, bacterial and fungi infections. Out of the 100 billion neutrophils produced daily, it is estimated that 10 % of these cells end up in oral biofluids. Because saliva is a fluid accessible through non-invasive techniques, it is an optimal source of cells and molecule surveillance in health and disease. While neutrophils are abundant in saliva, scientific advancements in neutrophil biology have been hampered likely due to their short life span, inability to divide once terminally differentiated, sensitivity to physical stress, and low RNA content. Here, we devise a protocol aiming to understand neutrophil heterogeneity by improving isolation methods, single-cell RNA extraction, sequencing and bioinformatic pipelines. Advanced flow cytometry 3D analysis, and machine learning validated our gating system model, by including positive neutrophil markers and excluding other immune cells and uncovered neutrophil heterogeneity. Considering specific cell markers, unique mitochondrial content, stringent and less stringent filtering strategies, our transcriptome single cell findings unraveled novel neutrophil subpopulations. Collectively, this methodology accelerates the discovery of salivary immune landscapes, with the promise of improving the understanding of diversification mechanisms, clinical diagnostics in health and disease, and guide targeted therapies.