Data analysis in flow cytometry: the future just started

MetaGate: Interactive analysis of high-dimensional cytometry data with metadata integration

Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level. Technical advances have substantially increased data complexity, but novel bioinformatical tools often show limitations in statistical testing, data sharing, cross-experiment comparability, or clinical data integration. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of metadata. MetaGate provides a data reduction algorithm based on a combinatorial gating system that produces a small, portable, and standardized data file. This is subsequently used to produce figures and statistical analyses through a fast web-based user interface. We demonstrate the utility of MetaGate through a comprehensive mass cytometry analysis of peripheral blood immune cells from 28 patients with diffuse large B cell lymphoma along with 17 healthy controls. Through MetaGate analysis, our study identifies key immune cell population changes associated with disease progression.

Semi-automated approaches for interrogating spatial heterogeneity of tissue samples

Tissues are spatially orchestrated ecosystems composed of heterogeneous cell populations and non-cellular elements. Tissue components' interactions shape the biological processes that govern homeostasis and disease, thus comprehensive insights into tissues' composition are crucial for understanding their biology. Recently, advancements in the spatial biology field enabled the in-depth analyses of tissue architecture at single-cell resolution, while preserving the structural context. The increasing number of biomarkers analyzed, together with whole tissue imaging, generate datasets approaching several hundreds of gigabytes in size, which are rich sources of valuable knowledge but require investments in infrastructure and resources for extracting quantitative information. The analysis of multiplex whole-tissue images requires extensive training and experience in data analysis. Here, we showcase how a set of open-source tools can allow semi-automated image data extraction to study the spatial composition of tissues with a focus on tumor microenvironment (TME). With the use of Lunaphore COMET platform, we interrogated lung cancer specimens where we examined the expression of 20 biomarkers. Subsequently, the tissue composition was interrogated using an in-house optimized nuclei detection algorithm followed by a newly developed image artifact exclusion approach. Thereafter, the data was processed using several publicly available tools, highlighting the compatibility of COMET-derived data with currently available image analysis frameworks. In summary, we showcased an innovative semi-automated workflow that highlights the ease of adoption of multiplex imaging to explore TME composition at single-cell resolution using a simple slide in, data out approach. Our workflow is easily transferrable to various cohorts of specimens to provide a toolset for spatial cellular dissection of the tissue composition.

Analytical Appraisal of Hematogones in B-ALL MRD Assessment Using Multidimensional Dot-Plots by Multiparametric Flow Cytometry: A Critical Review and Update

The spectrum of benign B-cell precursors, known as hematogones (HGs), shows a significant morphological and immunophenotypic overlap with their malignant counterpart i.e. B-lymphoid blasts (BLBs). This results in a diagnostic dilemma in assessment of cases wherein there is a physiological preponderance of HGs and also poses a significant challenge in measurable residual disease assessment in B-cell acute lymphoblastic leukaemia. Consequently, expression patterns of various immunophenotypic markers are considered the most important tool in identification and delineation of HGs from BLBs. However, certain aspects of B-cell compartment evaluation by flow cytometric immunophenotyping and its relevance in clinical scenarios is yet to be defined precisely. This review summarizes current flowcytometric data on HGs and its discrimination from BLBs based on thorough review of literature and evaluation of in-house data. Furthermore, it focuses on the utility of an additional analytical tool i.e., radar plot for a comprehensive representation of various subsets of the B-cell compartment and their differentiation from BLBs.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12288-023-01696-5.

MetaGate: Interactive Analysis of High-Dimensional Cytometry Data with Meta Data Integration

Flow cytometry is a powerful technology for high-throughput protein quantification at the single-cell level, widely used in basic research and routine clinical diagnostics. Traditionally, data analysis is carried out using manual gating, in which cut-offs are defined manually for each marker. Recent technical advances, including the introduction of mass cytometry, have increased the number of proteins that can be simultaneously assessed in each cell. To tackle the resulting escalation in data complexity, numerous new analysis algorithms have been developed. However, many of these show limitations in terms of providing statistical testing, data sharing, cross-experiment comparability integration with clinical data. We developed MetaGate as a platform for interactive statistical analysis and visualization of manually gated high-dimensional cytometry data with integration of clinical meta data. MetaGate allows manual gating to take place in traditional cytometry analysis software, while providing a combinatorial gating system for simple and transparent definition of biologically relevant cell populations. We demonstrate the utility of MetaGate through a comprehensive analysis of peripheral blood immune cells from 28 patients with diffuse large B-cell lymphoma (DLBCL) along with 17 age- and sex-matched healthy controls using two mass cytometry panels made of a total of 55 phenotypic markers. In a two-step process, raw data from 143 FCS files is first condensed through a data reduction algorithm and combined with information from manual gates, user-defined cellular populations and clinical meta data. This results in one single small project file containing all relevant information to allow rapid statistical calculation and visualization of any desired comparison, including box plots, heatmaps and volcano plots. Our detailed characterization of the peripheral blood immune cell repertoire in patients with DLBCL corroborate previous reports showing expansion of monocytic myeloid-derived suppressor cells, as well as an inverse correlation between NK cell numbers and disease progression.

Identifying dynamical persistent biomarker structures for rare events using modern integrative machine learning approach

The evolution of omics and computational competency has accelerated discoveries of the underlying biological processes in an unprecedented way. High throughput methodologies, such as flow cytometry, can reveal deeper insights into cell processes, thereby allowing opportunities for scientific discoveries related to health and diseases. However, working with cytometry data often imposes complex computational challenges due to high-dimensionality, large size, and nonlinearity of the data structure. In addition, cytometry data frequently exhibit diverse patterns across biomarkers and suffer from substantial class imbalances which can further complicate the problem. The existing methods of cytometry data analysis either predict cell population or perform feature selection. Through this study, we propose a "wisdom of the crowd" approach to simultaneously predict rare cell populations and perform feature selection by integrating a pool of modern machine learning (ML) algorithms. Given that our approach integrates superior performing ML models across different normalization techniques based on entropy and rank, our method can detect diverse patterns existing across the model features. Furthermore, the method identifies a dynamic biomarker structure that divides the features into persistently selected, unselected, and fluctuating assemblies indicating the role of each biomarker in rare cell prediction, which can subsequently aid in studies of disease progression.

Mebendazole targets essential proteins in glucose metabolism leading gastric cancer cells to death

Gastric cancer (GC) is among the most-diagnosed and deadly malignancies worldwide. Deregulation in cellular bioenergetics is a hallmark of cancer. Based on the importance of metabolic reprogramming for the development and cancer progression, inhibitors of cell metabolism have been studied as potential candidates for chemotherapy in oncology. Mebendazole (MBZ), an antihelminthic approved by FDA, has shown antitumoral activity against cancer cell lines. However, its potential in the modulation of tumoral metabolism remains unclear. Results evidenced that the antitumoral and cytotoxic mechanism of MBZ in GC cells is related to the modulation of the mRNA expression of glycolic targets SLC2A1, HK1, GAPDH, and LDHA. Moreover, in silico analysis has shown that these genes are overexpressed in GC samples, and this increase in expression is related to decreased overall survival rates. Molecular docking revealed that MBZ modifies the protein structure of these targets, which may lead to changes in their protein function. In vitro studies also showed that MBZ induces alterations in glucose uptake, LDH's enzymatic activity, and ATP production. Furthermore, MBZ induced morphologic and intracellular alterations typical of the apoptotic cell death pathway. Thus, this data indicated that the cytotoxic mechanism of MBZ is related to an initial modulation of the tumoral metabolism in the GC cell line. Altogether, our results provide more evidence about the antitumoral mechanism of action of MBZ towards GC cells and reveal metabolic reprogramming as a potential area in the discovery of new pharmacological targets for GC chemotherapy.

CRUSTY: a versatile web platform for the rapid analysis and visualization of high-dimensional flow cytometry data

Flow cytometry (FCM) can investigate dozens of parameters from millions of cells and hundreds of specimens in a short time and at a reasonable cost, but the amount of data that is generated is considerable. Computational approaches are useful to identify novel subpopulations and molecular biomarkers, but generally require deep expertize in bioinformatics and the use of different platforms. To overcome these limitations, we introduce CRUSTY, an interactive, user-friendly webtool incorporating the most popular algorithms for FCM data analysis, and capable of visualizing graphical and tabular results and automatically generating publication-quality figures within minutes. CRUSTY also hosts an interactive interface for the exploration of results in real time. Thus, CRUSTY enables a large number of users to mine complex datasets and reduce the time required for data exploration and interpretation. CRUSTY is accessible at https://crusty.humanitas.it/ .

Convergence of flow cytometry and bacteriology. Current and future applications: a focus on food and clinical microbiology

Since its development in the 1960s, flow cytometry (FCM) was quickly revealed a powerful tool to analyse cell populations in medical studies, yet, for many years, was almost exclusively used to analyse eukaryotic cells. Instrument and methodological limitations to distinguish genuine bacterial signals from the background, among other limitations, have hampered FCM applications in bacteriology. In recent years, thanks to the continuous development of FCM instruments and methods with a higher discriminatory capacity to detect low-size particles, FCM has emerged as an appealing technique to advance the study of microbes, with important applications in research, clinical and industrial settings. The capacity to rapidly enumerate and classify individual bacterial cells based on viability facilitates the monitoring of bacterial presence in foodstuffs or clinical samples, reducing the time needed to detect contamination or infectious processes. Besides, FCM has stood out as a valuable tool to advance the study of complex microbial communities, or microbiomes, that are very relevant in the context of human health, as well as to understand the interaction of bacterial and host cells. This review highlights current developments in, and future applications of, FCM in bacteriology, with a focus on those related to food and clinical microbiology.

COVID-19: Using high-throughput flow cytometry to dissect clinical heterogeneity

Here we consider how high-content flow cytometric methodology at appropriate scale and throughput rapidly provided meaningful biological data in our recent studies of COVID-19, which we discuss in the context of other similar investigations. In our work, high-throughput flow cytometry was instrumental to identify a consensus immune signature in COVID-19 patients, and to investigate the impact of SARS-CoV-2 exposure on patients with either solid or hematological cancers. We provide here some examples of our 'holistic' approach, in which flow cytometry data generated by lymphocyte and myelomonocyte panels were integrated with other analytical metrics, including SARS-CoV-2-specific serum antibody titers, plasma cytokine/chemokine levels, and in-depth clinical annotation. We report how selective differences between T cell subsets were revealed by a newly described flow cytometric TDS assay to distinguish actively cycling T cells in the peripheral blood. By such approaches, our and others' high-content flow cytometry studies collectively identified overt abnormalities and subtle but critical changes that discriminate the immuno-signature of COVID-19 patients from those of healthy donors and patients with non-COVID respiratory infections. Thereby, these studies offered several meaningful biomarkers of COVID-19 severity that have the potential to improve the management of patients and of hospital resources. In sum, flow cytometry provides an important means for rapidly obtaining data that can guide clinical decision-making without requiring highly expensive, sophisticated equipment, and/or "-omics" capabilities. We consider how this approach might be further developed.

A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy

Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.

Color-scalable flow cytometry with Raman tags

Flow cytometry is an indispensable tool in biology and medicine for counting and analyzing cells in large heterogeneous populations. It identifies multiple characteristics of every single cell, typically via fluorescent probes that specifically bind to target molecules on the cell surface or within the cell. However, flow cytometry has a critical limitation: the color barrier. The number of chemical traits that can be simultaneously resolved is typically limited to several due to the spectral overlap between fluorescence signals from different fluorescent probes. Here, we present color-scalable flow cytometry based on coherent Raman flow cytometry with Raman tags to break the color barrier. This is made possible by combining a broadband Fourier-transform coherent anti-Stokes Raman scattering (FT-CARS) flow cytometer, resonance-enhanced cyanine-based Raman tags, and Raman-active dots (Rdots). Specifically, we synthesized 20 cyanine-based Raman tags whose Raman spectra are linearly independent in the fingerprint region (400 to 1,600 cm^-1). For highly sensitive detection, we produced Rdots composed of 12 different Raman tags in polymer nanoparticles whose detection limit was as low as 12 nM for a short FT-CARS signal integration time of 420 µs. We performed multiplex flow cytometry of MCF-7 breast cancer cells stained by 12 different Rdots with a high classification accuracy of 98%. Moreover, we demonstrated a large-scale time-course analysis of endocytosis via the multiplex Raman flow cytometer. Our method can theoretically achieve flow cytometry of live cells with >140 colors based on a single excitation laser and a single detector without increasing instrument size, cost, or complexity.

Analysis of Antigen-Specific T and B Cells for Monitoring Immune Protection Against SARS-CoV-2

Immunological memory is the basis of protection against most pathogens. Long-living memory T and B cells able to respond to specific stimuli, as well as persistent antibodies in plasma and in other body fluids, are crucial for determining the efficacy of vaccination and for protecting from a second infection by a previously encountered pathogen. Antigen-specific cells are represented at a very low frequency in the blood, and indeed, they can be considered "rare events" present in the memory T-cell pool. Therefore, such events should be analyzed with careful attention. In the last 20 years, different methods, mostly based upon flow cytometry, have been developed to identify such rare antigen-specific cells, and the COVID-19 pandemic has given a dramatic impetus to characterize the immune response against the virus. In this regard, we know that the identification, enumeration, and characterization of SARS-CoV-2-specific T and B cells following infection and/or vaccination require i) the use of specific peptides and adequate co-stimuli, ii) the use of appropriate inhibitors to avoid nonspecific activation, iii) the setting of appropriate timing for stimulation, and iv) the choice of adequate markers and reagents to identify antigen-specific cells. Optimization of these procedures allows not only determination of the magnitude of SARS-CoV-2-specific responses but also a comparison of the effects of different combinations of vaccines or determination of the response provided by so-called "hybrid immunity," resulting from a combination of natural immunity and vaccine-generated immunity. Here, we present two methods that are largely used to monitor the response magnitude and phenotype of SARS-CoV-2-specific T and B cells by polychromatic flow cytometry, along with some tips that can be useful for the quantification of these rare events. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Identification of antigen-specific T cells Basic Protocol 2: Identification of antigen-specific B cells.

PRI: Re-Analysis of a Public Mass Cytometry Dataset Reveals Patterns of Effective Tumor Treatments

Recently, mass cytometry has enabled quantification of up to 50 parameters for millions of cells per sample. It remains a challenge to analyze such high-dimensional data to exploit the richness of the inherent information, even though many valuable new analysis tools have already been developed. We propose a novel algorithm “pattern recognition of immune cells (PRI)” to tackle these high-dimensional protein combinations in the data. PRI is a tool for the analysis and visualization of cytometry data based on a three or more-parametric binning approach, feature engineering of bin properties of multivariate cell data, and a pseudo-multiparametric visualization. Using a publicly available mass cytometry dataset, we proved that reproducible feature engineering and intuitive understanding of the generated bin plots are helpful hallmarks for re-analysis with PRI. In the CD4⁺T cell population analyzed, PRI revealed two bin-plot patterns (CD90/CD44/CD86 and CD90/CD44/CD27) and 20 bin plot features for threshold-independent classification of mice concerning ineffective and effective tumor treatment. In addition, PRI mapped cell subsets regarding co-expression of the proliferation marker Ki67 with two major transcription factors and further delineated a specific Th1 cell subset. All these results demonstrate the added insights that can be obtained using the non-cluster-based tool PRI for re-analyses of high-dimensional cytometric data.

Differences in glucose concentration shows new perspectives in gastric cancer metabolism

Gastric cancer (GC) is among the deadliest cancers worldwide despite available therapies, highlighting the need for novel therapies and pharmacological agents. Metabolic deregulation is a potential study area for new anticancer targets, but the in vitro metabolic studies are controversial, as different ranges of glucose used in the culture medium can influence results. In this study, we evaluated cellular viability, glucose uptake, and LDH activity in gastric cell lines when exposed to different glucose concentrations: high (HG, 25 mM), low (LG, 5.5 mM), and free (FG, 0 mM) glucose mediums. Moreover, we evaluated how glucose variations may influence cellular phenotype and the expression of genes related to epithelial-mesenchymal transition (EMT), metabolism, and cancer development in metastatic GC cells (AGP-01). Results showed that in the FG metastatic cells evidenced higher viability when compared with other cell lines and that when exposed to either LG or HG mediums most of the phenotypic assays did not differ. However, cells exposed to LG increased colony formation and mRNA levels of metabolic-related genes when compared to HG medium. Our results recommend LG medium to metabolic studies once glucose concentration is closer to physiological levels. These findings are important to point out new relevant targets in metabolic reprogramming that can be alternatives to current chemotherapies in patients with metastatic GC.

Advances in automated real-time flow cytometry for monitoring of bioreactor processes

Flow cytometry and its technological possibilities have greatly advanced in the past decade as analysis tool for single cell properties and population distributions of different cell types in bioreactors. Along the way, some solutions for automated real-time flow cytometry (ART-FCM) were developed for monitoring of bioreactor processes without operator interference over extended periods with variable sampling frequency. However, there is still great potential for ART-FCM to evolve and possibly become a standard application in bioprocess monitoring and process control. This review first addresses different components of an ART-FCM, including the sampling device, the sample-processing unit, the unit for sample delivery to the flow cytometer and the settings for measurement of pre-processed samples. Also, available algorithms are presented for automated data analysis of multi-parameter fluorescence datasets derived from ART-FCM experiments. Furthermore, challenges are discussed for integration of fluorescence-activated cell sorting into an ART-FCM setup for isolation and separation of interesting subpopulations that can be further characterized by for instance omics-methods. As the application of ART-FCM is especially of interest for bioreactor process monitoring, including investigation of population heterogeneity and automated process control, a summary of already existing setups for these purposes is given. Additionally, the general future potential of ART-FCM is addressed.

Anti-Tumor Effect of Marshallagia marshalli Somatic Antigen on Inhibition Cell Growth of K562

BACKGROUND

Today, the inhibitory effect of helminths on tumor cell growth has been proven. We investigated the anti-tumor activity of Marshallagia marshalli somatic against K562 cells.

METHODS

Different concentrations of M. marshalli somatic antigen were incorporated in the culture medium of K652 cells, and the proliferation and apoptosis were measured after 24, 48, and 72 h, using MTT and Annexin V/PI staining assay.

RESULTS

Treatment of cells with 1-2 mg/ml antigen for 24-72 h could suppress cell proliferation and increase apoptosis. While treating cells with 0.1 mg/ml antigen for 72 h could inhibit cell growth. There was no meaningful effect on treated cells in comparison with the control group (P< 0.05) after incubating cells for 24- 48 h with 0.1 mg/ml antigen.

CONCLUSION

M. marshalli somatic antigen had an anti-cancer property, and its role in cancer treatment could be considered as an effective therapeutic method.

Measurement of canine Th17 cells by flow cytometry

Th17 cells are T helper cells which play an important role during inflammation and autoimmune disease. To investigate the role of these cells in diseases in dogs in a clinical setting, methods for fast identification had to be established. Th17 cells are a rare cell population, for their measurement stimulation is recommended. To examine more samples simultaneously and to receive a relatively high purity of cell population of CD3 + CD4+ cells, different methods on various levels of preselection of cells as well as the possibility of storing blood overnight for measuring Th17 cells by flow cytometry were investigated. Firstly, to receive a high number of mononuclear cells, two different density gradients were compared and analysed. Furthermore, the enrichment of CD3 + CD4+ cells via depletion of CD8alpha+, CD11b + and CD21+ cells by cell sorting (autoMACS Pro Separator) was tested. It was also investigated whether stimulation processes led to better interpretation of results and whether there was a significant difference in measurement of directly processed blood samples and samples that had been stored overnight. In conclusion, the use of the density gradient (Lymph24+ Spin Medium) resulted in a purer cell population through a significant decrease in polymorphonuclear cells (*p = 0.01). After cell sorting, a significant difference in cell population purity was detected. Within the target fraction (containing mainly CD3 + CD4+ cells), CD8alpha+, CD21+, CD11b + cell percentages were significantly lower (***p < 0.001, *p < 0.02, ***p < .0001, respectively), and CD3 + CD4+ cell percentage was significantly higher (***p < .0001). There was a significant difference in Th17 cell percentage between unstimulated and stimulated cell populations (***p < .0001), but no significant difference in the percentage of unstimulated Th17 cells (p = 0.29) or stimulated Th17 cells (p = 0.71) in stored blood in comparison to directly processed EDTA blood samples. Finally, a modified protocol that offers an efficient way to investigate samples that were stored overnight by means of flow cytometry was evolved to research the role of Th17 cells in dogs with different diseases or in healthy populations.

High-throughput single cell data analysis - A tutorial

White blood cells protect the body against disease but may also cause chronic inflammation, auto-immune diseases or leukemia. There are many different white blood cell types whose identity and function can be studied by measuring their protein expression. Therefore, high-throughput analytical instruments were developed to measure multiple proteins on millions of single cells. The information-rich biochemistry information may only be fully extracted using multivariate statistics. Here we show an overview of the most essential steps for multivariate data analysis of single cell data. We used white blood cells (immunology) as a case study, but a similar approach may be used in environment or biotech research. The first step is analyzing the study design and subsequently formulating a research question. The three main designs are immunophenotyping (finding different cell types), cell activation and rare cell discovery. When preparing the data it is essential to consider the design and focus on the cell type of interest by removing all unwanted events. After pre-processing, the ten-thousands to millions of single cells per sample need to be converted into a cellular distribution. For immunophenotyping a clustering method such as Self-Organizing Maps is useful and for cell activation a model that describes the covariance such as Principal Component Analysis is useful. In rare cell discovery it is useful to first model all common cells and remove them to find the rare cells. Finally discriminant analysis based on the cellular distribution may highlight which cell (sub)types are different between groups.

A flow cytometric journey into cell cycle analysis

Cell cycle involves a series of changes that lead to cell growth and division. Cell cycle analysis is crucial to understand cellular responses to changing environmental conditions. Since its inception, flow cytometry has been particularly useful for cell cycle analysis at single cell level due to its speed and precision. Previously, flow cytometric cell cycle analysis relied solely on the measurement of cellular DNA content. Later, methods were developed for multiparametric analysis. This review explains the journey of flow cytometry to understand different molecular and cellular events underlying cell cycle using various protocols. Recent advances in the field that overcome the shortcomings of traditional flow cytometry and expand its scope for cell cycle studies are also discussed.

Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry

Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase imaging of single cells flowing through parallel microfluidic channels. Here, the approach was applied for characterizing the morphology of individual red blood cells during storage under regular blood bank conditions. Samples from five blood donors were examined, over 100,000 cells examined for each, at three time points. The approach allows high-throughput phase imaging of a large number of cells, greatly extending our ability to study cellular phenotypes using individual cell images. Holographic cytology images can provide measurements of multiple physical traits of the cells, including optical volume and area, which are observed to consistently change over the storage time. In addition, the large volume of cell imaging data can serve as training data for machine-learning algorithms. For the study here, logistic regression was used to classify the cells according to the storage time points. The analysis showed that at least 5000 cells are needed to ensure accuracy of the classifiers. Overall, results showed the potential of holographic cytometry as a diagnostic tool.

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.

Treatment with a neutrophil elastase inhibitor and ofloxacin reduces P. aeruginosa burden in a mouse model of chronic suppurative otitis media

Chronic suppurative otitis media (CSOM) is a widespread, debilitating problem with poorly understood immunology. Here, we assess the host response to middle ear infection over the course of a month post-infection in a mouse model of CSOM and in human subjects with the disease. Using multiparameter flow cytometry and a binomial generalized linear machine learning model, we identified Ly6G, a surface marker of mature neutrophils, as the most informative factor of host response driving disease in the CSOM mouse model. Consistent with this, neutrophils were the most abundant cell type in infected mice and Ly6G expression tracked with the course of infection. Moreover, neutrophil-specific immunomodulatory treatment using the neutrophil elastase inhibitor GW 311616A significantly reduces bacterial burden relative to ofloxacin-only treated animals in this model. The levels of dsDNA in middle ear effusion samples are elevated in both humans and mice with CSOM and decreased during treatment, suggesting that dsDNA may serve as a molecular biomarker of treatment response. Together these data strongly implicate neutrophils in the ineffective immune response to P. aeruginosa infection in CSOM and suggest that immunomodulatory strategies may benefit drug-tolerant infections for chronic biofilm-mediated disease.

Potential Therapeutic Effects of New Ruthenium (III) Complex with Quercetin: Characterization, Structure, Gene Regulation, and Antitumor and Anti-Inflammatory Studies (RuIII/Q Novel Complex Is a Potent Immunoprotective Agent)

The aim of this study was to evaluate the antioxidant and anti-inflammatory effects of the new [Ru(Q)(Cl)2(H2O)2] complex (RuIII/Q). A new vital complex containing quercetin flavonoid compound (Q) with ruthenium (III) ions was synthesized. The molar conductivity of the RuIII/Q complex was measured in dimethylsulfoxide (DMSO) with value 12 (Ω−1 mol−1 cm−1, indicating their non-electrolytic nature. Infrared (FTIR) spectroscopic investigation of the RuIII/Q complex indicated that Q is coordinated as a bidentate with Ru metal ions through the oxygen of carbonyl C(4)=O group and oxygen of phenolic C(3)−O group based on the wavenumber shifts at 1654 and 1335 cm−1 respectively. The electronic (UV−Vis) spectra and the magnetic susceptibility value (1.85 B.M.) revealed that the Ru(III) complex has an octahedral geometry. The average diameter of the RuIII/Q nanoparticles was approximately 7–15 nm according to the transmission electron microscopy. The thermogravimetric study (TG/DTG) indicates that the RuIII/Q compound is quite stable until 300 °C. To assess biological activity, 60 male rats were allocated to six groups, namely control, DG (D-galactose), Q, RuIII/Q, DG plus Q, and DG plus RuIII/Q. Antioxidant enzymes (SOD, CAT, GPx, and GRx), markers of lipid peroxidation (such as MDA), expression of genes (namely Nrf2, Cu-ZnSOD, CAT, GPx, cyto c, P53, Bax, BCl2, caspase-3, and caspase-9 in testicular tissue), glutamate, 4-hydroxynonenal (HNE), GSH, HCY, amyloid beta, and GABA levels were evaluated in brain tissues. Cytokines, such as IL-6 and TNF-α, histological and ultrastructural studies were estimated in both the brain and testicular tissues, while the comet assay was performed in the brain tissue. RuIII/Q administration either alone or combined with DG reduced oxidative injury to normal levels and decreased apoptotic activities. Thus, RuIII/Q inhibited injury in both the testis and brain and reduced oxidative stress in male rats. The (RuIII/Q) complex has a potent ameliorative effect against aging neurotoxicity, reproductive toxicity, and antihepatic cancer activity induced by D-galactose (DG).

K-means quantization for a web-based open-source flow cytometry analysis platform

Flow cytometry (FCM) is an analytic technique that is capable of detecting and recording the emission of fluorescence and light scattering of cells or particles (that are collectively called “events”) in a population¹. A typical FCM experiment can produce a large array of data making the analysis computationally intensive². Current FCM data analysis platforms (FlowJo³, etc.), while very useful, do not allow interactive data processing online due to the data size limitations. Here we report a more effective way to analyze FCM data on the web. Freecyto is a free and intuitive Python-flask-based web application that uses a weighted k-means clustering algorithm to facilitate the interactive analysis of flow cytometry data. A key limitation of web browsers is their inability to interactively display large amounts of data. Freecyto addresses this bottleneck through the use of the k-means algorithm to quantize the data, allowing the user to access a representative set of data points for interactive visualization of complex datasets. Moreover, Freecyto enables the interactive analyses of large complex datasets while preserving the standard FCM visualization features, such as the generation of scatterplots (dotplots), histograms, heatmaps, boxplots, as well as a SQL-based sub-population gating feature². We also show that Freecyto can be applied to the analysis of various experimental setups that frequently require the use of FCM. Finally, we demonstrate that the data accuracy is preserved when Freecyto is compared to conventional FCM software.

Immunologic responses to antifibrotic treatment in IPF patients

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease limited to the lungs. Immunological dysregulation may significantly participate in the pathophysiology of IPF. The immunological responses to nintedanib therapy in IPF patients were investigated for the first time in this study.

MATERIALS AND METHODS

Fifty IPF patients (median age (IQR) 69 (65-75) years; 38 males), were selected retrospectively. Flowcytometry analysis were performed to phenotype immunological biomarkers in peripheral blood from IPF patients after 1 year of antifibrotic therapy and a group of healthy volunteers.

RESULTS

Before starting antifibrotic treatment, IPF patients showed increased CD1d+CD5+ (p = 0.0460), Treg (p = 0.0354), T effector (CD25^highCD127^high) (p = 0.0336), central cells (CD4⁺CD45RA^-) (p = 0.0354), effector cells (CD4⁺CD45RA⁺) (p = 0.0249) and follicular cell percentages (p = 0.0006), notably Tfh1 (p = 0.0412) and Tfh17 (p = 0.0051) cell percentages, in respect with healthy controls (HC). After nintedanib therapy, Breg (p = 0.0302), T effector (p = 0.0468), Th17.1 (p = 0.0146) and follicular cells (p = 0.0006), notably Tfh1 (p = 0.0006) and Tfh17 (p = 0.0182) cell percentages, were significantly decreased. In the logistic regression, Tfh panel showed a significant area under the receiver operating characteristics curve (AUROC) to distinguish IPF than HC (90.5%), as well as t0 and t1 (99.3%).

CONCLUSION

In conclusion, the immunological results obtained in this study demonstrate that nintedanib significantly helps to restore immunological responses in IPF patients. These findings will be useful in the search for biomarkers predictive of response to antifibrotic treatment.

Regulatory T cell monitoring in severe eosinophilic asthma patients treated with mepolizumab

Severe eosinophilic asthma (SEA) has been associated with T-helper type 2 (Th2) inflammatory response. A good understanding of T cell functions in asthma is important for therapy, especially in the choice of biological treatments for severe cases. Mepolizumab, an IL-5 antagonist, is indicated for the treatment of severe asthma. Regulatory T cells (Tregs) suppress inflammation by secreting cytokines that inhibit Th2 cell proliferation. We investigated peripheral Treg, CD4, CD8, CD19 and NK cell percentages and their relationship to clinical and functional parameters, including peripheral eosinophils, before and after anti-IL5 treatment. Subjects were 14 adult SEA patients (9 male, 54.1 ± 11.6 years), treated with mepolizumab, and 10 controls. T cells (CD4 and CD8), CD19, NK and Tregs were evaluated by flow cytometry. Comparison of lung function parameters before and after treatment with mepolizumab (T0 and T1) showed an increase in FEV1, FEV1/FVC ratio and a reduction in blood eosinophil percentages. CD8 and CD16/56⁺ CD3⁺ were significantly higher in SEA patients than controls (P = .04 and P = .03, respectively). A decrease in CD45+, CD8 + and CD16/56⁺ CD3⁺ cell percentages was observed between T0 and T1 (P = .02, P = .04, P = .03, respectively). A significant increase in Treg percentages (P = .0001) was recorded between T0 and T1. Mepolizumab therapy was found to modulate immune response, restoring immune balance in patients with SEA.

Automated identification of leukocyte subsets improves standardization of database-guided expert-supervised diagnostic orientation in acute leukemia: a EuroFlow study

Precise classification of acute leukemia (AL) is crucial for adequate treatment. EuroFlow has previously designed an AL orientation tube (ALOT) to guide toward the relevant classification panel and final diagnosis. In this study, we designed and validated an algorithm for automated (database-supported) gating and identification (AGI tool) of cell subsets within samples stained with ALOT. A reference database of normal peripheral blood (PB, n = 41) and bone marrow (BM; n = 45) samples analyzed with the ALOT was constructed, and served as a reference for the AGI tool to automatically identify normal cells. Populations not unequivocally identified as normal cells were labeled as checks and were classified by an expert. Additional normal BM (n = 25) and PB (n = 43) and leukemic samples (n = 109), analyzed in parallel by experts and the AGI tool, were used to evaluate the AGI tool. Analysis of normal PB and BM samples showed low percentages of checks (<3% in PB, <10% in BM), with variations between different laboratories. Manual analysis and AGI analysis of normal and leukemic samples showed high levels of correlation between cell numbers (r² > 0.95 for all cell types in PB and r² > 0.75 in BM) and resulted in highly concordant classification of leukemic cells by our previously published automated database-guided expert-supervised orientation tool for immunophenotypic diagnosis and classification of acute leukemia (Compass tool). Similar data were obtained using alternative, commercially available tubes, confirming the robustness of the developed tools. The AGI tool represents an innovative step in minimizing human intervention and requirements in expertise, toward a "sample-in and result-out" approach which may result in more objective and reproducible data analysis and diagnostics. The AGI tool may improve quality of immunophenotyping in individual laboratories, since high percentages of checks in normal samples are an alert on the quality of the internal procedures.

Introduction to Multiparametric Flow Cytometry and Analysis of High-Dimensional Data

Multiparametric flow cytometry is a technique utilized in translational experiments that utilizes fluorescently tagged antibodies and functional fluorescent dyes to measure proteins on the surface or in the cytoplasm of cells and to measure processes occurring within cells themselves. These fluorescent molecules, or fluorophores, can be tagged to antibodies to measure specific biological molecules such as proteins inside or on the surface of cells. Small organic compounds such as the nucleic acid binding dye propidium iodide (PI) can permeate compromised cell membranes when cells are no longer viable or used to measure DNA content of cycling cells. Successful completion of flow cytometry experiments requires expertise in both the preparation of the samples, acquisition of the samples on instruments, and analyses of the results. This chapter describes the principles needed to conduct a successful multiparameter flow cytometry experiment needed for drug development with references to well established internet resources that are useful to those less experienced in the field. In addition, we provide a brief introduction to data analysis including complex analysis of 10+ parameters simultaneously. These high-dimensional datasets require novel methods for analysis due to the volume of data collected, which are also introduced in this chapter.

High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia

Simple Summary

B-cell Acute Lymphoblastic Leukaemia is one of the most common cancers in childhood, with 20% of patients eventually relapsing. Flow cytometry is routinely used for diagnosis and follow-up, but it currently does not provide prognostic value at diagnosis. The volume and the high-dimensional character of this data makes it ideal for its exploitation by means of Artificial Intelligence methods. We collected flow cytometry data from 56 patients from two hospitals. We analysed differences in intensity of marker expression in order to predict relapse at the moment of diagnosis. We finally correlated this data with biomolecular information, constructing a classifier based on CD38 expression.

Abstract

Artificial intelligence methods may help in unveiling information that is hidden in high-dimensional oncological data. Flow cytometry studies of haematological malignancies provide quantitative data with the potential to be used for the construction of response biomarkers. Many computational methods from the bioinformatics toolbox can be applied to these data, but they have not been exploited in their full potential in leukaemias, specifically for the case of childhood B-cell Acute Lymphoblastic Leukaemia. In this paper, we analysed flow cytometry data that were obtained at diagnosis from 56 paediatric B-cell Acute Lymphoblastic Leukaemia patients from two local institutions. Our aim was to assess the prognostic potential of immunophenotypical marker expression intensity. We constructed classifiers that are based on the Fisher’s Ratio to quantify differences between patients with relapsing and non-relapsing disease. We also correlated this with genetic information. The main result that arises from the data was the association between subexpression of marker CD38 and the probability of relapse.

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.

Double emulsion flow cytometry with high-throughput single droplet isolation and nucleic acid recovery

Droplet microfluidics has made large impacts in diverse areas such as enzyme evolution, chemical product screening, polymer engineering, and single-cell analysis. However, while droplet reactions have become increasingly sophisticated, phenotyping droplets by a fluorescent signal and sorting them to isolate individual variants-of-interest at high-throughput remains challenging. Here, we present sdDE-FACS (s[combining low line]ingle d[combining low line]roplet D[combining low line]ouble E[combining low line]mulsion-FACS), a new method that uses a standard flow cytometer to phenotype, select, and isolate individual double emulsion droplets of interest. Using a 130 μm nozzle at high sort frequency (12-14 kHz), we demonstrate detection of droplet fluorescence signals with a dynamic range spanning 5 orders of magnitude and robust post-sort recovery of intact double emulsion (DE) droplets using 2 commercially-available FACS instruments. We report the first demonstration of single double emulsion droplet isolation with post-sort recovery efficiencies >70%, equivalent to the capabilities of single-cell FACS. Finally, we establish complete downstream recovery of nucleic acids from single, sorted double emulsion droplets via qPCR with little to no cross-contamination. sdDE-FACS marries the full power of droplet microfluidics with flow cytometry to enable a variety of new droplet assays, including rare variant isolation and multiparameter single-cell analysis.

Multi-set Pre-processing of Multicolor Flow Cytometry Data

Flow Cytometry is an analytical technology to simultaneously measure multiple markers per single cell. Ten thousands to millions of single cells can be measured per sample and each sample may contain a different number of cells. All samples may be bundled together, leading to a ‘multi-set’ structure. Many multivariate methods have been developed for Flow Cytometry data but none of them considers this structure in their quantitative handling of the data. The standard pre-processing used by existing multivariate methods provides models mainly influenced by the samples with more cells, while such a model should provide a balanced view of the biomedical information within all measurements. We propose an alternative ‘multi-set’ preprocessing that corrects for the difference in number of cells measured, balancing the relative importance of each multi-cell sample in the data while using all data collected from these expensive analyses. Moreover, one case example shows how multi-set pre-processing may benefit removal of undesired measurement-to-measurement variability and another where class-based multi-set pre-processing enhances the studied response upon comparison to the control reference samples. Our results show that adjusting data analysis algorithms to consider this multi-set structure may greatly benefit immunological insight and classification performance of Flow Cytometry data.

Resolving complex hierarchies in chemical mixtures: how chemometrics may serve in understanding the immune system

In immunology, the resolution of complex chemical mixtures familiar from omics, comes with an added layer of hierarchy: bioactive immunological surface markers are embedded on the cell membranes of e.g. white blood cells. Therefore, each blood sample actually consists of a comprehensive mixture of cells. The cells need to be resolved based on their surface marker chemistry, to investigate their involvement in an immune response. This mixture may be measured on a single-cell level with Multicolour Flow Cytometry (MFC). Finding such cellular and molecular markers is of the utmost academic and diagnostic importance. Several advanced data analysis methods therefore aim to meet the considerable data challenge of resolving such cell mixtures. These multivariate methods are more resource-efficient than the manual analysis of MFC data, called sequential gating, but also likely provide additional biomedical insight compared to the conventional bivariate approach. To compare such methods more comprehensively than has been done until now, we have developed a list of criteria on how each method recovers the information on both the cell and the underlying molecular levels on an MFC sample of an asthma patient. We compare these methods for the chemometric data analysis commonly used in metabolomics. This shows that all compared methods have their own advantage in recovering the sequential gating results, giving insight into the limitations of sequential gating, providing insight into the chemical relationships between cells within the mixture and resolving information related to chemical heterogeneities between cells. We furthermore show how comparative analyses of different samples may lead to further insight into the subdivision of cells into different types based on their immunological involvement in asthma development, and how sparsity-a currently popular method to enhance the discriminative ability of multivariate models-may reduce the insight into the underlying hierarchical variability in cell chemistry. Although developed for cytometry, the presented chemometrics will be highly valuable to many more chemical systems where hierarchical arrangement of the molecules plays a crucial role.

Impact of Aging and HIV Infection on the Function of the C-Type Lectin Receptor MINCLE in Monocytes

Both aging and HIV infection are associated with an enhanced pro-inflammatory environment that contributes to impaired immune responses and is mediated in part by innate immune pattern-recognition receptors. MINCLE is a C-type lectin receptor that recognizes trehalose-6,6'-dimycolate or "cord factor," the most abundant glycolipid in Mycobacterium tuberculosis. Here, we evaluated MINCLE function in monocytes in a cohort of HIV-infected and uninfected young (21-35 years) and older adults (≥60 years) via stimulation of peripheral blood mononuclear cells with trehalose-6,6-dibehenate, a synthetic analog of trehalose-6,6'-dimycolate and measurement of cytokine production (interleukin [IL]-10, IL-12, IL-6, tumor necrosis factor-α) by multicolor flow cytometry. Our studies show an age- and HIV-associated increase in cytokine multifunctionality of monocytes both at the population and single cell level that was dominated by IL-12, IL-10, and IL-6. These findings provide insight into the host response to M. tuberculosis and possible sources for the pro-inflammatory environment seen in aging and HIV infection.

High-speed automatic characterization of rare events in flow cytometric data

A new computational framework for FLow cytometric Analysis of Rare Events (FLARE) has been developed specifically for fast and automatic identification of rare cell populations in very large samples generated by platforms like multi-parametric flow cytometry. Using a hierarchical Bayesian model and information-sharing via parallel computation, FLARE rapidly explores the high-dimensional marker-space to detect highly rare populations that are consistent across multiple samples. Further it can focus within specified regions of interest in marker-space to detect subpopulations with desired precision.

A Systematic Study on Transit Time and Its Impact on Accuracy of Concentration Measured by Microfluidic Devices

Gating or threshold selection is very important in analyzing data from a microflow cytometer, which is especially critical in analyzing weak signals from particles/cells with small sizes. It has been reported that using the amplitude gating alone may result in false positive events in analyzing data with a poor signal-to-noise ratio. Transit time (τ) can be set as a gating threshold along with side-scattered light or fluorescent light signals in the detection of particles/cells using a microflow cytometer. In this study, transit time of microspheres was studied systematically when the microspheres passed through a laser beam in a microflow cytometer and side-scattered light was detected. A clear linear relationship between the inverse of the average transit time and total flow rate was found. Transit time was used as another gate (other than the amplitude of side-scattering signals) to distinguish real scattering signals from noise. It was shown that the relative difference of the measured microsphere concentration can be reduced significantly from the range of 3.43%-8.77% to the range of 8.42%-111.76% by employing both amplitude and transit time as gates in analysis of collected scattering data. By using optimized transit time and amplitude gate thresholds, a good correlation with the traditional hemocytometer-based particle counting was achieved (R² > 0.94). The obtained results suggest that the transit time could be used as another gate together with the amplitude gate to improve measurement accuracy of particle/cell concentration for microfluidic devices.

Phenotypical T Cell Differentiation Analysis: A Diagnostic and Predictive Tool in the Study of Primary Immunodeficiencies

Multiparametric flow cytometry (MFC) represents a rapid, highly reproducible, and sensitive diagnostic technology for primary immunodeficiencies (PIDs), which are characterized by a wide range of T cell perturbations and a broad clinical and genetic heterogeneity. MFC data from CD4+ and CD8+ T cell subsets were examined in 100 patients referred for Primary Immunodeficiencies to our center. Naïve, central memory, effector memory, and terminal effector memory cell differentiation stages were defined by the combined expression CD45RA/CD27 for CD4 and CD45RA/CCR7 for CD8. Principal component analysis (PCA), a non-hypothesis driven statistical analysis, was applied to analyze MFC data in order to distinguish the diverse PIDs. Among severe lymphopenic patients, those affected by severe combined and combined immunodeficiency (SCID and CID) segregated in a specific area, reflecting a homogenous, and a more severe T cell impairment, compared to other lymphopenic PID, such as thymectomized and partial DiGeorge syndrome patients. PID patients with predominantly antibody defects were distributed in a heterogeneous pattern, but unexpectedly PCA was able to cluster some patients' resembling CID, hence warning for additional and more extensive diagnostic tests and a diverse clinical management. In conclusion, PCA applied to T cell MFC data might help the physician to estimate the severity of specific PID and to diversify the clinical and diagnostic approach of the patients.

An R-Derived FlowSOM Process to Analyze Unsupervised Clustering of Normal and Malignant Human Bone Marrow Classical Flow Cytometry Data

Multiparameter flow cytometry (MFC) is a powerful and versatile tool to accurately analyze cell subsets, notably to explore normal and pathological hematopoiesis. Yet, mostly supervised subjective strategies are used to identify cell subsets in this complex tissue. In the past few years, the implementation of mass cytometry and the big data generated have led to a blossoming of new software solutions. Their application to classical MFC in hematology is however still seldom reported. Here, we show how one of these new tools, the FlowSOM R solution, can be applied, together with the Kaluza® software, to a new delineation of hematopoietic subsets in normal human bone marrow (BM). We thus combined the unsupervised discrimination of cell subsets provided by FlowSOM and their expert-driven node-by-node assignment to known or new hematopoietic subsets. We also show how this new tool could modify the MFC exploration of hematological malignancies both at diagnosis (Dg) and follow-up (FU). This can be achieved by direct comparison of merged listmodes of reference normal BM, Dg, and FU samples of a representative acute myeloblastic case tested with the same immunophenotyping panel. This provides an immediate unsupervised evaluation of minimal residual disease. © 2019 International Society for Advancement of Cytometry.

Single-cell technologies in reproductive immunology

The maternal-fetal interface represents a unique immune privileged site that maintains the ability to defend against pathogens while orchestrating the necessary tissue remodeling required for placentation. The recent discovery of novel cellular families (innate lymphoid cells, tissue-resident NK cells) suggests that our understanding of the decidual immunome is incomplete. To understand this complex milieu, new technological developments allow reproductive immunologists to collect increasingly complex data at a cellular resolution. Polychromatic flow cytometry allows for greater resolution in the identification of novel cell types by surface and intracellular protein. Single-cell RNA-seq coupled with microfluidics allows for efficient cellular transcriptomics. The extreme dimensionality and size of data sets generated, however, requires the application of novel computational approaches for unbiased analysis. There are now multiple dimensionality reduction (tSNE, SPADE) and visualization tools (SPICE) that allow researchers to efficiently analyze flow cytometry data. Development of computational tools has also been extended to RNA-seq data (including scRNA-seq), which requires specific analytical tools. Here, we provide an overview and a brief primer for the reproductive immunology community on data acquisition and computational tools for the analysis of complex flow cytometry and RNA-seq data.

EuroFlow Lymphoid Screening Tube (LST) data base for automated identification of blood lymphocyte subsets

In recent years the volume and complexity of flow cytometry data has increased substantially. This has led to a greater number of identifiable cell populations in a single measurement. Consequently, new gating strategies and new approaches for cell population definition are required. Here we describe how the EuroFlow Lymphoid Screening Tube (LST) reference data base for peripheral blood (PB) samples was designed, constructed and validated for automated gating of the distinct lymphoid (and myeloid) subsets in PB of patients with chronic lymphoproliferative disorders (CLPD). A total of 46 healthy/reactive PB samples which fulfilled pre-defined technical requirements, were used to construct the LST-PB reference data base. In addition, another set of 92 PB samples (corresponding to 10 healthy subjects, 51 B-cell CLPD and 31 T/NK-cell CLPD patients), were used to validate the automated gating and cell-population labeling tools with the Infinicyt software. An overall high performance of the LST-PB data base was observed with a median percentage of alarmed cellular events of 0.8% in 10 healthy donor samples and of 44.4% in CLPD data files containing 49.8% (range: 1.3-96%) tumor cells. The higher percent of alarmed cellular events in every CLPD sample was due to aberrant phenotypes (75.6% cases) and/or to abnormally increased cell counts (86.6% samples). All 18 (22%) data files that only displayed numerical alterations, corresponded to T/NK-cell CLPD cases which showed a lower incidence of aberrant phenotypes (41%) vs B-cell CLPD cases (100%). Comparison between automated vs expert-bases manual classification of normal (r² = 0.96) and tumor cell populations (rho = 0.99) showed a high degree of correlation. In summary, our results show that automated gating of cell populations based on the EuroFlow LST-PB data base provides an innovative, reliable and reproducible tool for fast and simplified identification of normal vs pathological B and T/NK lymphocytes in PB of CLPD patients.

Analyzing the scaffold immune microenvironment using flow cytometry: practices, methods and considerations for immune analysis of biomaterials

The immune system has evolved as a powerful tool for our body to combat infections, and is being engineered for new treatments in cancer and autoimmune disease. More recently, the complex role of the immune system is being recognized in tissue repair, regenerative medicine and biomaterial responses. From these combined interests, the field of immunoengineering is rapidly growing. However, bridging immunology with engineering poses numerous challenges including the biological complexity, language of immunology and accurately leveraging the powerful techniques of immunology to new applications. Elucidating the identity and function of immune cell populations responding to engineering systems will be required for continued advancement. Multi-color flow cytometry is a central technique used by immunologists for this purpose that requires careful control of variables, data acquisition, and interpretation. Here, we present methods for multi-color flow cytometry experimental design and analysis focused on characterizing the scaffold immune microenvironment in regenerative medicine research.

Towards a Physiological Scale of Vocal Fold Agent-Based Models of Surgical Injury and Repair: Sensitivity Analysis, Calibration and Verification

Agent based models (ABM) were developed to numerically simulate the biological response to surgical vocal fold injury and repair at the physiological level. This study aimed to improve the representation of existing ABM through a combination of empirical and computational experiments. Empirical data of vocal fold cell populations including neutrophils, macrophages and fibroblasts were obtained using flow cytometry up to four weeks following surgical injury. Random Forests were used as a sensitivity analysis method to identify model parameters that were most influential to ABM outputs. Statistical Parameter Optimization Tool for Python was used to calibrate those parameter values to match the ABM-simulation data with the corresponding empirical data from Day 1 to Day 5 following surgery. Model performance was evaluated by verifying if the empirical data fell within the 95% confidence intervals of ABM outputs of cell quantities at Day 7, Week 2 and Week 4. For Day 7, all empirical data were within the ABM output ranges. The trends of ABM-simulated cell populations were also qualitatively comparable to those of the empirical data beyond Day 7. Exact values, however, fell outside of the 95% statistical confidence intervals. Parameters related to fibroblast proliferation were indicative to the ABM-simulation of fibroblast dynamics in final stages of wound healing.

Development, application and computational analysis of high-dimensional fluorescent antibody panels for single-cell flow cytometry

The interrogation of single cells is revolutionizing biology, especially our understanding of the immune system. Flow cytometry is still one of the most versatile and high-throughput approaches for single-cell analysis, and its capability has been recently extended to detect up to 28 colors, thus approaching the utility of cytometry by time of flight (CyTOF). However, flow cytometry suffers from autofluorescence and spreading error (SE) generated by errors in the measurement of photons mainly at red and far-red wavelengths, which limit barcoding and the detection of dim markers. Consequently, development of 28-color fluorescent antibody panels for flow cytometry is laborious and time consuming. Here, we describe the steps that are required to successfully achieve 28-color measurement capability. To do this, we provide a reference map of the fluorescence spreading errors in the 28-color space to simplify panel design and predict the success of fluorescent antibody combinations. Finally, we provide detailed instructions for the computational analysis of such complex data by existing, popular algorithms (PhenoGraph and FlowSOM). We exemplify our approach by designing a high-dimensional panel to characterize the immune system, but we anticipate that our approach can be used to design any high-dimensional flow cytometry panel of choice. The full protocol takes a few days to complete, depending on the time spent on panel design and data analysis.

A review of methods for the detection of pathogenic microorganisms

The testing and rapid detection of pathogenic organisms is a crucial protocol in the prevention and identification of crises related to health, safety and wellbeing. Pathogen detection has become one of the most challenging aspects in the food and water industries, because of the rapid spread of waterborne and foodborne diseases in the community and at significant costs. With the prospect of inevitable population growth, and an influx of tourism to certain water bodies testing will become a requirement to control and prevent possible outbreaks of potentially fatal illnesses. The legislation is already particularly rigorous in the food industry, where failure to detect pathogenic materials represents a catastrophic event, particularly for the elderly, very young or immune-compromised population types. In spite of the need and requirement for rapid analytical testing, conventional and standard bacterial detection assays may take up to seven days to yield a result. Given the advent of new technologies, biosensors, chemical knowledge and miniaturisation of instrumentation this timescale is not acceptable. This review presents an opportunity to fill a knowledge gap for an extremely important research area; discussing the main techniques, biology, chemistry, miniaturisation, sensing and the emerging state-of-the-art research and developments for detection of pathogens in food, water, blood and faecal samples.

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome

Background

Multicolor flow cytometry (MFC) analysis is widely used to identify minimal residual disease (MRD) after treatment for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). However, current manual interpretation suffers from drawbacks of time consuming and interpreter idiosyncrasy. Artificial intelligence (AI), with the expertise in assisting repetitive or complex analysis, represents a potential solution for these drawbacks.

Methods

From 2009 to 2016, 5333 MFC data from 1742 AML or MDS patients were collected. The 287 MFC data at post-induction were selected as the outcome set for clinical outcome validation. The rest were 4:1 randomized into the training set (n = 4039) and the validation set (n = 1007). AI algorithm learned a multi-dimensional MFC phenotype from the training set and input it to support vector machine (SVM) classifier after Gaussian mixture model (GMM) modeling, and the performance was evaluated in The validation set.

Findings

Promising accuracies (84·6% to 92·4%) and AUCs (0·921–0·950) were achieved by the developed algorithms. Interestingly, the algorithm from even one testing tube achieved similar performance. The clinical significance was validated in the outcome set, and normal MFC interpreted by the AI predicted better progression-free survival (10·9 vs 4·9 months, p < 0·0001) and overall survival (13·6 vs 6·5 months, p < 0·0001) for AML.

Interpretation

Through large-scaled clinical validation, we showed that AI algorithms can produce efficient and clinically-relevant MFC analysis. This approach also possesses a great advantage of the ability to integrate other clinical tests.

Fund

This work was supported by the Ministry of Science and Technology (107-2634-F-007-006 and 103–2314-B-002-185-MY2) of Taiwan.

Automatically generate two-dimensional gating hierarchy from clustered cytometry data

Cytometry is an important technique widely used in medicine and biological research. Biologists traditionally analyze single-cell cytometry data by manual gating, which can be subjective and labor intensive. To address this issue, many automated and semiautomated methods have been developed. These advanced methods are designed to speed up and standardize the analysis of cytometry data, but their popularity is limited by their visualizations which are not intuitive to biologists who are accustomed to the conventional biaxial gating plots. In this article, we present a new method called Cluster-to-Gate (C2G) that can take clustering results as input, and automatically generate a nested two-dimensional gating hierarchy, which is a visualization representation that biologists are familiar with. This method can generate gating sequences for multiple target populations simultaneously and summarize them in one hierarchical tree that represents the gating hierarchy. We have tested this method on target populations defined by manual gating, automated clustering algorithms (k-means for example), and visualization-assisted methods (SPADE and tSNE). We have demonstrated that C2G is able to generate gating sequences that capture cell populations defined by the various clustering strategies, and robust to over-clustered and overlapping target populations. © 2018 International Society for Advancement of Cytometry.

Automated flow cytometric identification of disease-specific cells by the ECLIPSE algorithm

Multicolor Flow Cytometry (MFC)-based gating allows the selection of cellular (pheno)types based on their unique marker expression. Current manual gating practice is highly subjective and may remove relevant information to preclude discovery of cell populations with specific co-expression of multiple markers. Only multivariate approaches can extract such aspects of cell variability from multi-dimensional MFC data. We describe the novel method ECLIPSE (Elimination of Cells Lying in Patterns Similar to Endogeneity) to identify and characterize aberrant cells present in individuals out of homeostasis. ECLIPSE combines dimensionality reduction by Simultaneous Component Analysis with Kernel Density Estimates. A Difference between Densities (DbD) is used to eliminate cells in responder samples that overlap in marker expression with cells of controls. Thereby, subsequent data analyses focus on the immune response-specific cells, leading to more informative and focused models. To prove the power of ECLIPSE, we applied the method to study two distinct datasets: the in vivo neutrophil response induced by systemic endotoxin challenge and in studying the heterogeneous immune-response of asthmatics. ECLIPSE described the well-characterized common response in the LPS challenge insightfully, while identifying slight differences between responders. Also, ECLIPSE enabled characterization of the immune response associated to asthma, where the co-expressions between all markers were used to stratify patients according to disease-specific cell profiles.

DAFi: A directed recursive data filtering and clustering approach for improving and interpreting data clustering identification of cell populations from polychromatic flow cytometry data

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.

A Method for the Interpretation of Flow Cytometry Data Using Genetic Algorithms

Background:

Flow cytometry analysis is the method of choice for the differential diagnosis of hematologic disorders. It is typically performed by a trained hematopathologist through visual examination of bidimensional plots, making the analysis time-consuming and sometimes too subjective. Here, a pilot study applying genetic algorithms to flow cytometry data from normal and acute myeloid leukemia subjects is described.

Subjects and Methods:

Initially, Flow Cytometry Standard files from 316 normal and 43 acute myeloid leukemia subjects were transformed into multidimensional FITS image metafiles. Training was performed through introduction of FITS metafiles from 4 normal and 4 acute myeloid leukemia in the artificial intelligence system.

Results:

Two mathematical algorithms termed 018330 and 025886 were generated. When tested against a cohort of 312 normal and 39 acute myeloid leukemia subjects, both algorithms combined showed high discriminatory power with a receiver operating characteristic (ROC) curve of 0.912.

Conclusions:

The present results suggest that machine learning systems hold a great promise in the interpretation of hematological flow cytometry data.