Unsupervised Analysis of Flow Cytometry Data in a Clinical Setting Captures Cell Diversity and Allows Population Discovery

Computational Flow Cytometry Accurately Identifies Sezary Cells Based on Simplified Aberrancy and Clonality Features

Flow cytometric identification of circulating neoplastic cells (Sezary cells) in patients with mycosis fungoides and Sezary syndrome is essential for diagnosis, staging, and prognosis. Although recent advances have improved the performance of this laboratory assay, the complex immunophenotype of Sezary cells and overlap with reactive T cells demand a high level of analytic expertise. We utilized machine learning to simplify this analysis using only 2 predefined Sezary cell-gating plots. We studied 114 samples from 59 patients with Sezary syndrome/mycosis fungoides and 66 samples from unique patients with inflammatory dermatoses. A single dimensionality reduction plot highlighted all TCR constant β chain-restricted (clonal) CD3+/CD4+ T cells detected by expert analysis. On receiver operator curve analysis, an aberrancy scale feature computed by comparison with controls (area under the curve = 0.98) outperformed loss of CD2 (0.76), CD3 (0.83), CD7 (0.77), and CD26 (0.82) in discriminating Sezary cells from reactive CD4+ T cells. Our results closely mirrored those obtained by exhaustive expert analysis for event classification (positive percentage agreement = 100%, negative percentage agreement = 99%) and Sezary cell quantitation (regression slope = 1.003, R squared = 0.9996). We demonstrate the potential of machine learning to simplify the accurate identification of Sezary cells.

Applications of Flow Cytometry in Drug Discovery and Translational Research

Flow cytometry is a mainstay technique in cell biology research, where it is used for phenotypic analysis of mixed cell populations. Quantitative approaches have unlocked a deeper value of flow cytometry in drug discovery research. As the number of drug modalities and druggable mechanisms increases, there is an increasing drive to identify meaningful biomarkers, evaluate the relationship between pharmacokinetics and pharmacodynamics (PK/PD), and translate these insights into the evaluation of patients enrolled in early clinical trials. In this review, we discuss emerging roles for flow cytometry in the translational setting that supports the transition and evaluation of novel compounds in the clinic.

flowSim: Near duplicate detection for flow cytometry data

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.

Improving Multicolor Colocalization in Single-Vesicle Flow Cytometry with Vesicle Transit Time

Immunophenotyping of vesicles, such as extracellular vesicles (EVs), is essential to understanding their origin and biological role. We previously described a custom-built flow analyzer that utilizes a gravity-driven flow, high numerical aperture objective, and micrometer-sized flow channels to reach the sensitivity needed for fast multidimensional analysis of the surface proteins of EVs, even down to the smallest EVs (e.g., ∼30-40 nm). It is difficult to flow focus small EVs, and thus, the transiting EVs exhibit a distribution in particle velocities due to the laminar flow. This distribution of vesicle velocities leads to potentially incorrect results when immunophenotyping nanometer-sized vesicles using cross-correlation analysis (Xcorr), as the order of appearance of the vesicles might not be the same at different spatially offset laser excitation regions. Here, we describe an alternative cross-correlation analysis strategy (Scorr), which uses information on particle transit time across the laser excitation beam width to improve multicolor colocalization in single-vesicle immunoprofiling. We tested the performance of the algorithm for colocalization analysis of multicolor nanobeads and EVs experimentally and via simulations and found that Scorr improved both the efficiency and accuracy of colocalization versus Xcorr. As shown from Monte Carlo simulations, Scorr provided an ∼1.2-4.7-fold increase in the number of colocalized peaks and ensured negligible colocalization of peaks. In silico results were in good agreement with experimental data, which showed an increase in colocalized peaks of ∼1.3-2.5-fold and ∼1.2-2-fold for multicolor beads and EVs, respectively.

Time series clustering of T cell subsets dissects heterogeneity in immune reconstitution and clinical outcomes among MUD-HCT patients receiving ATG or PTCy

Introduction

Anti-T-lymphocyte globulin (ATG) or post-transplant cyclophosphamide (PTCy) prevent graft-versus-host disease (GVHD) after hematopoietic cell transplantation (HCT), yet individual patients benefit differentially.

Methods

Given the sparse comparative data on the impact of cellular immune reconstitution in this setting, we studied flow cytometry and clinical outcomes in 339 recipients of 10/10 matched-unrelated donor (MUD) HCT using either ATG (n=304) or PTCy (n=35) for in vivo T cell manipulation along with a haploidentical PTCy control cohort (n=45). Longitudinal cellular immune reconstitution data were analyzed conventionally and with a data science approach using clustering with dynamic time warping to determine the similarity between time-series of T cell subsets.

Results

Consistent with published studies, no significant differences in clinical outcomes were observed at the cohort level between MUD-ATG and MUD-PTCy. However, cellular reconstitution revealed preferences for distinct T cell subpopulations associating with GVHD protection in each setting. Starting early after HCT, MUD-PTCy patients had higher regulatory T cell levels after HCT (p <0.0001), while MUD-ATG patients presented with higher levels of γδ T- or NKT cells (both p <0.0001). Time-series clustering further dissected the patient population’s heterogeneity revealing distinct immune reconstitution clusters. Importantly, it identified phenotypes that reproducibly associated with impaired clinical outcomes within the same in vivo T cell manipulation platform. Exemplarily, patients with lower activated- and αβ T cell counts had significantly higher NRM (p=0.032) and relapse rates (p =0.01).

Discussion

The improved understanding of the heterogeneity of cellular reconstitution in MUD patients with T cell manipulation both at the cohort and individual level may support clinicians in managing HCT complications.

Artificial intelligence in clinical multiparameter flow cytometry and mass cytometry-key tools and progress

There are many research studies and emerging tools using artificial intelligence (AI) and machine learning to augment flow and mass cytometry workflows. Emerging AI tools can quickly identify common cell populations with continuous improvement of accuracy, uncover patterns in high-dimensional cytometric data that are undetectable by human analysis, facilitate the discovery of cell subpopulations, perform semi-automated immune cell profiling, and demonstrate potential to automate aspects of clinical multiparameter flow cytometric (MFC) diagnostic workflow. Utilizing AI in the analysis of cytometry samples can reduce subjective variability and assist in breakthroughs in understanding diseases. Here we review the diverse types of AI that are being applied to clinical cytometry data and how AI is driving advances in data analysis to improve diagnostic sensitivity and accuracy. We review supervised and unsupervised clustering algorithms for cell population identification, various dimensionality reduction techniques, and their utilities in visualization and machine learning pipelines, and supervised learning approaches for classifying entire cytometry samples.Understanding the AI landscape will enable pathologists to better utilize open source and commercially available tools, plan exploratory research projects to characterize diseases, and work with machine learning and data scientists to implement clinical data analysis pipelines.

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.

The impact of single-cell genomics on the field of mycobacterial infection

Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.