Advances in Clinical Mass Cytometry.

The advent of high-dimensional single-cell technologies has enabled detection of cellular heterogeneity and functional diversity of immune cells during health and disease conditions. Because of its multiplexing capabilities and limited compensation requirements, mass cytometry or cytometry by time of flight (CyTOF) has played a superior role in immune monitoring compared with flow cytometry. Further, it has higher throughput and lower cost compared with other single-cell techniques. Several published articles have utilized CyTOF to identify cellular phenotypes and features associated with disease outcomes. This article introduces CyTOF-based assays to profile immune cell-types, cell-states, and their applications in clinical research.

tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis.

Summary: While many algorithms for analyzing high-dimensional cytometry data have now been developed, the software implementations of these algorithms remain highly customized-this means that exploring a dataset requires users to learn unique, often poorly interoperable package syntaxes for each step of data processing. To solve this problem, we developed {tidytof}, an open-source R package for analyzing high-dimensional cytometry data using the increasingly popular 'tidy data' interface. Availability and implementation: {tidytof} is available at https://github.com/keyes-timothy/tidytof and is released under the MIT license. It is supported on Linux, MS Windows and MacOS. Additional documentation is available at the package website (https://keyes-timothy.github.io/tidytof/). Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Characterization of the input material quality for the production of tisagenlecleucel by multiparameter flow cytometry and its relation to the clinical outcome.

Tisagenlecleucel (tisa-cel) is a CD19-specific CAR-T cell product approved for the treatment of relapsed/refractory (r/r) DLBCL or B-ALL. We have followed a group of patients diagnosed with childhood B-ALL (n = 5), adult B-ALL (n = 2), and DLBCL (n = 25) who were treated with tisa-cel under non-clinical trial conditions. The goal was to determine how the intensive pretreatment of patients affects the produced CAR-T cells, their in vivo expansion, and the outcome of the therapy. Multiparametric flow cytometry was used to analyze the material used for manufacturing CAR-T cells (apheresis), the CAR-T cell product itself, and blood samples obtained at three timepoints after administration. We present the analysis of memory phenotype of CD4/CD8 CAR-T lymphocytes (CD45RA, CD62L, CD27, CD28) and the expression of inhibitory receptors (PD-1, TIGIT). In addition, we show its relation to the patients' clinical characteristics, such as tumor burden and sensitivity to prior therapies. Patients who responded to therapy had a higher percentage of CD8+CD45RA+CD27+ T cells in the apheresis, although not in the produced CAR-Ts. Patients with primary refractory aggressive B-cell lymphomas had the poorest outcomes which was characterized by undetectable CAR-T cell expansion in vivo. No clear correlation of the outcome with the immunophenotypes of CAR-Ts was observed. Our results suggest that an important parameter predicting therapy efficacy is CAR-Ts' level of expansion in vivo but not the immunophenotype. After CAR-T cells' administration, measurements at several timepoints accurately detect their proliferation intensity in vivo. The outcome of CAR-T cell therapy largely depends on biological characteristics of the tumors rather than on the immunophenotype of produced CAR-Ts.

Single-cell technologies uncover intra-tumor heterogeneity in childhood cancers.

Childhood cancer is the second leading cause of death in children aged 1 to 14. Although survival rates have vastly improved over the past 40 years, cancer resistance and relapse remain a significant challenge. Advances in single-cell technologies enable dissection of tumors to unprecedented resolution. This facilitates unraveling the heterogeneity of childhood cancers to identify cell subtypes that are prone to treatment resistance. The rapid accumulation of single-cell data from different modalities necessitates the development of novel computational approaches for processing, visualizing, and analyzing single-cell data. Here, we review single-cell approaches utilized or under development in the context of childhood cancers. We review computational methods for analyzing single-cell data and discuss best practices for their application. Finally, we review the impact of several studies of childhood tumors analyzed with these approaches and future directions to implement single-cell studies into translational cancer research in pediatric oncology.

OMIP-080: 29-Color flow cytometry panel for comprehensive evaluation of NK and T cells reconstitution after hematopoietic stem cells transplantation.

This 29-color panel was developed and optimized for the monitoring of NK cell and T cell reconstitution in peripheral blood of patients after HSCT. We considered major post-HSCT complications during the design, such as relapses, viral infections, and GvHD and identification of lymphocyte populations relevant to their resolution. The panel includes markers for all major NK cell and T cell subsets and analysis of their development and qualitative properties. In the NK cell compartment, we focus mainly on CD57 + NKG2C+ cells and the expression of activating (NKG2D, DNAM-1) and inhibitory receptors (NKG2A, TIGIT). Another priority is the characterization of T cell reconstitution; therefore, we included detection of CD4+ RTEs based on CD45RA, CD62L, CD95, and CD31 as a marker of thymus function. Besides that, we also analyze the emergence and properties of major T cell populations with a particular interest in CD8, Th1, ThCTL, and Treg subsets. Overall, the panel allows for comprehensive analysis of the reconstituting immune system and identification of potential markers of immune cell dysfunction.

Generalized EmbedSOM on quadtree-structured self-organizing maps.

EmbedSOM is a simple and fast dimensionality reduction algorithm, originally developed for its applications in single-cell cytometry data analysis. We present an updated version of EmbedSOM, viewed as an algorithm for landmark-based embedding enrichment, and demonstrate that it works well even with manifold-learning techniques other than the self-organizing maps. Using this generalization, we introduce an inwards-growing variant of self-organizing maps that is designed to mitigate some earlier identified deficiencies of EmbedSOM output. Finally, we measure the performance of the generalized EmbedSOM, compare several variants of the algorithm that utilize different landmark-generating functions, and showcase the functionality on single-cell cytometry datasets from recent studies.