Making the most of high‐dimensional cytometry data

Abstract

High‐dimensional cytometry represents an exciting new era of immunology research, enabling the discovery of new cells and prediction of patient responses to therapy. A plethora of analysis and visualization tools and programs are now available for both new and experienced users; however, the transition from low‐ to high‐dimensional cytometry requires a change in the way users think about experimental design and data analysis. Data from high‐dimensional cytometry experiments are often underutilized, because of both the size of the data and the number of possible combinations of markers, as well as to a lack of understanding of the processes required to generate meaningful data. In this article, we explain the concepts behind designing high‐dimensional cytometry experiments and provide considerations for new and experienced users to design and carry out high‐dimensional experiments to maximize quality data collection.

CyTOF mass cytometry analysis of human memory CD4+ T cells and memory B cells

“…The number of “useable” events for downstream analysis then depends on any loss during pre-processing steps such as doublet discrimination and viability gating – see expected outcomes . Ultimately there is no hard and fast rule to the number of events to acquire per sample, and this should be informed by experimental design and analysis strategies ( Marsh-Wakefield et al., 2021 ). Wash cell suspensions.…”

“…The protocol here describes the acquisition of non-barcoded samples – however, sample barcoding is generally recommended as it has many advantages, including higher throughput (due to decreased between-sample washing), improved doublet discrimination during post-acquisition debarcoding, and decreased between-sample signal variation due to control of instrument signal drift. Barcoding approaches have been outlined elsewhere ( Marsh-Wakefield et al., 2021 ; Rybakowska et al., 2020 ).…”

Simplified mass cytometry protocol for in-plate staining, barcoding, and cryopreservation of human PBMC samples in clinical trials

“…After routine clean up gating using Gaussian parameters, we had an end point cell count of at least 200,000 CD45 + 103Rh - alive single cells per patient available to analyze ( Figure 4 .). For an in-depth immune phenotyping encompassing small cellular populations, samples need to have a minimum of 100,000 alive single cells analyzed per condition ( Marsh-Wakefield et al., 2021 ) ( Maecker and Harari, 2015 ).

Figure 4 Cell recovery at different timepoints during CyTOF preparation and protocol Cell recovery data for number of cells per sample before CyTOF staining, after machine acquisition, and after routine clean-up gating to obtain single, viable cells.

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How to Prepare Spectral Flow Cytometry Datasets for High Dimensional Data Analysis: A Practical Workflow

“…In mass cytometry a fixed cofactor for all channels between 5 and 15 is often appropriate. In conventional and spectral flow cytometry, an individual cofactor per channel/fluorochrome is recommend, usually between 10 and 10.000 (18). However, calculating individual cofactors can be time-consuming and if fast exploration of data is needed, a fixed cofactor can temporarily be used.…”

“…Not knowing where to start, new analysis strategies can be intimidating to work with and mistakes are easily made. Furthermore, specific characteristics of spectral flow cytometry, such as removal of auto-fluorescence per cell, higher maximum fluorescence intensities and minimal requirement for spectral compensation, also hinder easy use of already published workflows for data analysis in mass cytometry or conventional flow cytometry (14,15).A few groups published advanced workflows also suited for spectral flow cytometry (16)(17)(18). Commercial flow cytometry software, such as Cytobank, also provide automated pipelines, such as CITRUS (19).…”