![]() Additionally, autofluorescence signatures from cells or tissues can further reduce signal resolution. In turn, flow cytometry panels that include 12 or more fluorescent probes suffer from increased spectral spillover. However, high costs, sample loss, low acquisition speed and sample destruction upon analysis, are practical limitations that also need to be considered. The lack of autofluorescence, minimal overlap between tags that would typically require compensation, and the large range of metal tags available for MC, have made MC an important discovery tool for immunologists. In contrast to conventional flow cytometry, SFC assesses the full visible-light spectrum of each fluorophore and uses unmixing technology with non-square compensation matrices to differentiate individual markers ( 6). Among those advances has been the development of spectral flow cytometry (SFC), which is particularly useful for distinguishing fluorophores with a high degree of spectral overlap ( 5– 8). Over the last few years, advances in flow cytometry instrumentation and fluorophore availability also increased dramatically, allowing for the design of panels with up to 30 fluorophores ( 3, 4). ![]() In 2009, the development of mass cytometry (MC, also called cytometry by time-of-flight or CyTOF) which uses metal-conjugated antibodies, led to an unprecedented increase in the number of analytes that could be assessed ( 2). However, the number of laser lines, the breadth of fluorescent dye emission profiles, their subsequent spillover and limited hardware, restricted most experiments to no more than 14 markers. In the 1980s 3 markers could be analyzed at a time ( 1) and this number increased over the following 30 years. However, large scale studies combined with an in-depth technical analysis will be needed to assess differences between these technologies in more detail.įlow cytometry has been an important technology for cellular analysis. Overall, our small comparison study suggests that mass cytometry and spectral flow cytometry both yield comparable results when analyzed manually or by high-dimensional clustering or dimensionality reduction algorithms such as t-SNE, UMAP or FlowSOM. Differences between the data sets only became apparent when the maximum number of parameters in each data set were assessed, due to differences in the number of recorded events or the maximum number of assessed parameters. When we downsampled each data set to their equivalent cell numbers and parameters, our analysis yielded highly comparable results. In this small comparison study, we investigated splenocytes from the same sample by either MC or SFC and compared both high-dimensional data sets using expert gating, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP) analysis and FlowSOM. As a consequence, new computational techniques such as dimensionality reduction and/or clustering algorithms are necessary to analyze, clean, visualize and interpret these high-dimensional data sets. The arrival of mass cytometry (MC) and, more recently, spectral flow cytometry (SFC) has revolutionized the study of cellular, functional and phenotypic diversity, significantly increasing the number of characteristics measurable at the single-cell level. The expression level of cell-type specific markers for subsetting was further highlighted for each cell type. Separate t-SNE plots with 7,500 iterations and a perplexity of 50 were calculated for total CD4 T cells and dendritic cells (DCs) and displayed in 2D using the resultant t-SNE 1 and t-SNE 2 dimensions. ![]() Analysis of specific populations using SFC or MC data. Expression levels of XCR1, CD44, KLRG1, CD8, CD279, MHCII, CD11b, CD274, CD69 and F4/80 are shown for t-SNE (A) and UMAP (B) with scales normalized as indicated in the materials and methods section. ( A) t-SNE analysis was performed with 7,500 iterations with perplexity of 50 and displayed in 2D plots using the resultant t-SNE 1 and t-SNE 2 dimensions according to the per cell expression of 16 proteins. ![]() For both data sets, CD45+/Single/Live cells were used for analysis and downsampled to equal cell numbers. Comparison of marker expression by t-SNE and UMAP from two comparable MC and SFC data sets. Supplementary Material: Supplementary Figure 1.
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