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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
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High-Dimensional Data Analysis Algorithms Yield Comparable Results for Mass Cytometry and Spectral Flow Cytometry

Laura Ferrer-Font1,2, Johannes U Mayer1, Samuel Old1

  • 1Malaghan Institute of Medical Research, Wellington, New Zealand.

Cytometry. Part a : the Journal of the International Society for Analytical Cytology
|April 16, 2020
PubMed
Summary

Mass cytometry (MC) and spectral flow cytometry (SFC) provide comparable single-cell analysis results. Computational methods like t-SNE, UMAP, and FlowSOM effectively analyze high-dimensional data from both platforms.

Keywords:
FlowSOMUMAPhigh-dimensional data analysismass cytometryspectral flow cytometryt-SNE

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Area of Science:

  • Single-cell biology
  • Immunology
  • Computational biology

Background:

  • Mass cytometry (MC) and spectral flow cytometry (SFC) have advanced single-cell analysis by increasing measurable cellular characteristics.
  • High-dimensional data generated by these technologies necessitate advanced computational techniques for analysis, including dimensionality reduction and clustering algorithms.

Purpose of the Study:

  • To compare the performance of mass cytometry (MC) and spectral flow cytometry (SFC) in analyzing splenocytes.
  • To evaluate the effectiveness of computational methods (expert gating, t-SNE, UMAP, FlowSOM) on high-dimensional cytometry data.

Main Methods:

  • Splenocytes from a single sample were analyzed using both MC and SFC.
  • Data were compared using expert gating, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection (UMAP), and FlowSOM.
  • Datasets were downsampled to equivalent cell numbers and parameters for comparison.

Main Results:

  • Downsampled MC and SFC datasets yielded highly comparable results.
  • Differences emerged when maximum parameters were assessed, influenced by event numbers and parameter counts.
  • Manual gating and computational algorithms (t-SNE, UMAP, FlowSOM) produced similar outcomes for both technologies.

Conclusions:

  • Mass cytometry and spectral flow cytometry offer comparable results for single-cell data analysis, irrespective of analysis method.
  • Further large-scale studies are required to fully elucidate technical differences between MC and SFC.