Analyzing high-dimensional cytometry data using FlowSOM

Quintelier, Katrien; Couckuyt, Artuur; Emmaneel, Annelies; Aerts, Joachim; Saeys, Yvan; Gassen, Sofie Van

doi:10.1038/s41596-021-00550-0

Cited by 96 publications

(82 citation statements)

References 80 publications

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“…FlowSOM uses a self-organizing map algorithm for generating single cell subsets with unique marker protein expressions. FlowSOM is capable of clustering similar cells together and offers a robust way to determine which cellular subsets are differentially expressed between data sources [ 28 ]. We run a FlowSOM analysis and clustering on the CD8+ T cell data and determine that 6 of the 15 clusters are differentially expressed between healthy controls and COVID-19 patients ( S15 Fig ).…”

Section: Resultsmentioning

confidence: 99%

Determining clinically relevant features in cytometry data using persistent homology

et al. 2022

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Cytometry experiments yield high-dimensional point cloud data that is difficult to interpret manually. Boolean gating techniques coupled with comparisons of relative abundances of cellular subsets is the current standard for cytometry data analysis. However, this approach is unable to capture more subtle topological features hidden in data, especially if those features are further masked by data transforms or significant batch effects or donor-to-donor variations in clinical data. We present that persistent homology, a mathematical structure that summarizes the topological features, can distinguish different sources of data, such as from groups of healthy donors or patients, effectively. Analysis of publicly available cytometry data describing non-naïve CD8+ T cells in COVID-19 patients and healthy controls shows that systematic structural differences exist between single cell protein expressions in COVID-19 patients and healthy controls. We identify proteins of interest by a decision-tree based classifier, sample points randomly and compute persistence diagrams from these sampled points. The resulting persistence diagrams identify regions in cytometry datasets of varying density and identify protruded structures such as ‘elbows’. We compute Wasserstein distances between these persistence diagrams for random pairs of healthy controls and COVID-19 patients and find that systematic structural differences exist between COVID-19 patients and healthy controls in the expression data for T-bet, Eomes, and Ki-67. Further analysis shows that expression of T-bet and Eomes are significantly downregulated in COVID-19 patient non-naïve CD8+ T cells compared to healthy controls. This counter-intuitive finding may indicate that canonical effector CD8+ T cells are less prevalent in COVID-19 patients than healthy controls. This method is applicable to any cytometry dataset for discovering novel insights through topological data analysis which may be difficult to ascertain otherwise with a standard gating strategy or existing bioinformatic tools.

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Section: Resultsmentioning

confidence: 99%

Determining clinically relevant features in cytometry data using persistent homology

et al. 2022

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“…In order to get a broader overview of changes in relevant immune cell populations in CKD, we re-collected peripheral blood mononuclear cells (PBMC) from seven HC and six HD patients for immunophenotyping using flow cytometry. Unsupervised FlowSOM analysis 11 of our monocyte and dendritic cell targeting flow panel (see Supplementary table 1, supplementary figure 4, Figure 5b), showed phenotypic alterations of monocytes (cluster 3, Figure 5c) being decreased (Figure 5d) and dendritic cell (cluster 7, Figure 5c) being increased (Figure 5d) in HD patients. Using classical hierarchical gating, we observed similar abundances of total monocytes (identified by HLA-DR, CD14, and CD16 as described in 12 ), but a significant shift from classical (CD14+CD16-) towards non-classical (CD14-CD16+) and intermediate (CD14+CD16+) monocytes in HD (Figure 5e), the latter two being known for All rights reserved.…”

Section: Monocyte Subsets Contribute To the Pro-inflammatory Phenotype In Ckdmentioning

confidence: 94%

Inflammation in children with chronic kidney disease linked to gut dysbiosis and metabolite imbalance

Holle

Bartolomaeus

Loeber

et al. 2022

Preprint

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Chronic kidney disease (CKD) is characterized by a sustained pro-inflammatory response. The underlying mechanisms are incompletely understood, but may be linked to gut dysbiosis. Dysbiosis has been described in adults with CKD; however, comorbidities limit CKD-specific conclusions. We analyzed the fecal microbiome, metabolites and immune phenotypes in children at three different CKD stages (G3-G4, G5 (hemodialysis), after kidney transplantation) and healthy controls. Serum TNF-α and sCD14 were stage-dependently elevated, indicating inflammation and gut barrier dysfunction. We observed microbiome alterations in CKD, including a diminished production of short-chain fatty acids. Bacterial tryptophan metabolites were increased in CKD. CKD serum activated the aryl hydrocarbon receptor and stimulated TNF-α production by monocytes, corresponding to a shift towards intermediate/non-classical monocytes. Unsupervised T cell analysis revealed pro-inflammatory shifts in MAIT and Treg cells. Thus, gut barrier dysfunction and microbial metabolites exacerbate inflammation and may therefore contribute to the increased cardiovascular burden in CKD.

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“…Meta-clusters with an abundance <1% of all events were pooled with the most phenotypically similar meta-cluster. Then, the proportion of corrected FlowSOM meta-clusters in each node on the initial FlowSOM minimum spanning tree was visualized to control reassignment consistency ( 35 ).…”

Section: Methodsmentioning

confidence: 99%

In-Depth Immunophenotyping With Mass Cytometry During TB Treatment Reveals New T-Cell Subsets Associated With Culture Conversion

et al. 2022

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Tuberculosis (TB) is a difficult-to-treat infection because of multidrug regimen requirements based on drug susceptibility profiles and treatment observance issues. TB cure is defined by mycobacterial sterilization, technically complex to systematically assess. We hypothesized that microbiological outcome was associated with stage-specific immune changes in peripheral whole blood during TB treatment. The T-cell phenotypes of treated TB patients were prospectively characterized in a blinded fashion using mass cytometry after Mycobacterium tuberculosis (Mtb) antigen stimulation with QuantiFERON-TB Gold Plus, and then correlated to sputum culture status. At two months of treatment, cytotoxic and terminally differentiated CD8+ T-cells were under-represented and naïve CD4+ T-cells were over-represented in positive- versus negative-sputum culture patients, regardless of Mtb drug susceptibility. At treatment completion, a T-cell immune shift towards differentiated subpopulations was associated with TB cure. Overall, we identified specific T-cell profiles associated with slow sputum converters, which brings new insights in TB prognostic biomarker research designed for clinical application.

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Analyzing high-dimensional cytometry data using FlowSOM

Cited by 96 publications

References 80 publications

Determining clinically relevant features in cytometry data using persistent homology

Determining clinically relevant features in cytometry data using persistent homology

Inflammation in children with chronic kidney disease linked to gut dysbiosis and metabolite imbalance

In-Depth Immunophenotyping With Mass Cytometry During TB Treatment Reveals New T-Cell Subsets Associated With Culture Conversion

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