Mass cytometry data were concatenated using the .fcs File Concatenation Tool (Cytobank, Inc.), normalized using the MATLAB version of the Normalizer tool (Finck et al., 2013 (link)), and debarcoded using the CATALYST R/Bioconductor package (Chevrier et al., 2018 (link)). Debarcoded files were compensated for channel crosstalk using single-stained polystyrene beads as previously described (Chevrier et al., 2018 (link)). The compensated .fcs files were uploaded to the Cytobank server (Cytobank, Inc.) for manual gating on populations of interest. For Figure 1 , manual gates were set to exclude nonspecific background signal and cisplatin-positive dead cells (Figure S1 C). The resulting population was exported as .fcs files and loaded into R (R Core Team, 2016 ) for downstream analysis. Sample duplicates that were used to ensure high data quality between two barcoding plates (Figure S1 D) were concatenated for downstream analysis.
Cytobank server
Manufactured by Beckman Coulter
The Cytobank server is a centralized platform designed to store, analyze, and manage flow cytometry data. It provides a secure and collaborative environment for researchers to access, process, and share their flow cytometry experiments.
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Lab products found in correlation
2 protocols using cytobank server
1
Mass Cytometry Data Processing Protocol
2
High-Throughput Cytometry Analysis of Tumor Samples
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High-throughput mass cytometry (cytometry by time of flight (CyTOF)) analysis was performed as previously described.14 (link) Briefly, tumors were prepared as single-cell suspensions. Cells were pooled (3×106) and immunostained with a mixture of metal-tagged antibodies using the different surface markers as indicated in online supplemental table S4 . All antibodies were conjugated using the MAXPAR reagent (Fluidigm, South San Francisco, CA, USA) and tittered prior to staining. Rhodium and iridium intercalators were used to identify live/dead cells. Cells were washed twice with PBS, fixed in 1.6% formaldehyde (Sigma-Aldrich), washed again in ultrapure H2O, and acquired by CyTOF mass cytometry system (Fluidigm). The acquired data were uploaded to the Cytobank web server (Cytobank). CD11b+ myeloid live cells were used for the analysis, and the gated cells were segregated into subpopulation clusters by expression markers. Data analysis was performed by viSNE algorithm,15 (link) via the Cytobank server. Changes in specific populations were validated by flow cytometry.
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