High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning

Abstract

Modern immunologic research increasingly requires high-dimensional analyses to understand the complex milieu of cell types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the coexpression patterns of hundreds of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and the identification of previously unknown cellular heterogeneity in the lungs of melanoma metastasis-bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost, and accessible solution to single-cell proteomics in complex tissues.

Imprinted SARS-CoV-2-specific memory lymphocytes define hybrid immunity

“…PBMC flow-through were stimulated with peptide megapools as described above. For surface phenotyping, cells were washed and barcoded using four different fluorescently labeled CD45 antibodies to create eight unique barcodes as previously described (Becht et al, 2021). Using this method to limit technical variability, all four stimulation conditions for both pre-and post-vaccination samples were combined and fully stained simultaneously for 20 minutes at 37 C. Cells were then fixed for 10 minutes at room temperature using 1% paraformaldehyde (Sigma-Aldrich).…”

Protein Adsorption Loss─The Bottleneck of Single-Cell Proteomics

“…Since its initial report in 1969, flow cytometry has revolutionized our study of proteins in single cells. The method was limited in multiplexing due to overlapping spectra; however, improvements such as the recent machine learning approach have pushed its capacity to analyze the concurrent expression of 100 proteins . Using transition metal labeling instead of fluorescence-labeling on targeted proteins via antibodies, mass cytometry-based single-cell analysis (cyTOF), although constrained by high-quality antibodies and distinct rare metal isotope channels, can achieve high throughputs at a scale of millions of cells.…”

Combined multidimensional single-cell protein and RNA profiling dissects the cellular and functional heterogeneity of thymic epithelial cells

“…For the Infinity Flow computational analysis of the LEGENDScreen datasets, the acquired fcs files were gated on CD45 negative cells or specifically on EpCAM1 + TEC using the FlowJo software. The newly exported fcs files were then used as the dataset for the Infinity Flow pipeline as recently published (Becht et al, 2021). The augmented data matrices generated during this process were then further analysed using the Seurat package for hierarchical clustering of the cells and differential expression analysis (Hao et al, 2021).…”

“…In a next step, each of these cells were stained separately with individual antibodies specific for any of the 260 exploratory markers. Infinity Flow, a computational machine learning algorithm based on the non-linear detection of the backbone markers, was subsequently used to impute at single-cell level the co-expression of individual exploratory markers (Becht et al, 2021). The resulting heterogeneity of phenotypes was then visualised and interpreted by the single-cell analysis pipeline, Seurat (Hao et al, 2021).…”

“…To address this limitation, we sought to screen mouse TEC for the expression of 260 cell surface markers employing massively parallel flow cytometry and the Infinity Flow computational pipeline to infer a co-expression pattern for any of the tested epitopes (Becht et al, 2021). This approach identified several novel TEC surface markers that when suitably combined identified perinatal cTEC, intertypical TEC and tuft-like mTEC which had previously only been classified either by their distinct RNA expression profiles or a combination of cell surface and intracellular markers (Lucas et al, 2020; Miller et al, 2018).…”