Fluorescent Cell Barcoding for Immunophenotyping

Cell Viability Multiplexing: Quantification of Cellular Viability by Barcode Flow Cytometry and Computational Analysis

Fluorescent cell barcoding (FCB) is a useful flow cytometric technique for high-throughput multiplexed analyses and can minimize technical variations after preliminary optimization and validation of protocols. To date, FCB is widely used for measurement of phosphorylation status of certain proteins, while it can be also employed for cellular viability assessment. In this chapter, we describe the protocol to perform FCB combined with viability assessment on lymphocytes and monocytes using manual and computational analysis. We also provide recommendations for FCB protocol optimization and validation for clinical sample analysis.

Inter-intra instrument comparison and standardization of a 10-color immunophenotyping for B and T cell non-Hodgkin lymphoma diagnosis and monitoring

Harmonization of flow cytometry protocols from instrument settings to antibody panel and reagents is highly encouraged for inter-laboratory data comparison in both research and clinical settings, especially for minimal residual disease monitoring evaluation in hematological diseases across centers. Here, we described inter-intra instrument comparison of two standardized 10-color staining dried tubes for B- and T-cell lymphoproliferative disorder diagnosis and monitoring on two different flow cytometers, a Beckman Coulter NaviosEx and a Beckman Coulter DxFlex. A total of 47 consecutive patients were enrolled, and 39 of them were evaluable for further studies. We show highly comparable results between the two cytometers for cell frequency and fluorescence intensity signals for both standardized 10-color staining dried tubes. For this latter, fluorescence of each antibody and subject was normalized on the mean value obtained from the entire study cohort thus reducing the effects of biological variability and allowing comparison between instruments with different detector sensitivity. In summary, dried tubes were confirmed as an optimal standardized diagnostic tool, especially when associated with EuroFlow standardized procedures by minimizing technical and biological variability. However, data analysis is still operator-dependent, and more efforts are needed to develop automated or semi-automated software for flow cytometry data analysis for diagnostic purposes.

Barcoding of live peripheral blood mononuclear cells to assess immune cell phenotypes using full spectrum flow cytometry

Barcoded flow cytometry is a multiplexing technique allowing for the simultaneous acquisition of cells from different donors or experimental conditions in a high-throughput manner. This approach allows to synchronize acquisition of samples and reduce variance introduced through the operator or technical platform. However, to date, only very few flow cytometry barcoding protocols have been developed, which often suffer from technical limitations. Here, we developed a novel barcoding protocol for a full-spectrum flow cytometry platform. We developed a 21-color immunophenotyping assay for up to 20 different samples analyzed simultaneously with comparable variance between repeated single-tube acquisition and postde-multiplexing. Barcoding offers great potential in parallelizing the analysis of complex cell populations such as peripheral blood mononuclear cells (PBMCs). Consequently, we assessed the performance of our method in situations where PBMCs were challenged with phytohaemagglutinin (PHA), a strong mitogen and broad activator of B cells and T cells, and superantigen Staphylococcus enterotoxin B (SEB) that has been reported to induce polyclonal T cell activation. PBMCs were either barcoded before pooled challenge or challenged individually pre-barcoding. Our final workflow included pooled immunophenotyping followed by machine learning aided single-cell data analysis and enabled us to identify robust PHA and SEB mode of action related phenotypic changes in PBMC immune cell lineages. Conclusively, we present a novel technique allowing the barcoded acquisition and analysis of PBMCs from up to 20 different donors and present a valid basis for the future development of complex immunophenotyping protocols.

Data processing workflow for large-scale immune monitoring studies by mass cytometry

Mass cytometry is a powerful tool for deep immune monitoring studies. To ensure maximal data quality, a careful experimental and analytical design is required. However even in well-controlled experiments variability caused by either operator or instrument can introduce artifacts that need to be corrected or removed from the data. Here we present a data processing pipeline which ensures the minimization of experimental artifacts and batch effects, while improving data quality. Data preprocessing and quality controls are carried out using an R pipeline and packages like CATALYST for bead-normalization and debarcoding, flowAI and flowCut for signal anomaly cleaning, AOF for files quality control, flowClean and flowDensity for gating, CytoNorm for batch normalization and FlowSOM and UMAP for data exploration. As proper experimental design is key in obtaining good quality events, we also include the sample processing protocol used to generate the data. Both, analysis and experimental pipelines are easy to scale-up, thus the workflow presented here is particularly suitable for large-scale, multicenter, multibatch and retrospective studies.

Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry

High-dimensional, single-cell cell technologies revolutionized the way to study biological systems, and polychromatic flow cytometry (FC) and mass cytometry (MC) are two of the drivers of this revolution. As up to 30-50 dimensions respectively can be measured per single-cell, they allow deep phenotyping combined with cellular functions studies, like cytokine production or protein phosphorylation. In parallel, the bioinformatics field develops algorithms that are able to process incoming data and extract the most useful and meaningful biological information. However, the success of automated analysis tools depends on the generation of high-quality data. In this review we present the most recent FC and MC computational approaches that are used to prepare, process and interpret high-content cytometry data. We also underscore proper experimental design as a key step for obtaining good quality data.