Stabilization of Human Whole Blood Samples for Multicenter and Retrospective Immunophenotyping Studies

Protocol to characterize immune cell subpopulations in cerebrospinal fluid of patients with neuroinflammatory diseases using mass cytometry

Phenotypic and compositional changes of immune cells in cerebrospinal fluid (CSF) can be used as biomarkers to help diagnose and track disease activity for neuroinflammatory and neurodegenerative diseases. Here, we present a workflow to perform high-dimensional immune profiling at single-cell resolution using cytometry by time-of-flight (CyTOF) on cells isolated from the CSF of patients with neuroinflammation. We describe steps for sample collection and preparation, barcoding to allow for multiplexing, and downstream data analysis using R. For complete details on the use and execution of this protocol, please refer to Fernández-Zapata et al.1.

Approaching Mass Cytometry Translational Studies by Experimental and Data Curation Settings

Clinical studies are conducted to better understand the pathological mechanism of diseases and to find biomarkers associated with disease activity, drug response, or outcome prediction. Mass cytometry (MC) is a high-throughput single-cell technology that measures hundreds of cells per second with more than 40 markers per cell. Thus, it is a suitable tool for immune monitoring and biomarker discovery studies. Working in translational and clinical settings requires a careful experimental design to minimize, monitor, and correct the variations introduced during sample collection, preparation, acquisition, and analysis. In this review, we will focus on these important aspects of MC-related experiments and data curation in the context of translational clinical research projects.

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.