The complexity of the immune system entails the simultaneous analysis of several parameters to gather meaningful insights. The advancements in flow cytometry instrumentation and the availability of reagents in recent years have facilitated high-dimensional analysis of immune cells to a tremendous extent.

However, increasing the number of parameters to gain a more thorough understanding of the immune cells has inherent risks of introducing unintended errors at various stages. Errors can occur during many of the steps in complex flow cytometry experiments, starting from sample preparation to instrument set up and performance tracking, increasing the possibility of occurrence of both experimental and non-biological technical artifacts. Moreover, when large-scale studies are conducted, the sheer number of experiments needed can pose risks of variabilities occurring between assays and may require strong analysis pipelines that can effectively address irregularities arising from large datasets. High-dimensional and high-throughput analysis of immune cells often suffer from inherent risks of high variability being introduced through these artificial non-biological variables.

In a recent publication in Cell Reports Methods, Liechti et al. present a robust pipeline that can address these issues and provide a systematic method for conducting high-throughput flow cytometry assays at scale. Their pipeline included both sample handling and data analysis; and they applied it to perform a high-throughput analysis of a large dataset comprising 3,357 samples in 19 experiments and reported minimal non-biological variation. Considering the large sample size and the number of experiments, enough care had to be taken to avoid sample loss and experimental errors. To this end, the authors developed an extensive quality control (QC) process at every step.

For example, they pre-prepared the entire staining reagent cocktail and performed quality control on them before sample processing. After reagent QC was performed, the samples were acquired on the BD FACSymphony™ A5 Cell Analyzer to analyze their 28-color flow cytometry panel. They used FlowAI for data pre-processing and QC to remove data artifacts arising from irregular data. To address data variations arising from biological and non-biological factors, they adopted regular instrument control, standardization and performance control measures. To account for variations in instrument performance, they used BD® FC Beads, and to account for technical and experimental variations across experiments they used sample QC by including a PBMC sample from the same donor and batch in each experiment. All their methods have been documented in STAR Methods, in their paper.

The combination of the high-throughput sample processing pipeline and high-dimensional flow cytometry in this experiment paved the way for handling data from multiexperimental settings. All the QC measures included in the pipeline help reduce errors arising from technical and experimental variations and increase data precision. Automated pre-processing of data and the ability to analyze unsupervised data with great precision helped with expediting time to results. The tool could be extended to other experimental settings as well.

Read the full article entitled “A robust pipeline for high-content, high-throughput immunophenotyping reveals age- and genetics dependent changes in blood leukocytes.” Cell Reports Methods.