Abstract
Multicolor flow cytometry is an essential tool for studying the immune system in health and disease, allowing users to extract longitudinal multiparametric data from patient samples. The process is complicated by substantial variation in performance between each flow cytometry instrument, and analytical errors are therefore common. Here, we present an approach to overcome such limitations by applying a systematic workflow for pairing colors to markers optimized for the equipment intended to run the experiments. The workflow is exemplified by the design of four comprehensive flow cytometry panels for patients with hematological cancer. Methods for quality control, titration of antibodies, compensation, and staining of cells for obtaining optimal results are also addressed. Finally, to handle the large amounts of data generated by multicolor flow cytometry, unsupervised clustering techniques are used to identify significant subpopulations not detected by conventional sequential gating.
Highlights
There are many occasions where it is important to examine the composition of immune cells in our body
The clustering method identified that in the CD8 compartment, Terminal effector cells (TE) cells (CD45RA + CCR7-) and effector memory (EM) cells (CD45RA- CCR7-) were more frequent in the myelodysplastic syndrome (MDS) patient, while naïve (CD45RA + CCR7+) and central memory (CM) cells (CD45RA- CCR7+) were less frequent compared to the healthy donors (HD), which corresponds to the findings based on our manual gating
We have presented four flow cytometry panels for analyzing immune cell compartments and a workflow for constructing new panels
Summary
There are many occasions where it is important to examine the composition of immune cells in our body. The technology has been in use since the 1960s, and even though the general principles of the technology have not changed much, its application and complexity have vastly increased and allows us to retrieve high dimensional data in parallel from a broad range of different cell types [3]. Follows the need for a systematic approach for designing flow cytometry panels. To generate the most accu rate results from flow cytometry experiments, the user must optimize panels to function with the specific machines intended to run the ex periments. We here describe a systematic approach to designing multicolor flow cytometry panels (Table 1) and provide a use case with examples of four comprehensive and complementary panels that cover some of the most important immune cell subtypes and their functional state in patients with cancer or autoimmune disorders. The workflow and principles described can be used to build and optimize research panels suitable for your own specific needs and available instruments
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