Open AccessWith great power comes great responsibility: high-dimensional spectral flow cytometry to support clinical trials
Author(s) -
Megan McCausland,
Yi-Dong Lin,
Tania Nevers,
Christopher J. Groves,
Vilma Decman
Publication year - 2021
Publication title -
bioanalysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.566
H-Index - 58
eISSN - 1757-6199
pISSN - 1757-6180
DOI - 10.4155/bio-2021-0201
Subject(s) - flow cytometry , instrumentation (computer programming) , cytometry , computer science , flow (mathematics) , sample (material) , computational biology , characterization (materials science) , nanotechnology , biochemical engineering , data mining , data science , biological system , biology , chemistry , engineering , mathematics , immunology , materials science , chromatography , geometry , operating system
Flow cytometry is a powerful technology used in research, drug development and clinical sample analysis for cell identification and characterization, allowing for the simultaneous interrogation of multiple targets on various cell subsets from limited samples. Recent advancements in instrumentation and fluorochrome availability have resulted in significant increases in the complexity and dimensionality of flow cytometry panels. Though this increase in panel size allows for detection of a broader range of markers and sub-populations, even in restricted biological samples, it also comes with many challenges in panel design, optimization, and downstream data analysis and interpretation. In the current paper we describe the practices we established for development of high-dimensional panels on the Aurora spectral flow cytometer to aid clinical sample analysis.