Flow Cytometry Has a Significant Impact on the Cellular Metabolome

Abstract

The characterization of specialized cell subpopulations in a heterogeneous tissue is essential for understanding organ function in health and disease. A popular method of cell isolation is fluorescence-activated cell sorting (FACS) based on probes that bind surface or intracellular markers. In this study, we analyse the impact of FACS on the cell metabolome of mouse peritoneal macrophages. Compared with directly pelleted macrophages, FACStreated cells had an altered content of metabolites related to the plasma membrane, activating a mechanosensory signalling cascade causing inflammation-like stress. The procedure also triggered alterations related to energy consumption and cell damage. The observed changes mostly derive from the physical impact on cells during their passage through the instrument. These findings provide evidence of FACS-induced biochemical changes, which should be taken into account in the design of robust metabolic assays of cells separated by flow cytometry.

Metabolomic profiling of rare cell populations isolated by flow cytometry from tissues

“…Cells can undergo metabolic changes upon removal from their in vivo environment (Lau et al, 2020). This is a particular problem when cells are enzymatically dissociated at 37°C, when they exchange metabolites with the dissociation medium, or when cells are sorted into buffers that require additional processing steps before cell lysis and metabolite extraction (Lau et al, 2020, Binek et al, 2019, Llufrio et al, 2018. For this reason, we have avoided analyzing enzymatically dissociated cells.…”

“…This limitation is particularly apparent when considering rare cells, such as stem cells or circulating cancer cells, that may be metabolically different from other cells. The difficulty of performing metabolomics on small numbers of these cells is compounded by the need to purify them from tissues, introducing additional technical challenges for metabolic analyses (Binek et al, 2019, Llufrio et al, 2018, Lau et al, 2020. It is extremely time consuming and difficult to isolate a million cells from a rare cell population by flow cytometry.…”

Dissecting cell type-specific metabolism in pancreatic ductal adenocarcinoma

“…These changes, especially the larger changes in levels, indicate that metabolism is not at steady-state when metabolites are analyzed in sorted cell populations, and complicates the interpretation of differential isotope labeling patterns (Buescher et al, 2015). In fact, changes in metabolite levels and labeling may be even greater when using flow cytometry to sort cells in practice because temperature as well as factors such as mechanical stress are less easily controlled (Binek et al, 2019). Thus, assessment of M+3 labeling of TCA cycle intermediates in sorted cell populations from organoids or tumors may not fully portray the contribution of each cell type to the pyruvate carboxylation phenotype observed when material containing multiple cell types is analyzed.…”

“…Furthermore, cell sorting exposes cells to conditions that are different from those experienced by cells in tissues, and can change metabolism in many ways. For example, sorting can induce mechanical and oxidative stress and reduce the levels of certain metabolites (Binek et al, 2019;Llufrio et al, 2018;Roci et al, 2016), and even including small amounts of bovine serum to the buffer was not sufficient to prevent changes to metabolite level changes during cell sorting (Llufrio et al, 2018). Indeed, metabolite labeling patterns can be more robust than metabolite levels when assessing metabolites in flow cytometry sorted cells, although labeling can also be affected by cell sorting, particularly in metabolites with rapid turnover such as lactate (Roci et al, 2016).…”

Open Source ImmGen: network perspective on metabolic diversity among mononuclear phagocytes

“…Additional dimension of this diversity is manifested by the metabolic characteristics of individual mononuclear phagocytes which can vary significantly based on the cell type and its location [3][4][5] . At present, direct metabolomics profiling of tissue residing subpopulations is not feasible, as the process of ex vivo sorting can be lengthy and cause significant metabolic perturbations 6,7 . However, RNA levels are significantly more stable to the sorting process and can serve as a reasonably reliable proxy to activities of metabolic pathways 8,9 .…”