Impact of Long-Term Cryopreservation on Blood Immune Cell Markers in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Implications for Biomarker Discovery

Abstract

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex neuroimmune disorder characterized by numerous symptoms of unknown etiology. The ME/CFS immune markers reported so far have failed to generate a clinical consensus, perhaps partly due to the limitations of biospecimen biobanking. To address this issue, we performed a comparative analysis of the impact of long-term biobanking on previously identified immune markers and also explored additional potential immune markers linked to infection in ME/CFS. A correlation analysis of marker cryostability across immune cell subsets based on flow cytometry immunophenotyping of fresh blood and frozen PBMC samples collected from individuals with ME/CFS (n = 18) and matched healthy controls (n = 18) was performed. The functionality of biobanked samples was assessed on the basis of cytokine production assay after stimulation of frozen PBMCs. T cell markers defining Treg subsets and the expression of surface glycoprotein CD56 in T cells and the frequency of the effector CD8 T cells, together with CD57 expression in NK cells, appeared unaltered by biobanking. By contrast, NK cell markers CD25 and CD69 were notably increased, and NKp46 expression markedly reduced, by long-term cryopreservation and thawing. Further exploration of Treg and NK cell subsets failed to identify significant differences between ME/CFS patients and healthy controls in terms of biobanked PBMCs. Our findings show that some of the previously identified immune markers in T and NK cell subsets become unstable after cell biobanking, thus limiting their use in further immunophenotyping studies for ME/CFS. These data are potentially relevant for future multisite intervention studies and cooperative projects for biomarker discovery using ME/CFS biobanked samples. Further studies are needed to develop novel tools for the assessment of biomarker stability in cryopreserved immune cells from people with ME/CFS.

Keywords: chronic fatigue syndrome; cryopreservation; freeze-thaw process; immune biomarkers; immunophenotyping; myalgic encephalomyelitis.

Conflict of interest statement

JR is an employee at the Rigau Private Clinic. JB is the founder and CEO and JC is also co-founder and CSO (AlbaJuna Therapeutics, Barcelona, Spain). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Gómez-Mora, Carrillo, Urrea, Rigau, Alegre, Cabrera, Oltra, Castro-Marrero and Blanco.

Figures

Figure 1

Analysis of T cell subset markers in fresh and frozen samples from ME/CFS patients (A–D). Frequencies of the analyzed T cell subsets in fresh (2013) and frozen (2016) PBMC samples from the same participants are shown. Complete identity between fresh and frozen cells is illustrated by blue dotted lines. Linear regression of data is illustrated by red lines. Spearman correlation coefficients and P-values are shown for each panel.

Figure 2

Analysis of T cell subset markers in frozen PBMC samples from individuals with ME/CFS and healthy controls. The indicated T cell subsets were assessed in frozen PBMC samples from 18 ME/CFS individuals (brown) and 18 healthy controls (green). Data is shown as median values with interquartile range (boxes), plus minimal and maximal observations (bars). P-values are indicated for each set of groups compared.

Figure 3

Analysis of NK cell subset markers in fresh and frozen samples from ME/CFS patients (A–D). Frequencies of the analyzed NK cell subsets in fresh (2013) and frozen (2016) blood samples from the same participants are shown. As in Figure 1, complete identity between fresh and frozen cells is illustrated by blue dotted lines. Linear regression of data is illustrated by red lines. Spearman correlation coefficients and P-values are shown for each panel.

Figure 4

Analysis of new Treg subset markers. (A) Gating strategy of Treg cells, first identified by high CD25+ and FOXP3+ expression (classic Treg) and defined as natural Tregs by the lack of CD127 expression. Coexpression of CD45RA, FOXP3 or co-expression of CD39 and CD73 as new potential Treg markers were assessed as indicated. (B, C) Show the frequencies of the indicated cell subsets obtained for healthy controls (n = 18; green) and ME/CFS patients (n = 18; brown). Data are presented as median with interquartile range (boxes) plus minimal and maximal values (bars). P-values are indicated for each set of groups compared.

Figure 5

Analysis of cytokine expression in frozen/thawed stimulated samples. (A) Representative example of cytokine production from ME/CFS PBMCs by gated CD4+ T cells. IFN-γ and IL-17 pro-inflammatory cytokines, and IL-4 and TFG-β1 anti-inflammatory cytokines were evaluated as indicated. (B) Percentages of CD4+ cytokine producing T cells are shown for healthy controls (n = 18; green) and ME/CFS patients (n = 18; brown). Data are shown as median with interquartile range (boxes) and minimal and maximal values (bars). Unstimulated conditions were included as negative controls in each experiment. P-values are indicated for each set compared.

Figure 6

Analysis of new NK cell markers. (A) Representative dot-plots for the co-expression of CD57 and either NKG2C or NKp46 in gated CD56+CD16+NK cells. (B) Correlation between CD57 and NKp46 expression in CD56+CD16+ NK cells from frozen PBMC samples (18 healthy controls in green and 18 ME/CFS patients in brown). Linear regression of data is illustrated by a red line. Spearman correlation coefficients and P-values are shown. (C, D) Marker coexpression for healthy controls (n = 18; green) and individuals with ME/CFS (n = 18; brown) are shown as indicated. Data are shown as median with interquartile ranges (boxes) plus minimal and maximal values (bars). P-values are indicated for each set compared.