Irreversible alteration of extracellular vesicle and cell-free messenger RNA profiles in human plasma associated with blood processing and storage

Abstract

The discovery and utility of clinically relevant circulating biomarkers depend on standardized methods that minimize preanalytical errors. Despite growing interest in studying extracellular vesicles (EVs) and cell-free messenger RNA (cf-mRNA) as potential biomarkers, how blood processing and freeze/thaw impacts the profiles of these analytes in plasma was not thoroughly understood. We utilized flow cytometric analysis to examine the effect of differential centrifugation and a freeze/thaw cycle on EV profiles. Utilizing flow cytometry postacquisition analysis software (FCMpass) to calibrate light scattering and fluorescence, we revealed how differential centrifugation and post-freeze/thaw processing removes and retains EV subpopulations. Additionally, cf-mRNA levels measured by RT-qPCR profiles from a panel of housekeeping, platelet, and tissue-specific genes were preferentially affected by differential centrifugation and post-freeze/thaw processing. Critically, freezing plasma containing residual platelets yielded irreversible ex vivo generation of EV subpopulations and cf-mRNA transcripts, which were not removable by additional processing after freeze/thaw. Our findings suggest the importance of minimizing confounding variation attributed to plasma processing and platelet contamination.

Figure 1

Light scattering calibration. (A) Histogram of NIST-traceable polystyrene beads (152, 203, 303, 401, 510, and 600 nm) are shown using side scattering (SSC-H) on a bioexponential scale using FlowJo. Each bead size relative to SSC-H is identified to obtain median side scattering in arbitrary units (a.u.) for light scattering calibration. (B) Regression plot of acquired light scattering power in arbitrary units compared to the predicted scattering cross-section in nm² is calculated using FCMpass software. (C) Scatter-diameter curve showing light scatter intensity relationships with EV diameter established in FCMpass software. The acquired NIST-traceable polystyrene bead scattering intensity are overlaid with the predicted scattering data for NIST-traceable polystyrene beads with refractive index of 1.5900. The scatter-diameter relationship given high, average, and low effective EV refractive indices are shown, which can be used to estimate EV diameter from corresponding scattering intensity in arbitrary units.

Figure 2

Fluorescence calibration. (A) Representative flow cytometry dot plots of Quantum Alexa Fluor 488 MESF (top), Quantum Alexa Fluor 647 MESF beads (middle), and Quantum PE MESF (bottom) gated using SSC-A and FSC-A in arbitrary units (a.u.) from FlowJo. (B) The gated beads are shown in each fluorescence channel (488–530/30-A, 640–670/30-A, and 561–586/15-A respectively) against SSC-A in arbitrary units using FlowJo. (C) Histogram of Quantum Alexa Fluor 488 MESF, Quantum Alexa Fluor 647 MESF, and Quantum PE MESF beads are shown using fluorescent intensity in arbitrary units from FlowJo. The subsets of each bead differing in fluorescence intensity are drawn to obtain median fluorescence in arbitrary units for fluorescence calibration. (D) Regression of acquired fluorescence intensity in arbitrary units to MESF bead reference values for each population established in FCMpass software.

Figure 3

Effect of differential centrifugation on EVs using flow cytometry. (A) Schematic diagram of differentially processed plasma using single spin (S1: 1000 × g centrifugation) and double spin (S2: 15,000 × g secondary spin after the initial single spin S1). (B) Platelet concentration in differentially processed plasma from three healthy individuals (n = 3) was measured in independent technical replicates using a haemocytometer. The error bar represented standard deviations for the indicated blood processing conditions. P-value was calculated using Wilcoxon test (*P < 0.05). (C) Representative flow cytometry dot plot of EV diameter (nm) versus fluorescent intensity in Quantum Alexa Fluor MESF units for S1 and S2 using FlowJo. Quantum Alexa Fluor 647 MESF was used for Alexa Fluor 647 conjugated CD9 stained plasma, Quantum Alexa Fluor 488 MESF was used for Alexa Fluor 488 conjugated CD63 stained plasma, and Quantum PE MESF was used for PE conjugated CD41 stained plasma. Events were gated into two subpopulations: 150 to 1000 nm (green box) and from 1000 to 3000 nm (red box).

Figure 4

Effect of a freeze thaw cycle on EVs using flow cytometry. (A) Schematic diagram of differentially processed plasma (S1, S2) and respective freeze thaw processes (S1FR, S2FR). (B) Box plot of CD9⁺, CD63⁺, and CD41⁺ of gated events from 1000–3000 nm (red) and 150–1000 nm (green) for S1, S1FR, S2, and S2FR using R. CD9⁺, CD63⁺, CD41⁺ events were converted to concentrations using calibrated flow rate in a given acquisition time. EV concentration defined as the number of EVs per μl was determined by number of EVs detected in a given sample volume multiplied by the dilution factor. The sample volume was calculated by the product of measured flow rate and acquisition time. Statistical significance were obtained from three healthy volunteers for each freeze thaw processing condition using Tukey’s multiple comparisons (ns = not significant, P > 0.05; *P < 0.05, ***P < 0.001, ****P < 0.0001).

Figure 5

Effect of post-thaw processing on EVs using flow cytometry. (A) Schematic diagram of differentially processed plasma (S1, S2), respective freeze thaw samples (S1FR, S2FR), and secondary spin after post-freeze/thaw plasma S1FR (S1FRS2). (B) Representative flow cytometry dot plot of EV diameter (nm) versus fluorescent intensity in Quantum MESF units for CD9⁺ EVs, CD63⁺, and CD41⁺ EVs in S1FR, S2FR, and S1FRS2 conditions using FlowJo. Quantum Alexa Fluor 647 MESF is used for Alexa Fluor 647 conjugated CD9 stained plasma, Quantum Alexa Fluor 488 MESF is used for Alexa Fluor 488 conjugated CD63 stained plasma, and Quantum PE MESF is used for PE conjugated CD41 stained plasma. Events were gated from 150 to 1000 nm (green box) and from 1000 to 3000 nm (red box). (C) Box plot of CD9⁺, CD63⁺, CD41⁺ EV concentration from 1000–3000 nm (red) and 150–1000 nm (green) for S1FR, S2FR, and S1FRS2 using R. Statistical significance were obtained from three healthy volunteers for each freeze thaw processing condition using Tukey’s multiple comparisons (ns = not significant, P > 0.05; *P < 0.05, ***P < 0.001, ****P < 0.0001).

Figure 6

Effect of freeze thaw and post-thaw processing on cf-mRNAs using qRT-PCR. (A) Hierarchical clustering analysis of relative levels (in ΔCt) of 16 custom selected genes using RT-qPCR. Ct difference (ΔCt) between S1 and individual processing conditions are indicated from lowest (blue) to highest (red) using R. Non-tissue specific genes that are fully or partially removed, and tissue-specific genes which are retained in S1FRS2 with respect to S1 are shown. (B) Box plot of the median expression levels (in Cts) for representative non-tissue specific genes which are fully removed (i.e. HBG1 and SMC4) or partially removed (i.e. PF4, and B2M), and tissue-specific genes (i.e. APOE, ALB) which are retained in S1FRS2 with respect to S1 are shown using R. Higher raw Ct indicates lower levels of cfRNA transcripts.