Evaluation of Sample Preservation and Storage Methods for Metaproteomics Analysis of Intestinal Microbiomes

Abstract

A critical step in studies of the intestinal microbiome using meta-omics approaches is the preservation of samples before analysis. Preservation is essential for approaches that measure gene expression, such as metaproteomics, which is used to identify and quantify proteins in microbiomes. Intestinal microbiome samples are typically stored by flash-freezing and storage at -80°C, but some experimental setups do not allow for immediate freezing of samples. In this study, we evaluated methods to preserve fecal microbiome samples for metaproteomics analyses when flash-freezing is not possible. We collected fecal samples from C57BL/6 mice and stored them for 1 and 4 weeks using the following methods: flash-freezing in liquid nitrogen, immersion in RNAlater, immersion in 95% ethanol, immersion in a RNAlater-like buffer, and combinations of these methods. After storage, we extracted protein and prepared peptides for liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis to identify and quantify peptides and proteins. All samples produced highly similar metaproteomes, except for ethanol-preserved samples that were distinct from all other samples in terms of protein identifications and protein abundance profiles. Flash-freezing and RNAlater (or RNAlater-like treatments) produced metaproteomes that differed only slightly, with less than 0.7% of identified proteins differing in abundance. In contrast, ethanol preservation resulted in an average of 9.5% of the identified proteins differing in abundance between ethanol and the other treatments. Our results suggest that preservation at room temperature in RNAlater or an RNAlater-like solution performs as well as freezing for the preservation of intestinal microbiome samples before metaproteomics analyses. IMPORTANCE Metaproteomics is a powerful tool to study the intestinal microbiome. By identifying and quantifying a large number of microbial, dietary, and host proteins in microbiome samples, metaproteomics provides direct evidence of the activities and functions of microbial community members. A critical step for metaproteomics workflows is preserving samples before analysis because protein profiles are susceptible to fast changes in response to changes in environmental conditions (air exposure, temperature changes, etc.). This study evaluated the effects of different preservation treatments on the metaproteomes of intestinal microbiome samples. In contrast to prior work on preservation of fecal samples for metaproteomics analyses, we ensured that all steps of sample preservation were identical so that all differences could be attributed to the preservation method.

Keywords: LC-MS/MS; gut microbes; intestinal microbiome; metaproteomics; microbiota; sample preservation; storage.

FIG 1

There were no significant differences in total numbers of PSMs, peptides, proteins, and protein groups between samples coextracted after 1 week of preservation, and only minimal differences existed in samples coextracted after 4 weeks. FF, flash-freezing; R, RNAlater; RF, RNAlater + flash-freezing; N, NAP buffer; AN, autoclaved NAP buffer; E, 95% ethanol; 1 week, preserved for 1 week and first extraction batch; 4 weeks, preserved for 4 weeks and second extraction batch. Bars represent the arithmetic mean (n = 4 for all except 95% ethanol at 4 weeks, for which n = 3). Error bars represent standard deviation. Asterisks indicate statistical significance (t test, P value of <0.05). (A) Total proteins identified at 5% FDR include the microbial, host, and dietary proteins. (B) Total protein groups identified at 5% FDR. (C) Total peptides identified at 5% FDR. (D) Total peptide spectrum matches (PSMs) identified at 5% FDR.

FIG 2

Over 76% of microbial, host, and dietary protein identifications overlapped between treatments, and these proteins account for more than 99% of all PSMs. Replicates of both time points were combined (n = 8 samples/treatment except for ethanol, for which n = 7). Proteins were included if they were identified with an FDR of <5% and at least one protein unique peptide and were present in at least three samples in the whole data set. The number of PSMs that the proteins represent are displayed in panels B and D. (A and B) Comparison of treatments that differed most in terms of physical/chemical properties: flash-freezing, RNAlater, 95% ethanol, and NAP buffer. (C and D) Comparison of the chemically similar treatments (RNAlater and RNAlater-like treatments): RNAlater, RNAlater frozen, NAP buffer, and autoclaved NAP.

FIG 3

Ethanol-preserved samples were distinct from all other samples in their protein abundance profiles. (A) Principal-component analysis (PCA) of the relative protein abundances from each sample (CLR-transformed). Diamonds, 1 week; circles, 4 weeks. (B) Number of significant differences between each treatment (two-sided t test, FDR of 0.05, and S0 of 0.1). A significant difference represents one protein that is more abundant in one treatment over the other for each paired comparison (refer to Supplemental Data Set 2 for directionality). Percentages in parentheses indicate the percentage of significant proteins out of the total proteins considered (n = 6,086).

FIG 4

Small but significant differences in the representation of microbial taxa in the metaproteomes based on the preservation method. Bars represent the mean percent proteinaceous biomass for each taxon at the phylum level (A) or the genus level (B). Biomass contributions of specific taxa were calculated using the method described by Kleiner et al. (32). Error bars represent the standard deviation (n = 8, except for the ethanol treatment, for which n = 7). Asterisks represent statistical significance (t test, paired, two-tailed, P < 0.05). The eight most abundant genera are displayed in the figure. Percentages are low because genus-level taxonomy could be assigned for 11.1 ± 0.53% (n = 47) of the total proteinaceous biomass in our samples, distributed over 28 different microbial genera.

FIG 5

Distribution of biochemical properties of identified proteins. (A) Molecular weight (MW; in kDa). (B) Isoelectric point. (C) Number of predicted transmembrane helices (TMH). Bars represent the proportion (%) of identified proteins belonging in each range.