ChipFilter: Microfluidic-Based Comprehensive Sample Preparation Methodology for Microbial Consortia

Abstract

The metaproteomic approach is an attractive way to describe a microbiome at the functional level, allowing the identification and quantification of proteins across a broad dynamic range as well as the detection of post-translational modifications. However, it remains relatively underutilized, mainly due to technical challenges that should be addressed, including the complexity of extracting proteins from heterogeneous microbial communities. Here, we show that a ChipFilter microfluidic device coupled to a liquid chromatography tandem mass spectrometry (LC-MS/MS) setup can be successfully used for the identification of microbial proteins. Using cultures of Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae, we have shown that it is possible to directly lyse the cells and digest the proteins in the ChipFilter to allow the identification of a higher number of proteins and peptides than that by standard protocols, even at low cell density. The peptides produced are overall longer after ChipFilter digestion but show no change in their degree of hydrophobicity. Analysis of a more complex mixture of 17 species from the gut microbiome showed that the ChipFilter preparation was able to identify and estimate the amounts of 16 of these species. These results show that ChipFilter can be used for the proteomic study of microbiomes, particularly in the case of a low volume or cell density. The mass spectrometry data have been deposited on the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD039581.

Keywords: metaproteomic; proteomic; sample preparation.

Figure 1

Whole-cell lysis and bottom-up proteomic sample preparation with ChipFilter. Number of proteins (A) and peptides (B) identified for Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae using ChipFilter.

Figure 2

Performance assessment of the ChipFilter workflow compared to other methods for Mix 1 (3E6 cells/sample). Identification of target proteins (A) and total peptides (B) was carried out by different methods for four replicates. Pearson coefficient for the four protocols (C). The distribution of proteins (D) and peptides (E) was identified at least once by different methods. Number and percentage of proteins identified by each method for E. coli, S. cerevisiae, and B. subtilis (F).

Figure 3

Label-free quantification of the peptides and proteins. Total number of peptides with the abundance value for each method according to the number of identifications in the four replicates (A). Distribution of the coefficient of variation at the peptide (B) and protein (C) levels.

Figure 4

Performance assessment of the ChipFilter workflow compared to other methods for Mix 2 (3E2 cells/sample). Identification of target proteins (A) and peptides (B) by different methods was carried out for four replicates. Pearson coefficient for the four protocols (C). The distribution of proteins (D) and peptides (E) was identified at least once across different methods. Number and percentage of proteins identified by each method for E. coli, S. cerevisiae, and B. subtilis (F).

Figure 5

Physical–chemical characteristics of the peptides generated by different preparation methods for the 3× 1E6 cell sample. The number of missed cleavages generated by each method for all the peptides (A) and for peptides exclusively identified by one protocol (B). Amino acid length distribution. (C) Positive ion mass (MH+) distribution. Kyte–Doolittle hydrophobicity index (E). For (C–E), the mean value is plotted in the graph along with the error bars indicating the standard deviation between four replicates.

Figure 6

Biomass estimations using proteomic approaches (in-solution and ChipFilter proteolysis) for next-generation sequencing methods and cell number for species present in the standard gut mix. Cell number, genome copy, 16S and 18S, and 16S data were provided by the manufacturer.

Figure 7

Number of proteins identified according to the phylum (A). Phylogenetic relationships between the identified phylum determined using Unipept Desktop v2.0.1 software (B).