High-Throughput Stool Metaproteomics: Method and Application to Human Specimens

Abstract

Stool-based proteomics is capable of significantly augmenting our understanding of host-gut microbe interactions. However, compared to competing technologies, such as metagenomics and 16S rRNA sequencing, it is underutilized due to its low throughput and the negative impact sample contaminants can have on highly sensitive mass spectrometry equipment. Here, we present a new stool proteomic processing pipeline that addresses these shortcomings in a highly reproducible and quantitative manner. Using this method, 290 samples from a dietary intervention study were processed in approximately 1.5 weeks, largely done by a single researcher. These data indicated a subtle but distinct monotonic increase in the number of significantly altered proteins between study participants on fiber- or fermented food-enriched diets. Lastly, we were able to classify study participants based on their diet-altered proteomic profiles and demonstrated that classification accuracies of up to 89% could be achieved by increasing the number of subjects considered. Taken together, this study represents the first high-throughput proteomic method for processing stool samples in a technically reproducible manner and has the potential to elevate stool-based proteomics as an essential tool for profiling host-gut microbiome interactions in a clinical setting. IMPORTANCE Widely available technologies based on DNA sequencing have been used to describe the kinds of microbes that might correlate with health and disease. However, mechanistic insights might be best achieved through careful study of the dynamic proteins at the interface between the foods we eat, our microbes, and ourselves. Mass spectrometry-based proteomics has the potential to revolutionize our understanding of this complex system, but its application to clinical studies has been hampered by low-throughput and laborious experimentation pipelines. In response, we developed SHT-Pro, the first high-throughput pipeline designed to rapidly handle large stool sample sets. With it, a single researcher can process over one hundred stool samples per week for mass spectrometry analysis, conservatively approximately 10× to 100× faster than previous methods, depending on whether isobaric labeling is used or not. Since SHT-Pro is fairly simple to implement using commercially available reagents, it should be easily adaptable to large-scale clinical studies.

An integrated workflow for enhanced taxonomic and functional coverage of the mouse fecal metaproteome

“…Our initial experiment involved the establishment of an optimal sample preparation workflow applied to the mouse fecal metaproteome. In this context, we assessed two sample preparation steps that are commonly employed in metaproteomic studies: 1) the usage of LSC 19 , 24 , 34 versus nLSC; 24 , 35 and 2) in-solution digestion 19 , 22 versus filter-aided sample preparation (FASP) 20 , 21 (Figure S1A, Table S1). The resulting LC-MS/MS data were processed using the MaxQuant software.…”

Evaluating live microbiota biobanking using an ex vivo microbiome assay and metaproteomics

“…We used quantitative metaproteomics to measure the expressed proteins and functions, because we were particularly interested in the functional state of the microbiomes, which is best assessed at the protein level instead of DNA level (16S rRNA gene 7 sequencing or metagenomics) 26 . Moreover, recent development of MS-based deep metaproteomics approaches [26][27][28] allows rapid and comprehensive profiling of the functions of the cultured microbiomes in this study. In agreement with previous FMT and animal studies 20,21 , we did not observe obvious detrimental effects on the functional profiles of the microbiomes frozen up to a year.…”

An Integrated Workflow for Enhanced Taxonomic and Functional Coverage of the Mouse Faecal Metaproteome

“…The LSC approach also leads to more consistent identifications and as a result fewer missing values, which is a general and extensive problem in metaproteomic datasets. Regarding the topic of reproducible protein identification and quantification, a recent metaproteomic study demonstrated the use of Tandem Mass Tag (TMT) approach in human stool samples [22].…”

“…Many studies have reported increased protein identification due to laboratory optimisation for the analysis of metaproteome samples [19][20][21][22][23]. In humans, different sample preparation methodologies have been shown to result in significant changes in the taxonomic composition and functional activities representated [19,24,25].…”