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Li et al. Microbiome Res Rep 2024;3:26 https://dx.doi.org/10.20517/mrr.2023.57 Page 3 of 16

microbiomes to various xenobiotics. We have developed RapidAIM, namely Rapid Assay of Individual
Microbiome, taking advantage of fast-pass metaproteomics to study the human gut microbiome’s protein
expression responses to xenobiotics in a high-throughput compatible setting [18,19] . RapidAIM has been
widely used in numerous research studies. As a proof of concept of RapidAIM, we first used it to assess the
effect of 43 xenobiotic compounds on five individual gut microbiomes and discovered that seven of the
tested compounds showed consistent effects across individual samples, while some other compounds
showed significant but individually distinct effects . RapidAIM was then used to evaluate the effect of a
[18]
[20]
[21]
panel of structurally similar compounds of berberine , different structures of resistant starches , and
commonly used sweeteners on individual gut microbiomes. RapidAIM was also used to evaluate the effect
[22]
[23]
of a bacteriophage preparation on microbiome composition and function , as well as being applied in two
ongoing clinical trials for the selection of therapeutic interventions (NCT04520594 and NCT04522271). The
optimized culture model (MiPro) of RapidAIM has been used by other researchers to investigate gut
[24]
microbiome responses to oligomannate , dietary cholesterol , dietary fibers , antibiotics , and
[26]
[27]
[25]
nanoparticles . Notably, the previous efforts of RapidAIM did not realize automated high-throughput
[28]
metaproteomic analysis. With the rapid rise of the automation and big data era, the development of a 2.0
version of the protocol to expand the capability to study microbiome responses to various stimuli is timely,
and its broad application is highly expected.
In RapidAIM 2.0, we incorporate automation and multiplexing techniques into the metaproteomic analysis
workflow to observe the response of protein expression in the in vitro microbiome in a high-throughput
manner. The use of isobaric chemical labels, such as tandem mass tag (TMT) approach, offers great
potential in the analysis of large sample sets such as those generated from our individual microbiome-
xenobiotics assay designs. TMT-based quantitation has recently been used for large-scale proteomics studies
owing to its high multiplexing capacity and deep proteome coverage . The use of TMT labeling also
[29]
significantly reduces LC-MS/MS time and cost . A recent study reported the development of a high-
[30]
throughput stool metaproteomics workflow . The protocol used Protifi S-trap to clean up the proteins in
[31]
combination with TMT labeling and automation, which also greatly saves time. However, only around 5,000
[31]
microbial proteins were identified from a total of 290 human stool samples . Our recently optimized
TMT-metaproteomics workflow identified 28,605 microbial peptides and 10,656 microbial protein groups
from 97 samples. This streamlined TMT labeling workflow is fully compatible with automation of the
metaproteomics sample preparation steps which altogether can significantly increase the robustness of
liquid handling compared to manual operation, speed up the experimental workflow, and further increase
the throughput . In this paper, we further adapted this workflow to an automated version, and show that
[32]
metaproteomic profiling derived from cultured samples of four human gut microbiomes resulted in a total
of over 5,000 quantified protein groups per sample. This is comparable to previous studies using manual,
label-free metaproteomics protocols [18,33] . To demonstrate the typical outcome of the protocol, we show an
example of using RapidAIM 2.0 to evaluate the effect of prebiotic kestose on ex vivo individual human gut
microbiomes preprocessed with five different workflows; we also show that kestose had consistent
functional effects across individuals and can be used as a positive control in the assay.

METHODS
Proof-of-concept of the RapidAIM 2.0 approach
We designed a study to exemplify the expected outcome of the RapidAIM 2.0 approach. The study includes
an evaluation of different sample biobanking methods and sample storage methods. Although these tests do
not provide specific examples of different compounds, the comparison of the sample pre-processing
workflow described above still yields a diverse range of samples. This result serves to effectively showcase
the stability and versatility of the high-throughput strategy employed. To evaluate sample biobanking
methods, four different stool sample processing strategies, namely gauze filtration (Gauze), 100 µm vacuum