#METABOLOMICS WORKBENCH HanLab_20231111_130114 DATATRACK_ID:4454 STUDY_ID:ST002973 ANALYSIS_ID:AN004882 PROJECT_ID:PR001850 VERSION 1 CREATED_ON November 11, 2023, 4:53 pm #PROJECT PR:PROJECT_TITLE A protocol for metabolomics-based gut microbiome investigations PR:PROJECT_SUMMARY A significant hurdle that has limited progress in microbiome science has been PR:PROJECT_SUMMARY identifying and studying the diversity of metabolites produced by the gut PR:PROJECT_SUMMARY microbes. Gut microbial metabolism produces thousands of difficult-to-identify PR:PROJECT_SUMMARY metabolites, which present a challenge to study their roles in host biology. PR:PROJECT_SUMMARY Over the recent years, mass spectrometry-based metabolomics has become one of PR:PROJECT_SUMMARY the core technologies for identifying small metabolites. However, metabolomics PR:PROJECT_SUMMARY expertise, ranging from sample preparation, instrument use, to data analysis, is PR:PROJECT_SUMMARY often lacking in academic labs. Most targeted metabolomics methods provide high PR:PROJECT_SUMMARY levels of sensitivity and quantification, while they are limited to a panel of PR:PROJECT_SUMMARY predefined molecules that may not be informative to microbiome-focused studies. PR:PROJECT_SUMMARY Here we have developed a gut microbe-focused and wide-spectrum metabolomic PR:PROJECT_SUMMARY protocol using Liquid Chromatography-Mass Spectrometry (LC-MS) and bioinformatic PR:PROJECT_SUMMARY analysis. This protocol enables users to carry out experiments from sample PR:PROJECT_SUMMARY collection to data analysis, only requiring access to a LC-MS instrument, which PR:PROJECT_SUMMARY is often available at local core facilities. By applying this protocol to PR:PROJECT_SUMMARY samples containing human gut microbial metabolites, spanning from culture PR:PROJECT_SUMMARY supernatant to human biospecimens, our approach enables high confidence PR:PROJECT_SUMMARY identification of >800 metabolites that can serve as candidate mediators of PR:PROJECT_SUMMARY microbe-host interactions. We expect this protocol will lower the barrier in PR:PROJECT_SUMMARY tracking gut bacterial metabolism in vitro and in mammalian hosts, propelling PR:PROJECT_SUMMARY hypothesis-driven mechanistic studies and accelerating our understanding of the PR:PROJECT_SUMMARY gut microbiome at the chemical level. PR:INSTITUTE Duke University School of Medicine PR:DEPARTMENT Biochemistry PR:LABORATORY Han PR:LAST_NAME Han PR:FIRST_NAME Shuo PR:ADDRESS 307 Research Drive, Nanaline Duke Building, Room 159, Durham, NC, 27710, USA PR:EMAIL shuo.han@duke.edu PR:PHONE 909-732-2788 #STUDY ST:STUDY_TITLE Examine the through-filter recovery of metabolites extracted from a complex ST:STUDY_TITLE bacterial medium ST:STUDY_SUMMARY Based on this metabolomic protocol, the specific dataset submitted here ST:STUDY_SUMMARY addresses whether passing metabolite extracts through a 0.2 micron filter plate ST:STUDY_SUMMARY impacts the overall detection of metabolites. We recommend the use of filter ST:STUDY_SUMMARY plate to remove particulate, in turn, prolonging column and instrument life. ST:STUDY_SUMMARY Here we have tested the through-filter recovery of metabolites extracted from a ST:STUDY_SUMMARY rich, complex bacterial culture media (mega media) used to culture diverse gut ST:STUDY_SUMMARY bacterial species in our study. We select mega media as our biological matrix ST:STUDY_SUMMARY for this experiment, because it enables us to assess a diverse set of ST:STUDY_SUMMARY metabolites. Leveraging this dataset, we have observed that the ion-abundance a ST:STUDY_SUMMARY large number of molecular features detected in pre- vs. post-filtered samples ST:STUDY_SUMMARY closely correlate with each other. We have performed this experiment with two ST:STUDY_SUMMARY independent batches of mega media and observed consistent results. Collectively, ST:STUDY_SUMMARY our observations indicate a good retention of ion abundance of molecular ST:STUDY_SUMMARY features after passing them through the 0.2 micron membrane filter. ST:INSTITUTE Duke University ST:DEPARTMENT Biochemistry ST:LABORATORY Han ST:LAST_NAME Han ST:FIRST_NAME Shuo ST:ADDRESS 307 Research Drive, Nanaline Duke Building, Room 159 ST:EMAIL shuo.han@duke.edu ST:PHONE 909-732-2788 #SUBJECT SU:SUBJECT_TYPE Other abiotic sample #SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Raw file names and additional sample data SUBJECT_SAMPLE_FACTORS - SH_01 Treatment:post-filter | Batch:1 RAW_FILE_NAME=Post-filtered_1_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_02 Treatment:post-filter | Batch:1 RAW_FILE_NAME=Post-filtered_2_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_03 Treatment:post-filter | Batch:1 RAW_FILE_NAME=Post-filtered_3_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_04 Treatment:post-filter | Batch:1 RAW_FILE_NAME=Post-filtered_4_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_05 Treatment:post-filter | Batch:1 RAW_FILE_NAME=Post-filtered_5_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_06 Treatment:pre-filter | Batch:1 RAW_FILE_NAME=Pre-filtered_1_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_07 Treatment:pre-filter | Batch:1 RAW_FILE_NAME=Pre-filtered_2_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_08 Treatment:pre-filter | Batch:1 RAW_FILE_NAME=Pre-filtered_3_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_09 Treatment:pre-filter | Batch:1 RAW_FILE_NAME=Pre-filtered_4_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_10 Treatment:pre-filter | Batch:1 RAW_FILE_NAME=Pre-filtered_5_Experiment_1.RAW SUBJECT_SAMPLE_FACTORS - SH_11 Treatment:post-filter | Batch:2 RAW_FILE_NAME=Post-filtered_1_Experiment_2.RAW SUBJECT_SAMPLE_FACTORS - SH_12 Treatment:post-filter | Batch:2 RAW_FILE_NAME=Post-filtered_2_Experiment_2.RAW SUBJECT_SAMPLE_FACTORS - SH_13 Treatment:post-filter | Batch:2 RAW_FILE_NAME=Post-filtered_3_Experiment_2.RAW SUBJECT_SAMPLE_FACTORS - SH_14 Treatment:post-filter | Batch:2 RAW_FILE_NAME=Post-filtered_4_Experiment_2.RAW SUBJECT_SAMPLE_FACTORS - SH_15 Treatment:post-filter | Batch:2 RAW_FILE_NAME=Post-filtered_5_Experiment_2.RAW SUBJECT_SAMPLE_FACTORS - SH_16 Treatment:pre-filter | Batch:2 RAW_FILE_NAME=Pre-filtered_1_Experiment_2.RAW SUBJECT_SAMPLE_FACTORS - SH_17 Treatment:pre-filter | Batch:2 RAW_FILE_NAME=Pre-filtered_2_Experiment_2.RAW SUBJECT_SAMPLE_FACTORS - SH_18 Treatment:pre-filter | Batch:2 RAW_FILE_NAME=Pre-filtered_3_Experiment_2.RAW SUBJECT_SAMPLE_FACTORS - SH_19 Treatment:pre-filter | Batch:2 RAW_FILE_NAME=Pre-filtered_4_Experiment_2.RAW SUBJECT_SAMPLE_FACTORS - SH_20 Treatment:pre-filter | Batch:2 RAW_FILE_NAME=Pre-filtered_5_Experiment_2.RAW #COLLECTION CO:COLLECTION_SUMMARY Two independently made batches of bacteria media was used for this study. For CO:COLLECTION_SUMMARY each batch, five aliquots from the same batch were used as replicates. Each CO:COLLECTION_SUMMARY aliquot was then split into halves for metabolite extraction. Following CO:COLLECTION_SUMMARY extraction, the one half was used as the pre-filtered controls and the other CO:COLLECTION_SUMMARY half was used for post-filtered sample that passed through the 0.2 micron filter CO:COLLECTION_SUMMARY membrane. CO:SAMPLE_TYPE bacterial media #TREATMENT TR:TREATMENT_SUMMARY Metabolites extracted from mega medium, a rich and undefined bacterial medium, TR:TREATMENT_SUMMARY are filtered using a 96-well 0.2 micron filter plate. Here we compare the TR:TREATMENT_SUMMARY detection of metabolites in pre-filtered vs. post-filtered conditions from the TR:TREATMENT_SUMMARY same replicate, and five replicates are used for each of the two independent TR:TREATMENT_SUMMARY batches of media tested. #SAMPLEPREP SP:SAMPLEPREP_SUMMARY The sample preparation procedure is described in detail in our preprint of this SP:SAMPLEPREP_SUMMARY metabolomic protocol: SP:SAMPLEPREP_SUMMARY https://protocolexchange.researchsquare.com/article/pex-2055/v1 #CHROMATOGRAPHY CH:CHROMATOGRAPHY_SUMMARY A published C18 reveres phase method was implemented with minor modifications. CH:CHROMATOGRAPHY_SUMMARY The C18 positive method (ESI+) used mobile phase solvents (LC-MS grade) CH:CHROMATOGRAPHY_SUMMARY consisting of 0.1% formic acid (Fisher) in water (A) and 0.1% formic acid in CH:CHROMATOGRAPHY_SUMMARY methanol (B). The gradient profile was from 0.5% B to 70% B in 4 minutes, from CH:CHROMATOGRAPHY_SUMMARY 70% B to 98% B in 0.5 minutes, and holding at 98% B for 0.9 minute before CH:CHROMATOGRAPHY_SUMMARY returning to 0.5% B in 0.2 minutes. The flow rate was 350 µL per minute. The CH:CHROMATOGRAPHY_SUMMARY sample injection volume was 5 µL. LC separations were made at 40C on separate CH:CHROMATOGRAPHY_SUMMARY columns fitted with a Vanguard pre-column of the same composition: Waters CH:CHROMATOGRAPHY_SUMMARY Acquity BEH 1.7 µm particle size, 2.1 mm id x 100 mm length (C18). Data were CH:CHROMATOGRAPHY_SUMMARY collected at a mass range of 70-1000 m/z at an acquisition rate of 2 spectra per CH:CHROMATOGRAPHY_SUMMARY second. Specific ion source parameters included Fragmentor (140V), Gas Temp CH:CHROMATOGRAPHY_SUMMARY (250oC), Sheath Gas Temp (200oC), and VCap (4000V). CH:CHROMATOGRAPHY_TYPE Reversed phase CH:INSTRUMENT_NAME Thermo Vanquish CH:COLUMN_NAME Waters ACQUITY UPLC BEH C18 (100 x 2.1mm,1.7um) CH:SOLVENT_A 100% water + 0.1% formic acid CH:SOLVENT_B 100% methanol + 0.1% formic acid CH:FLOW_GRADIENT From 0.5% B to 70% B in 4 minutes, from 70% B to 98% B in 0.5 minutes, and CH:FLOW_GRADIENT holding at 98% B for 0.9 minute before returning to 0.5% B in 0.2 minutes. CH:FLOW_RATE 0.350 mL/minute CH:COLUMN_TEMPERATURE 40C #ANALYSIS AN:ANALYSIS_TYPE MS #MS MS:INSTRUMENT_NAME Thermo Orbitrap Exploris 240 MS:INSTRUMENT_TYPE Orbitrap MS:MS_TYPE ESI MS:ION_MODE POSITIVE MS:MS_COMMENTS Please see step-by-step details in our preprint for this metabolomic protocol: MS:MS_COMMENTS https://protocolexchange.researchsquare.com/article/pex-2055/v1 MS:MS_RESULTS_FILE ST002973_AN004882_Results.txt UNITS:raw ion count Has m/z:Yes Has RT:Yes RT units:Minutes #END