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