{ "METABOLOMICS WORKBENCH":{"STUDY_ID":"ST002052","ANALYSIS_ID":"AN003340","VERSION":"1","CREATED_ON":"January 6, 2022, 12:26 pm"}, "PROJECT":{"PROJECT_TITLE":"Multi-omic attributes and unbiased computational modeling for the prediction of immunomodulatory potency of mesenchymal stromal cells","PROJECT_SUMMARY":"Mesenchymal stromal cells (MSCs) are “living medicines” that continue to be evaluated in clinical trials to treat various clinical indications, yet remain unapproved. Because these cell therapies can be harvested from different tissue sources, are manufactured ex vivo, and are composed of highly responsive cells from donors of varying demographics, significant complexities limit the current understanding and advancements to clinical practice. However, we propose a model workflow used to overcome challenges by identifying multi-omic features that can serve as predictive therapeutic outcomes of MSCs. Here, features were identified using unbiased symbolic regression and machine learning models that correlated multi-omic datasets to results from in vitro functional assays based on putative mechanisms of action of MSCs. Together, this study provides a compelling framework for achieving the identification of candidate CQAs specific to MSCs that may help overcome current challenges, advancing MSCs to broad clinical use. This upload contains the metabolomic dataset which were correlated with quality metrics, such as potency.","INSTITUTE":"Georgia Institute of Technology","LAST_NAME":"Gaul","FIRST_NAME":"David","ADDRESS":"311 Ferst Drive Atlanta, GA 30332","EMAIL":"david.gaul@chemistry.gatech.edu","PHONE":"4048943870"}, "STUDY":{"STUDY_TITLE":"Multi-omic Attributes and Unbiased Computational Modeling for the Prediction of Immunomodulatory Potency of Mesenchymal Stromal Cells","STUDY_SUMMARY":"Mesenchymal stromal cells (MSCs) are “living medicines” that continue to be evaluated in clinical trials to treat various clinical indications, yet remain unapproved. Because these cell therapies can be harvested from different tissue sources, are manufactured ex vivo, and are composed of highly responsive cells from donors of varying demographics, significant complexities limit the current understanding and advancements to clinical practice. However, we propose a model workflow used to overcome challenges by identifying multi-omic features that can serve as predictive therapeutic outcomes of MSCs. Here, features were identified using unbiased symbolic regression and machine learning models that correlated multi-omic datasets to results from in vitro functional assays based on putative mechanisms of action of MSCs. Together, this study provides a compelling framework for achieving the identification of candidate CQAs specific to MSCs that may help overcome current challenges, advancing MSCs to broad clinical use. This upload contain the metabolomic datasets, which were correlated with quality metrics, such as potency.","INSTITUTE":"Georgia Institute of Technology","LABORATORY":"System Mass Spectrometry Core","LAST_NAME":"Gaul","FIRST_NAME":"David","ADDRESS":"311 Ferst Drive Atlanta, GA 30332","EMAIL":"david.gaul@chemistry.gatech.edu","PHONE":"4048943870"}, "SUBJECT":{"SUBJECT_TYPE":"Cultured cells","SUBJECT_SPECIES":"Homo sapiens","TAXONOMY_ID":"9606"}, "SUBJECT_SAMPLE_FACTORS":[ { "Subject ID":"-", "Sample ID":"Blank01", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Blank","RAW_FILE_NAME":"Blank01"} }, { "Subject ID":"-", "Sample ID":"Blank02", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Blank","RAW_FILE_NAME":"Blank02"} }, { "Subject ID":"-", "Sample ID":"Blank04", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Blank","RAW_FILE_NAME":"Blank04"} }, { "Subject ID":"-", "Sample ID":"Blank05", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Blank","RAW_FILE_NAME":"Blank05"} }, { "Subject ID":"-", "Sample ID":"QC01", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Pooled","RAW_FILE_NAME":"QC01"} }, { "Subject ID":"-", "Sample ID":"QC02", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Pooled","RAW_FILE_NAME":"QC02"} }, { "Subject ID":"-", "Sample ID":"QC03", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Pooled","RAW_FILE_NAME":"QC03"} }, { "Subject ID":"-", "Sample ID":"QC04", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Pooled","RAW_FILE_NAME":"QC04"} }, { "Subject ID":"-", "Sample ID":"QC05", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Pooled","RAW_FILE_NAME":"QC05"} }, { "Subject ID":"-", "Sample ID":"QC06", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Pooled","RAW_FILE_NAME":"QC06"} }, { "Subject ID":"-", "Sample ID":"QC07", "Factors":{"Factor":"NA"}, "Additional sample data":{"sample_type":"QC_Pooled","RAW_FILE_NAME":"QC07"} }, { "Subject ID":"-", "Sample ID":"BM1_1", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample01_RB139_OOT"} }, { "Subject ID":"-", "Sample ID":"BM1_2", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample02_RB139_OOT"} }, { "Subject ID":"-", "Sample ID":"BM2_1", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample03_RB168"} }, { "Subject ID":"-", "Sample ID":"BM2_2", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample04_RB168"} }, { "Subject ID":"-", "Sample ID":"BM2_3", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample05_RB168"} }, { "Subject ID":"-", "Sample ID":"BM3_1", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample06_RB171"} }, { "Subject ID":"-", "Sample ID":"BM3_2", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample07_RB171"} }, { "Subject ID":"-", "Sample ID":"BM3_3", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample08_RB171"} }, { "Subject ID":"-", "Sample ID":"BM4_1", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample09_RB174"} }, { "Subject ID":"-", "Sample ID":"BM4_2", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample10_RB174"} }, { "Subject ID":"-", "Sample ID":"BM4_3", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample11_RB174"} }, { "Subject ID":"-", "Sample ID":"BM5_1", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample12_RB177"} }, { "Subject ID":"-", "Sample ID":"BM5_2", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample13_RB177"} }, { "Subject ID":"-", "Sample ID":"BM5_3", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample14_RB177"} }, { "Subject ID":"-", "Sample ID":"BM6_1", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample15_RB179"} }, { "Subject ID":"-", "Sample ID":"BM6_2", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample16_RB179"} }, { "Subject ID":"-", "Sample ID":"BM6_3", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample17_RB179"} }, { "Subject ID":"-", "Sample ID":"BM7_1", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample18_RB183"} }, { "Subject ID":"-", "Sample ID":"BM7_2", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample19_RB183"} }, { "Subject ID":"-", "Sample ID":"BM7_3", "Factors":{"Factor":"MSC_BoneMarrow"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample20_RB183"} }, { "Subject ID":"-", "Sample ID":"CT1", "Factors":{"Factor":"MSC_CordTissue"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample26_GMP075EXP"} }, { "Subject ID":"-", "Sample ID":"CT2", "Factors":{"Factor":"MSC_CordTissue"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample27_GMP088EXP"} }, { "Subject ID":"-", "Sample ID":"CT3", "Factors":{"Factor":"MSC_CordTissue"}, "Additional sample data":{"sample_type":"Sample","RAW_FILE_NAME":"Sample28_GMP087EXP"} } ], "COLLECTION":{"COLLECTION_SUMMARY":"Bone Marrow derived Mesenchymal Stromal Cells were purchased from RoosterBio, and the cord tissue derived Mesenchymal Stromal Cells were from Duke University School of Medicine.","SAMPLE_TYPE":"Stem cells"}, "TREATMENT":{"TREATMENT_SUMMARY":"BM-MSCs were expanded in either regular media or xeno-free media, while the CT-MSCs were expanded in xeno-free media. Harvested cells were resuspended in CryoStor CS5."}, "SAMPLEPREP":{"SAMPLEPREP_SUMMARY":"Metabolites were extracted using a modified bligh-Dyer on 1 million MSC pellet with bead homogenization.The Aqueous layer was dried, reconstituted with 80% MeOH with internal standards prior to analysis. the organic layer was also dried, reconstituted with IPA with internal standards prior to analysis"}, "CHROMATOGRAPHY":{"CHROMATOGRAPHY_TYPE":"Reversed phase","INSTRUMENT_NAME":"Thermo Vanquish","COLUMN_NAME":"Thermo Accucore C30 (150 x 2.1 mm, 2.6um)"}, "ANALYSIS":{"ANALYSIS_TYPE":"MS"}, "MS":{"INSTRUMENT_NAME":"Thermo Q Exactive HF hybrid Orbitrap","INSTRUMENT_TYPE":"Orbitrap","MS_TYPE":"ESI","ION_MODE":"NEGATIVE","MS_COMMENTS":"Compound Discoverer used to process","MS_RESULTS_FILE":"ST002052_AN003340_Results.txt UNITS:peak area Has m/z:Neutral masses Has RT:Yes RT units:Minutes"} }