Raw metabolomics data for glutamine and fish collagen peptides in alleviating post-antibiotic Streptococcus pneumoniae injury in feline lung cells

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Authors: Huasong Bai
Cited by: 0

Abstract

1. Data OverviewThis dataset contains the untargeted metabolomics raw files for feline lung cells. The data includes two ionization modes: Positive (POS) and Negative (NEG). Files are organized into two folders (or identified by suffixes): pos/ and neg/. The filenames within both folders are identical and correspond to the Raw name listed in the mapping table below. 2. Sample Mapping and Experimental GroupsThe following table describes the relationship between the raw mass spectrometry filenames, the biological sample IDs, and their respective experimental groups: Raw name Sample Name Group Name Description FZTM250086589-2A NC1 NC Negative Control (Group 1) FZTM250086590-2A NC2 NC Negative Control (Group 2) FZTM250086591-1A NC3 NC Negative Control (Group 3) FZTM250086592-1A NC4 NC Negative Control (Group 4) FZTM250086593-1A SP1 SP S. pneumoniae Injury (Group 1) FZTM250086594-1A SP2 SP S. pneumoniae Injury (Group 2) FZTM250086595-1A SP3 SP S. pneumoniae Injury (Group 3) FZTM250086596-1A SP4 SP S. pneumoniae Injury (Group 4) FZTM250086597-1A GLU1 GLU Glutamine Treatment (Group 1) FZTM250086598-1A GLU2 GLU Glutamine Treatment (Group 2) FZTM250086599-1A GLU3 GLU Glutamine Treatment (Group 3) FZTM250086600-1A GLU4 GLU Glutamine Treatment (Group 4) FZTM250086601-1A FCP1 FCP Fish Collagen Peptides (Group 1) FZTM250086602-1A FCP2 FCP Fish Collagen Peptides (Group 2) FZTM250086603-1A FCP3 FCP Fish Collagen Peptides (Group 3) FZTM250086604-1A FCP4 FCP Fish Collagen Peptides (Group 4) QC1 QC1 QC Quality Control 1 QC2 QC2 QC Quality Control 2 QC3 QC3 QC Quality Control 3

Fields of Study & Subjects

Metabolomics and Mass Spectrometry Studies Pneumonia and Respiratory Infections Antimicrobial Peptides and Activities

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
30 January 2026
Document type
Dataset