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Discovery proteomics by mass spectrometry comparing secreted proteins from young and old mouse lung mesenchymal stromal cells

Citation

Chanda, Diptiman; Thannickal, Victor; Mobley, James (2021), Discovery proteomics by mass spectrometry comparing secreted proteins from young and old mouse lung mesenchymal stromal cells, Dryad, Dataset, https://doi.org/10.5061/dryad.x0k6djhj1

Abstract

Lung mesenchymal stromal cells (L-MSCs) were isolated from the young ( 3 months) and old (22-24 months) mice (N = 4 each) following collagenase digestion of the lungs and anchorage-dependent growth. Culture media was collected from young and old L-MSCs (10^6 cells) grown in culture for 24 hrs in serum-free condition. Culture media was then  centrifuged to remove any cellular debris. Five (5) ml of supernatants were concentrated using 3kDa Molecular weight cut-off filters, and 10 micrograms of proteins were run as multiple MW 1D PAGE fractions using a standard GeLC approach with a nano-HPLC in-line with a Velos Pro Orbitrap MS. Individual data files were searched using Sequest, the results were then combined for each sample, followed by grouping, filtering, and quantifying by normalized spectral counts. 

 

Methods

Search Engine: Sequest 

  Version: 27, rev. 12

  Parent Tolerance: 0.015 Da (Monoisotopic)

  Variable Modifications: +16 on M (Oxidation), +57 on C (Carbamidomethyl)

  Database: the mjdb_mouse-20150516 database (24937 entries)

  Digestion Enzyme: Trypsin

  Max Missed Cleavages: 3

 

Scaffold: Version: Scaffold_4.8.7 

  Protein Grouping Strategy: Experiment-wide grouping with binary peptide-protein weights

  Peptide Thresholds: 80.0% minimum

  Protein Thresholds: 99.0% minimum and 2 peptides minimum

  Peptide FDR: 2.8% (Prophet)

  Protein FDR: 0.1% (Prophet)

  ID Source(s): FASTA:UniRef/NREF (UniProt)

Usage Notes

Ø There were 503 high confidence proteins identified with the filtered indicated on the previous slide. Of these, 235 proteins were found to have non-zero quantifiable values in 3 of 4 experimental repeats per group for down-stream statistical analysis.
 
Ø There were 30 proteins (20↓ and 10↑ in old v young) that passed both a single pairwise statistic in addition to a fold change of ><+/- 1.5. There were additionally 8 proteins (4↓ and 4↑ in old v young) that had measurable levels of protein in 3 of 4 experiments within one group vs no measurable levels in the other group, with 6 of these proteins (3 and 3 per group) that presented with average NSC’s above >1.5; therefore, these 6 proteins were added to the 30 significantly changed proteins for systems analysis.
 
Ø UniProtKB accession numbers were cross correlated to Gene Names and ID’s prior to systems analysis using the DAVID GO database (https://david.ncifcrf.gov/), and all proteins did cross match, the GeneID names were then used along with the fold change values (OvY) for systems analysis in Metacore (Genego/ https://ccbr.ccr.cancer.gov/project/genego-metacore/).
 
Ø Within Metacore, Network ID’s are generated and utilized as often much shorter and universal gene ontology (GO) ID’s for systems analysis. The top 36 Protein Hits list is illustrated on the following slide along with UniProtKB, Gene, and Network ID’s.
 
Ø Metacore was utilized to ID the top localizations for the 36 protein list, and how they are associated with biomarker of disease. The primary localization was related to proteins known to go to extracellular space and exosomal, the top disease process was associated with bronchogenic carcinoma.
 
Ø Under toxic pathologies in Metacore, the most significant association was with lung fibrosis, and the following 8 proteins were in that group corresponding  with OvsY fold changes; Elastin (-2.080), FKBP10 (-1.98), Integrin (2.62), LDHA (-2.1), Peroxiredoxin (-1.76), PRDX1 (-1.76),  SDF-1 (-1.94), and Stromelysin-1 (-3.15).
 
Ø It should be stated that all of the systems analysis were carried out unsupervised from the standpoint of including any biology, pathway, or pathology associated terms, these data came up with endpoints observed solely based on the protein list for those that changed significantly between the two groups.

Funding

NIH Office of the Director, Award: P01 HL114470

NIH Office of the Director, Award: R01 AG046210

Veteran Affairs Administration, USA

Veteran Affairs Administration, USA