Project PXD010861

PRIDE Assigned Tags:
Biological Dataset



Kidney proteomics in the unilateral ureter obstruction (UUO) mouse model


Treatments for kidney fibrosis represent an urgent yet unmet clinical need. Effective therapies are limited due to not well understood molecular pathogenesis. We aimed at generating a comprehensive and integrated multi-omics data set (RNA/ microRNA transcriptomics and proteomics) of fibrotic kidneys which will be searchable through a user-friendly web application. Therefore, two commonly used mouse models were utilized: a reversible chemical-induced injury model (folic acid (FA) induced nephropathy) and an irreversible surgical-induced fibrosis model (unilateral ureteral obstruction (UUO)). RNA and small RNA sequencing as well as MS/MS with 10-plex tandem mass tags proteomics were performed with kidney samples from different time points over the course of fibrosis development. In summary, we present temporal and integrated multi-omics data from fibrotic mouse kidneys which are accessible through an interrogation tool to provide a searchable transcriptome and proteome for kidney fibrosis researcher.

Sample Processing Protocol

Kidney samples were mechanically homogenized in lysis buffer (8 M urea, 1% SDS, Roche complete protease inhibitors and phosphatase inhibitors, 50 mM Tris pH 8.5). Approximately one third of a kidney was used for sample preparation. Protein concentration was determined using the BCA assay. The homogenate was reduced with 5 mM DTT and alkylated with 15 mM iodoacetamide. 0.15 mg of protein was precipitated using chloroform:methanol. Pellets were washed twice with cold methanol and re-solubilized in 8M urea with 20 mM EPPS, pH 8.5. After diluting the samples to 4M urea using 20 mM EPPS they were digested with Lys-C overnight at room temperature. On the next day, samples were further diluted to 1.5 M urea using 20 mM EPPS and digested for 6h at 37°C using Trypsin. 60 µg of each sample were then brought to 10% (v/v) acetonitrile and labeled with 2:1 (TMT:Peptide) by mass of TMT-10 reagent . The reaction was quenched with hydroxylamine (0.5% final volume). Afterwards, samples were acidified by adding formic acid to 2% final volume, combined, and desalted using a C18 Sep-Pak . The now combined sample was fractionated using basic pH reversed phase chromatography using a 1200 HPLC (Agilent) equipped with a UV-DAD detector and fraction collection system. Then, the resulting 12 fractions were desalted using the C18 StageTip procedure. Each fraction was loaded onto a 100 µm id, 35 cm long column packed with 1.8 µm beads and separated using a 3h gradient from 8-27% buffer B (99% acetonitrile and 1% formic acid) and buffer A (96% water, 3% acetonitrile and 1% formic acid) using an Easy 1000 nano-LC (Thermo-Fisher Scientific). All MS analyses were performed on an Orbitrap Fusion Lumos mass spectrometer (Thermo-Fisher Scientific) applying a multi-notch MS3 method.

Data Processing Protocol

Raw data was converted to mzXML and searched via Sequest version 28 against a concatenated Uniprot database downloaded 02/04/2014. Variable modifications of oxidized methionine and over-labelling of TMT on serine, threonine and tyrosine were considered. To distinguish forward and reverse hits Linear discriminate analysis was used and reverse hits were filtered to an FDR of 1% at the protein level. Using rules of parsimony shared peptides were collapsed into the minimally sufficient number of proteins. Quantitation filters of > 200 sum reporter ion S:N and > 0.7 isolation specificity were incorporated.


Matthew Berberich, Harvard Program In Therapeutic Science/Harvard Medical School
Vishal S. Vaidya, Ph.D., Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA Department of Medicine – Renal Division, Brigham and Women’s Hospital, Boston, MA ( lab head )

Submission Date


Publication Date


Corresponding dataset(s) in other omics resources

RNA and small RNA seq is submitted to GEO
but no number yet




renal fibrosis


Not available

Experiment Type

Shotgun proteomics


    Pavkovic M, Pantano L, Gerlach CV, Brutus S, Boswell SA, Everley RA, Shah JV, Sui SH, Vaidya VS. Multi omics analysis of fibrotic kidneys in two mouse models. Sci Data. 2019 6(1):92 PubMed: 31201317