Dataset Information

Label-free DIA proteomics of WT, HET, and SMA mouse liver at postnatal day 10 (P10)

ABSTRACT: This dataset contains label-free DIA proteomics of liver tissue from postnatal day 10 (P10) Taiwanese SMA mice (genotype: Smn−/−; SMN2tg/0), heterozygous carriers (HET, Smn+/−; SMN2tg/0), and wild-type controls (WT, Smn+/+; SMN2 0/0). SMA and HET samples were littermates generated from the same FVB/N × C57BL/6 mixed-background colony, whereas WT controls were age-matched animals on a C57BL/6 background. Whole-liver lysates were processed by in-solution protein digestion and analyzed using data-independent acquisition (DIA) LC–MS/MS on a Thermo Orbitrap Exploris 480 mass spectrometer equipped with a FAIMS Pro differential ion mobility device. A Vanquish Neo nano-HPLC system (Thermo Scientific) was coupled to the mass spectrometer for chromatographic separation. Peptide and protein identification and quantification were performed with DIA-NN v1.8.1using a predicted spectral library generated from the Mus musculus UniProt canonical FASTA file (accession UP000000589, downloaded 2024-01-04). The DIA-NN output was filtered at 1% precursor- and protein-level FDR (global q-value ≤ 0.01), with additional quality filters applied in R (≥ 4 fragment ions per precursor, library q-value ≤ 0.01, unique peptides only). Final LFQ intensities were calculated using the DIA-NN R package, and downstream statistical analysis was performed in Perseus v1.6.15. All RAW files generated during data acquisition are included in this dataset. One SMA biological replicate was identified as an outlier by principal component analysis and excluded from downstream statistical comparisons in the accompanying manuscripts: this corresponds to SMN biological replicate 5 (Raw file: E3_CoIID_656_3632_LI_165). Its RAW file and corresponding DIA-NN output tables are provided here for completeness, transparency, and reproducibility. The dataset contains the original RAW files, DIA-NN search output tables (.tsv), the predicted spectral library used for identification, and the Mus musculus UniProt FASTA database employed for database searching. Experimental factors encoded in the metadata include genotype (WT, HET, SMA) and tissue (liver).

INSTRUMENT(S):

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Liver

DISEASE(S): Spinal Muscular Atrophy

SUBMITTER: SOFIA VRETTOU

LAB HEAD: Brunhilde Wirth

PROVIDER: PXD070887 | Pride | 2026-01-11

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	E3_CoIID_6563632_LI_151_165_mbr.tsv	Tabular
	E3_CoIID_656_3632_LI_151_165_mbr_gg_matrix.tsv	Tabular
	E3_CoIID_656_3632_LI_151_165_mbr_lib.speclib	Other
	E3_CoIID_656_3632_LI_151_165_mbr_pg_matrix.tsv	Tabular
	E3_CoIID_656_3632_LI_151_165_mbr_pr_matrix.tsv	Tabular

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Project description:Dear Juan Francisco, Dear Pablo we finished analyzing your latest order. In short, the data looks great! Your neg. controls behave as expected and cluster tightly. The probe (JT-195) enriches for a lot of prey proteins. Many are shared with the cysteinome control. Replicates are very similar. Kind regards, Tobias *** Always reply to workflow results by using the order comments function in b-fabric! Please do NOT send emails to proteomics@ or individual people involved in workflow processing (order coach). *** 1 Downloading results packaged in a b-fabric workunits [https://fgcz-bfabric.uzh.ch/wiki/tiki-index.php?page=WorkunitDownload] 2 General data processing logic LC-MS raw data => Dia-NN => proLFQ/SAINTexpress => Interaction proteomics report |-> ScaffoldDIA 3 Workunit guidance 3.1 Dia-NN The main output of Dia-NN is called <WU#>_report.tsv and is a precursor-centric table in long format. It describes chromatographic features mapped to peptides. This file can be loaded in downstream processing tools like DIAgui [https://github.com/mgerault/DIAgui] or amica [https://github.com/tbaccata/amica] or a spread sheet calculator. For direct exploration of Dia-NN output we recommend using the so called matrix files named <WU#>_report.<level>_matrix.tsv These files summarize the main report for pr (precursor), pg (protein group), or gg (gene group) level by the maxLFQ algorithm in matrix-like tables (feature x sample). Be aware that maxLFQ estimates are tailored towards comparisons between experimental conditions (also called horizontal comparisions). They should not be used for rank sorting proteins, as estimator for the abs. amount of proteins. For this special estimators like topN or iBAQ should be used (available in DIAgui) [https://github.com/vdemichev/DiaNN] 3.2 SAINTexpress The main interaction scoring results are summarized in the interaction proteomics report. This report is named SaintExpressReport*_<WU#>.html and can be viewed in any web browser. All numbers shown in the report are extracted from the SAINTexpress output which is available as DIANN_<WU#>_data.xlsx. The list tab summarizes all bait-prey pairs scored by the ML model for the different comparisons. We judge preys by co-filtering in effect size (FoldChange) and significance dimension (BFDR). Suitable cutoffs are very experiment/data dependent, especially in the effect size dimension. We tend to use BFDR <5% and FC >1 as initial thresholds. Refiltering according to known interactions is always a good approach to arrive at meaningful candidate lists. The ORA_Bait_<baitName>_<preyName>.txt files can be used to upload candidate lists to web tools like String-DB [https://string-db.org] for network/enrichment analysis. They are always filtered according to parameters shown in the interaction proteomics report. [https://saint-apms.sourceforge.net/Main.html] 3.3 ScaffoldDIA The main output of protein identification and quantification performed by Dia-NN is also available as ScaffoldDIA formatted file (<WU#>_result.sdia). ScaffoldDIA allows to browse LC-MS datasets using a GUI. The ScaffoldDIA app can be downloaded free of charge from [https://www.proteomesoftware.com/products/scaffold-dia] and runs on all mayor (OS) platforms. Be aware that Dia-NN matrix files might be different to ScaffoldDIA outputs, since ScaffoldDIA takes the Dia-NN main report (which describes chromatographic features mapped to peptides) and recalculates protein inference, reestimate protein quantifies. The output in the app also heavily depends on the set filter options. We do NOT recommend using ScaffoldDIA exports for statistical downstream processing, but only for interactive explorative analysis.

Dataset Information

Label-free DIA proteomics of WT, HET, and SMA mouse liver at postnatal day 10 (P10)

Dataset's files

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