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MermelekasMass SpectrometryBottom-up proteomicsData-independent acquisitionLung CancerStatistical methodDifferential abundanceLc-ms/msDifferential expressionDiaLabel-free proteomicshttps://www.ebi.ac.uk/pride/archive/projects/PXD063099Epithelial CellNCI-H1944 cells were cultured in RPMI-1640 media supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. The cells were seeded into cell culture dishes in eight replicates for each condition. After 12 hours, the media was replaced with standard medium or containing interferon gamma (10 ng/ml) for controls and treated cells, respectively. After 24 h, the cells were harvested using trypsinization. The cells were lysed by the addition of a lysis buffer (4% SDS, 1 mM DTT, and 25 mM HEPES, pH 7.6) followed by heating for 5 min at 95 °C and sonication for 1 min. The protein concentration was measured using the DC protein assay with BSA as a standard. The cell pellet were lysed by 4 % SDS lysis buffer and prepared for mass spectrometry analysis using a modified version of the SP3 protein clean up and digestion protocol (Moggridge et al., 2018). In brief, 100 µg protein from each sample was alkylated with 4 mM Chloroacetamide. Sera‐Mag SP3 bead mix (20 µl) was transferred into the protein sample together with 100% Acetonitrile to a final concentration of 70 %. The mix was incubated under rotation at room temperature for 18 min. The mix was placed on the magnetic rack and the supernatant was discarded, followed by two washes with 70 % ethanol and one with 100 % acetonitrile. The beads-protein mixture was reconstituted in 100 µl LysC buffer (10 % Acetonitrile, 50 mM HEPES pH: 7.6 and 1:50 enzyme (LysC) to protein ratio) and incubated overnight. Finally, trypsin was added in 1:50 enzyme to protein ratio in 100 µl (10 % Acetonitrile, 50 mM HEPES pH 7.6) and incubated overnight followed by SCX clean up. The final peptide concentration was determined by the Bio-Rad DC Assay. An aliquot of approximately 10 µg was suspended in LC mobile phase A and 0.5 µg was injected on the LC-MS/MS system. Peptide mixtures were prepared combining the controls and interferon-treated samples in a range of different proportions: Ctrl:IFN-treated 100:0, 95:5, 90:10, 75:25, 50:50, 25:75, 10:90, 5:95, 0:100. The resultant 72 samples (8 replicates x 9 mixtures) were dried in a SpeedVac and dissolved in MS-grade water prior to MS analysis.PrideFor protein identification and quantification, all raw files were analyzed by Spectronaut using the direct DIA approach. All parameters were kept as factory default for protein identification. Briefly, runs were recalibrated using iRT standard peptides in a local and non-linear regression. Precursors, peptides and proteins were filtered with FDR 1%. The decoy database was created by mutation method. For quantification, only peptides unique to a protein group were used. Protein groups were defined base on gene symbols to obtain a gene symbol centric quantification. Stripped peptide quantification was defined as the top precursor quantity. Protein group quantification was calculated by the median value of the top 3 most abundant peptides. The quantitative values were filtered using Qvalue for each sample.ProteomicsJanne LehtiöDepartment of oncology and pathology, Karolinska Institute, Stockholm, SwedenPARTIALHomo Sapiens (human)georgios.mermelekas@scilifelab.se42020565 Zhu Y, Berkovska O, Wang L, Yang M, Johansson HJ, Mermelekas G, Zarrineh M, Yin D, Orre LM, Lehtiö J. Differential protein expression analysis of quantitative mass spectrometry data using DEqMS. Nat Protoc. 2026 10.1038/s41596-026-01349-7Karolinska InstitutetSwedenDEqMS is an R package-based statistical tool for differential protein expression analysis in quantitative mass spectrometry-based proteomics. It implements a robust Bayesian method for accurate variance estimation that accounts for the number of mass spectrometry features used for protein quantification (number of peptide precursors or peptide spectrum matches). Originally validated for data-dependent acquisition proteomics, DEqMS now extends to data-independent acquisition workflows, as demonstrated using both spike-in and real-world datasets. Given a peptide- or protein-level quantification table with mass spectrometry feature count as inputs, DEqMS outputs a protein- or gene-level results table containing fold changes and multiple statistics (t-values, P value, among others) adjusted according to mass spectrometry feature count. Here we detail the use of the DEqMS R package. This updated workflow broadens DEqMS's applicability, enabling researchers with basic R programming knowledge to identify proteins with significantly altered abundance between sample groups across diverse quantitative proteomics datasets. DEqMS is available to install at https://bioconductor.org/packages/DEqMS/ .Differential protein expression analysis of quantitative mass spectrometry data using DEqMS.Zhu Yafeng Y, Berkovska Olena O, Wang Lingshuo L, Yang Mei M, Johansson Henrik J HJ, Mermelekas Georgios G, Zarrineh Mahshid M, Yin Dong D, Orre Lukas M LM, Lehtiö Janne JfalseDEqMS - a method for differential protein expression analysis of quantitative mass spectrometry dataDEqMS is a statistical method to perform differential protein expression (abundance) analysis for quantitative mass spectrometry-based proteomics. Developed on top of Limma, DEqMS implements a robust Bayesian method for accurate variance estimation while taking into account the number of peptide precursors or PSMs used for protein quantification. DEqMS has a demonstrated improved accuracy and statistical power compared to previous methods and has since its initial publication been used by a wide range of researchers in various proteomics applications. Originally, DEqMS was validated to analyze proteomics data from data dependent acquisition (DDA). Based on users’ feedback, we now demonstrate DEqMS usability in performing differential analysis also in data independent acquisition (DIA)-based proteomics data. Given this updated protocol, we envision that DEqMS will continue to be a useful tool for defining sets of proteins with significantly different levels between sample groups based on complex quantitative proteomics data.2026-06-032025-04-18PXD063099NEWT:330879NEWT:377960NEWT:2042546NEWT:259447NEWT:295546NCBITaxon:2719036NCBITaxon:1280NEWT:112503NEWT:1129NEWT:309807NEWT:59753NEWT:89184NEWT:309800NEWT:281395NEWT:1211601NEWT:876138NEWT:44271NEWT:193516NEWT:111205NEWT:1117NEWT:498257NEWT:10036NEWT:1590NEWT:661410NEWT:638632NEWT:224326NEWT:376619NCBITaxon:79857NEWT:1096976NEWT:1589NEWT:135622NEWT:67352NEWT:35786NEWT:1580NEWT:399784NEWT:96731NEWT:383379NEWT:418106NEWT:10029NEWT:913645NEWT:641809NEWT:317447NEWT:4688NEWT:111225NEWT:7719NEWT:868565NEWT:135674NEWT:79329NEWT:30069NEWT:12637NEWT:59729NEWT:2164133NEWT:295105NEWT:108061NEWT:60711NEWT:224308NEWT:3347NEWT:160621NEWT:212790NEWT:1310161NEWT:77133NEWT:145481NEWT:1310165NEWT:29058NCBITaxon:79824NEWT:1912919NEWT:44688NEWT:44689NEWT:498211NEWT:347256NEWT:5518NEWT:398007NEWT:1527468NEWT:498217NEWT:498216NEWT:11320NEWT:246196NEWT:246197NEWT:145458NEWT:44685NEWT:161934NEWT:1148NEWT:5508NEWT:3329NEWT:5507NEWT:410661NEWT:1140NCBITaxon:2157NEWT:1143NEWT:1287689NEWT:1094343NEWT:1462472NEWT:1336795NEWT:644042NEWT:1182590NEWT:3712NEWT:3711NEWT:270643NEWT:2065263NEWT:177437NEWT:10418NEWT:118698NEWT:1616117NEWT:118696NEWT:34865NEWT:52283NEWT:284812NEWT:8175NEWT:43330NEWT:980415NEWT:1603293NEWT:44664NEWT:3702NEWT:1245466NEWT:244366NEWT:1246791NEWT:118694NEWT:2850NEWT:118691NEWT:34871NEWT:33548NEWT:96794NEWT:3708NEWT:332648NEWT:44670NEWT:536231NEWT:376219NEWT:219813NEWT:1510NEWT:460519NEWT:1515NEWT:572307NEWT:1432138NEWT:1424507NEWT:1194599NEWT:272844NEWT:1348799NEWT:1303443NEWT:9483NEWT:485NEWT:56636NEWT:2709072NEWT:2853422NEWT:1679718NEWT:480NEWT:67767NEWT:46835NEWT:109757NEWT:582580NEWT:294607NEWT:1502NEWT:128017NEWT:376686NEWT:95486NEWT:1883446NEWT:1233435NEWT:109760NEWT:29031NEWT:235443NEWT:108458NEWT:5936NEWT:320637NEWT:3750NEWT:983964NEWT:11706NEWT:32644NEWT:527796NEWT:499175NEWT:109779NEWT:3745NEWT:1715989NCBITaxon:4751NEWT:3747NEWT:1116234NEWT:1255228NEWT:410289NEWT:373153NEWT:472NEWT:529507NEWT:1071661NEWT:470NEWT:5911NCBITaxon:50557NEWT:39251NEWT:29491NEWT:101841NEWT:446NEWT:153481NEWT:2014887NEWT:33952NEWT:445NEWT:153009NEWT:261756NEWT:63366NEWT:63367NEWT:215402NEWT:1547NEWT:9031NEWT:27292NEWT:1050722NEWT:108931NEWT:1293497NEWT:1055524NEWT:150475NEWT:267872NEWT:172269NEWT:9534NEWT:5180NEWT:256737NEWT:9541NEWT:8694NEWT:33936NEWT:8692NEWT:2903NEWT:185579NEWT:13076NEWT:1006581NEWT:8673NEWT:33940NEWT:550NEWT:554NEWT:451516NEWT:552NEWT:1325291NEWT:36185NEWT:1054211NEWT:1225786NEWT:575412NEWT:28112NEWT:6493NEWT:6494NEWT:6491NEWT:507601NEWT:520NEWT:186441NEWT:643680NEWT:214092NCBITaxon:6157NEWT:13095NEWT:162425NEWT:104105NEWT:216257NEWT:9986NEWT:8654NEWT:8658NEWT:1268063NEWT:8657NEWT:8655NEWT:5147NEWT:28104NEWT:407821NCBITaxon:2NEWT:568708NEWT:986NEWT:52641NEWT:28532NEWT:353152NEWT:40674NEWT:1194669NEWT:51329NEWT:443906NEWT:519NEWT:2510939NEWT:6063NEWT:1328388NEWT:1548728NEWT:667127NEWT:9557NEWT:377586NEWT:300641NEWT:39655NEWT:9554NEWT:38323NEWT:256318NEWT:206411NCBITaxon:6191NEWT:229533NEWT:2925NEWT:214053NEWT:80863NEWT:90675NEWT:52638NEWT:57075NEWT:8697NEWT:8695NEWT:884204NEWT:1123869NEWT:9544NEWT:9545NEWT:979NEWT:7370NEWT:83906NEWT:1134506NEWT:255470NEWT:38783NEWT:6426NEWT:33090NEWT:9935NEWT:287889NEWT:305959NEWT:13489NEWT:92867NEWT:92866NCBITaxon:3055NEWT:51750NEWT:202950NEWT:295027NCBITaxon:11320NEWT:632957NEWT:9925NCBITaxon:9606NEWT:90690NEWT:1436183NEWT:4232NEWT:416348NEWT:11298NEWT:196627NEWT:200308NEWT:242507NEWT:200302NEWT:870435NEWT:9913NEWT:9915NEWT:105841NEWT:2666255NEWT:999810NCBITaxon:5693NEWT:28995NEWT:1392998NEWT:380394NEWT:114796NEWT:226900NEWT:1266738NEWT:231490NEWT:244704NEWT:7725NEWT:430615NEWT:563041NEWT:72664NEWT:326423NEWT:452467NEWT:198822NEWT:36111NEWT:326424NEWT:1678078NEWT:749906NEWT:418985NEWT:749907NEWT:150847NEWT:431947NEWT:69014NEWT:142809NEWT:302409NEWT:130821NEWT:27606NEWT:1519788NEWT:59202NEWT:9975NEWT:8643NEWT:1159899NEWT:502780NEWT:860688NEWT:13443NEWT:8644NEWT:5141NEWT:46360NCBITaxon:3044782NEWT:202914NEWT:160235NEWT:502779NEWT:297246NEWT:8639NEWT:1928434NEWT:8637NEWT:1114970NEWT:2096NEWT:192222NEWT:9963NEWT:1617910NEWT:701504NEWT:2785785NEWT:411901NEWT:1298722NEWT:8626NEWT:105884NCBITaxon:9615NEWT:8621NEWT:58334NEWT:58331NEWT:8624NEWT:5116NEWT:8623NEWT:5597NCBITaxon:3077NEWT:33035NEWT:194439NCBITaxon:12637NEWT:38727NEWT:8618NEWT:70448NEWT:9940NEWT:1247190NEWT:118702NEWT:118700NEWT:2126719NEWT:118705NEWT:6945NEWT:2104NEWT:2627626NEWT:6943NEWT:157546NEWT:870726NEWT:91374NEWT:91373NEWT:474186NEWT:10136NEWT:38942NEWT:4762NEWT:307972NEWT:2102NEWT:34NEWT:38949NEWT:1299114NEWT:1590841NEWT:1276473NEWT:2282334NEWT:1247NEWT:70667NEWT:869728NEWT:6938NCBITaxon:39054NEWT:1736309NEWT:4751NEWT:194963NEWT:15819NEWT:109461NEWT:1085956NEWT:6928NEWT:1239NEWT:36745NEWT:400727NEWT:272563NEWT:11009NEWT:3885NEWT:3888NEWT:649408NEWT:6915NEWT:101510NEWT:3880NEWT:272559NEWT:216816NEWT:153151NEWT:515849NEWT:10141NEWT:95767NEWT:466585NEWT:1000589NEWT:85963NEWT:29NEWT:85962NEWT:42197NEWT:143361NEWT:1299NEWT:424718NEWT:316407NEWT:1794699NCBITaxon:183674NEWT:167387NEWT:846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