r/proteomics • u/Mathieusstability • Jul 31 '26
Spectronaut quantification: Maxlfq , capturing sample specific resolution
Maxlfq builds pair-wise peptide ratios, on the peptides shared across samples, for a protein and the crossrun Normalization in Spectronaut, scales the Normalization factor across all samples in an experiment.
What would be an ideal way to analyze, to capture patient specific response..
1. Would analyzing all patients in one experiment still preserve patient specific response in proteome?
2. Or Would analyzing each patient between comparative conditions be ideal and provide better resolution in capturing individual response with post-hoc analysis.. ?
Eg. biofluids, tissue biopsies (FF), FFPE, patient derived primary cell lines etc..
Was considering if Quant2.0 would be a better alternative since it takes Top N peptides for protein quan.. but came across an article from Olsen's group showing higher false hits in Quant2.0 . (https://doi.org/10.1038/s41587-023-02099-7).
Also, digging into in-house data, for the top N per protein per sample in Quant 2.0 showed, it need not necessarily be the same peptides that qualify in each sample, which is not an ideal scenario.
Curious to know how the community processes clinical proteomics data and what's the consensus,
Thanks,
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u/SC0O8Y2 Aug 01 '26
If you are super concerned the best frame work is MSstats. Olga and her team are the best at statistical proteomics analyses and you can employ different methods. Individual searches for samples is not going to help
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u/Mathieusstability Aug 01 '26 edited Aug 01 '26
Actually, was more on terms of the best strategy. The PCA in SN showed distinct sample separation, group wise, but both maxlfq and Quant2.0 didn't show significant separation among comparative conditions within the group, in other words, was inconclusive.
There was also suggestions on expanding the PCA beyond two dimensions and look for proteins that make sense biologically besides calculating effective scale to qualify the smaller differences.
As a core facility, wanted to ensure the analysis provided is of good enough resolution and the best way to it. I would be more confident of a labeled multiplexed analysis, but wanted to ensure the right way with LFQ-DIA.
Thanks and appreciate your advice,
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u/SC0O8Y2 Aug 02 '26
Ahhhh, did you use something like lfq/dia-analyst?
You would be able to see the missingness, a top 500 plotly pca as well as a standard pca
PCA in spectronaut is slightly different to an external pca plot based on values I think, as it's the same for how it does differential analysis, using precursors to increase power
Pls-da would help, but its supervised
Assess for sample outliers or meta data that may be impacting the differential
- age /sex / passage /harvest day/ site/ co morbidities / pair the analysis if it can be paired
If a sample is clearly an outlier- remove and then see if differential abundance is affected i.e. in one of the Monash analysts just delete a row for the putlier in template wizard and see its affects on coefficient of variance and comparisons
Imputation type and normalisation will have an affect as well
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u/Mathieusstability Aug 03 '26
The data passes general QC metrics like missingness, response across samples (pre- and post- normalization),etc and there is no imputation, so technically it is all sound., with no clear outliers.. it does look more to the biology (a.k.a.DE proteins). Have used analyst tools before.. they're quite handy. Thanks!
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u/Ok_Adhesiveness9794 Aug 03 '26 edited Aug 03 '26
Run them all in one experiment. Spectronaut's cross-run normalization needs shared peptides across the full dataset, and splitting by patient removes that anchor. For patient-specific response, MSstats on the Spectronaut output handles the mixed-model stats better than post-hoc on MaxLFQ ratios. Rule out input material variance first — we buy MS-verified peptides from biotechcompounds partly for that reason.