version: "1.0.1" name: bio-proteomics-quantification description: Quantifies protein abundance from mass spectrometry using label-free (LFQ/MaxLFQ, DIA fragment-level), isobaric (TMT/iTRAQ reporter ions, MS2 vs SPS-MS3), and metabolic (SILAC) approaches, including peptide-to-protein summarization (Tukey median polish, MaxLFQ, msqrob), sample-loading and IRS cross-plex normalization, and isotopic impurity correction. Use when turning peptide/PSM/reporter signal into a protein-by-sample abundance matrix for downstream analysis. Statistical testing of that matrix is differential-abundance; DIA quant mechanics and DIA-NN runs are dia-analysis; reading search-engine outputs is data-import; razor/shared-peptide group assignment is protein-inference. tool_type: mixed primary_tool: MSstats

Version Compatibility

Reference examples tested with: MSstats 4.10+, MSnbase 2.28+, iq 1.9+, numpy 1.26+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Protein Quantification -- Reconstructing Protein Abundance from Ions Whose Physical Origin Dictates the Irreducible Error

"Quantify proteins from my mass spec data" -> Reconstruct a protein-by-sample abundance matrix from peptide/reporter ion signals, choosing a summarizer and normalizer that match where the signal physically came from -- because the measurement's physical origin sets an error that no normalization can remove.

• R: MSstats::dataProcess() (Tukey median polish summarization) for label-free feature-to-protein
• R: iq::maxLFQ() for the real MaxLFQ algorithm (delayed normalization + maximal peptide-ratio least-squares)
• R: MSnbase::quantify(reporters=TMT10) + purityCorrect() for isobaric reporter extraction
• R/Python: sample-loading + IRS scaling to bridge TMT plexes; per-sample median centering for LFQ

Scope: this skill OWNS converting peptide/PSM/reporter signal into a normalized protein abundance matrix (LFQ, TMT/iTRAQ, SILAC; summarization; normalization; IRS). Statistical testing of the matrix -> differential-abundance. DIA quant mechanics and DIA-NN execution -> dia-analysis. Parsing MaxQuant/DIA-NN outputs -> data-import. Razor/shared-peptide group assignment -> protein-inference. OUT OF SCOPE: missing-value imputation and the downshift false-positive trap (modeled in differential-abundance), and absolute copy-number calibration beyond a one-line pointer.

The Single Most Important Modern Insight

1. Every quant method answers "where does the signal physically come from?" differently, and that physical origin dictates the error structure no normalization can remove. LFQ measures MS1 precursor area (or DIA fragment area) in SEPARATE runs -> the irreducible error is run-to-run variation plus stochastic, left-censored (MNAR) missingness. Isobaric TMT/iTRAQ measures low-m/z reporter ions from CO-ISOLATED, co-eluting peptides in ONE spectrum -> the irreducible error is RATIO COMPRESSION toward 1:1, a PHYSICAL co-isolation effect (interloper reporters add roughly equally to every channel), attacked at the instrument by SPS-MS3 and never fully undone in software. SILAC measures a heavy/light MS1 pair in the SAME scan -> lowest per-ratio variance, but its irreducible vulnerabilities are incomplete labeling and Arg->Pro label scrambling, which bias every ratio and cannot be corrected post hoc because channels are combined before any MS (a correctly mixed sample carries no inherent mixing error). Teach the signal origin and every threshold and failure mode below follows from it.

1. The peptide-to-protein SUMMARIZATION choice is the highest-leverage decision in the pipeline, and it is invisible in the output. In log space a peptide intensity is protein abundance + a peptide effect (ionization efficiency, flyability, missed cleavages, modifications) that spans orders of magnitude and is partly context-dependent, plus a run effect. Sum is dominated by the highest-flying peptide (dropout collapses it -> a fold change driven by detectability, not biology); mean is unbiased only if the detected peptide SET is identical across runs (it is not under MNAR); median discards relative-intensity information. Benchmarks confirm the quantification method is a dominant driver of which proteins are called differential (Lin 2022). Report the summarizer as prominently as the test, and run a sensitivity analysis across >=2 summarizers -- that is where the answer is most likely to move.

Tool Taxonomy

Decision Tree by Scenario

Default when uncertain: label-free DDA -> MSstats::dataProcess with summaryMethod='TMP', normalization='equalizeMedians'; report the summarizer alongside results and sanity-check against iq::maxLFQ().

Label-Free Summarization and Normalization

Summarize peptides to proteins with MSstats

Goal: Turn MaxQuant feature-level evidence into a normalized protein-level abundance matrix.

Approach: Reformat to MSstats input, then dataProcess applies median equalization and Tukey median polish (robust to outlier peptides, 50% breakdown) with censored-value handling for label-free missingness.

library(MSstats)
 
maxquant_input <- MaxQtoMSstatsFormat(
    evidence = read.table('evidence.txt', sep = '\t', header = TRUE),
    proteinGroups = read.table('proteinGroups.txt', sep = '\t', header = TRUE),
    annotation = read.csv('annotation.csv')
)
 
# TMP = Tukey median polish; censoredInt='NA' treats missing intensities as left-censored
processed <- dataProcess(maxquant_input, normalization = 'equalizeMedians',
                         summaryMethod = 'TMP', censoredInt = 'NA', MBimpute = FALSE)
 
protein_abundance <- processed$ProteinLevelData

Run the real MaxLFQ (not median centering)

Goal: Produce MaxLFQ protein intensities from a peptide quant matrix.

Approach: Call iq::maxLFQ(), which implements Cox 2014 delayed normalization and maximal peptide-ratio least-squares. Per-sample median centering shares only the name and silently gives a different answer.

library(iq)
 
# rows = peptide ions, columns = samples, values = log2 intensities for ONE protein group
result <- maxLFQ(peptide_log2_matrix)
protein_estimate <- result$estimate    # one MaxLFQ value per sample

Median-center label-free intensities (a normalizer, not a summarizer)

Goal: Correct per-sample loading differences before testing.

Approach: Subtract each sample's median log2 intensity (corrects LOCATION only; it cannot manufacture variance, so it is the safe default). Median centering normalizes; it does NOT summarize peptides to proteins.

import numpy as np
import pandas as pd
log_int = np.log2(intensities.replace(0, np.nan))    # MaxQuant writes 0 for missing; log2(0) = -inf
sample_medians = log_int.median(axis=0)
normalized = log_int - sample_medians + sample_medians.median()

Isobaric (TMT/iTRAQ) Quantification

Extract and impurity-correct reporter ions

Goal: Pull TMT reporter intensities from spectra and correct cross-channel isotope bleed.

Approach: Read spectra on disk, quantify the reporter region, then purityCorrect with a LOT-SPECIFIC impurity matrix from the reagent Certificate of Analysis. readMSnSet reads an already-quantified text matrix and does NOT extract reporters.

library(MSnbase)
 
raw <- readMSData('experiment.mzML', mode = 'onDisk')
# method='max' for centroided spectra; reporters=TMT10 defines the 126-131 reporter m/z
quant <- quantify(raw, reporters = TMT10, method = 'max')
 
# makeImpuritiesMatrix has manufacturer-default templates; REPLACE with lot-specific Certificate values
imp <- makeImpuritiesMatrix(x = 10)
quant <- purityCorrect(quant, imp)

Bridge multiple TMT plexes with IRS

Goal: Make reporter intensities comparable across separate TMT runs.

Approach: Absolute reporter intensities for the same protein differ 2-5x between plexes because each plex samples a random point on the elution profile. Sample-loading normalization fixes within-run loading; the Internal Reference Scaling bridge (Plubell 2017) then pins each plex's pooled reference channel to a common per-protein value. Order: SL, then IRS.

import numpy as np
import pandas as pd
# protein_psm_sums: protein x channel, summed PSM reporter ions; one reference channel per plex
def sample_loading_normalize(plex):
    target = plex.sum(axis=0).mean()    # common target = mean column sum within the plex
    return plex * (target / plex.sum(axis=0))
def irs_scale(plexes, ref_cols):
    refs = pd.concat([p[ref] for p, ref in zip(plexes, ref_cols)], axis=1)
    geomean = np.exp(np.log(refs.replace(0, np.nan)).mean(axis=1))    # per-protein geometric mean of references
    out = []
    for p, ref in zip(plexes, ref_cols):
        factor = geomean / p[ref]    # per-protein per-plex scaling factor
        out.append(p.mul(factor, axis=0))
    return out

SILAC Quantification

Goal: Compute heavy/light ratios while preserving on/off biology and flagging label artifacts.

Approach: A protein present only in the heavy channel is the interesting biology, not a NaN to discard. Verify labeling efficiency (>=95%, target 97-98%) on a heavy-only pilot and assess Arg->Pro conversion before trusting any ratio.

import numpy as np
# Arg10/Lys8 is the common pairing (avoids overlap with the +6 isotope envelope)
SILAC_SHIFTS = {'Arg10': 10.008269, 'Lys8': 8.014199, 'Arg6': 6.020129, 'Lys6': 6.020129}
def silac_log2_ratio(heavy, light):
    if heavy > 0 and light > 0:
        return np.log2(heavy / light)
    if heavy > 0 and light == 0:
        return np.inf     # present only in heavy: real on/off biology, do NOT discard as NaN
    if light > 0 and heavy == 0:
        return -np.inf
    return np.nan

Per-Method Failure Modes

MaxLFQ reimplemented as median centering

Trigger: A homebrew function named maxlfq that only subtracts per-sample medians. Mechanism: Real MaxLFQ is delayed normalization plus a maximal peptide-ratio least-squares solve; median centering shares only the name. Symptom: Plausible-looking but systematically different intensities; unbalanced peptide sets handled wrongly. Fix: Call iq::maxLFQ(), DIA-NN, or MaxQuant.

TMT ratio compression

Trigger: MS2-only reporter quant on a complex sample. Mechanism: Co-isolated interloper peptides add reporters roughly equally to every channel, pulling large true ratios toward 1:1; PHYSICAL, not removable by normalization. Symptom: "Nothing is significant"; attenuated fold changes, inflated false negatives. Fix: SPS-MS3 acquisition, narrower isolation windows, FAIMS/ion mobility, or complement-reporter methods; a PIF filter helps but MS1 purity underestimates true interference (Savitski 2013).

TMT plexes concatenated without IRS

Trigger: Stacking reporter intensities from multiple plexes directly. Mechanism: Absolute reporter intensities for one protein differ 2-5x across runs from random elution-profile sampling, unrelated to abundance. Symptom: Plex appears as the dominant axis of variation; spurious cross-plex differences. Fix: Include a pooled reference channel in EVERY plex; apply sample-loading then IRS (Plubell 2017).

Isotopic impurity matrix mis-applied

Trigger: Using the default/example impurity matrix, the wrong lot, or a transposed/mis-ordered (127N vs 127C) matrix. Mechanism: A few percent of each channel bleeds to +/-1 Da neighbors; wrong values mis-subtract, negative corrected intensities get clipped. Symptom: Adjacent channels silently biased; extreme contrasts placed in adjacent channels confounded. Fix: Use the lot-specific Certificate of Analysis values; randomize channel-to-condition assignment.

SILAC Arg->Pro conversion / incomplete labeling

Trigger: Pro-containing peptides or a labeling efficiency below ~95%. Mechanism: Cells convert heavy Arg to heavy Pro (+6 Da), splitting Pro-peptide signal and underestimating the heavy channel; residual light masquerades as down-regulation. Symptom: Ratios biased toward light, worse for Pro-rich proteins; invisible without a check. Fix: Proline supplementation, measure conversion per cell line, verify >=95% incorporation on a heavy-only pilot.

SILAC on/off proteins discarded as NaN

Trigger: Returning NaN whenever either channel is zero. Mechanism: A protein present only in heavy (or only light) is the interesting biology, thrown away. Symptom: Largest true changes silently dropped before analysis. Fix: Record present-in-one-channel cases as +/-Inf or flag them; route honest absence handling to differential-abundance.

Spectral counting / NSAF used for fold changes

Trigger: Reaching for PSM counts for quantitative comparison. Mechanism: Count statistics are catastrophic at low abundance and saturate; dynamic exclusion deliberately breaks count-abundance proportionality. NSAF divides intensity by protein LENGTH then total -- a count divided by total spectra is not NSAF. Symptom: Noisy, biased estimates; the wrong normalization labeled NSAF. Fix: Use MS1/MS2 intensity (LFQ/DIA); keep spectral counting as historical context only.

AP-MS / enrichment pulldown normalized as a balanced proteome

Trigger: Median/sample-loading/IRS normalization applied to an affinity-purification or biotin-enrichment pulldown. Mechanism: Data-internal normalization assumes most signal is an unchanging background; a successful pulldown is deliberately non-representative (bait plus a few interactors over background), so equalizing medians or loading rescales away the enrichment being measured. Symptom: Real interactors flattened toward background; bait abundance dominates the axis of variation. Fix: Do not data-internal-normalize an enrichment; score against negative-control pulldowns (SAINT/CompPASS/CRAPome) or normalize to bait abundance.

Quantitative Thresholds

Common Errors

References

• Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. 2014. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ. Mol Cell Proteomics 13(9):2513-2526.
• Silva JC, Gorenstein MV, Li GZ, Vissers JPC, Geromanos SJ. 2006. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol Cell Proteomics 5(1):144-156.
• Wisniewski JR, Hein MY, Cox J, Mann M. 2014. A "proteomic ruler" for protein copy number and concentration estimation without spike-in standards. Mol Cell Proteomics 13(12):3497-3506.
• Zybailov B, Mosley AL, Sardiu ME, et al. 2006. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J Proteome Res 5(9):2339-2347.
• Ong SE, Blagoev B, Kratchmarova I, et al. 2002. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1(5):376-386.
• Ting L, Rad R, Gygi SP, Haas W. 2011. MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods 8(11):937-940.
• McAlister GC, Nusinow DP, Jedrychowski MP, Wuhr M, et al. 2014. MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal Chem 86(14):7150-7158.
• Savitski MM, Mathieson T, Zinn N, et al. 2013. Measuring and managing ratio compression for accurate iTRAQ/TMT quantification. J Proteome Res 12(8):3586-3598.
• Thompson A, Wolmer N, Koncarevic S, et al. 2019. TMTpro: design, synthesis, and initial evaluation of a proline-based isobaric 16-plex tandem mass tag reagent set. Anal Chem 91(24):15941-15950.
• Plubell DL, Wilmarth PA, Zhao Y, et al. 2017. Extended multiplexing of tandem mass tags (TMT) labeling reveals age and high fat diet specific proteome changes in mouse epididymal adipose tissue. Mol Cell Proteomics 16(5):873-890.
• Choi M, Chang CY, Clough T, et al. 2014. MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 30(17):2524-2526.
• Goeminne LJE, Gevaert K, Clement L. 2016. Peptide-level robust ridge regression improves estimation, sensitivity, and specificity in data-dependent quantitative label-free shotgun proteomics. Mol Cell Proteomics 15(2):657-668.
• Sticker A, Goeminne L, Martens L, Clement L. 2020. Robust summarization and inference in proteome-wide label-free quantification. Mol Cell Proteomics 19(7):1209-1219.
• Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. 2020. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 17(1):41-44.
• Lin MH, Wu PS, Wong TH, Lin IY, Lin J, Cox J, Yu SH. 2022. Benchmarking differential expression, imputation and quantification methods for proteomics data. Brief Bioinform 23(3):bbac138.

Related Skills

• data-import - Parse MaxQuant/DIA-NN outputs and pick the right intensity column before quantifying
• protein-inference - Razor/shared-peptide group assignment that determines which protein a peptide counts toward
• differential-abundance - Statistical testing, missing-value modeling, and the downshift false-positive trap
• proteomics-qc - CV, correlation, and PCA checks that confirm normalization worked
• dia-analysis - DIA fragment-level MaxLFQ and DIA-NN execution
• differential-expression/de-results - Shared empirical-Bayes and FDR conventions for expression matrices
• data-visualization/heatmaps-clustering - Visualize the normalized abundance matrix
• workflows/proteomics-pipeline - End-to-end pipeline that calls this skill for the quant step