Using Two Peptide Isotopologues as Internal Standards for the Streamlined Quantification of Low-Abundance Proteins by Immuno-MRM and Immuno-MALDI
Bibliographic record
Abstract
Mass spectrometry (MS), particularly targeted proteomics, is increasingly being used for quantifying specific proteins and peptides in clinical specimens. The coupling of immuno-enrichment of proteotypic peptides with MS [e.g., immuno-multiple reaction monitoring (MRM) and immuno-matrix-assisted laser desorption ionization (MALDI)] enables the development of highly sensitive and specific assays for low-abundance signaling proteins. By incorporating stable isotope-labeled standards, these workflows allow the determination of endogenous protein concentrations. This is typically achieved through external calibration, often using surrogate matrices, which has inherent limitations for the analysis of clinical specimens as there are often substantial variations in the sample matrix, and sample amounts are typically limited. We have previously introduced the use of two peptide isotopologues for generating external calibration curves in plasma. Here, we present a two-point internal calibration (2-PIC) strategy using two isotopologues for immuno-MS assays and demonstrate its flexibility and robustness. Quantification of the tumor suppressor PTEN in Colo-205 cells by immuno-MRM and immuno-MALDI using 2-PIC and external calibration yielded very similar results (relative standard deviation between 2-PIC and external calibration: 4.9% for immuno-MRM; 1.1% for immuno-MALDI), without the need for a surrogate matrix or additional patient material for calibration, while concurrently reducing the instrument time and cost. Although our PTEN immuno-MRM and immuno-MALDI assays can be considered to be orthogonal as they utilized entirely different sample preparation and MS analysis workflows, targeted different PTEN peptides, and were performed in different laboratories, the endogenous Colo-205 PTEN levels determined with 2-PIC showed a good correlation ( r 2 = 0.9966) and good agreement (0.48 ± 0.01 and 0.29 ± 0.02 fmol/μg of total protein) between immuno-MRM and immuno-MALDI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".