Systematic Optimization of the iMALDI Workflow for the Robust and Straightforward Quantification of Signaling Proteins in Cancer Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Immuno-MALDI (iMALDI) combines immuno-enrichment of biomarkers with MALDI-MS for fast, precise, and specific quantitation, making it a valuable tool for developing clinical assays. iMALDI assays are optimized for the PI3-kinase signaling pathway members phosphatase and tensin homolog (PTEN) and PI3-kinase catalytic subunit alpha (p110α), with regard to sensitivity, robustness, and throughput. A standardized template for developing future iMALDI assays, including automation protocols to streamline assay development and translation, is provided. EXPERIMENTAL DESIGN: Conditions for tryptic digestion and immuno-enrichment (beads, bead:antibody ratios, incubation times, direct vs. indirect immuno-enrichment) are rigorously tested. Different strategies for calibration and data readout are compared. RESULTS: Digestion using 1:2 protein:trypsin (wt:wt) for 1 h yielded high and consistent peptide recoveries. Direct immuno-enrichment (antibody-bead coupling prior to antigen-enrichment) yielded 30% higher peptide recovery with a 1 h shorter incubation time than indirect enrichment. Immuno-enrichment incubation overnight yielded 1.5-fold higher sensitivities than 1 h incubation. Quantitation of the endogenous target proteins is not affected by the complexity of the calibration matrix, further simplifying the workflow. CONCLUSIONS AND CLINICAL RELEVANCE: This optimized and automated workflow will facilitate the clinical translation of high-throughput sensitive iMALDI assays for quantifying cell-signaling proteins in individual tumor samples, thereby improving patient stratification for targeted treatment.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it