Mass Spectrometry With Data-Independent Acquisition for the Identification of Target Antigens in Membranous Nephropathy
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: In recent years, the strategy of using laser capture microdissection and mass spectrometry (LCM/MS) has expanded the landscape of antigens associated with membranous nephropathy (MN). Specific associations with phenotypes, diseases, and sometimes reversible triggers led to an antigen-based classification of MN, informing precision medicine and highlighting the potential value of routine use of proteomics in classifying MN. This study reproduces and further improves the original LCM/MS for antigen detection in MN. STUDY DESIGN: Retrospective cohort study using residual material from kidney biopsies. SETTING & PARTICIPANTS: R-negative MN; and 5 individuals with other glomerular diseases. PREDICTOR: Proteomic analysis of microdissected glomeruli. OUTCOME: R-negative MN. ANALYTICAL APPROACH: The technique of LCM/MS was expanded by integrating a data-independent acquisition (DIA) approach to enable the identification and quantification of peptides of varying abundance. RESULTS: R-negative MN. LIMITATIONS: Retrospective design; sample size; no identification of novel antigens. CONCLUSIONS: An integrative approach combining LCM/MS and DIA enabled identification of more target antigens than LCM/MS with DDA, potentially informing our understanding of disease mechanisms in MN. PLAIN-LANGUAGE SUMMARY: Membranous nephropathy is an autoimmune kidney disease characterized by circulating autoantibodies that recognize antigens in podocytes or in the glomerular basement membrane. To date, more than 10 different antigens have been identified, with specific associations with various etiologies and potential impact on management. In this study, proteomic analyses were implemented on glomeruli microdissected from kidney biopsies in patients with membranous nephropathy and appropriate controls. The original technique of proteomic analysis developed by Sethi and coworkers was expanded by applying a specific and more sensitive mass spectrometry method (data-independent acquisition) combined with bioinformatics analysis. We showed that this approach is a powerful tool to detect target antigens, and it may provide insights into disease mechanisms, with the potential to inform clinical diagnosis and classification of membranous nephropathy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".