A Targeted Multiplexed MALDI MS Assay Platform using Affinity-Bead Assisted Mass Spectrometry (Affi-BAMS) for Monitoring Brain and CSF Biomarkers
Bibliographic record
Abstract
Proteomic studies often employ multi-dimensional analytical methods such as nano-LC-ESI-MS/MS to simplify the sample complexity. The time and expertise required to implement LCMS methods can be a barrier to integrating targeted proteomics within translational research programs. We present a robust method that combines multiplexed immuno-affinity capture with MALDI MS, called Affi-BAMS. This platform enables development of highly specific assays for simultaneous profiling of multiple analytes. The workflow utilizes enrichment on single beads that contain one antibody, having enough binding capacity to quantify within 3 orders of magnitude. Multiplexing is achieved by combining assay beads with different specificities. Assay beads are spatially arrayed and captured peptides are eluted into individual micro-wells. The resulting array of micro-spots contain concentrated analytes for direct measurement by MALDI MS. While both intact proteins and protein fragments can be monitored by Affi-BAMS, we focused this work for bottom-up and middle-down proteomics. We illustrate Affi-BAMS assays to several protein targets associated with progression of Alzheimer's disease. Examples include a multiplexed assay for beta-amyloid to monitor fifteen C-terminal fragments, localized within the region of aa672 - aa711 (including AB1-38, AB1-40, AB1-42 & APP669-711). We have also configured assays to monitor twelve different regions of tau (MAPT), spanning from the N-terminus to the C-terminus, including assays for both total and known phosphorylation sites. Lastly, we demonstrate how Affi-BAMS can be used to monitor epigenetic marks on Histone H3 in normal and disease brain. The unique features of this technology include multiplexing capacity that exceeds the current limit of bead-based sandwich immunoassay platforms and the ability to independently screen multiple sites within a single protein. This platform should aid protein panel profiling across a wide range of research applications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".