A blood‐based diagnostic for Alzheimer’s disease using spectral microscopy of immuno‐enriched Aβ from frozen PBMCs
Bibliographic record
Abstract
Abstract Background In Alzheimer’s disease (AD), toxic Aβ peptides aggregate into higher molecular weight assemblies and accumulate not only in the extracellular space, but also in the walls of blood vessels in the brain, increasing their permeability, and promoting immune cell migration and activation. As immune cells contact these pathological brain materials, they may act as “sentinels” that are detectable once they return to the circulation. Previously, we have demonstrated that leukocytes, when stained with an amyloid sensitive probe, K114 (Tocris Bioscience), display distinct spectral changes in subjects with AD and mild cognitive impairment (MCI) (Black et al. 2022). Here we extend this work by introducing an enhanced method that combines the specificity of immunocapture of Aβ peptides together with spectral interrogation to reveal their amyloid character from easily obtainable frozen peripheral blood mononuclear cells (PBMCs). Methods 30 frozen PBMC samples were obtained from the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD) with 15 clinically‐ and histologically‐confirmed MCI/AD and 15 cognitively normal subjects based on the National Alzheimer’s Coordinating Center (NACC) database. To adapt our method to frozen samples, we used immunoprecipitation to purify target amyloid proteins by anti‐Aβ antibody (4G8)‐coated magnetic beads (Protein G SureBeads™, BioRad Laboratories), which were then labelled with K114, and imaged with a spectral confocal microscope. Results Comparing subjects with neuropathologically‐confirmed AD (n=6), Braak stage for neurofibrillary degeneration (B score) >5 and density of neocortical neuritic plaques (C score) >2 to cognitively normal controls (n=6), our technique detected highly significant differences between groups (P<0.005, Fig. 1). Correlation between our AD Score and B scores was R = 0.824, and with cognitive status was R = 0.735 (Fig. 2). Conclusions These observations indicate that our method is capable of detecting AD from circulating human PBMCs, potentially mirroring the “Aβ load” in the brain. Our technique could constitute a reliable and inexpensive biomarker for early AD and AD‐related MCI.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".