Author response: Determining composition of micron-scale protein deposits in neurodegenerative disease by spatially targeted optical microproteomics
Bibliographic record
Abstract
Neurodegenerative diseases such as Alzheimer's disease affect millions of people worldwide. In many of these diseases, toxic proteins accumulate in the brain and build up as small ‘plaques’ in the gaps, or synapses, that cells called neurons communicate across. Eventually, the plaques prevent the neurons signaling to each other correctly, leading to problems such as memory loss. Identifying the proteins present in plaques is technically challenging, partly because the plaques are very small. Hadley, Rakhit et al. have now developed a new method called spatially targeted optical microproteomics (or STOMP) that can collect proteins from small areas of cells. In this method, plaques are identified under a light microscope, and their contents are attached to a molecule called a photo-affinity tag using lasers. The photo-tagged proteins are then pulled out using beads that specifically bind to the photo-affinity tag. The proteins can then be identified using a well-established method called mass spectrometry. Hadley, Rakhit et al. used STOMP to analyze plaques present in the brains of mice that develop similar symptoms to those seen in humans with Alzheimer's disease. This revealed that these plaques contain more than 50 different proteins, some of which had not previously been found in plaques. In particular, several proteins from the ‘presynaptic’ neuron that sends signals across the synapse were found in the plaques. However, no proteins from the receiving (‘postsynaptic’) neuron on the other side of the synapse were present in the plaque. Fixed human brain tissue is more difficult to analyze than mouse samples because it is modified for storage. In spite of these issues, Hadley, Rakhit et al. successfully also used STOMP to identify the proteins in human plaques. STOMP can be used to identify the proteins present in any area of a cell and thus has the potential to be widely used by scientists, not just those studying plaques.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.176 | 0.079 |
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".