Novel Monoclonal Antibodies Against Mouse C1q: Characterisation and Development of a Quantitative ELISA for Mouse C1q
Bibliographic record
Abstract
Abstract Recent studies have identified roles for complement in synaptic pruning, both physiological during development and pathological in Alzheimer’s disease (AD). These reports suggest that C1q initiates complement activation on synapses and C3 fragments then tag them for removal by microglia. There is an urgent need to characterise these processes in rodent AD models; this requires the development of reagents and methods for detection and quantification of rodent C1q in fluids and pathological tissues. These will enable better evaluation of the role of C1q in disease and its value as disease biomarker. We describe the generation in C1q-deficient mice of novel monoclonal antibodies against mouse and rat C1q that enabled development of a sensitive, specific, and quantitative ELISA for mouse and rat C1q capable of measuring C1q in biological fluids and tissue extracts. Serum C1q levels were measured in wild-type (WT), C1q knockout (KO), C3 KO, C7 KO, Crry KO, and 3xTg and APP NL-G-F AD model mice through ageing. C1q levels significantly decreased in WT, APP NL-G-F , and C7 KO mice with ageing. C1q levels were reduced in APP NL-G-F compared to WT at all ages and in 3xTg at 12 months; C3 KO and C7 KO, but not Crry KO mice, also demonstrated significantly lower C1q levels compared to matched WT. In brain homogenates, C1q levels increased with age in both WT and APP NL-G-F mice. This robust and adaptable assay for quantification of mouse and rat C1q provides a vital tool for investigating the expression of C1q in rodent models of AD and other complement-driven pathologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".