Human brain atlas: miRNA version
Bibliographic record
Abstract
Human brain is a complex organ comprising multiple cell types of differing function. Although histological evaluation remains the mainstay approach for evaluating tissue, comprehensive molecular characterization is now possible due to advanced -omic approaches. microRNAs (miRNAs) are small (~22 nt) RNA molecules that regulate gene expression and mediate cellular differentiation in normal brain development. miRNAs also make excellent tissue markers due to their abundance, cell-type and disease-stage specificity, and stability in solid/liquid clinical samples. To advance our knowledge of miRNA-mediated gene regulation in human brain, we generated comprehensive miRNA expression profiles from 117 fresh normal brain samples through barcoded small RNA sequencing; tissues included neocortex, allocortex, white matter, cerebellum, olfactory bulb, optic nerve, pineal gland and spinal cord. FASTQ sequence files were annotated using state-of-the-art sequence annotation available through the Renwick lab. Following data pre-processing, high expression analysis of miRNA profiles showed that miR-9 was the highest expressed miRNA in neocortex, cerebellum and olfactory bulb, whereas miR-22 was highest expressed in cingulate cortex, optic nerve and spinal cord; interestingly, miR-29 was the highest expressed miRNA in hippocampus. Our analyses showed a trend towards unique miRNA signatures in different anatomical areas of the brain. Our next step is to perform miRNA fluorescence in situ hybridization on formalin-fixed paraffin-embedded tissues using a novel method developed in the Renwick lab. Accurate miRNA characterization of normal tissues will provide a firm basis for understanding miRNA changes in neurological diseases. LEARNING OBJECTIVES This presentation will enable the learner to: 1. Describe the function of miRNAs and their suitability as tissue/cell specific signatures 2. Describe the miRNA expression trends in profiling various anatomical regions of the central nervous system
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.261 | 0.187 |
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".