NanoString Technologies Neuropathology Panel Produces Unreliable Measurements of Mouse Hippocampal Gene Expression
Bibliographic record
Abstract
Abstract Technologies for measuring gene expression (i.e., the number of RNA transcripts) of large numbers of genes simultaneously in specific tissues have exploded in recent years. Current methods include high-throughput RNA sequencing (RNA-seq), transcript counting platforms like NanoString’s nCounter®, and spatially resolved techniques based on fluorescent in situ hybridization (FISH). Several studies have evaluated the reliability of these different methods and performance in comparison to one another. Typically, technical reliability, as measured by Pearson’s correlation of two measurements of the same sample, is usually well above 90%, and is statistically significant even for small sample sizes (e.g., 8 samples measured twice). We performed an experiment where we aimed to compare hippocampal gene expression between 3 groups (n=5 per group) of young adult male C57BL/6J mice. Before sampling, the groups were treated with either repeated injections of PBS (vehicle), extracellular vesicles taken from the blood plasma of sedentary mice (SedVs) or exercising mice (ExerVs). The hippocampus was dissected, and RNA purified using standard methods. The samples were analyzed using the NanoString Neuropathology panel, that measures 770 genes simultaneously. To estimate reliability, we measured 8 of the samples twice in two separate assays. Surprisingly, only 85 genes showed a significant Pearson’s correlation (p<0.05), and none of these met false discovery significance (all q<0.05). To confirm that no errors were made transferring labels, the individual samples were permuted to see whether a different assignment could recover a greater number of positive correlations. Results showed that the original assignment was best suggesting no errors in sample assignments were made. We conclude that the Nanostring neuropathology panel produces unreliable data for mouse hippocampal gene expression.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".