Technical note: Use of laser capture microdissection for the localization of tissue-specific global gene expression in rumen papillae
Bibliographic record
Abstract
Gene expression profiling of bovine rumen tissue has provided insight into dietary regulation of rumen epithelial function. However, most studies have relied on a heterogeneous sample with multiple tissue and cell types. The objective of this study was to use laser capture microdissection to characterize RNA expression profiling of epithelial and connective tissues of rumen papillae. Papillae were biopsied from 3 lactating dairy cows, frozen in cryomolds, cut into sections, stained, and dehydrated, and epithelial and connective cells were collected using laser capture microdissection. Total RNA was isolated from epithelial and connective tissue and global gene expression was assessed using the Affymetrix GeneChip Bovine Gene 1.0 ST array (Affymetrix, Santa Clara, CA). Data preprocessing was conducted using the robust multi-array average method, and detection of differentially expressed genes (DEG) was determined using ANOVA. The model included the fixed effect of tissue, and a Benjamini-Hochberg false discovery rate of 0.1 was applied to DEG. We found 382 DEG between epithelial and connective tissues. Analysis of these DEG using Ingenuity Pathway Analysis (Redwood City, CA) found that epithelial and connective tissues in rumen papillae expressed distinct RNA profiles (signatures). The epithelial signature was enriched with RNA encoding tight junction and metabolic genes, whereas connective signatures were enriched with RNA encoding proteins involved in cell structure and extracellular matrix composition. The molecular functions enriched within the top networks between the 2 tissues from the Ingenuity Pathway Analysis included connective tissue disorders, dermatological diseases and conditions, gastrointestinal disease, tissue morphology, and tissue development. In summary, it is possible to use laser capture microdissection for the localization of tissue-specific global gene expression in rumen papillae. This approach may be useful to improve the accuracy and interpretation of molecular measurements in future studies.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".