Identification of genes specifically expressed by human Müller cells by use of subtractive hybridization.
Bibliographic record
Abstract
PURPOSE: Müller cells are the predominant type of glial cells in the retina. They play a critical role in the retina. The purpose of this study was to generate a profile of the genes specifically expressed by human retinal Müller cells and to identify genes that may be responsible for retinal diseases. METHODS: Subtractive hybridization is a method process by which two populations of mRNA are compared in order to obtain clones of genes expressed in one population but not in the other. A cDNA subtraction library was constructed using RNA isolated from human Müller cells and human astrocytes. PCR-select differential screening was used to further verify the differentially expressed cDNA clones. Positive clones were sequenced and analyzed using the NCBI BLASTN program to identify sequence homologies. RESULTS: We identified 194 clones specifically expressed in human Müller cells. Among these clones, 102 corresponded to known human genes. Of the remaining 94 clones, 75 corresponded to expressed sequence tags or genomic clones and 19 transcripts did not match with any sequence in databases, and are possibly novel genes. CONCLUSIONS: The analysis of the subtraction library revealed genes that are specifically expressed by human Müller cells. Some of these genes are unidentified, novel genes that are specific to Müller cells as determined by RT-PCR and Northern blot analyses. These novel genes thus represent candidate genes for retinal diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".