Proteomic analysis of opsins and thyroid hormone-induced retinal development using isotope-coded affinity tags (ICAT) and mass spectrometry.
Bibliographic record
Abstract
PURPOSE: Analyses that reveal the relative abundance of proteins are informative in elucidating mechanisms of retinal development and disease progression. However, popular high-throughput proteomic methods do not reliably detect opsin protein abundance, which serve as markers of photoreceptor differentiation. We utilized thyroid-hormone (TH) treatment of rainbow trout (Oncorhynchus mykiss) as a model of cone apoptosis and cone regeneration. We used this model to investigate if emerging proteomic technology allows effective analysis of retinal development and opsin protein abundance. We also sought to begin a characterization of proteomic changes in the retina occurring with TH treatment and address whether TH affects proliferation or photoreceptor differentiation. METHODS: Retinal homogenates were prepared from control and TH-treated fish. Peptides from control and treated homogenates were differentially labeled, using isotope-code affinity tags (ICAT) and analyzed using capillary liquid chromatography-electrospray ionization-tandem mass spectrometry (capLC-ESI-MS/MS). This method identifies proteins and quantifies their relative abundance between two samples. RESULTS: The relative abundance of many retinal proteins changed during TH treatment. These included proteins from every functional class. We detected 1,684 different peptides, and our quantification suggests that 94 increased and 146 decreased in abundance more than 50% during TH treatment. Cell-cycle proteins appear to be increased, consistent with TH-inducing cell proliferation, similar to its effect in Xenopus. Other proteins associated with retinal development, such as deltaA and tubulins, changed in abundance during TH treatment. Rod opsin and three cone opsins were identified and the relative abundance of each changed with TH treatment. CONCLUSIONS: ICAT and capLC-ESI-MS/MS are an effective complement to other molecular approaches that investigate the mechanisms of retinal development. Unlike other proteomic techniques, this approach does not require development of species- or tissue-specific methodology, such as characterizing two dimensional (2D) gels or antibodies, in order to be practical as a high-throughput approach. Importantly, this technology was able to assess the relative abundance of opsin proteins. These findings represent the first high-throughput proteomic analysis of the retina and demonstrate the technique's ability to provide useful information in retinal development.
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 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".