LIRTS Viewer: A Web-Based Resource to View the Transcriptional Response of Lens Epithelial Cells to Injury
Bibliographic record
Abstract
Purpose: Residual lens epithelial cells (LECs) respond to injury after cataract surgery, leading to posterior capsular opacification (PCO). Transcriptomic profiling of lens capsule-associated cells (CACs) post-cataract surgery (PCS) revealed that LECs quickly alter their transcriptome, producing numerous pro-inflammatory cytokines within a few hours PCS. In contrast, the significant activation of TGFβ signaling and fibrotic extracellular matrix deposition related to PCO only begins 1 to 3 days later. However, the global changes in gene expression in CACs, following the establishment of robust TGFβ signaling, remain unknown. Methods: Lens fiber cells were removed from wild-type mice, and CACs were isolated at 0, 72, or 120 hours PCS to perform bulk RNA sequencing (RNA-seq) to obtain estimates of RNA abundance. These data were combined with existing RNA-seq datasets to create a web-based visualization resource to explore the expression dynamics of most protein coding genes in CACs. Results: At 72 hours PCS, CACs differentially express genes consistent with a surge in proliferation and changes in actin filament organization while also robustly expressing fibrotic marker genes by 120 hours PCS. We developed a data visualization resource, the Lens Injury Response Time Series (LIRTS) Viewer, which integrates all data to gather valuable insights from gene expression in CACs over the first 5 days PCS. Conclusions: The LIRTS Viewer is useful for generating hypotheses related to PCO pathogenesis, as it reveals CAC gene expression dynamics, gene correlations, and biological pathways during the first 5 days following lens injury in an in vivo cataract surgery model.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| 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".