Late Breaking Abstract - Reversing cleared lung samples for multi-dimensional analysis in therapeutic discovery
Bibliographic record
Abstract
Light-sheet fluorescence microscopy (LSFM) is an emerging imaging technique that enables high-resolution 3D imaging of large, intact samples (up to several centimeters) with subcellular resolution. It is especially powerful for visualizing structural changes in disease and for localizing rare cell types identified in single-cell datasets, capabilities beyond current in vivo imaging modalities. LSFM relies on optical clearing to render tissues transparent and on the immersion of samples in hydrophobic solutions for imaging. Since most labeling approaches use water-based solutions, this precludes further labeling after clearing. This is a key bottleneck for co-localizing multiple cell types in 3D tissues, which is essential for identifying populations defined by multiple phenotypic markers (e.g., immune cell subtypes). To address this, we developed a novel workflow to reverse optical clearing, enabling subsequent histological and RNA-based analyses in the same lung sample. Mouse lungs, embryos, and human fibrotic lung samples were cleared and imaged with LSFM, then reverse-cleared and paraffin-embedded for immunofluorescence and in situ RNAscope analysis. Lung markers such as pro-SPC and AQP5 remained detectable, extracellular matrix components (collagens, glycosaminoglycans) were retained, and RNA yields were comparable to standard paraffin-embedded tissue. Finally, we developed a virtual reality-based co-registration workflow to precisely align 2D histological slices within 3D LSFM datasets. This pipeline expands LSFM's utility, enabling comprehensive, multimodal analysis from a single sample, supporting advanced preclinical and clinical studies of intact tissues at scale.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".