Nile Red Fluorescence and Spectroscopy Reveal Unique Lipid Droplet Distribution and Physicochemical Changes in Glomerulonephritis
Bibliographic record
Abstract
Background: Abnormalities in lipid deposition and lipid droplet (LD) accumulation have not been well established in glomerulonephritis (GN). Nile Red (NR), a well-known lipophilic stain for intracellular LDs, is a solvatochromic fluorophore that provides high-resolution spatial assessment of lipid distribution and chemistry. Solvatochromatic spectroscopy is a sensitive imaging modality with the potential to characterize the subtle, early changes in lipid chemistry associated with glomerular injury and disease. Methods: A total of 72 kidney biopsies of histologically diagnosed glomerular diseases including minimal change disease, membranous nephropathy, primary FSGS, class IV lupus nephritis (LN), ANCA-associated vasculitis, and IgA nephropathy (IgAN), and healthy tissue controls were retrieved from the Biobank for the Molecular Classification of Kidney Disease. Quantitative spectral analysis of NR emission patterns were performed on NR-stained biopsies to generate unique physicochemical profiles for each type of GNl. Segmented regions of biopsies (glomeruli, tubules, and interstitial) were imaged using confocal microscopy allowing for LD quantitatation using an algorithm developed in MATLAB. Results: Lipid droplet distribution greatly differed between the GN with IgAN and LN demonstrating the highest number LDs in glomeruli. By spectral analyses, control tissue showed significant differences in lipid polarity profile between histological compartments of the kidney (glomeruli, interstitium and tubules). Tubules consistently displayed more lipid rich domains compared to interstitial and glomerular regions. In diseased states, these patterns varied between GNs, with glomerular regions becoming more polar (for example in LN) and tubular regions (predominantly for IgAN). Within histologically identical disease types, we noted distinct populations based on lipid profiles, suggesting significant variance within GN. Conclusions: Nile Red spectral analysis of human kidney tissue provides unique insights into lipid physicochemical changes in GN. Marked variance within identical disease types suggests that histological determinants of GNs are limited and sensitive techniques such as high-resolution imaging and spectroscopy can identify earlier changes in disease.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".