Nanoscale reorganization of sarcoplasmic reticulum in pressure-overload cardiac hypertrophy visualized by dSTORM
Bibliographic record
Abstract
Abstract Pathological cardiac hypertrophy is a debilitating condition characterized by deleterious thickening of the myocardium, dysregulated Ca2+ signaling within cardiomyocytes, and contractile dysfunction. Importantly, the nanoscale organization, localization, and patterns of expression of critical Ca2+ handling regulators including dihydropyridine receptor (DHPR), ryanodine receptor 2 (RyR2), phospholamban (PLN), and sarco/endoplasmic reticulum Ca2+-ATPase 2A (SERCA2A) remain poorly understood, especially during pathological hypertrophy disease progression. In the current study, we induced cardiac pathological hypertrophy via transverse aortic constriction (TAC) on 8-week-old CD1 mice, followed by isolation of cardiac ventricular myocytes. dSTORM super-resolution imaging was then used to visualize proteins at nanoscale resolution at two time points and we quantified changes in protein cluster properties using Voronoi tessellation and 2D Fast Fourier Transform analyses. We showed a decrease in the density of DHPR and RyR2 clusters with pressure-overload cardiac hypertrophy and an increase in the density of SERCA2A protein clusters. PLN protein clusters decreased in density in 2-week TAC but returned to sham levels by 4-week TAC. Furthermore, 2D-FFT analysis revealed changes in molecular organization during pathological hypertrophy, with DHPR and RyR2 becoming dispersed while both SERCA2A and PLN sequestered into dense clusters. Our work reveals molecular adaptations that occur in critical SR proteins at a single molecule during pressure overload-induced cardiomyopathy. Nanoscale alterations in protein localization and patterns of expression of crucial SR proteins within the cardiomyocyte provided insights into the pathogenesis of cardiac hypertrophy, and specific evidence that cardiomyocytes undergo significant structural remodeling during the progression of pathological hypertrophy.
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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) | 0.002 | 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".