Additional file 1 of Translatome analysis of tuberous sclerosis complex 1 patient-derived neural progenitor cells reveals rapamycin-dependent and independent alterations
Bibliographic record
Abstract
Additional file 1. Figure S1. Quality control of RNA sequencing data of cytosolic and polysome-associated mRNA isolated from NPCs of different conditions. A Barplot showing overall number of reads that are aligned to adapter sequences, rRNA sequences, assigned to genes as well as unmapped/unassigned reads. B Boxplot showing TMM-log2 normalized counts of TSC1 transcript in TSC1−/− (red) and TSC1+/+ (blue) NPCs. C Scree-plot showing the percentage of variance explained by PC1-PC10. D Correlation of principal components (PC1-PC9) to experimental factors. E Projection of samples in principal components 1 and 2, with samples shaped according to library type (circle: polysome-associated mRNA; triangle: cytosolic mRNA) and colored according to genotype of samples (TSC1−/−, red; TSC1+/+, blue). Figure S2. Quality control of RNA sequencing data of postmortem samples of BA19 ASD and control. A Barplot showing overall number of reads that are aligned to adapter sequences, assigned to genes as well as unmapped/unassigned reads. B Scree-plot showing the percentage of variance explained by PC1-PC10. C Correlation of principal components (PC1-PC9) to experimental factors. D Projection of samples in principal components 1 and 2, with samples shaped according to library type (circle: polysome-associated mRNA; triangle:cytosolic mRNA) and colored according to condition (ASD: red; Control: blue). Figure S3. Immunoblotting in NPCs. A Immunoblotting for TSC1 in TSC1−/− compared with CRISPR-corrected TSC1+/+ NPCs. Ribosomal S6 protein serves as a loading control. B Immunoblot of NPCs treated with rapamycin (50 nM) and RMC-6272 (10 nM) for indicated proteins. β-tubulin served as a loading control. Images have been cropped for clarity and conciseness, and entire blots are shown in Additional file 9. Figure S4. Gene ontology analysis comparison for ASD and TSC1−/− NPCs. A and B Gene ontology analysis (similar to Fig. 1F) for genes categorized as “translation up” in TSC1−/− versus TSC1+/+ and ASD versus Ctrl BA19 comparisons (A); and genes categorized as “translation down” in TSC1−/− and TSC1+/+ and ASD versus Ctrl BA19 comparisons (B). The analysis was performed using ClueGO in “cluster mode.” Figure S5. Comparison of rapamycin-treated TSC1−/− to non-treated TSC1+/+ NPCs. A and B anota2seq analysis (A) and kernel densities for p value or FDR from anota2seq analysis (B) are shown comparing rapamycin-treated TSC1−/− to non-treated TSC1+/+ NPCs. (similar to Fig. 1B-C). Figure S6. Additional data related to changes in cells size and proliferation. A Bright field images (left panel) and cell size quantitation (right panel) of TSC1+/+ and TSC1−/− NPCs treated with 50 nM rapamycin or 10 nM RMC-6272 (n = 3 ± SD) are shown. Scale bar = 100 µm. B TSC1+/+ and TSC1−/− NPCs were treated with 10 nM RMC-6272 or DMSO as a control along with 1:500 dilution of the fluorescent nuclear marker NucSpot650 to visualize cell nuclei in the near infrared (NIR) spectrum. Using the Incucyte SX5 system, images were taken every 2 h for a total of 48 h. Graphs of NIR mean intensity (NIRCU) were generated using GraphPad Prism9 showing the average nuclei number/image field (36 non-overlapping image fields/well). Data represent three biological replicates per treatment group (± SEM). *p < 0.05, **p < 0.01, ***p < 0.001 calculated by Student’s t test. Figure S7. Additional data related to changes in neurite outgrowth. A and B Quantitation of neurite number (left), length (middle) and extremities (right) from trace images are shown for immunofluorescence staining of TSC1± (A) and TSC1+/+ (B) NPCs treated with DMSO, 50 nM rapamycin or 10 nM RMC-6272 using the neuronal marker MAP2 and HCA-Vision image quantitation software. Data represent eight non-overlapping field images/treatment generated using GraphPad Prism9 with relative fold change normalized to DMSO-treated NPCs (mean, ± SD). ** p < 0.01, ***p < 0.001, ****p < 0.0001, ns = not significant calculated by Student’s t test (A-B).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.936 | 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".