Additional file 1 of Dynamic profiling of medulloblastoma surfaceome
Bibliographic record
Abstract
Supplementary Material 1: Fig. 1, related to Fig. 1 Glycocapture box blot (A) prior to and (B) after SUC2-normalization (yellow dot; NPVLAANSTQFRDPK peptide). (C) Hierarchical clustering heatmap based on Euclidean distance of differentially expressed proteins demonstrating close clustering of technical triplicates. (D) ITGA5 peptide tag intensity, (E)ITGA5 mRNA expression and (F) ITGB1 protein expression in Group 3 MB cells isolated at pre-determined timepoints through therapy. (G) Comparison of ITGA5 intensity to SUC2 demonstrating selective enrichment for ITGA5 in Group 3 MB through therapy. Bars represent mean of at least three technical replicates. *p ≤ 0.05, **p ≤ 0.001, ***p ≤ 0.0001, ****p ≤ 0.00001; unpaired t-test or one-way ANOVA with Sidak’s method for multiple comparisons. Fig. 2, related to Fig. 1. (A) A Venn diagram demonstrating the number of unique and overlapping surface proteins enriched at each stage of therapy when compared to their expression at engraftment and control timepoints. (B) A list of proteins used to generate the Venn diagram. R = post-radiation; RC = post-chemoradiotherapy; Re = relapse. Fig. 2, related to Fig. 2. In silico evaluation of ITGA5 in publicly available MB repository. (A) Kaplan-Meir curve demonstrating worse overall survival in patients (n = 288) with relative mRNA expression of ITGA5 over 4.6 (RMA- Normalized). (B) Transcriptional expression of ITGA5 of 632 patients across 12 MB subtypes described in Cavalli et al. and (C) based on age group affiliation. **p ≤ 0.001; one-way ANOVA with Dunnett’s method for multiple comparisons. Fig. 3, related to Fig. 3. Characterization and validation of ITGA5 KD. (A) mRNA expression of ITGA5 in HEK293FT cells post ITGA5 KD. (B) Microscopic images of HEK293FT cells post ITGA5 KD. Changes in (C)ITGA5 mRNA expression in reccurent Group 3 MB cells post ITGA5 KD. (D) Flow cytometric evaluation of changes in ITGA5 surface expression in HD-MB03-Re cells post ITGA5 KD. Bars represent mean of at least three technical replicates. *p ≤ 0.05, **p ≤ 0.001, ***p ≤ 0.0001, ****p ≤ 0.00001; unpaired t-test or one-way ANOVA with Sidak’s method for multiple comparisons. Fig. 4, related to Fig. 4. Selectivity characterization of dioscin in recurrent Group 3 MB cells and hNSCs. (A) Dose response curves and (B) corresponding IC50 concentrations and hill slopes of dioscin in hNSCs and three recurrent Group 3 MB lines. Points represent mean of three technical replicates, normalized to DMSO. Error bars represent standard error of the mean. IC50 and Hill slope values standardized to two decimal places. Fig. 5, related to Fig. 4. Expression of ITGA5 in healthy human tissue samples (A) ITGA5 protein levels as detected by whole cell proteomics in health tissues, reported by Wang et al. Protein intensity is reported as intensity-based absolute quantification (iBAQ) values, normalized using median centering across tissues. (B) ITGA5 protein expression as detected by antibody staining using the Human Proteome Atlas public repository. Staining strength corresponds to expression levels, including high (3), medium (2), low (1), and not detected (0). (C) mRNA expression of ITGA5 in various tissues according to Genotype-Tissue Expression (GTEx) dataset ( https://www.proteinatlas.org/ ); expressed in transcripts per million (TPM).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.748 | 0.169 |
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".