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Record W4296078552 · doi:10.1101/2022.09.13.507846

Single cell spatial analysis identifies regulators of brain tumor initiating cells

2022· preprint· en· W4296078552 on OpenAlexaff
Reza Mirzaei, Charlotte D’Mello, Marina Liu, Ana Nikolić, Mehul Kumar, Frank Visser, Pinaki Bose, Marco Gallo, V. Wee Yong

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2022
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsInstitute of Cancer ResearchAlberta Children's HospitalHotchkiss Brain InstituteUniversity of Calgary
FundersSchool of Medicine, Indiana University
KeywordsBiglycanWnt signaling pathwayTranscriptomeCell biologyExtracellular matrixBiologyProteoglycanTumor microenvironmentCancer researchSignal transductionGene expressionTumor cellsGeneDecorinGenetics

Abstract

fetched live from OpenAlex

Abstract Glioblastomas (GBMs) are aggressive brain tumors with extensive intratumoral heterogeneity. Here, we used spatial transcriptomics and single-cell ATAC-seq to dissect the transcriptome of distinct anatomical regions of the tumor microenvironment. We identified numerous extracellular matrix (ECM) molecules including biglycan elevated in areas infiltrated with brain tumor-initiating cells (BTICs). Single-cell RNA sequencing showed that the ECM molecules were differentially expressed by cells including injury response versus developmental BTICs. Exogeneous biglycan or overexpression of biglycan resulted in a higher proliferation rate of BTICs, and this was associated mechanistically with LDL receptor-related protein 6 (LRP6) binding and activation of the Wnt/β-catenin pathway. Biglycan-overexpressing BTICs grew to a larger tumor mass when implanted intracranially in mice. This study points to the spatial heterogeneity of ECM molecules in the GBM microenvironment and suggests biglycan-LRP6 axis as a therapeutic target to curb GBM growth.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.209
Teacher spread0.197 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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