Differential Effects of PDGFA and PDGFC Ligands on Neural Stem and Progenitor Cells
Bibliographic record
Abstract
My thesis describes the behaviour of subventricular zone (SVZ)-derived neural stem and progenitor cells (here called NPCs) from young adult mice during exposure to members of the platelet-derived growth factor (PDGF) family of ligands. Two novel findings arise from my work. First, P53 null NPCs cultured in PDGFA transform in vitro acquiring a phenotype and genotype that closing resembles that of human glioblastoma (GBM). Second, NPCs cultured in PDGFC form small quiescent spheres of pluripotential neural stem cells (NSCs) that can be maintained in a stem-like state in PDGFC or induced to proliferate or differentiate by modifying the growth factor environment. My work is of interest to cancer biologists studying the origins of GBM and oncologists seeking ways to prevent this cancer or intercept it at an early more treatable stage. My thesis is also of interest to stem cell biologists and neurobiologists seeking new models to study the stem cell niche in the mammalian brain and brain maturation in health and disease. I have uncovered the mechanism of transformation of P53 null NPCs in PDGFA by analyzing an in vitro model of oncogenesis in which the earliest events can be detected, and their consequences tracked over time. I discovered that defective mitosis initiates a series of events that culminate in a GBM-like cancer. I also discovered that cells isolated from the SVZ using PDGFC have the behavioral and biomarker profile that would be expected of true stem cells. My findings stand in contrast to the gold standard method for isolating and studying NSCs in which cells from the SVZ are cultured in epidermal growth factor plus fibroblast growth factor (EGF/FGF) and where they divide rapidly and continuously. I also found that PDGFC could replace PDGFA in the isolation and propagation of oligodendrocyte progenitor cells (OPCs), a cell type of increasing interest. Overall, my work adds to our understanding of the biology of two important members of the PDGF family of ligands and describes new model systems that can be exploited further.
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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 | 0.000 | 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".