Processing of Primary Brain Tumor Tissue for Stem Cell Assays and Flow Sorting
Bibliographic record
Abstract
Brain tumors are typically comprised of morphologically diverse cells that express a variety of neural lineage markers. Only a relatively small fraction of cells in the tumor with stem cell properties, termed brain tumor initiating cells (BTICs), possess an ability to differentiate along multiple lineages, self-renew, and initiate tumors in vivo. We applied culture conditions originally used for normal neural stem cells (NSCs) to a variety of human brain tumors and found that this culture method specifically selects for stem-like populations. Serum-free medium (NSC) allows for the maintenance of an undifferentiated stem cell state, and the addition of bFGF and EGF allows for the proliferation of multi-potent, self-renewing, and expandable tumorspheres. To further characterize each tumor's BTIC population, we evaluate cell surface markers by flow cytometry. We may also sort populations of interest for more specific characterization. Self-renewal assays are performed on single BTICs sorted into 96 well plates; the formation of tumorspheres following incubation at 37 °C indicates the presence of a stem or progenitor cell. Multiple cell numbers of a particular population can also be sorted in different wells for limiting dilution analysis, to analyze self-renewal capacity. We can also study differential gene expression within a particular cell population by using single cell RT-PCR. The following protocols describe our procedures for the dissociation and culturing of primary human samples to enrich for BTIC populations, as well as the dissociation of tumorspheres. Also included are protocols for staining for flow cytometry analysis or sorting, self-renewal assays, and single cell RT-PCR.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.023 |
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".