Abstract 4324: Establishing a high-resolution <i>in vivo</i> imaging technique for examining the dynamic contribution of bone marrow derived progenitor cells to intracranial tumor vasculature
Bibliographic record
Abstract
Abstract Introduction: Recent data suggests that tumor vascularization can occur by differentiation of bone marrow derived progenitor cells (BMDCs) into new vessels. However the precise mechanisms by which this process occurs remains controversial and to date very few studies have examined the contribution of BMDC to brain tumor vascularity. One of the challenges in studying vascular biology, and more so intracranial vascular biology, is the ability to study the temporal evolution and dynamic progression of vessel formation in response to oncogenic signals and stressors. We present a strategy that couples two-photon excitation laser microscopy (2PLM) with glioma xenograft models generated in an intracranial window chamber (ICW), in order to obtain high resolution (e.g. single cell) longitudinal real-time imaging of BMDC and examine their homing, differentiation and integration into intracranial tumor vasculature. Methodology: Animal Models: Bone Marrow (BM) of NOD/SCID mice were stably reconstituted with BM harvested from green-fluorescent protein (GFP) transgenic mice. Briefly, NOD/SCID mice received total body irradiation at 3Gy followed by tail vein injection of harvested GFP+BM. Brain tumor xenografts in an ICW were generated in these chimera mice using U87 glioma cells engineered to stably express mCherry fluorochrome. Intracranial glioma xenograft in ICW: Intracranial window chambers were generated using a 2.7mm trephine drill to remove the skull overlying the right frontal cortex, leaving the dura intact. 4×10E5 U87-mCherry cells were injected at a 3mm cortical depth in the centre of the window. The window was covered with a coverslip sealed in place with dental acrylic. In vivo Imaging - 2PLM: Mice were anesthetized and imaged in an inverted position. Mice were imaged over a longitudinal period, d1-35, post cell transplantation. Results: We have successfully imaged 40 mice using intracranial glioma xenograft in ICW model coupled with 2PLM. We obtained longitudinal imaging up to 5 weeks following glioma cell implantation with no evidence of intracranial infection, inflammation or damage to the window model. High-resolution images of tumor cells, tumor vasculature and single cell resolution of the GFP+BM cells can be obtained. We can visualize tumor mass upto a 4mm depth from the cortical surface as confirmed by histological sections. The dynamic flow of GFP+BM within tumor vasculature can be visualized and quantified in 3D. Longitudinal differentiation of BMDCs can be traced by following their migration and integration into the glioma tumor microenvironment over time. Conclusion: We introduce a non-invasive and reproducible strategy for examining brain tumor microenvironment, its vascular progression and more precisely understanding at a single-cell level the contribution of BMDC brain tumor vascularity. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 4324.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".