Three-Dimensional Reconstructed Bone Marrow Matrix Culture Improves the Viability of Primary Myeloma Cells In-Vitro via a STAT3-Dependent Mechanism
Bibliographic record
Abstract
Primary myeloma (PM) cells are short-lived in conventional culture, which limited their usefulness as a study model. Here, we evaluated if three-dimensional (3D) culture can significantly prolong the longevity of PM cells in-vitro. We employed a previously established 3D model for culture of bone marrow mononuclear cells isolated from 15 patients. We assessed the proportion of PM cells, viability and proliferation using CD38 staining, trypan blue exclusion assays and carboxy fluorescein succinimidyl ester (CFSE) staining, respectively. We observed significantly more CD38+ viable cells in 3D than in conventional culture (65% vs. 25%, p = 0.006) on day 3. CFSE staining showed no significant difference in cell proliferation between the two culture systems. Moreover, we found that PM cells in 3D culture are more STAT3 active by measure of pSTAT3 staining (66% vs. 10%, p = 0.008). Treatment of IL6, a STAT3 activator significantly increased CD38+ cell viability (41% to 68%, p = 0.021). In comparison, inhibition of STAT3 with Stattic significantly decreased PM cell viability in 3D culture (38% to 17% p = 0.010). Neither IL6 nor Stattic affected the PM cell viability in conventional culture. This study suggests that 3D culture can significantly improve the longevity of PM cells in-vitro, and STAT3 activation can further improve their viability.
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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 | 0.001 |
| 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".