In vitro Culture of Naïve Human Bone Marrow Mesenchymal Stem Cells: A Stemness Based Approach
Bibliographic record
Abstract
Human bone marrow derived mesenchymal stem cells (BM-MSCs) resides in their niches in close proximity to hematopoietic stem cells (HSCs). These naïve MSCs have tremendous potential in regenerative therapeutics, and may also be exploited by cancer and infectious disease agents. Hence, it is important to study the physiological and pathological roles of naïve MSC. However, our knowledge of naïve MSCs is limited by lack of appropriate isolation and in vitro culture methods. Established culture methods use serum rich media, and serial passaging for retrospective isolation of MSCs. These primed MSCs may not reflect the true physiological and pathological roles of naive MSCs (Figure 1). Therefore, there is a strong need for direct isolation and in vitro culture of naïve MSCs to study their stemness (self-renewal and undifferentiated state) and developmental ontogeny. We have taken a niche-based approach on stemness to better maintain naïve MSCs in vitro. In this approach, stemness is broadly divided as a niche dependent (extrinsic), niche independent (intrinsic) and niche modulatory (altruistic or competitive). Using this approach, we were able to maintain naïve CD271+/CD133+ BM-MSCs for two weeks. Furthermore, this in vitro culture system helped us to identify naïve MSCs as a protective niche site for Mycobacterium tuberculosis, the causative organism of pulmonary tuberculosis. In this review, we discuss the in vitro culture of primed versus naïve human BM derived MSCs with a special focus on how a stemness based approach could facilitate the study of naïve BM-MSCs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".