Bone Marrow-Derived Mesenchymal Stem Cells: Isolation, Expansion, Characterization, Viral Transduction, and Production of Conditioned Medium
Bibliographic record
Abstract
Mesenchymal stem cells (MSCs) are defined as self-renewing and multipotent cells capable of differentiating into multiple cell types, including osteocytes, chondrocytes, adipocytes, hepatocytes, myocytes, neurons, and cardiomyocytes. MSCs were originally isolated from the bone marrow stroma but they have recently been identified also in other tissues, such as fat, epidermis, and cord blood. Several methods have been used for MSC isolation. The most common method is based on the ability of the MSCs to selectively adhere to plastic surfaces. Phenotypic characterization of MSCs is usually carried out using immunocytochemical detection or fluorescence-activated cell sorting (FACS) analysis of cell surface molecule expression. However, the lack of specific markers renders the characterization of MSCs difficult and sometimes ambiguous. MSCs posses remarkable expansion potential in culture and are highly amenable to genetic modification with various viral vectors rendering them optimal vehicles for cell-based gene therapy. Most importantly, MSC plasticity and the possibility to use them as autologous cells render MSCs suitable for cell therapy and tissue engineering. Furthermore, it is known that MSCs produce and secrete a great variety of cytokines and chemokines that play beneficial paracrine actions when MSCs are used for tissue repair. In this chapter, we describe methods for isolation, ex vivo expansion, phenotypic characterization, and viral infection of MSCs from mouse bone marrow. We also describe a method for preparation of conditioned and concentrated conditioned medium from MSCs. The conditioned medium can be easily tested both in vitro and in vivo when a particular paracrine effect (i.e., cytoprotection) is hypothesized to be an important mechanism of action of the MSCs and/or screened to identify a target paracrine/autocrine mediator.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.004 |
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".