Comparison of Different Strategies of MSC Isolation Revels Advantage To Expand MSC Directly from Purified CD105+ and CD271+ Cells.
Bibliographic record
Abstract
Abstract The potential for multilineage differentiation together with the ability to expand in cultures are the reasons why Mesenchymal Stem Cells (MSC) are considered to be the population of stem cells for potential treatment for variety of disorders (e. g. Osteogenesis Imperfecta, Myocardium Infarction, GvHD). MSC are isolated from the bone marrow mononuclear cells (MNC) based on their adhesive properties. There have been few attempts to isolate MSC directly based on the expression of selected surface antigens, but these isolation strategies were not compared with “the gold standard” procedure which is still selection by plastic adherence. Nevertheless, it is obvious that a presence of different populations of cells “contaminating” MSC in adherent cell cultures (e.g., endothelial cells, macrophages, dendritic cells) may affect expansion of MSC. In this study we proposed new isolation strategies of bone marrow MSC based on RosetteSep Isolation Kit (Stem Cells Technologies Inc., Vancouver, Canada) and immunomagnetic isolation of CD105+ or CD271+ cell populations (Miltenyi Biotec, Germany). Four fractions of bone marrow mononuclear cells i) non-purified MNC, ii) MNC enriched in MSC by RosetteSep Isolation Kit, iii) sorted CD105+ and iv) sorted CD271+ cells were cultured for three passages. Subsequently, we evaluated i) number of CFU-F colonies, ii) expression of selected surface antigens (CD105, CD166, CD44, CD73, CD45, CD34), iii) in vitro osteogenic differentiation of expanded cells and iv) changes in the expression of genes related to osteogenesis (RQ-PCR). We found that the mean number of CFU-F colonies counted on the 9th day of culture was 26 (range 14,5–41,4), 49 (range 21,2–97,1), 105 (range 36,5–221) and 148 (range 55,3–211) per 107 MNC for non-purified MNC, MNC enriched in MSC by RosetteSep Isolation Kit, purified CD105+cells and purified CD271+cells, respectively. After 3rd passage the phenotype of cells was similar as we observed a comparable percentage of cells positive (over 90%) for CD105, CD166, CD44, CD73 and negative (below 5%) for CD45, CD34 surface antigens in all fractions. The RQ - PCR analysis of mRNA level of osteogenic (osteocalcin, PTHR, α1collagen), adipogenic (lipoprotein lipase, leptin, PPARγ2) and chondrogenic (aggrecan1) genes in all four populations revealed that MSC isolated by means of expression of 105 and CD271 antigens had higher level of mRNA for all assessed genes except for lipoprotein lipase and α1collagen prior to differentiation. After 30 days of osteogenic differentiation RQ - PCR analysis was repeated and compared with that before differentiation. We noticed an increased level of mRNA for osteocalcin and PTHR (markers of osteogenic differentiation) in all four populations, with the highest expression in MSC derived from non-purified MNC. However, this fraction had also the highest mRNA level of PPARγ2, lipoprotein lipase, and aggrecan genes (adipogenic and chondrogenic lineage respectively). Since the highest number of CFU-F was derived from purified CD105+ and CD271+ cells as well as these two populations seem to be the most homogenous based on RQ-PCR data, these cell fractions should be employed to expand most efficiently MSC for potential therapeutic purposes. Our data suggest that, non-MSC cells present in MNC and RosetteSep cultures may negatively affect both the expansion efficiency and differentiation along desired MSC lineage.
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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 | 0.000 |
| 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".