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Record W3001728494 · doi:10.1097/corr.0000000000001138

CORR Insights®: Fibrin Clots Maintain the Viability and Proliferative Capacity of Human Mesenchymal Stem Cells: An In Vitro Study

2020· letter· en· W3001728494 on OpenAlexaff
T. Mark Campbell

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2020
Typeletter
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineMesenchymal stem cellRotator cuffTearsFibrinStem cellRegenerative medicinePopulationPathologySurgeryImmunologyCell biologyBiology

Abstract

fetched live from OpenAlex

Where Are We Now? Soft-tissue musculoskeletal injuries like meniscus and rotator cuff tears are extremely common and are becoming even more so as the population in developed nations ages [9, 11]. Degenerative meniscal tears are closely associated with knee osteoarthritis, which affects more than 10% of those older than 60 years of age, and rotator cuff tears affect nearly one-third of individuals within that same age group [11]. Surgery for meniscal tears in this age group [6] does not outperform placebo surgery or non-surgical treatments, and surgically treated rotator cuff tears often do not heal [11]. In an effort to improve healing, regenerative approaches, such as the use of biological scaffolds, have kindled interest. Fibrin clots are natural hydrogel scaffolds that represent the end-product of the physiologic blood coagulation cascade [4]. During hemostasis, fibrin acts as a natural support matrix and reservoir for numerous cell types and growth factors, thus playing a critical supportive role in tissue healing [4]. In addition to scaffold considerations, a number of studies have evaluated the use of mesenchymal stem cells (MSCs) to heal injured tissue [10]. These multi-potent cells are found in many human tissues and are believed to promote and direct tissue repair through several mechanisms, including their ability to differentiate into cells important for tissue repair (fibroblasts and chondrocytes), to dampen the local immune/inflammatory response, and by acting through paracrine and endocrine effects that direct other cells assisting in tissue repair [14]. One of the challenges faced in integrating MSCs into an area of damaged tissue is the local environment of tissue injury, which is often highly-inflammatory, and may be hostile towards the repair capabilities and even viability of the MSCs. Scaffolds, such as fibrin matrices, might provide an ideal shelter for MSCs to perform their repair function at the site of injury, while protecting them from the local inflammatory environment. Though preliminary research into the use of MSCs for tissue regeneration has shown promise, the Food and Drug Administration has published a position statement indicating that there is presently a lack of high-quality evidence demonstrating their regenerative effect and that further study is needed before they can be approved for clinical use [7, 8]. In the current study, Warth and colleagues [13] demonstrate that muscle-derived MSCs incorporate readily into a natural fibrin clot and maintain their viability for at least 7 days in vitro. Further, they demonstrated that viability was sustained in clot-incorporated MSCs when incubated with either growth media or non-nutritive saline, the latter suggesting that the fibrin clot provided adequate metabolic support for the MSCs over the incubation period. Previous proof-of-concept studies have demonstrated viability of bone marrow-derived MSCs, embryonic stem cells, and induced pluripotent stem cells in commercially-produced fibrin gels, providing additional evidence that MSCs can maintain viability within such matrices [4]. Where Do We Need to Go? The ideal cell-based delivery system would bring the optimal number of regenerative cells to the injury site and promote a milieu that sustains the viability, proliferation, and repair capacity of those cells to maximize healing efficiency. Towards this goal, one of the advantages of using fibrin as a delivery system for regenerative cellular and molecular biomaterials is its flexibility with respect to affinity towards growth factors important for tissue regeneration. Structural and mechanical properties of the fibrin matrix can also be modified to control the release kinetics for both cellular (MSCs) and molecular (growth factors) constituents by varying clot microstructure (pore size, density), stiffness and resistance to fibrinolysis [4]. Thus, the scaffold can be specifically adapted for the tissue in need of repair. Research describing tissue-specific adaptations, such as for the rotator cuff or meniscus, is still in the early stages. Another challenge faced in regenerative therapy is ensuring that MSCs engage in the repair process once delivered to the site of injury. One study found that, when embedded in a fibrin matrix, MSCs are capable of creating vascular structures within avascular three-dimensional (3-D) fibrin matrices [12]. Other studies have highlighted the difficulty in promoting MSC differentiation within fibrin matrices [1, 3]. For example, bone-derived fibrin-embedded MSCs showed osteogenic potential, but did not fully differentiate into mature osteoblasts after 28 days in vitro [1]. Another study [3] demonstrated that both muscle- and bone marrow-derived MSCs showed osteogenic capacity when incorporated into a fibrin sealant once applied to a murine calvarial defect; however, both cell types required genetic modification in order to regenerate bone in vivo. Optimizing MSC repair capacity at the site of injury is also an ongoing area of research, but heterogeneity across studies remains a hurdle. Factors to consider include stem cell tissue source, number and concentration of cells, autologous or allogenic, expanded in the lab or not, method of delivery (injected at the bedside versus implanted during surgery), and use of a scaffold. Further, scaffold properties may vary in terms of architecture, solubility, and affinity for cells and growth factors. Thus, in addition to optimization, standardization of cell-based therapies is needed to promote consistent, reliable tissue repair. How Do We Get There? Emerging technologies, such as biofabrication through the use of 3-D printing, allow standardized computer-assisted creation of scaffolds with precise control over composition, pore size, stiffness and geometry [5]. Growth factors, which are important for cell viability, proliferation, and differentiation can also be selectively embedded within a scaffold, and adapted for the specific tissue requiring repair [5]. With this fine level of control, scaffolds can be reproducibly manufactured in any facility having access to adequate biofabrication technology. This would allow the opportunity to systematically study and build upon standardized manufacturing protocols, reduce heterogeneity in both the scaffold architecture and cellular composition, and improve the evidence upon which stem cell therapies are delivered. Ensuring that the cells used within the scaffolds can be reliably and reproducibly characterized and that they fulfill minimal criteria for cell identity is a necessity, to ensure consistency [2]. Further in vivo studies, such as cellular- and protein-labelling experiments, demonstrating direct or directed tissue repair by the delivered cells as well as the optimal scaffold properties supporting these activities are needed. The ideal scaffold and cellular properties will likely need to be adapted for the individual tissues targeted for repair, such as the rotator cuff and the meniscus.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

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.

Opus teacher head0.187
GPT teacher head0.428
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2020
Admission routes1
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