Implanted Tissue-Engineered Vascular Graft Cell Isolation with Single-Cell RNA Sequencing Analysis
Bibliographic record
Abstract
The advent of single-cell RNA sequencing (scRNA-Seq) has brought with it the ability to gain greater insights into the cellular composition of tissues and heterogeneity in gene expression within specific cell types. For tissue-engineered blood vessels, this is particularly impactful to better understand how neotissue forms and remodels into tissue resembling a native vessel. A notable challenge, however, is the ability to separate cells from synthetic biomaterials to generate high-quality single-cell suspensions to interrogate the cellular composition of our tissue-engineered vascular grafts (TEVGs) during active remodeling in situ. We present here a simple, commercially available approach to separate cells within our TEVG from the residual scaffold for downstream use in a scRNA-Seq workflow. Utilizing this method, we identified the cell populations comprising explanted TEVGs and compared these with results from immunohistochemical analysis. The process began with explanted TEVGs undergoing traditional mechanical and enzymatic dissociation to separate cells from scaffold and extracellular matrix proteins. Magnetically labeled antibodies targeting murine origin cells were incubated with enzymatic digests of TEVGs containing cells and scaffold debris in suspension allowing for separation by utilizing a magnetic separator column. Single-cell suspensions were processed through 10 × Genomics and data were analyzed utilizing R to generate cell clusters. Expression data provided new insights into a diverse composition of phenotypically unique subclusters within the fibroblast, macrophage, smooth muscle cell, and endothelial cell populations contributing to the early neotissue remodeling stages of TEVGs. These populations were correlated qualitatively and quantitatively with immunohistochemistry highlighting for the first time the potential of scRNA-Seq to provide exquisite detail into the host cellular response to an implanted TEVG. These results additionally demonstrate magnetic cell isolation is an effective method for generating high-quality cell suspensions for scRNA-Seq. While this method was utilized for our group's TEVGs, it has broader applications to other implantable materials that use biodegradable synthetic materials as part of scaffold composition. Impact statementSingle-cell RNA sequencing is an evolving technology with the ability to provide detailed information on the cellular composition of remodeling biomaterials in vivo. This present work details an effective approach for separating nondegraded biomaterials from cells for downstream RNA-sequencing analysis. We applied this method to implanted tissue-engineered vascular grafts and for the first time describe the cellular composition of the remodeling graft at a single-cell gene expression level. While this method was effective in our scaffold, it has broad applicability to other implanted biomaterials that necessitate separation of cell from residual scaffold materials for single-cell RNA sequencing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".