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Record W4318667756 · doi:10.1089/ten.tec.2022.0189

Implanted Tissue-Engineered Vascular Graft Cell Isolation with Single-Cell RNA Sequencing Analysis

2023· article· en· W4318667756 on OpenAlexfundno aff
Gabriel Mirhaidari, Jenny C. Barker, Jesse J. Westfall, Katherine E. Miller, Tracy A. Bedrosian, Christopher K. Breuer, James W. Reinhardt

Bibliographic record

VenueTissue Engineering Part C Methods · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of HealthAlberta Water Research InstituteOhio State UniversityAdditional VenturesNationwide Children's HospitalAmerican Heart Association
KeywordsCellCell typeExtracellular matrixCell cultureCell biologyFibroblastBiologyProgenitor cellScaffoldChemistryMolecular biologyComputational biologyStem cellBiochemistryBiomedical engineeringGenetics

Abstract

fetched live from OpenAlex

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.

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)0.001
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.003

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.

Opus teacher head0.023
GPT teacher head0.273
Teacher spread0.250 · 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".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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