Evaluation of Therapeutic Collagen-Based Biomaterials in the Infarcted Mouse Heart by Extracellular Matrix Targeted MALDI Imaging Mass Spectrometry
Bibliographic record
Abstract
The goal of this study was to develop strategies to localize human collagen-based hydrogels within an infarcted mouse heart, as well as analyze its impact on endogenous extracellular matrix (ECM) remodeling. Collagen is a natural polymer that is abundantly used in bioengineered hydrogels because of its biocompatibility, cell permeability, and biodegradability. However, without the use of tagging techniques, collagen peptides derived from hydrogels can be difficult to differentiate from the endogenous ECM within tissues. Imaging mass spectrometry is a robust tool capable of visualizing synthetic and natural polymeric molecular structures yet is largely underutilized in the field of biomaterials outside of surface characterization. In this study, our group leveraged a recently developed matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) technique to enzymatically target collagen and other ECM peptides within the tissue microenvironment that are both endogenous and hydrogel-derived. Using a multimodal approach of fluorescence microscopy and ECM-IMS techniques, we were able to visualize and relatively quantify significantly abundant collagen peptides in an infarcted mouse heart that were localized to regions of therapeutic hydrogel injection sites. On-tissue MALDI MS/MS was used to putatively identify sites of collagen peptide hydroxyproline site occupancy, a post-translational modification that is critical in collagen triple helical stability. Additionally, the technique could putatively identify over 35 endogenously expressed ECM peptides that were expressed in hydrogel-injected mouse hearts. Our findings show evidence for the use of MALDI-IMS in assessing the therapeutic application of collagen-based biomaterials.
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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) | 0.000 | 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) | 0.000 | 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".