Monitoring matrix metalloproteinase activity at the epidermal–dermal interface by SILAC‐iTRAQ‐TAILS
Bibliographic record
Abstract
Secreted proteases act on interstitial tissue secretomes released from multiple cell types. Thus, substrate proteins might be part of higher molecular complexes constituted by many proteins with diverse and potentially unknown cellular origin. In cell culture, these may be reconstituted by mixing native secretomes from different cell types prior to incubation with a test protease. Although current degradomics techniques could identify novel substrate proteins in these complexes, all information on the cellular origin is lost. To address this limitation, we combined iTRAQ-based terminal amine isotopic labeling of substrates (iTRAQ-TAILS) with SILAC to assign proteins to a specific cell type by MS1- and their cleavage by MS2-based quantification in the same experiment. We demonstrate the power of our newly established workflow by monitoring matrix metalloproteinase (MMP) 10 dependent cleavages in mixtures from light-labeled keratinocyte and heavy-labeled fibroblast secretomes. This analysis correctly assigned extracellular matrix components, such as laminins and collagens, to their respective cellular origins and revealed their processing in an MMP10-dependent manner. Hence, our newly devised degradomics workflow facilitates deeper insight into protease activity in complex intercellular compartments such as the epidermal-dermal interface by integrating multiple modes of quantification with positional proteomics. All MS data have been deposited in the ProteomeXchange with identifier PXD001643 (http://proteomecentral.proteomexchange.org/dataset/PXD001643).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".