Analysis of proteomic profiles and functional properties of human peripheral blood myeloid dendritic cells, monocyte-derived dendritic cells and the dendritic cell-like KG-1 cells reveals distinct characteristics
Bibliographic record
Abstract
BACKGROUND: Dendritic cells (DCs) are specialized antigen presenting cells that play a pivotal role in bridging innate and adaptive immune responses. Given the scarcity of peripheral blood myeloid dendritic cells (mDCs) investigators have used different model systems for studying DC biology. Monocyte-derived dendritic cells (moDCs) and KG-1 cells are routinely used as mDC models, but a thorough comparison of these cells has not yet been carried out, particularly in relation to their proteomes. We therefore sought to run a comparative study of the proteomes and functional properties of these cells. RESULTS: Despite general similarities between mDCs and the model systems, moDCs and KG-1 cells, our findings identified some significant differences in the proteomes of these cells, and the findings were confirmed by ELISA detection of a selection of proteins. This was particularly noticeable with proteins involved in cell growth and maintenance (for example, fibrinogen gamma chain (FGG) and ubiquinol cytochrome c) and cell-cell interaction and integrity (for example, fascin and actin). We then examined the surface phenotype, cytokine profile, endocytic and T-cell-activation ability of these cells in support of the proteomic data, and obtained confirmatory evidence for differences in the maturation status and functional attributes between mDCs and the two DC models. CONCLUSION: We have identified important proteomic and functional differences between mDCs and two DC model systems. These differences could have major functional implications, particularly in relation to DC-T cell interactions, the so-called immunological synapse, and, therefore, need to be considered when interpreting data obtained from model DC systems.
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 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) | 0.000 | 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) | 0.001 | 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".