Decoding communication patterns of the innate immune system by quantitative proteomics
Bibliographic record
Abstract
The innate immune system is a collective network of cell types involved in cell recruitment and activation using a robust and refined communication system. Engagement of receptor-mediated intracellular signaling initiates communication cascades by conveying information about the host cell status to surrounding cells for surveillance and protection. Comprehensive profiling of innate immune cells is challenging due to low cell numbers, high dynamic range of the cellular proteome, low abundance of secreted proteins, and the release of degradative enzymes (e.g., proteases). However, recent advances in mass spectrometry-based proteomics provides the capability to overcome these limitations through profiling the dynamics of cellular processes, signaling cascades, post-translational modifications, and interaction networks. Moreover, integration of technologies and molecular datasets provide a holistic view of a complex and intricate network of communications underscoring host defense and tissue homeostasis mechanisms. In this Review, we explore the diverse applications of mass spectrometry-based proteomics in innate immunity to define communication patterns of the innate immune cells during health and disease. We also provide a technical overview of mass spectrometry-based proteomic workflows, with a focus on bottom-up approaches, and we present the emerging role of proteomics in immune-based drug discovery while providing a perspective on new applications in the future.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".