ArticleMolecular & cellular proteomics : MCP2015
Targeted Proteomics-Driven Computational Modeling of Macrophage S1P Chemosensing.
Article in Molecular & cellular proteomics : MCP, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 17 citations in OpenAlex.
- Quantitative Modeling of TLR Signaling Reveals Missing Negative Feedback Guiding Identification of TANK-IKKε Checkpoint.bioRxiv : the preprint server for biology · 2026Article
- Omics and systems view of innate immune pathways.Proteomics · 2023Review
- Absolute protein quantitation of the mouse macrophage Toll-like receptor and chemotaxis pathways.Scientific data · 2022Article
- Proteomic Changes of Osteoclast Differentiation in Rheumatoid and Psoriatic Arthritis Reveal Functional Differences.Frontiers in immunology · 2022Article
- Review
- Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer.Molecules (Basel, Switzerland) · 2021Review
- Phosphoproteome Analysis in Immune Cell Signaling.Current protocols in immunology · 2020Article
- Multi-Omics Strategies Uncover Host-Pathogen Interactions.ACS infectious diseases · 2019Review
- Mechanistic Models of Cellular Signaling, Cytokine Crosstalk, and Cell-Cell Communication in Immunology.Frontiers in immunology · 2019Article
- Application of targeted mass spectrometry in bottom-up proteomics for systems biology research.Journal of proteomics · 2018Review
- A Methodology for Comprehensive Analysis of Toll-Like Receptor Signaling in Macrophages.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Review
- Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification.Journal of visualized experiments : JoVE · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
Osteoclasts are monocyte-derived multinuclear cells that directly attach to and resorb bone. Sphingosine-1-phosphate (S1P)(1) regulates bone resorption by functioning as both a chemoattractant and chemorepellent of osteoclast precursors through two G-protein coupled receptors that antagonize each other in an S1P-concentration-dependent manner. To quantitatively explore the behavior of this chemosensing pathway, we applied targeted proteomics, transcriptomics, and rule-based pathway modeling using the Simmune toolset. RAW264.7 cells (a mouse monocyte/macrophage cell line) were used as model osteoclast precursors, RNA-seq was used to identify expressed target proteins, and selected reaction monitoring (SRM) mass spectrometry using internal peptide standards was used to perform absolute abundance measurements of pathway proteins. The resulting transcript and protein abundance values were strongly correlated. Measured protein abundance values, used as simulation input parameters, led to in silico pathway behavior matching in vitro measurements. Moreover, once model parameters were established, even simulated responses toward stimuli that were not used for parameterization were consistent with experimental findings. These findings demonstrate the feasibility and value of combining targeted mass spectrometry with pathway modeling for advancing biological insight.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.