SILAC Ribosomal Proteomic Quantification Service
Are you currently facing challenges in accurately quantifying protein expression changes, deciphering complex cellular responses, or identifying robust biomarker candidates? Our SILAC (stable isotope labeling by amino acids in cell culture) proteomics quantification service helps you achieve highly accurate relative protein quantification and understand dynamic cellular processes through advanced metabolic labeling with stable isotopes coupled with state-of-the-art mass spectrometry (MS).
Introduction of SILAC Proteomics Quantification Service
SILAC is a powerful metabolic labeling strategy for accurate MS-based quantitative proteomics. Cells are cultured in media where natural amino acids are replaced with heavy stable isotope-labeled counterparts. This results in the in vivo incorporation of these isotopes into all proteins synthesized anew. When differently labeled cell populations are mixed, the relative protein abundance is precisely determined by the ratio of heavy to light peptide signals in the MS, offering high accuracy and reproducibility.
Fig.1 Overview of a SILAC experiment.1
Workflow
Our SILAC workflow is designed for clarity, precision, and comprehensive data output, ensuring you receive the highest quality results for your research.
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Client Consultation & Experimental Design Finalization
We begin with a thorough discussion to understand your research goals and refine the experimental plan, ensuring the SILAC strategy is optimized for your specific needs. -
Cell Culture & Isotopic Labeling
Your cells are cultured in specialized media containing either normal or stable isotope-labeled essential amino acids. This process is conducted over multiple cell divisions to guarantee nearly complete integration of the labeled amino acids into the proteome.
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Sample Preparation & Protein Digestion
Labeled cell populations are harvested, combined in precise ratios, and then lysed. Proteins are extracted, quantified, and then enzymatically digested into peptides.
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LC-MS/MS Analysis
The peptide mixture is subjected to high-resolution liquid chromatography-tandem MS (LC-MS/MS). Peptides are separated by chromatography and then analyzed by the MS, which measures the mass-to-charge ratio of the peptides and their fragments. The disparity in mass between light-and heavy-labeled peptide pairs enables their identification and relative quantification.
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Data Processing & Bioinformatics AnalysisSophisticated software is used to process the raw MS data. This involves identifying peptides and proteins, quantifying the relative abundance of proteins based on the intensity ratios of heavy and light peptide pairs, and performing statistical analysis to determine significant changes in protein expression.
Final Deliverables
1. Comprehensive Project Report
A detailed report including the experimental design, methodology, a full list of identified and quantified proteins with their relative expression ratios, and statistical significance values.
2. Raw Data Files
Access to the raw MS data files and processed data tables.
3. Bioinformatics Summary
Key biological insights can be derived, encompassing potential pathway analysis and gene ontology enrichment analysis based on the differentially expressed proteins.
Applications
SILAC proteomics quantification is a versatile technique with a wide range of applications in biomedical research. Its ability to accurately measure relative protein abundance makes it invaluable for:
- Understanding Dynamic Cellular Responses
Quantifying proteome changes in response to various stimuli such as drug treatments, growth factors, stress conditions, or environmental perturbations.
- Biomarker Discovery
Identifying proteins that are differentially expressed between healthy and diseased states, or between different stages of a disease, to discover potential diagnostic, prognostic, or predictive biomarkers.
- Drug Mechanism of Action Studies
Elucidating how therapeutic compounds affect cellular proteomes, identifying primary drug targets, downstream signaling pathways, and potential off-target effects.
- Analysis of Protein Turnover and Stability
Measuring the rates of protein synthesis and degradation under different conditions to understand protein homeostasis.
Functional Proteomics: Characterizing proteomic differences between various cell types, developmental stages, or genetically modified organisms to understand protein function in a biological context.
- Signal Transduction Pathway Analysis
Investigating changes in protein abundance within signaling pathways upon activation or inhibition, providing insights into cellular communication networks.
- Quantitative Analysis of Post-Translational Modifications (PTMs)
While often requiring PTM-specific enrichment, SILAC can be used to quantify changes in the abundance of PTM-modified proteins, offering insights into regulatory events like phosphorylation, ubiquitination, or acetylation dynamics.
Why Choose Us?
Choosing Creative Biolabs for your SILAC proteomics quantification means partnering with experts dedicated to providing high-quality, reliable, and insightful proteomics data. Our service leverages the inherent strengths of the SILAC methodology, including exceptional accuracy due to early-stage sample mixing which minimizes experimental variability, and in vivo labeling reflecting true physiological changes. We utilize cutting-edge LC-MS/MS platforms and robust bioinformatics pipelines to deliver comprehensive results. Our team offers expert consultation from experimental design through to data interpretation, ensuring your project's success.
FAQs
Q: What types of samples are suitable for your SILAC service?
A: Our SILAC service is primarily designed for any culturable cells that can efficiently incorporate the stable isotope-labeled amino acids from the growth medium. If you have specific cell lines or conditions, we recommend discussing them with our experts.
Q: How can SILAC help in my drug discovery research?
A: SILAC is invaluable for drug discovery by enabling precise quantification of proteome changes in response to drug candidates, elucidating mechanisms of action, identifying off-target effects, and discovering potential biomarkers of drug efficacy or toxicity.
Q: What makes SILAC more suitable than other quantitative methods for my study?
A: SILAC's key advantages include in vivo labeling which reflects true biological states, and the mixing of samples at an early stage, which significantly reduces sample handling variability compared to methods that label peptides post-extraction. This generally leads to higher accuracy and reproducibility, especially for complex experimental designs.
Q: Are there any constraints or particular factors to take into account when conducting SILAC experiments?
A: Successful SILAC requires cells to be actively dividing and incorporating amino acids. Some cell types may have slower incorporation rates. Complete labeling is crucial, typically requiring at least 5-6 cell doublings. Also, it's primarily suited for cell culture systems rather than tissue samples directly, though adaptations exist.
Q: How does SILAC compare to label-free quantification (LFQ)?
A: Both are powerful techniques. SILAC often provides higher accuracy and reproducibility due to early sample mixing and internal standards for every peptide pair. LFQ can be more versatile for samples not amenable to metabolic labeling and can be more cost-effective for very large sample sets, though it can be more susceptible to run-to-run variation.
Creative Biolabs is committed to providing cutting-edge SILAC proteomics quantification services that deliver accurate, reproducible, and insightful data. Our expertise in metabolic labeling, high-resolution MS, and advanced bioinformatics empowers researchers to delve deeper into the complexities of the proteome. Our team of experienced scientists is ready to discuss your project needs and help you design the optimal SILAC strategy.
Reference
- Bousquet, Paula A., et al. "SILAC-based quantitative proteomics and microscopy analysis of cancer cells treated with the N-glycolyl GM3-specific anti-tumor antibody 14F7." Frontiers in immunology 13 (2022): 994790. Distributed under Open Access license CC BY 4.0, without modification.
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