ArticleJournal of proteome research2018
Surfactant and Chaotropic Agent Assisted Sequential Extraction/On-Pellet Digestion (SCAD) for Enhanced Proteomics.
Article in Journal of proteome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed, 16 citations in OpenAlex.
- Proteomics of the heart.Physiological reviews · 2024Review
- Extracellular Matrix Proteomics: TheBiomolecules · 2023Review
- Analysis of pancreatic extracellular matrix protein post-translational modificationsMolecular omics · 2021Article
- Proteome-wide and matrisome-specific alterations during human pancreas development and maturation.Nature communications · 2021Article
- MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts.Mass spectrometry reviews · 2019Review
- In Depth Quantification of Extracellular Matrix Proteins from Human Pancreas.Journal of proteome research · 2019Article
- Investigating Phosphorylation Patterns of the Ion Channel TRPM7 Using Multiple Extraction and Enrichment Techniques Reveals New Phosphosites.Journal of the American Society for Mass Spectrometry · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 2 countries.
Funding
Abstract
As a popular sample preparation approach, filter-aided sample preparation (FASP) has been widely used in proteomic analysis. However, several limitations have been noted, including sample loss during filtration, repetitive centrifugation steps, and the possibility of breakage of filtration membrane. Extraction bias among different sample preparation strategies presents another challenge. To overcome these limitations and address remaining challenges, we developed a novel surfactant and chaotropic agent assisted sequential extraction/on-pellet digestion (SCAD) protocol. The new strategy resulted in higher protein yield and improved peptide recovery and protein coverage compared to two conventional sample preparation methods (FASP and urea). In combination of three strategies, more than 10,000 distinct protein groups were identified with 1% FDR from MDA-MB-231 cells without any prefractionation. This in-depth proteome analysis was accomplished by optimization of protein extraction, enzymatic digestion, LC gradient, and peptide sequencing method. Ingenuity Pathways Analysis (IPA) of proteins exclusively identified in SCAD revealed several crucial signaling pathways that regulate breast cancer progression. SCAD also enabled an unbiased extraction of different categories of proteins (membrane, intracellular, nuclear) associated with tumorigenesis, which integrates the advantages of FASP and urea extraction. This novel strategy expedites comprehensive protein identification, which is applicable for biomarker discovery in various types of cancers.
Indexed as
Identifiers
What OpenQuestion holds
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.