Dataset Information

Benchmarking SILAC Proteomics Workflows and Data Analysis Platforms

ABSTRACT: Stable isotopic labeling by amino acids in cell culture (SILAC) is a powerful metabolic labeling technique with widespread applications and diverse study designs. SILAC proteomics requires the confident identification and quantification of complete isotopic versions of proteins and peptides during data acquisition and analysis. However, different SILAC workflows and data analysis platforms have not been comparatively evaluated. To fill this critical gap and provide practical user guidelines for SILAC proteomics data analysis, we designed a comprehensive benchmarking pipeline to evaluate different SILAC workflows and commonly used data analysis software. Ten different data analysis platforms were evaluated for static and dynamic SILAC labeling with both DDA and DIA methods. Both in-house generated and repository SILAC proteomics datasets were used for benchmarking with hundreds of raw data files from HeLa and neuron samples. We evaluated twelve performance metrics for SILAC proteomics including identification, quantification, accuracy, precision, reproducibility, filtering criteria, missing values, false discovery rate, protein half-life measurement, completeness, unique software features, and speed of data analysis. In summary, this study provided the first systematic evaluation of different SILAC data analysis platforms with practical guidelines to assist decision-making in SILAC proteomics study design and data analysis.

INSTRUMENT(S): Q Exactive HF-X

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Ling Hao

PROVIDER: MSV000096401 | MassIVE | Wed Nov 13 08:43:00 GMT 2024

SECONDARY ACCESSION(S): PXD057850

REPOSITORIES: MassIVE

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Parkinson disease (PD) is a neurodegenerative disease characterized by the accumulation of alpha-synuclein (SNCA) and other proteins in aggregates termed âLewy Bodiesâ within neurons. PD has both genetic and environmental risk factors, and while processes leading to aberrant protein aggregation are unknown, past work points to abnormal levels of SNCA and other proteins. Although several genome-wide studies have been performed for PD, these have focused on DNA sequence variants by genome-wide association studies (GWAS) and on RNA levels (microarray transcriptomics), while genome-wide proteomics analysis has been lacking. After appropriate filters, proteomics identified 3,558 unique proteins and 283 of these (7.9%) were significantly different between PD and controls (q-value<0.05). RNA-sequencing identified 17,580 protein-coding genes and 1,095 of these (6.2%) were significantly different (FDR p-value<0.05), but only 166 of the FDR significant protein-coding genes (0.94%) were present among the 3,558 proteins characterized. Of these 166, eight genes (4.8%) were significant in both studies, with the same direction of effect. Functional enrichment analysis of the proteomics results strongly supports mitochondrial-related pathways, while comparable analysis of the RNA-sequencing results implicates protein folding pathways and metallothioneins. Ten of the implicated genes or proteins co-localized to GWAS loci. Evidence implicating SNCA was stronger in proteomics than in RNA-sequencing analyses. Notably, differentially expressed protein-coding genes were more likely to not be characterized in the proteomics analysis, which lessens the ability to compare across platforms. Combining multiple genome-wide platforms offers novel insights into the pathological processes responsible for this disease by identifying pathways implicated across methodologies. The study consists of mRNA-Seq (29 PD, 44 neurologically normal controls) and three-stage Mass Spectrometry Tandem Mass Tag Proteomics (12 PD, 12 neurologically normal controls) performed in post-mortem BA9 brain tissue. The proteomics samples are a subset of the RNA-Seq samples.

Dataset Information

Benchmarking SILAC Proteomics Workflows and Data Analysis Platforms

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets