Dataset Information

Label-free single-cell RNA Multiplexing leveraging genetic variability [bulk RNA-seq]

ABSTRACT: Label-free single-cell RNA Multiplexing leveraging genetic variability [bulk RNA-seq]

PROVIDER: PRJNA1040192 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	SRR26826519_1.fastq.gz	Fastqsanger.gz
	SRR26826519_2.fastq.gz	Fastqsanger.gz
	SRR26826520_1.fastq.gz	Fastqsanger.gz
	SRR26826520_2.fastq.gz	Fastqsanger.gz
	SRR26826521_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 44

Similar Datasets

Project description:Proteome wide protein quantification has become facile in the last years supported by label-free discovery proteomics approaches and powerful, easy to use software. We set out to independently evaluate this highly peptide centric technology focusing on the performance of some of the currently most popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant and Progenesis QIP. The sample to sample variability introduced into protein abundance estimates by the technology itself, thereby affecting the validity of changes in protein abundance reported to be biological in nature, was determined. Using the same experimental design we were interested in the potential of shotgun proteomics to uncover biological contributions to changes in protein abundance in essentially isogenic plants grown under the same environmental conditions. The accuracy, precision, limit of quantification, intra-analysis repeatability and other performance metrics of protein quantification were determined with the softwares’ ready-to-run, default parameters and some modified settings. MaxQuant and Progenesis QIP each had decided advantages and exhibited direct proportionality between measured and actual protein abundance. The inherent variability in protein quantification in measurements of Arabidopsis thaliana proteomes was substantially higher than the biological sample to sample variability. Strikingly, correlation between Arabidopsis proteins quantified with MaxQuant and Progenesis QIP was poor due to differential protein inference. Both of these points warrant caution in the interpretation of shotgun proteomics studies. The abundance of around 99% of proteins was constant in repeated sampling of the Arabdiopsis thaliana Col-0 inbred accession; however a handful showed substantial quantitative variability. The function stress/stimulus response was highly significantly overrepresented for these proteins. Heritable information beyond genetic polymorphism that affects gene expression levels has recently been documented for these functions. We speculate on the potential of discovery proteomics to measure gene expression levels as a conduit of the epigenotype shaping quantitative traits.

Dataset Information

Label-free single-cell RNA Multiplexing leveraging genetic variability [bulk RNA-seq]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets