Zhu et al. 2018 (Nat. Comm.): HeLa lysates

Near single-cell proteomics data of HeLa lysates at different concentrations (10, 40 and 140 cell equivalent). Each concentration is acquired in triplicate.

zhu2018NC_lysates

Format

A QFeatures object with 4 assays, each assay being a SingleCellExperiment object:

  • peptides: quantitative information for 14,921 peptides from 9 lysate samples

  • proteins_intensity: quantitative information for 2,199 proteins from 9 lysate samples

  • proteins_LFQ: LFQ intensities for 2,199 proteins from 9 lysate samples

  • proteins_iBAQ: iBAQ values for 2,199 proteins from 9 lysate samples

Sample annotation is stored in colData(zhu2018NC_lysates()).

Source

The PSM data can be downloaded from the PRIDE repository PXD006847. The source link is: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2018/01/PXD006847

Acquisition protocol

The data were acquired using the following setup. More information can be found in the original article (see References).

  • Cell isolation: HeLas were collected from cell cultures.

  • Sample preparation performed in bulk (5E5 cells/mL). Protein extraction using RapiGest (+ DTT) + dilution to target concentration + alkylation (IAA) + Lys-C digestion + trypsin digestion + cleave RapiGest (formic acid).

  • Separation: nanoACQUITY UPLC pump (60nL/min) with an Self-Pack PicoFrit 70cm x 30um LC columns.

  • Ionization: ESI (1,900V).

  • Mass spectrometry: Thermo Fisher Orbitrap Fusion Lumos Tribrid. MS1 settings: accumulation time = 246ms; resolution = 120,000; AGC = 1E6. MS/MS settings, depend on the sample size, excepted for the AGC = 1E5. Blank and approx. 10 cells (time = 502ms; resolution = 240,000), approx. 40 cells (time = 246ms; resolution = 120,000), approx. 140 cells (time = 118ms; resolution = 60,000).

  • Data analysis: MaxQuant (v1.5.3.30) + Perseus + OriginLab 2017.

Data collection

The data were collected from the PRIDE repository (accession ID: PXD006847). We downloaded the Vail_Prep_Vail_peptides.txt and the Vail_Prep_Vail_proteinGroups.txt files containing the combined identification and quantification results. The sample annotations were inferred from the names of columns holding the quantification data and the information in the article. The peptides data were converted to a SingleCellExperiment object. We split the protein table to separate the three types of quantification: protein intensity, label-free quantitification (LFQ) and intensity based absolute quantification (iBAQ). Each table is converted to a SingleCellExperiment object along with the remaining protein annotations. The 4 objects are combined in a single QFeatures object and feature links are created based on the peptide leading razor protein ID and the protein ID.

References

Zhu, Ying, Paul D. Piehowski, Rui Zhao, Jing Chen, Yufeng Shen, Ronald J. Moore, Anil K. Shukla, et al. 2018. “Nanodroplet Processing Platform for Deep and Quantitative Proteome Profiling of 10-100 Mammalian Cells.” Nature Communications 9 (1): 882 (link to article).

See also

The same experiment was conducted directly on HeLa cells samples rather than lysates. The data is available in zhu2018NC_hela.

Examples

# \donttest{
zhu2018NC_lysates()
#> see ?scpdata and browseVignettes('scpdata') for documentation
#> loading from cache
#> An instance of class QFeatures containing 1 assays:
#>  [1] peptides: SingleCellExperiment with 14921 rows and 9 columns 
# }