zhu2018NC_hela.RdNear single-cell proteomics data of HeLa samples containing different number of cells. There are three groups of cell concentrations: low (10-14 cells), medium (35-45 cells) and high (137-141 cells). The data also contain measures for blanks, HeLa lysates (50 cell equivalent) and 2 cancer cell line lysates (MCF7 and THP1, 50 cell equivalent).
zhu2018NC_helaA QFeatures object with 4 assays, each assay being a SingleCellExperiment object:
peptides: quantitative information for 37,795 peptides from
21 samples
proteins_intensity: protein intensities for 3,984 proteins
from 21 samples
proteins_LFQ: LFQ intensities for 3,984 proteins from 21
samples
proteins_iBAQ: iBAQ values for 3,984 proteins from 21 samples
Sample annotation is stored in colData(zhu2018NC_hela()).
The PSM data can be downloaded from the PRIDE repository PXD006847. FTP link: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2018/01/PXD006847
The data were acquired using the following setup. More information
can be found in the original article (see References).
Cell isolation: HeLa cell concentration was adjusted by serial dilution and cell counting was performed manually using an inverted microscope.
Sample preparation performed using the nanoPOTs device. Protein extraction using RapiGest (+ DTT) + alkylation (IAA) + Lys-C 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
The data were collected from the PRIDE repository (accession
ID: PXD006847). We downloaded the CulturedCells_peptides.txt
and the CulturedCells_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.
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).
The same experiment was conducted on HeLa lysates: zhu2018NC_lysates.
# \donttest{
zhu2018NC_hela()
#> see ?scpdata and browseVignettes('scpdata') for documentation
#> loading from cache
#> An instance of class QFeatures containing 2 assays:
#> [1] peptides: SingleCellExperiment with 37795 rows and 21 columns
#> [2] proteins: SingleCellExperiment with 3984 rows and 21 columns
# }