zhu2018MCP.RdNear single-cell proteomics data of laser captured
micro-dissection samples. The samples are 24 brain sections from
rat pups (day 17). The slices are 12 um thick squares of either
50, 100, or 200 um width. 5 samples were dissected from the corpus
callum (CC), 4 samples were dissected from the corpus collosum
(CP), 13 samples were extracted from the cerebral cortex
(CTX), and 2 samples are labeled as (Mix).
zhu2018MCPA QFeatures object with 4 assays, each assay being a SingleCellExperiment object:
peptides: quantitative information for 13,055 peptides from
24 samples
proteins_intensity: protein intensities for 2,257 proteins
from 24 samples
proteins_LFQ: LFQ intensities for 2,257 proteins from 24 samples
proteins_iBAQ: iBAQ values for 2,257 proteins from 24 samples
Sample annotation is stored in colData(zhu2018MCP()).
The PSM data can be downloaded from the PRIDE repository PXD008844. FTP link ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2018/07/PXD008844
The data were acquired using the following setup. More information
can be found in the original article (see References).
Cell isolation: brain patches were collected using laser-capture microdissection (PALM MicroBeam) on flash frozen rat (Rattus norvergicus) brain tissues. Note that the samples were stained with H&E before dissection for histological analysis. DMSO is used as sample collection solution
Sample preparation performed using the nanoPOTs device: DMSO evaporation + protein extraction (DMM + DTT) + alkylation (IAA)
Lys-C digestion + trypsin digestion.
Separation: nanoLC (Dionex UltiMate with an in-house packed 60cm x 30um LC columns; 50nL/min)
Ionization: ESI (2,000V)
Mass spectrometry: Thermo Fisher Orbitrap Fusion Lumos Tribrid (MS1 accumulation time = 246ms; MS1 resolution = 120,000; MS1 AGC = 3E6). The MS/MS settings depend on the sample size, excepted for the AGC = 1E5. 50um (time = 502ms; resolution = 240,000), 100um (time = 246ms; resolution = 120,000), 200um (time = 118ms; resolution = 60,000).
Data analysis: MaxQuant (v1.5.3.30) + Perseus (v1.5.6.0) + Origin Pro 2017
The data were collected from the PRIDE repository (accession
ID: PXD008844). We downloaded the MaxQuant_Peptides.txt
and the MaxQuant_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, Maowei Dou, Paul D. Piehowski, Yiran Liang, Fangjun Wang, Rosalie K. Chu, William B. Chrisler, et al. 2018. “Spatially Resolved Proteome Mapping of Laser Capture Microdissected Tissue with Automated Sample Transfer to Nanodroplets.” Molecular & Cellular Proteomics: MCP 17 (9): 1864–74 (link to article).
# \donttest{
zhu2018MCP()
#> see ?scpdata and browseVignettes('scpdata') for documentation
#> loading from cache
#> An instance of class QFeatures containing 4 assays:
#> [1] peptides: SingleCellExperiment with 13055 rows and 24 columns
#> [2] proteins_intensity: SingleCellExperiment with 840 rows and 0 columns
#> [3] proteins_LFQ: data.frame with 840 rows and 0 columns
#> [4] proteins_iBAQ: SingleCellExperiment with 840 rows and 0 columns
# }