dou2019_lysates.RdSingle-cell proteomics using nanoPOTS combined with TMT multiplexing. It contains quantitative information at PSM and protein level. The samples are commercial Hela lysates diluted to single-cell amounts (0.2 ng). The boosting wells contain the same digest but at higher amount (10 ng).
dou2019_lysatesA QFeatures object with 3 assays, each assay being a SingleCellExperiment object:
Hela_run_1: PSM data with 10 columns corresponding to the
TMT-10plex channels. Columns hold quantitative information for
HeLa lysate samples (either 0, 0.2 or 10ng). This is the data
for run 1.
Hela_run_1: PSM data with 10 columns corresponding to the
TMT-10plex channels. Columns hold quantitative information for
HeLa lysate samples (either 0, 0.2 or 10ng). This is the data
for run 2.
peptides: peptide data containing quantitative data for 13,934
peptides in 20 samples (run 1 and run 2 combined).
proteins: protein data containing quantitative data for 1641
proteins in 20 samples (run 1 and run 2 combined).
Sample annotation is stored in colData(dou2019_lysates()).
The PSM data can be downloaded from the massIVE repository MSV000084110. FTP link: ftp://massive.ucsd.edu/MSV000084110/
The protein data can be downloaded from the ACS Publications website (Supporting information section).
The data were acquired using the following setup. More information
can be found in the source article (see References).
Cell isolation: commercially available HeLa protein digest (Thermo Scientific).
Sample preparation performed using the nanoPOTs device. Protein extraction (DMM + TCEAP) + alkylation (IAA) + Lys-C digestion + trypsin digestion + TMT-10plex labeling and pooling.
Separation: nanoLC (Dionex UltiMate with an in-house packed 50cm x 30um LC columns; 50nL/min)
Ionization: ESI (2,000V)
Mass spectrometry: Thermo Fisher Orbitrap Fusion Lumos Tribrid (MS1 accumulation time = 50ms; MS1 resolution = 120,000; MS1 AGC = 1E6; MS2 accumulation time = 246ms; MS2 resolution = 60,000; MS2 AGC = 1E5)
Data analysis: MS-GF+ + MASIC (v3.0.7111) + RomicsProcessor (custom R package)
The PSM data were collected from the MassIVE repository
MSV000084110 (see Source section). The downloaded files are:
Hela_run_*_msgfplus.mzid: the MS-GF+ identification result
files
Hela_run_*_ReporterIons.txt: the MASIC quantification result
files
For each batch, the quantification and identification data were
combined based on the scan number (common to both data sets). The
combined datasets for the different runs were then concatenated
feature-wise. To avoid data duplication due to ambiguous matching
of spectra to peptides or ambiguous mapping of peptides to proteins,
we combined ambiguous peptides to peptides groups and proteins to
protein groups. Feature annotations that are not common within a
peptide or protein group are are separated by a ;. The sample
annotation table was manually created based on the available
information provided in the article. The data were then converted
to a QFeatures object using the scp::readSCP() function.
We generated the peptide data. First, we removed PSM matched to contaminants or decoy peptides and ensured a 1% FDR. We aggregated the PSM to peptides based on the peptide (or peptide group) sequence(s) using the median PSM instenity. The peptide data for the different runs were then joined in a single assay (see QFeatures::joinAssays), again based on the peptide sequence(s). We then removed the peptide groups. Links between the peptide and the PSM data were created using QFeatures::addAssayLink. Note that links between PSM and peptide groups are not stored.
The protein data were downloaded from Supporting information
section from the publisher's website (see Sources). The data is
supplied as an Excel file ac9b03349_si_003.xlsx. The file
contains 7 sheets from which we only took the sheet 6 (named
5 - Run 1 and 2 raw data) with the combined protein data for the
two runs. We converted the data to a SingleCellExperiment
object and added the object as a new assay in the QFeatures
dataset (containing the PSM data). Links between the proteins and
the peptides were created. Note that links to protein groups are
not stored.
Dou, Maowei, Geremy Clair, Chia-Feng Tsai, Kerui Xu, William B. Chrisler, Ryan L. Sontag, Rui Zhao, et al. 2019. “High-Throughput Single Cell Proteomics Enabled by Multiplex Isobaric Labeling in a Nanodroplet Sample Preparation Platform.” Analytical Chemistry, September (link to article).
# \donttest{
dou2019_lysates()
#> see ?scpdata and browseVignettes('scpdata') for documentation
#> loading from cache
#> An instance of class QFeatures containing 4 assays:
#> [1] Hela_run_1: SingleCellExperiment with 24562 rows and 10 columns
#> [2] Hela_run_2: SingleCellExperiment with 24310 rows and 10 columns
#> [3] peptides: SingleCellExperiment with 13934 rows and 20 columns
#> [4] proteins: SingleCellExperiment with 1641 rows and 20 columns
# }