dou2019_boosting: Dou et al. 2019 (Anal. Chem.): testing boosting ratios
In UCLouvain-CBIO/scpdata: Single-Cell Proteomics Data Package

dou2019_boosting

R Documentation

Dou et al. 2019 (Anal. Chem.): testing boosting ratios

Description

Single-cell proteomics using nanoPOTS combined with TMT isobaric labeling. It contains quantitative information at PSM and protein level. The cell types are either "Raw" (macrophage cells), "C10" (epihelial cells), or "SVEC" (endothelial cells). Each cell is replicated 2 or 3 times. Each cell type was run using 3 levels of boosting: 0 ng (no boosting), 5 ng or 50 ng. When boosting was applied, 1 reference well and 1 boosting well were added, otherwise 1 empty well was added. Each boosting setting (0ng, 5ng, 50ng) was run in duplicate.

Usage

dou2019_boosting

Format

A QFeatures object with 7 assays, each assay being a SingleCellExperiment object:

Boosting_X_run_Y: PSM data with 10 columns corresponding to the TMT-10plex channels. The X indicates the boosting amount (0ng, 5ng or 50ng) and Y indicates the run number (1 or 2).
peptides: peptide data containing quantitative data for 13,462 peptides in 60 samples (run 1 and run 2 combined).
proteins: protein data containing quantitative data for 1436 proteins and 60 samples (all runs combined).

Sample annotation is stored in colData(dou2019_boosting()).

Acquisition protocol

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

Cell isolation: single-cells from the three murine cell lines were isolated using FACS (BD Influx II cell sorter ). Boosting sample were prepared (presumably in bulk) from 1:1:1 mix of the three cell lines.
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)

Data collection

The PSM data were collected from the MassIVE repository MSV000084110 (see Source section). The downloaded files are:

⁠Boosting_*ng_run_*_msgfplus.mzid⁠: the MS-GF+ identification result files.
⁠Boosting_*ng_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_004.xlsx. The file contains 7 sheets from which we took the 2nd, 4th and 6th sheets (named ⁠01 - No Boost raw data⁠, ⁠03 - 5ng boost raw data⁠, ⁠05 - 50ng boost raw data⁠, respectively). The sheets contain the combined protein data for the duplicate runs given the boosting amount. We joined the data for all boosting ration based on the protein name and converted the data to a SingleCellExperiment object. We then added the object as a new assay in the QFeatures dataset (containing the PSM data). Links between the proteins and the corresponding PSM were created. Note that links to protein groups are not stored.

Source

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).

References

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).