gregoire2023_mixCTRL: Grégoire et al. 2023 - mixCTRL (arXiv): benchmark using...
In UCLouvain-CBIO/scpdata: Single-Cell Proteomics Data Package

gregoire2023_mixCTRL

R Documentation

Grégoire et al. 2023 - mixCTRL (arXiv): benchmark using monocytes/macrophages

Description

Single cell proteomics data acquired using the SCoPE2 protocol. The dataset contains two monocytes cell lines (THP1 and U937) as well as controled mixtures of both and macrophage-like cells produced upon PMA treatment. It contains quantitative information at PSM, peptide and protein levels. Data was acquired using Lumos Orbitrap (mainly) and timsTOF SCP mass spectrometers.

Usage

gregoire2023_mixCTRL

Format

A QFeatures object with 119 assays, each assay being a SingleCellExperiment object:

Assays 1-42: PSM data acquired with a TMT-16plex protocol, hence those assays contain 16 columns. Columns hold quantitative information from single-cell channels, carrier channels, blank (negative control) channels and unused channels.
Assays 43-84: peptide data resulting from the PSM to peptide aggregation of the 42 PSM assays.
Assays 85-91: peptide data for each of the 7 acquisition batches. Peptide data were joined based on their respective acquisition batches.
Assays 92-98: normalised peptide data.
Assays 99-105: normalised and log-transformed peptide data.
Assays 106-112: protein data for each of the 7 acquisition batches. Normalised and log-transformed peptide data were agreggated to protein.
Assays 113-119: Batch corrected protein data. Normalised and log-transformed protein data were batch corrected to remove technical variability induced by runs and channels.

All the data has been filtered to keep high quality features and samples.

The colData(gregoire2023_mixCTRL()) contains cell type annotation and batch annotation that are common to all assays. The description of the rowData fields for the PSM data can be found in the sage documentation.

Acquisition protocol

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

Cell isolation: BD FACSAria III cell sorting.
Sample preparation performed using the SCoPE2 protocol: mPOP cell lysis + trypsin digestion + TMT-16plex labeling and pooling.
Separation: online nLC (Ultimate 3000 LC System or Vanquish Neo UHPLC System) with a BioZen Peptide Polar C18 250 x 0.0075mm column.
Mass spectrometry: Orbitrap Fusion Lumos Tribrid (MS1 resolution = 70,000; MS2 accumulation time = 120ms; MS2 resolution = 70,000) and timsTOF SCP.
Data preprocessing: Sage.

Data collection

The PSM data were collected from a Zenodo archive (see Source section). The folder contains the following files of interest:

results.sage.cbio.tsv: the sage identification output file for batches acquired on the Lumos MS.
results.sage.giga.tsv: the sage identification output file for batches acquired on the timsTOF SCP MS.
quant.cbio.tsv: the sage quantification output file for batches acquired on the Lumos MS.
quant.giga.tsv: the sage quantification output file for batches acquired on the timsTOF SCP MS.
sampleAnnotation_batch.csv: sample annotation for each acquisition batch. There are in total 8 different annotation files.

We combined the sample annotations in a single table. We also combined cbio and giga tables together and merged resulting identification and quantification tables. Both annotation and features tables are then combined in a single QFeatures object using the scp::readSCP() function.

The QFeatures object was processed as described in the author's manuscript (see source). Note that the imputed assays were used in the paper for illustrative purposes only and have not been reproduced here.

Source

The data were downloaded from the Zenodo repository. The raw data and the quantification data can also be found in the ProteomeXchange Consortium via the PRIDE partner repository, project PXD046211.

References

Samuel Grégoire, Christophe Vanderaa, Sébastien Pyr dit Ruys, Gabriel Mazzucchelli, Christopher Kune, Didier Vertommen and Laurent Gatto. 2023. Standardised workflow for mass spectrometry- based single-cell proteomics data processing and analysis using the scp package. arXiv. DOI:10.48550/arXiv.2310.13598