woo2022_lung.RdSingle-cell proteomics data from dissociated primary human lung cells. The data were acquired using the TIFF (transfer identification based on FAIMS filtering) acquisition method. The data contain 26 single cells.
woo2022_lungA QFeatures object with 5 assays, each assay being a SingleCellExperiment object:
peptides_[intensity or LFQ]: 2 assays containing peptide
quantities or normalized quantities using the maxLFQ method
as computed by MaxQuant.
proteins_[intensity or iBAQ or LFQ]: 3 assays containing
protein quantities or normalized proteins using the iBAQ or
maxLFQ methods as computed by MaxQuant.
Sample annotation is stored in colData(woo_lung()).
The peptide and protein data can be downloaded from the MASSIVE repository MSV000085937
The data were acquired using the following setup. More information
can be found in the source article (see References).
Sample isolation: primary human lung cells were dissociated following the protocol in Bandyopadhyay et al., 2018. The cells were sorted using the Influx II cell sorter and deposited on a nanoPOTS chip.
Sample preparation: cells are lysed using using a DDM+DTT lysis and reduction buffer. The proteins are alkylated with IAA and digested with LysC and trypsin. Samples are then acidified with FA, vacuum dried and stored in freezer until data acquisition.
Liquid chromatography: peptides are loaded using an in-house autosampler (Williams et al. 2020). The samples are concentrated through a SPE column (4cm x 100µm i.d. packed with 5µm C18) with microflow LC pump. The peptides are then eluted from an LC column (25cm x 50 µm i.d. packed with 1.7µm C18) from a 60 min gradient (100nL/min).
Mass spectrometry: MS/MS was performed on an Orbitrap Fusion Lumos Tribrid MS with FAIMSpro coupled to a 2.4 kV ESI. FAIMS setup: 4-CV method (-45, -55, -65, -75 V). MS1 setup: resolution = 120.000, range = 350-1500 m/z,AGC target of 1E6, accumulation of 254ms. MS2 setup: 30% HCD, resolution AGC 2E4, accumulation of 254ms.
Raw data processing: preprocessing using Maxquant v1.6.2.10 that use Andromeda search engine (with UniProtKB 2016-21-29). MBR was enabled.
All data were collected from the MASSIVE repository (accession ID: MSV000085937).
The peptide and protein data were extracted from the
peptides_nondepleted_Lung_scProteomics.txt or
proteinGroups_nondepleted_Lung_scProteomics.txt files,
respectively, in the NonDepleted_Lung_SingleCellProteomics
folders.
The tables were split so that intensities, maxLFQ, and iBAQ data are contained in separate tables. Tables are then converted to SingleCellExperiment objects. Sample annotations were inferred from the sample names. All data is combined in a QFeatures object. AssayLinks were stored between peptide assays and their corresponding proteins assays based on the leading razor protein (hence only unique peptides are linked to proteins).
The script to reproduce the QFeatures object is available at
system.file("scripts", "make-data_woo2022_lung.R", package = "scpdata")
Source article: Woo, Jongmin, Geremy C. Clair, Sarah M. Williams, Song Feng, Chia-Feng Tsai, Ronald J. Moore, William B. Chrisler, et al. 2022. “Three-Dimensional Feature Matching Improves Coverage for Single-Cell Proteomics Based on Ion Mobility Filtering.” Cell Systems 13 (5): 426–34.e4. (link to article).
# \donttest{
woo2022_lung()
#> see ?scpdata and browseVignettes('scpdata') for documentation
#> loading from cache
#> An instance of class QFeatures containing 5 assays:
#> [1] peptides_intensity: SingleCellExperiment with 5415 rows and 26 columns
#> [2] peptides_LFQ: SingleCellExperiment with 5415 rows and 26 columns
#> [3] proteins_intensity: SingleCellExperiment with 1243 rows and 26 columns
#> [4] proteins_iBAQ: SingleCellExperiment with 1243 rows and 26 columns
#> [5] proteins_LFQ: SingleCellExperiment with 1243 rows and 26 columns
# }