leduc2022_plexDIA.RdSingle cell proteomics data acquired by the Slavov Lab. This is the dataset associated to the fourth version of the preprint (and the Genome Biology publication). It contains quantitative information of melanoma cells at precursor, peptide and protein level. This version of the data was acquired using the plexDIA MS acquisition protocol.
leduc2022_plexDIAA QFeatures object with 48 assays, each assay being a SingleCellExperiment object:
Assay 1-45: precursor data acquired with a mTRAQ-3 protocol, hence those assays contain 3 columns. Columns hold quantitative information from single cells or negative control samples.
Ms1Extracted: the DIA-NN MS1 extracted signal, it combines the
information from assays 1-45.
peptides: peptide data containing quantitative data for 3,608
peptides and 104 single cells. The data were filtered to 1%
protein FDR.
proteins: protein data containing quantitative data for 508
proteins and 105 single cells. Note that the peptide and protein
data provided by the authors differ by 3 samples. The precursor
data were aggregated to protein intensity using maxLFQ. The
protein data were further median normalized by column and by row,
log2 transformed, impute using KNN (k = 3), again median
normalized by column and by row, batch corrected using ComBat,
and median normalized by column and by row once more.
The colData(leduc2022_plexDIA()) contains cell type annotation and
batch annotation that are common to all assays. The description of
the rowData fields for the precursor data can be found in the
DIA-NN documentation.
The links to the data were found on the
Slavov Lab website.
The data were downloaded from the
Google drive folder 1 and
Google drive folder 2.
The raw data and the quantification data can also be found in the
massIVE repository MSV000089159:
ftp://massive.ucsd.edu/MSV000089159.
The data were acquired using the following setup. More information
can be found in the source article (see References).
Cell isolation: CellenONE cell sorting.
Sample preparation performed using the improved SCoPE2 protocol using the CellenONE liquid handling system. nPOP cell lysis (DMSO) + trypsin digestion + mTRAQ-3 labeling and pooling.
Separation: online nLC (DionexUltiMate 3000 UHPLC with a 25cm x 75um IonOpticks Aurora Series UHPLC column; 200nL/min).
Ionization: ESI (1,800V).
Mass spectrometry: Thermo Scientific Q-Exactive. The duty cycle = 1 MS1 + 4 DIA MS2 windows (120 Th, 120 Th, 200 Th and 580 Th, spanning 378-1,402 m/z). Each MS1 and MS2 scan was conducted at 70,000 resolving power, 3×10E6 AGC and 300ms maximum injection time.
Data analysis: DIA-NN.
The PSM data were collected from a shared Google Drive folder that
is accessible from the SlavovLab website (see Source section).
The folder contains the following files of interest:
annotation_plexDIA.csv: sample annotation
report_plexDIA_mel_nPOP.tsv: the DIA-NN output file
with the precursor data
report.pr_matrix_channels_ms1_extracted.tsv: the DIA-NN
output file with the combined precursor data
plexDIA_peptide.csv: the processed data table containing the
peptide data
plexDIA_protein_imputed.csv: the processed data table
containing the protein data
We removed the failed runs as identified by the authors. We also
formatted the annotation and precuror quantification tables to
facilitate matching between corresponding columns. Both annotation
and quantification tables are then combined in a single QFeatures
object using scp::readSCPfromDIANN().
The plexDIA_peptide.csv and plexDIA_protein_imputed.csv files
were loaded and formatted as SingleCellExperiment objects. The
columns names were adapted to match those in the QFeatures
object. The SingleCellExperiment objects were then added to the
QFeatures object and the rows of the peptide data are linked to
the rows of the precursor data based on the peptide sequence or
the protein name through an AssayLink object.
Andrew Leduc, Gray Huffman, and Nikolai Slavov. 2022. “Droplet Sample Preparation for Single-Cell Proteomics Applied to the Cell Cycle.” bioRxiv. Link to article
Andrew Leduc, Gray Huffman, Joshua Cantlon, Saad Khan, and Nikolai Slavov. 2022. “Exploring Functional Protein Covariation across Single Cells Using nPOP.” Genome Biology 23 (1): 261. Link to article
Jason Derks, Andrew Leduc, Georg Wallmann, Gray Huffman, Matthew Willetts, Saad Khan, Harrison Specht, Markus Ralser, Vadim Demichev, and Nikolai Slavov. 2023. “Increasing the Throughput of Sensitive Proteomics by plexDIA.” Nature Biotechnology 41 (1): 50–59. Link to article
# \donttest{
leduc2022_plexDIA()
#> see ?scpdata and browseVignettes('scpdata') for documentation
#> loading from cache
#> An instance of class QFeatures containing 48 assays:
#> [1] D..AL.AL.wAL_plexMel01.raw: SingleCellExperiment with 2495 rows and 3 columns
#> [2] D..AL.AL.wAL_plexMel03.raw: SingleCellExperiment with 1738 rows and 3 columns
#> [3] D..AL.AL.wAL_plexMel05.raw: SingleCellExperiment with 2299 rows and 3 columns
#> ...
#> [46] Ms1Extracted: SingleCellExperiment with 4682 rows and 135 columns
#> [47] peptides: SingleCellExperiment with 3608 rows and 104 columns
#> [48] proteins: SingleCellExperiment with 508 rows and 105 columns
# }