krull2024.Rd
They develop a new strategy for data-independent acquisition (DIA) that leverages the co-analysis of low-input samples alongside a corresponding enhancer (ME) of higher input. Using DIA-ME, they investigate the proteomic response of U-2 OS cells to interferon gamma (IFN-y) at the single-cell level.
krull2024
A QFeatures object with 159 assays, each assay being a SingleCellExperiment object.
Assay 1-158: DIA-NN main output report table split for each acquisition run. First 15 run acquires 10 single cells (MEs) and, remaining 143 run acquires 1 single cell. It contains the results of the spectrum identification and quantification.
proteins
: DIA-NN protein group matrix, containing normalised
quantities for 1553 protein groups in 143 single cells. Proteins
are filtered at (Q.Value <= 0.01), (Lib.Q.Value <= 0.01), and
(Lib.PG.Q.Value <= 0.01).
The colData(krull2024())
contains cell type annotations. The description
of the rowData
fields for the different assays can be found in the
DIA-NN
documentation.
The data were downloaded from PRIDE
repository
with accession ID PXD053464
.
The data were acquired using the following setup. More information
can be found in the source article (see References
).
Cell isolation: cells were detached with trypsin digestion, followed by dilution in 1.5 mL PBS, and isolated using BD FACSAria III instrument.
Sample preparation: Sorted single cells were collected in lysis buffer (50 mM TEAB, pH 8.5, and 0.025% DDM), denatured at 70 degrees Celsius for 30 minutes. Samples were acidified with 0.5% FA and transferred to auto sampler plates for mass spectrometry analysis.
Separation: Peptides were injected in a 2 microliter volume onto a (25 cm x 75 micrometer) ID column at a flow rate of 300 nL/min, separated using a gradient of ACN in water with 0.1% FA over 15 minutes, connected to a nano-ESI source.
Ionization: Ionization was performed using a 1,500 V capillary voltage with 3.0 L/min dry gas and a dry temperature of 180 degrees Celsius. MS data acquisition was conducted in diaPASEF mode using a timsTOF Pro mass spectrometer.
Mass spectrometry: MS1 scans covered a range of 200-1,700 m/z, while DIA window isolation targeted 475-1,000 m/z with eight DIA scans per cycle. Fragmentation was triggered by collision energy ranging from 45 eV to 27 eV depending on the ion mobility.
Data analysis: Data was processed using DIA-NN (v1.8.0) and Spectronaut 18 in a library-free approach, using deep learning for spectrum prediction, retention times, and ion mobility.
The data were collected from the PRIDE
repository
in the 03_SingleCell_Searches.zip
file.
We loaded the DIA-NN main report table and generated a sample
annotation table based on the MS file names. We next combined the
sample annotation and the DIANN tables into a QFeatures object
following the scp
data structure. We loaded the proteins group
matrix as a SingleCellExperiment object, and added the protein data
as a new assay and link the precursors to proteins using the
Protein.Group
variable from the rowData
.
Krull, K. K., Ali, S. A., & Krijgsveld, J. 2024. "Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of Cell States." Nature Communications, 15(1). Link to article
# \donttest{
krull2024()
#> see ?scpdata and browseVignettes('scpdata') for documentation
#> loading from cache
#> An instance of class QFeatures containing 159 set(s):
#> [1] D:\Data\KK\KK35\03_MEs\20230629_KK_10SC_control_01_A11_1_14185.d: SingleCellExperiment with 3521 rows and 1 columns
#> [2] D:\Data\KK\KK35\03_MEs\20230629_KK_10SC_control_02_B11_1_14186.d: SingleCellExperiment with 3349 rows and 1 columns
#> [3] D:\Data\KK\KK35\03_MEs\20230629_KK_10SC_control_03_C11_1_14188.d: SingleCellExperiment with 3277 rows and 1 columns
#> ...
#> [157] D:\Data\KK\KK35\20230630_KK_SC_IFNy_79_E10_1_14262.d: SingleCellExperiment with 1110 rows and 1 columns
#> [158] D:\Data\KK\KK35\20230630_KK_SC_IFNy_81_G10_1_14264.d: SingleCellExperiment with 1483 rows and 1 columns
#> [159] proteins: SingleCellExperiment with 1553 rows and 143 columns
# }