Williams et al. 2020 (Anal. Chem.): MCF10A cell line

Single-cell label free proteomics data from a MCF10A cell line culture. The data were acquired using a label-free quantification protocole based on the nanoPOTS technology. The objective was to test 2 elution gradients for single-cell applications and to demonstrate successful use of the new nanoPOTS autosampler presented in the article. The samples contain either no cells, single cells, 3 cells, 10 cells 50 cells.

williams2020_lfq

Format

A QFeatures object with 9 assays, each assay being a SingleCellExperiment object:

  • peptides_[30 or 60]min_[intensity or LFQ]: 3 assays containing peptide intensities or LFQ normalized quantifications (see References) for either a 30min or a 60 min gradient.

  • proteins_[30 or 60]min_[intensity or iBAQ or LFQ]: 6 assays containing protein intensities, iBAQ normalized or LFQ normalized quantifications (see References) for either a 30min or a 60 min gradient.

Sample annotation is stored in colData(williams2020_lfq()).

Source

The PSM and protein data can be downloaded from the MASSIVE repository MSV000085230.

Acquisition protocol

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

  • Sample isolation: cultured MCF10A cells were isolated using flow-cytometry based cell sorting and deposit on nanoPOTS microwells

  • Sample preparation: cells are lysed using using a DDM+DTT lysis buffer. Alkylation was then performed using an IAA solution. Proteins are digested with Lys-C and trypsin followed by acidification with FA. Sample droplets are then dried until LC-MS/MS analysis.

  • Liquid chromatography: peptides are loaded using the new autosampler described in the paper. Samples are loaded using a a homemade miniature syringe pump. The samples are then desalted and concentrated through a SPE column (4cm x 100µm i.d. packed with 5µm C18) with microflow LC pump. The peptides are then eluted from a long LC column (60cm x 50 µm i.d. packed with 3µm C18) coupled to a nanoflox LC pump at 150nL/mL with either a 30 min or a 60 min gradient.

  • Mass spectrometry: MS/MS was performed on an Orbitrap Fusion Lumos Tribrid MS coupled to a 2kV ESI. MS1 setup: Orbitrap analyzer at resolution 120.000, AGC target of 1E6, accumulation of 246ms. MS2 setup: ion trap with CID at resolution 60.000, AGC target of 2E4, accumulation of 120ms (50 cells) or 250ms (0-10 cells).

  • Raw data processing: preprocessing using Maxquant v1.6.2.10 that use Andromeda search engine (with UniProtKB 2016-21-29), MBR and LFQ normalization were enabled.

Data collection

All data were collected from the MASSIVE repository (accession ID: MSV000085230).

The peptide and protein data were extracted from the Peptides_[...].txt or ProteinGroups[...].txt files, respectively, in the MCF10A_LC_[30 or 60]minutes folders.

The tables were duplicated so that peptide intensisities, peptide LFQ, protein intensities, protein LFQ and protein intensities are contained in separate tables. Tables are then converted to SingleCellExperiment objects. Sample annotations were infered from the sample names and from the paper. 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_williams2020_lfq.R", package = "scpdata")

Suggestion

See QFeatures::joinAssays if you want to join the 30min and 60min assays in a single assay for an integrated analysis.

References

Source article: Williams, Sarah M., Andrey V. Liyu, Chia-Feng Tsai, Ronald J. Moore, Daniel J. Orton, William B. Chrisler, Matthew J. Gaffrey, et al. 2020. “Automated Coupling of Nanodroplet Sample Preparation with Liquid Chromatography-Mass Spectrometry for High-Throughput Single-Cell Proteomics.” Analytical Chemistry 92 (15): 10588–96. (link to article).

LFQ normalization: Cox, Jürgen, Marco Y. Hein, Christian A. Luber, Igor Paron, Nagarjuna Nagaraj, and Matthias Mann. 2014. “Accurate Proteome-Wide Label-Free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ.” Molecular & Cellular Proteomics: MCP 13 (9): 2513–26. (link to article).

iBAQ normalization: Schwanhäusser, Björn, Dorothea Busse, Na Li, Gunnar Dittmar, Johannes Schuchhardt, Jana Wolf, Wei Chen, and Matthias Selbach. 2011. “Global Quantification of Mammalian Gene Expression Control.” Nature 473 (7347): 337–42. (link to article).

Examples

# \donttest{
williams2020_lfq()
#> see ?scpdata and browseVignettes('scpdata') for documentation
#> loading from cache
#> An instance of class QFeatures containing 9 assays:
#>  [1] peptides_30min_intensity: SingleCellExperiment with 8643 rows and 26 columns 
#>  [2] peptides_30min_LFQ: SingleCellExperiment with 8643 rows and 26 columns 
#>  [3] peptides_60min_intensity: SingleCellExperiment with 6265 rows and 9 columns 
#>  ...
#>  [7] proteins_60min_intensity: SingleCellExperiment with 1600 rows and 9 columns 
#>  [8] proteins_60min_iBAQ: SingleCellExperiment with 1600 rows and 9 columns 
#>  [9] proteins_60min_LFQ: SingleCellExperiment with 1600 rows and 1 columns 
# }