Zhu et al. 2018 (Nat. Comm.): human pancreatic islets

Near single-cell proteomics data human pancreas samples. The samples were collected from pancreatic tissue slices using laser dissection. The pancreata were obtained from organ donors through the JDRFNetwork for Pancreatic Organ Donors with Diabetes (nPOD) program. The sample come either from control patients (n=9) or from type 1 diabetes (T1D) patients (n=9).

zhu2018NC_islets

Format

A QFeatures object with 4 assays, each assay being a SingleCellExperiment object:

  • peptides: quantitative information for 24,321 peptides from 18 islet samples

  • proteins_intensity: quantitative information for 3,278 proteins from 18 islet samples

  • proteins_LFQ: LFQ intensities for 3,278 proteins from 18 islet samples

  • proteins_iBAQ: iBAQ values for 3,278 proteins from 18 islet samples

Sample annotation is stored in colData(zhu2018NC_islets()).

Source

The PSM data can be downloaded from the PRIDE repository PXD006847. The source link is: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2018/01/PXD006847

Acquisition protocol

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

  • Cell isolation: The islets were extracted from the pacreatic tissues using laser-capture microdissection.

  • Sample preparation performed using the nanoPOTs device. Protein extraction using RapiGest (+ DTT) + alkylation (IAA) + Lys-C digestion + cleave RapiGest (formic acid)

  • Separation: nanoACQUITY UPLC pump with an Self-Pack PicoFrit 70cm x 30um LC columns; 60nL/min)

  • Ionization: ESI (1,900V)

  • Mass spectrometry: Thermo Fisher Orbitrap Fusion Lumos Tribrid. MS1 settings: accumulation time = 246ms; resolution = 120,000; AGC = 1E6. MS/MS settings: accumulation time = 118ms; resolution = 60,000; AGC = 1E5.

  • Data analysis: MaxQuant (v1.5.3.30) + Perseus + OriginLab 2017

Data collection

The data were collected from the PRIDE repository (accession ID: PXD006847). We downloaded the Islet_t1d_ct_peptides.txt and the Islet_t1d_ct_proteinGroups.txt files containing the combined identification and quantification results. The sample types were inferred from the names of columns holding the quantification data. The peptides data were converted to a SingleCellExperiment object. We split the protein table to separate the three types of quantification: protein intensity, label-free quantitification (LFQ) and intensity based absolute quantification (iBAQ). Each table is converted to a SingleCellExperiment object along with the remaining protein annotations. The 4 objects are combined in a single QFeatures object and feature links are created based on the peptide leading razor protein ID and the protein ID.

References

Zhu, Ying, Paul D. Piehowski, Rui Zhao, Jing Chen, Yufeng Shen, Ronald J. Moore, Anil K. Shukla, et al. 2018. “Nanodroplet Processing Platform for Deep and Quantitative Proteome Profiling of 10-100 Mammalian Cells.” Nature Communications 9 (1): 882 (link to article).

Examples

# \donttest{
zhu2018NC_islets()
#> see ?scpdata and browseVignettes('scpdata') for documentation
#> loading from cache
#> An instance of class QFeatures containing 1 assays:
#>  [1] peptides: SingleCellExperiment with 24321 rows and 18 columns 
# }