zhu2018NC_islets | R Documentation |
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
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())
.
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
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.
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
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).
zhu2018NC_islets()
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