Spatial proteomics datasets

The pRolocdata package

pRolocdata is a Bioconductor experiment package (release and devel pages) that collects published (mainly, although some unpublished datasets are also available) mass spectrometry-based spatial/organelle and protein-complex dataset. The data are distributed as MSnSet instances (see the MSnbase for details) and are used throughout the pRoloc and pRolocGUI software for spatial proteomics data analysis and visualisation.

Current build status:

Installation

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("pRolocdata")

Once installed, the package needs to be loaded

library("pRolocdata")

Available datasets

Currently, there are 144 datasets available in pRolocdata. Use the pRolocdata() function to obtain a list of data names and their description.

pRolocdata()

Data	Description
Barylyuk2020ToxoLopit	Whole-cell spatial proteome of Toxoplasma: molecular anatomy of an apicomplexan cell
E14TG2aR	LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016)
E14TG2aS1	LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016)
E14TG2aS1goCC	LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016)
E14TG2aS1yLoc	LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016)
E14TG2aS2	LOPIT experiment on Mouse E14TG2a Embryonic Stem Cells from Breckels et al. (2016)
HEK293T2011	LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013)
HEK293T2011goCC	LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013)
HEK293T2011hpa	LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013)
Kozik_con	Small molecule enhancers of endosome-to-cytosol import augment anti-tumour immunity
Kozik_pra	Small molecule enhancers of endosome-to-cytosol import augment anti-tumour immunity
Kozik_tam	Small molecule enhancers of endosome-to-cytosol import augment anti-tumour immunity
Shin2019MitoControlrep1	Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers
Shin2019MitoControlrep2	Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers
Shin2019MitoControlrep3	Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers
Shin2019MitoGcc88rep1	Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers
Shin2019MitoGcc88rep2	Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers
Shin2019MitoGcc88rep3	Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers
Shin2019MitoGol97rep1	Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers
Shin2019MitoGol97rep2	Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers
Shin2019MitoGol97rep3	Spatial proteomics defines the content of trafficking vesicles captured by golgin tethers
andreyev2010	Six sub-cellular fraction data from mouse macrophage-like RAW264.7 cells from Andreyev et al. (2009)
andreyev2010activ	Six sub-cellular fraction data from mouse macrophage-like RAW264.7 cells from Andreyev et al. (2009)
andreyev2010rest	Six sub-cellular fraction data from mouse macrophage-like RAW264.7 cells from Andreyev et al. (2009)
andy2011	LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013)
andy2011goCC	LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013)
andy2011hpa	LOPIT experiment on Human Embryonic Kidney fibroblast HEK293T cells from Breckels et al. (2013)
at_chloro	The AT_CHLORO data base
baers2018	Synechocystis spatial proteomics
beltran2016HCMV120	Data from Beltran et al. 2016
beltran2016HCMV24	Data from Beltran et al. 2016
beltran2016HCMV48	Data from Beltran et al. 2016
beltran2016HCMV72	Data from Beltran et al. 2016
beltran2016HCMV96	Data from Beltran et al. 2016
beltran2016MOCK120	Data from Beltran et al. 2016
beltran2016MOCK24	Data from Beltran et al. 2016
beltran2016MOCK48	Data from Beltran et al. 2016
beltran2016MOCK72	Data from Beltran et al. 2016
beltran2016MOCK96	Data from Beltran et al. 2016
courtland_control	Genetic Disruption of WASHC4 Drives Endo-lysosomal Dysfunction and Cognitive-Movement Impairments in Mice and Humans
courtland_mutant	Genetic Disruption of WASHC4 Drives Endo-lysosomal Dysfunction and Cognitive-Movement Impairments in Mice and Humans
davies2018ap4b1	AP-4 vesicles contribute to spatial control of autophagy via RUSC-dependent peripheral delivery of ATG9A
davies2018ap4e1	AP-4 vesicles contribute to spatial control of autophagy via RUSC-dependent peripheral delivery of ATG9A
davies2018wt	AP-4 vesicles contribute to spatial control of autophagy via RUSC-dependent peripheral delivery of ATG9A
dunkley2006	LOPIT data from Dunkley et al. (2006)
dunkley2006goCC	LOPIT data from Dunkley et al. (2006)
fabre2015r1	Data from Fabre et al. 2015
fabre2015r2	Data from Fabre et al. 2015
foster2006	PCP data from Foster et al. (2006)
groen2014cmb	LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014)
groen2014r1	LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014)
groen2014r1goCC	LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014)
groen2014r2	LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014)
groen2014r3	LOPIT experiments on Arabidopsis thaliana roots, from Groen et al. (2014)
hall2009	LOPIT data from Hall et al. (2009)
havugimana2012	Data from Havugimana et al. 2012
hirst2018	Data from Hirst et al. 2018
hyperLOPIT2015	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPIT2015_se	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPIT2015goCC	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPIT2015ms2	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPIT2015ms2psm	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPIT2015ms3r1	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPIT2015ms3r1psm	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPIT2015ms3r2	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPIT2015ms3r2psm	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPIT2015ms3r3	Protein and PMS-level hyperLOPIT datasets on Mouse E14TG2a embryonic stem cells from Christoforou et al. (2016).
hyperLOPITU2OS2017	2017 and 2018 hyperLOPIT on U2OS cells
hyperLOPITU2OS2017b	2017 and 2018 hyperLOPIT on U2OS cells
hyperLOPITU2OS2018	2017 and 2018 hyperLOPIT on U2OS cells
itzhak2016helaCtrl	Global, quantitative and dynamic mapping of protein subcellular localization
itzhak2016helaEgf	Global, quantitative and dynamic mapping of protein subcellular localization
itzhak2016stcSILAC
itzhak2017	Data from Itzhak et al. 2017
itzhak2017markers	Data from Itzhak et al. 2017
kirkwood2013	Data from Kirkwood et al. 2013.
krahmer2018pcp	Subcellular Reorganization in Diet-Induced Hepatic Steatosis
krahmer2018phosphopcp	Subcellular Reorganization in Diet-Induced Hepatic Steatosis
kristensen2012r1	Data from Kristensen et al. 2012
kristensen2012r2	Data from Kristensen et al. 2012
kristensen2012r3	Data from Kristensen et al. 2012
lopimsSyn1	LOPIMS data for the Synapter 2.0 paper
lopimsSyn2	LOPIMS data for the Synapter 2.0 paper
lopimsSyn2_0frags	LOPIMS data for the Synapter 2.0 paper
lopitdcU2OS2018	2017 and 2018 hyperLOPIT on U2OS cells
lpsTimecourse_mulvey2021	Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS
lpsTimecourse_rep1_mulvey2021	Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS
lpsTimecourse_rep2_mulvey2021	Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS
lpsTimecourse_rep3_mulvey2021	Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS
moloneyTbBSF	Spatial proteomics datasets from two African trypanosome species
moloneyTbPCF	Spatial proteomics datasets from two African trypanosome species
moloneyTcBSF	Spatial proteomics datasets from two African trypanosome species
moloneyTcPCF	Spatial proteomics datasets from two African trypanosome species
mulvey2015	Data from Mulvey et al. 2015
mulvey2015_se	Data from Mulvey et al. 2015
mulvey2015norm	Data from Mulvey et al. 2015
mulvey2015norm_se	Data from Mulvey et al. 2015
nikolovski2012	Meta-analysis from Nikolovski et al. (2012)
nikolovski2012imp	Meta-analysis from Nikolovski et al. (2012)
nikolovski2014	LOPIMS data from Nikolovski et al. (2014)
orre2019a431	SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization
orre2019h322	SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization
orre2019hcc827	SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization
orre2019hcc827gef	SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization
orre2019hcc827rep1	SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization
orre2019hcc827rep2	SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization
orre2019hcc827rep3	SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization
orre2019mcf7	SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization
orre2019u251	SubCellBarCode: Proteome-wide Mapping of Protein Localization and Relocalization
psms_lpsTimecourse_rep1_mulvey2021	Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS
psms_lpsTimecourse_rep2_mulvey2021	Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS
psms_lpsTimecourse_rep3_mulvey2021	Protein and PMS-level datasets from temporal abundance profiling experiments of THP-1 human leukaema cells stimulated with LPS
psms_thpLOPIT_lps_rep1_set1	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_lps_rep1_set2	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_lps_rep2_set1	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_lps_rep2_set2	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_lps_rep3_set1	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_lps_rep3_set2	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_unstim_rep1_set1	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_unstim_rep1_set2	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_unstim_rep2_set1	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_unstim_rep2_set2	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_unstim_rep3_set1	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
psms_thpLOPIT_unstim_rep3_set2	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
rodriguez2012r1	Spatial proteomics of human inducible goblet-like LS174T cells from Rodriguez-Pineiro et al. (2012)
rodriguez2012r2	Spatial proteomics of human inducible goblet-like LS174T cells from Rodriguez-Pineiro et al. (2012)
rodriguez2012r3	Spatial proteomics of human inducible goblet-like LS174T cells from Rodriguez-Pineiro et al. (2012)
stekhoven2014	Data from Stekhoven et al. 2014
tan2009r1	LOPIT data from Tan et al. (2009)
tan2009r1goCC	LOPIT data from Tan et al. (2009)
tan2009r2	LOPIT data from Tan et al. (2009)
tan2009r3	LOPIT data from Tan et al. (2009)
thpLOPIT_lps_mulvey2021	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
thpLOPIT_lps_rep1_mulvey2021	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
thpLOPIT_lps_rep2_mulvey2021	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
thpLOPIT_lps_rep3_mulvey2021	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
thpLOPIT_unstimulated_mulvey2021	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
thpLOPIT_unstimulated_rep1_mulvey2021	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
thpLOPIT_unstimulated_rep2_mulvey2021	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
thpLOPIT_unstimulated_rep3_mulvey2021	Protein and PMS-level hyperLOPIT datasets from THP-1 human leukaema cells
trotter2010	LOPIT data sets used in Trotter et al. (2010)
trotter2010shallow	LOPIT data sets used in Trotter et al. (2010)
trotter2010steep	LOPIT data sets used in Trotter et al. (2010)
yeast2018	Saccharomyces cerevisiae spatial proteomics (2018)

Loading data

Data is loaded into the R session using the load function; for instance, to get the data from Dunkley et al (2006), one would type

data(dunkley2006)

To get more information about a given dataset, see its manual page

?dunkley2006
## or
help("dunkley2006")

The datasets

Each data object in pRolocdata is available as an MSnSet instance. The instances contain the actual quantitative data, sample and features annotations (see pData and fData respectively). Additional MIAPE data [1, 2] experimental data is available in the experimentData slot, as described in section Required metadata below.

The source of these data is generally one or several text-based spreadsheet (csv, tsv) produced by a third-party application. These original files are often distributed as supplementary material to the research paper and used to generate the R objects. These source spreadsheets are available in the package's inst/extdata directory. The R script files, that read the spreadsheets and create the R data is distributed in the inst/scripts directory.

Suggested metadata

Additional metadata is available with the pRolocmetadata() function as detailed below.

Species

Documented in experimentData(.)@samples$species

Tissue

Documented in experimentData(.)@samples$tissue. If tissue is Cell, then details about the cell line are available in experimentData(.)@samples$cellLine.

PMID

Documented in pubMedIds(.).

Spatial proteomics experiment annotation

Documented in experimentData(.)@other:

• MS ($MS) type of mass spectrometry experiment: iTRAQ8, iTRAQ4, TMT6, LF, SC, ...
• Experiment ($spatexp) type of spatial proteomics experiment: LOPIT, LOPIMS, subtractive, PCP, other, PCP-SILAC, ...
• MarkerCol ($markers.fcol) name of the markers feature data. Default is markers.
• PredictionCol ($prediction.fcol) name of the localisation prediction feature data.

Example

experimentData(dunkley2006)@samples

## $species
## [1] "Arabidopsis thaliana"
## 
## $tissue
## [1] "Callus"

pubMedIds(dunkley2006)

## [1] "16618929"

otherInfo(experimentData(dunkley2006))

## $MS
## [1] "iTRAQ4"
## 
## $spatexp
## [1] "LOPIT"
## 
## $markers.fcol
## [1] "pd.markers"
## 
## $prediction.fcol
## [1] "pd.2013"

## all at once
pRolocmetadata(dunkley2006)

## pRoloc experiment metadata:
##  Species: Arabidopsis thaliana
##  Tissue: Callus
##  CellLine: NA
##  PMID: 16618929
##  MS: iTRAQ4
##  Experiment: LOPIT
##  MarkerCol: pd.markers
##  PredictionCol: pd.2013

Adding new data

The procedure to data in pRolocdata is as follows. Here, we assume that 3 new data files are available from the manuscript of Smith et al. 2017, and these files will be added to pRolocdata as three MSnSet objects.

1. the original data (often from supplementary material) are added to inst/extdata, say Smith_expA.csv, Smith_expB.csv and Smith_expC.csv (the name should ideally be the same as the original files), and the files and provenance is documented in inst/extdata/README. If the data files are really big, then they should be compressed. If they are too big (for example don't fit on github or would substantially increase the size of the package), then we might decide not to added them, but they should still be documented in the README file and the script (see point 2) should still assume they are there.

2. A script, typically called Smith2017.R, is added to inst/scripts/. That script reads the files above and saves the corresponding (compressed) MSnSet objects directly in data, typically called Smith2016a.rda, Smith2016a.rda, ..., and the objects themselves would be named Smith2016a, Smith2016b, ...

3. Write a man/Smith2016.Rd documentation file documenting all relevant data objects, providing some information about the experiment and data provenance, and a reference to the original paper.

4. Build and check the package and, if successful, send a github pull request.

If you do not have the R expertise to prepare the data, please open an issue in the pRolocdata Github repo or send me an email at laurent.gatto<AT>uclouvain<dot>be with the source csv files and appropriate metadata and I will add it for you.