DOI: 10.5061/dryad.r4xgxd2q8
title: iTRAQ proteomics dataset on ceramide-dependent exosomal cargoes from SW480 and SW620 cells

description: Cancer metastasis is largely influenced by cell–cell communication, to
 which exosomes play a vital role. Exosomes are small extracellular
 vesicles (sEVs) that originate as intraluminal vesicles (ILVs) within
 multivesicular bodies (MVBs) during endosome maturation. ILV formation
 depends on several pathways, including that of ceramide synthesis by
 neutral sphingomyelinase 2 [nSMase2]. Colorectal cancer (CRC)-derived sEVs
 are reported to carry a diverse range of metastatic cargo proteins;
 however, segregation of them in the ceramide-dependent sEV pool (sEVCer)
 remains unexplored. The current study aimed to identify the metastatic
 proteins that are secreted through sEVCer, from CRC cells of variable
 metastatic potentials. Primary (SW480) and metastatic (SW620) CRC cells
 were treated with nSMase2 blocker and sEVs were isolated, followed by
 extraction of the sEV proteins for a quantitative proteomic profiling
 using isobaric tags for relative and absolute quantitation (iTRAQ). In
 total, 1781 proteins were identified with unused protein score
 &gt;1.3. Of these identified proteins, 22.8% and 17.01% were found to
 be depleted within sEVs of the treated SW480 and SW620 cells,
 respectively. These depleted protein pools represented the cargo that are
 preferentially secreted through sEVCer in respective cell types
 (CargoCer-SW480 and CargoCer-SW620). CargoCer-SW480 overrepresented
 integrin signalling pathway members and CargoCer-SW620 overrepresented
 integrin as well as platelet-derived growth factor (PDGF) signalling
 pathway members. Interestingly, the uniquely overrepresented
 CargoCer-SW480 and CargoCer-SW620 were biologically connected, rendering
 possible transfer of metastatic cues via sEVCer. Overall, this study
 identified CargoCer and their dynamics over progressive CRC stages, and
 thereby opens up a new research direction for exploring the flow of
 metastatic cues through uptake and release of sEVCer.
description: The dataset was generated using 2D LC followed by tandem
 MS. The tandem MS analysis was performed using a 5600
 Triple TOF system (SCIEX) under Information Dependent Mode. The mass range
 of 400–1800 m/z and accumulation times of 250 ms per spectrum were chosen
 for precursor ion selections. MS/MS spectra were recorded in high
 sensitivity mode (resolution≥ 15 000) with rolling collision energy. In
 each cycle, a maximum of 20 precursors were selected for
 fragmentation. <strong>Peptide and Protein
 Identification</strong> Protein identification
 and relative iTRAQ quantification were performed with ProteinPilot™
 Software 5.0 (AB SCIEX, revision number 4769) using the Paragon™ Algorithm
 (5.0.0.0, 4767) for the peptide identification, which was further
 processed by Pro Group<sup>TM</sup> algorithm where
 isoform-specific quantification was adopted to trace the differences
 between expressions of various isoforms. The Pro Group Algorithm
 calculates protein ratios using only ratios from the spectra that are
 distinct to each protein or protein form and thus eliminates any masking
 of changes in expression because of peptides that are shared between
 proteins. User-defined search parameters were as follows: (1) Sample Type:
 iTRAQ 8plex (Peptide Labeled); (2) Cysteine Alkylation: MMTS; (3)
 Digestion: Trypsin; (4) Instrument: TripleTOF 5600; (5) Special Factors:
 None; (6) Species: Homo sapiens; (7) ID Focus: Biological modifications;
 (8) Database: 20190919_SwissProt_human_20659_plus crap.fasta; (9) Search
 Effort: Thorough; (10) FDR Analysis: Yes, using reverse database search
 strategy; (11) User Modified Parameter Files: Yes. For iTRAQ quantitation,
 the peptide for quantification was automatically selected by the Pro
 Group<sup>TM</sup> algorithm to calculate the reporter peak
 area, error factor (EF), and p-value. The resulting data set was
 auto-bias-corrected and background corrected to remove any variations
 imparted because of the unequal mixing during the combination of different
 labelled samples. This software counts each modified peptide as a unique
 one. The peak areas and the S/N ratios are extracted from the database by
 ProteinPilot<sup>TM</sup> to process the raw data to yield
 quantification data. A reverse database search strategy
 was adopted to estimate the false discovery rate (FDR) for peptide
 identification. For both of our iTRAQ studies, a strict unused confidence
 score of 1.3 was used as the qualification criteria, which corresponds to
 a peptide confidence level of 95% and a global FDR of 0.6%. The results
 were then exported into Microsoft Excel for manual data interpretation.
 Subsequently, the meaningful cutoff for up-regulation (iTRAQ ratio
 &gt;1.3) and down-regulation (iTRAQ ratio &lt;0.77) of proteins
 was finalized at 1.3 fold as previously reported [1-4]
 <strong>References:</strong> 1)
 Zhang Z, Wang W, Jin L, Cao X, Jian G, Wu N, Xu X, Yao Y & Wang D
 (2017) iTRAQ-Based Quantitative Proteomics Analysis of the Protective
 Effect of Yinchenwuling Powder on Hyperlipidemic
 Rats. <em>Evidence-Based Complement Altern Med</em>
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 Choong L-Y, Lin Q, Philp R, Wong C-H, Ang B-K, Tan Y-L, Loh M-C-S, Hew
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description: # iTRAQ proteomics dataset on ceramide-dependent exosomal cargoes from
 SW480 and SW620 cells
 [https://doi.org/10.5061/dryad.r4xgxd2q8](https://doi.org/10.5061/dryad.r4xgxd2q8) ## Description of the data and file structure Blocking the ceramide-dependent sEV biogenesis in SW480 and SW620 cells showed significant reduction in the quantity of released sEVs. As a consequence, the metastatic cargoes that are preferentially secreted through the ceramide-dependent route, are expected to be depleted in their sEV protein levels. These depleted cargoes are named as “Cargo-Cer” by us and will be referred as the same throughout the article. We hypothesized that the Cargo-Cer can vary with respect to the CRC stages, while some of them might remain overlapped. To specifically identify the Cargo-Cer for SW480 and SW620, we then performed iTRAQ-based quantitative proteomics with the proteins extracted from the sEVs of the respective cells. The mentioned cells were treated with GW4869, to block nSMase2 and thus the ceramide-dependent pathway for sEV biogenesis, prior to sEV isolation. DMSO treatment was used as the control for the study. sEVs isolated from the control and GW4869-treated SW480 cells are termed as sEV-SW480+DMSO and sEV-SW480+GW4869 respectively and similarly sEVs isolated from control and GW4869-treated SW620 cells are termed as sEV-SW620+DMSO and sEV-SW620+GW4869 respectively. Proteins extracted from sEV-SW480+DMSO, sEV-SW480+GW4869, sEV-SW620+DMSO and sEV-SW620+GW4869 were digested and labelled using iTRAQ labels 114, 115, 116 and 117 respectively. The labelled samples were then pooled together and processed for 2-Dimensional (2D) Liquid Chromatography (LC) followed by Tandem Mass Spectroscopy (MS/MS). Total 1479 proteins were identified and quantified with unused protein score&gt;2 with 99% confidence and 1781 proteins were identified and quantified with unused protein score&gt;1.3 and 95% confidence. Protein identification at 95% confidence level corresponds to a global false discovery rate (FDR) of 0.6%, calculated using a reverse database search strategy. Among the 1781 proteins quantified with unused protein score&gt;1.3, 1487 (83.49%) were reported in the *Homo sapiens* proteins section of the two publicly available extracellular vesicle databases, Vesiclepedia and Exocarta. In addition to this, 17 proteins were uniquely overlapped with Exocarta *Homo sapiens* proteins database only. Out of the remaining 277 proteins, 12 were archived in the Exocarta Rat Proteins database and 4 were archived in the Exocarta Mouse Protein database. This interprets that the majority of proteins (1520; 85.35%) quantified in the current study are well documented for their presence in the extracellular fraction. Notably, 87 out of the TOP100 proteins in the Exocarta database were quantified with high confidence in the current dataset and showed diverse regulation pattern in treated versus untreated cells. ### Files and variables #### File: Supplementary\_Table\_S9.xlsx **Description:** List of overrepresented CargoCer that are unique and common between SW480 and SW620. ##### Variables * Sheet 1: List of overrepresented pathway members from CargoCer-SW480. * Sheet 2: List of overrepresented pathway members from CargoCer-SW620. * Sheet 3: List of overrepresented pathway members common between CargoCer-SW480 and CargoCer-SW620. * Sheet 4: List of overrepresented pathway members that are unique in CargoCer-SW480. * Sheet 5: List of overrepresented pathway members that are unique in CargoCer-SW620. #### File: Supplementary\_Table\_S10.xls **Description:** List of CargoCer-SW480 and their interacting partners from CargoCer-SW620 along with respective interaction scores. Highlighted columns are only for the convenience of the readers to focus on, and are not essential for re-analysis. ##### Variables * Sheet 1: List of CargoCer-SW480 and their interacting partners from CargoCer-SW620 along with respective interaction scores. * Sheet 2: List of CargoCer-SW480 with their interactors. #### File: Supplementary\_Table\_S1.xlsx **Description:** List of all identified proteins using iTRAQ proteomics. Highlighted columns are only for the convenience of the readers to focus on, and are not essential for re-analysis. ##### Variables * Sheet 1: Proteins identified with unused protein score &gt;2 * Sheet 2: Proteins identified with unused protein score &gt;1.3. #### File: Supplementary\_Table\_S2.xlsx **Description:** Comparative analysis of all identified proteins from current dataset with Exocarta and Vesiclepedia *Homo sapiens* proteins datasets. ##### Variables * Sheet 1: Overall overlap statistics of the current dataset proteins with that of Exocarta and Vesiclepedia protein databases for human, rat and mouse. * Sheet 2: Overlap of proteins between current dataset and exocarta protein datasets. Out of 5402 exocarta human proteins, 1504 overlaps within current dataset. Since the current dataset contains total 1781 proteins, remaining 277 were searched for overlap with exocarta rat protein database containing total 1684 proteins. 12 of the current dataset proteins overlapped with the exocarta rat protein database. Remaining 265 proteins were searched for overlap with exocarta mouse protein database containing total 1017 proteins, with which 4 of the current dataset proteins overlapped. Green highlight represents overlapped proteins. * Sheet 3:Overlap of exocarta and vesiclepedia human protein datasets in the background of the current proteomics dataset. 1697 proteins from current dataset overlapped with vesiclepedia proteins (13809 total). 210, out of these 1697 proteins are unique to vesiclepedia and rest are commonly found within exocarta and vesiclepedia. Exocarta and vesiclepedia human proteins that are not overlapped with the current dataset, are subjected to overlap with each other. Red highlighted proteins represent overlapped ones between exocarta and the current protein dataset. Green highlight represents the proteins that are not found in the current dataset and are common between the Exocarta and vesiclepedia human protein databases. #### File: Supplementary\_Table\_S8.xlsx **Description:** List of overrepresented pathways for CargoCer-SW620 along with the individual protein members with their relative iTRAQ ratios. Highlighted columns are only for the convenience of the readers to focus on, and are not essential for re-analysis. ##### Variables * Sheet 1: List of overrepresented pathways for CargoCer-SW620 upon PANTHER Overrepresentation test. Details of test types and analysis are explained within each excel sheets. * Sheet 2: Individual protein members of the Integrin signalling pathway along with their relative iTRAQ ratios. * Sheet 3: Individual protein members of the PDGF signaling pathway along with their relative iTRAQ ratios. #### File: Supplementary\_Table\_S7.xlsx **Description:** List of 303 (17.01% of total) sEV proteins from SW620, with iTRAQ ratio&lt;0.77 against samples labelled with 117 versus 116 iTRAQ labels. These are the proteins that are significantly down-regulated in sEVSW620+GW4869 compared to sEVSW620+DMSO and thus are referred as CargoCer-SW620 that are preferentially released by ceramide-dependent exosomal route in SW620 cells. Panther Protein classes of these 303 proteins are mentioned alongside. Proteins extracted from GW4869-treated sEVs (sEVSW620+GW4869) and DMSO-treated (sEVSW620+DMSO) sEVs were labelled with 117 and 116 iTRAQ labels respectively. GW4869 is a nSMase2 blocker that negatively regulates ceramide synthesis within cells. Highlighted columns are only for the convenience of the readers to focus on, and are not essential for re-analysis. #### File: Supplementary\_Table\_S3.xlsx **Description:** List of 87 proteins (along with their iTRAQ quantification) that are overlapped with the TOP100 proteins in Exocarta. #### File: Supplementary\_Table\_S4.xlsx **Description:** List of TOP10 Gene Ontology (GO) terms with highest significance and fold enrichment as per Panther Pathway GO analysis for all identified proteins. Details of test types and analysis are explained within each excel sheets. Highlighted columns are only for the convenience of the readers to focus on, and are not essential for re-analysis. ##### Variables * Sheet 1: GOBP (GO by Biological Process) * Sheet 2: GOCC (GO by cellular compartment). #### File: Supplementary\_Table\_S6.xlsx **Description:** List of overrepresented pathways for CargoCer-SW480 along with the individual protein members with their relative iTRAQ ratios. Highlighted columns are only for the convenience of the readers to focus on, and are not essential for re-analysis. ##### Variables * Sheet 1: List of overrepresented pathways for CargoCer-SW480 upon PANTHER Overrepresentation test. Details of test types and analysis are explained within each excel sheets. * Sheet 2: Individual protein members of the Ubiquitin proteasome pathway along with their relative iTRAQ ratios. * Sheet 3: Individual protein members of the Integrin signalling pathway along with their relative iTRAQ ratios. #### File: Supplementary\_Table\_S5.xlsx **Description:** List of 406 (22.8% of total) sEV proteins from SW480, with iTRAQ ratio&lt;0.77 against samples labelled with 115 versus 114 iTRAQ labels. These are the proteins that are significantly down-regulated in sEVSW480+GW4869 compared to sEVSW480+DMSO and thus are referred as CargoCer-SW480 that are preferentially released by ceramide-dependent exosomal route in SW480 cells. Panther Protein classes of these 406 proteins are mentioned alongside. Proteins extracted from GW4869-treated sEVs (sEVSW480+GW4869) and DMSO-treated (sEVSW480+DMSO) sEVs were labelled with 115 and 114 iTRAQ labels respectively. GW4869 is a nSMase2 blocker that negatively regulates ceramide synthesis within cells. Highlighted columns are only for the convenience of the readers to focus on, and are not essential for re-analysis. Code/software MS Office Excel can be used to view the files. ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * NA