Dataset Information

OpenCell: Proteome-scale endogenous tagging enables the cartography of human cellular organization

ABSTRACT: Mapping the global proteome circuitry of the human cell is one of the central goals of the post-genomic era. Here, we combine high-throughput genome engineering of ~1,300 cell lines endogenously tagged with fluorescent protein fusions, 3D live-cell imaging, mass spectrometry (MS)-based high-speed interactomics and advanced machine learning to decode the interaction and localization architecture of the human proteome. We delineate interacting protein families and facilitate unbiased biological discovery by unsupervised clustering, while hierarchical analyses of the interactome superimposed to localization uncover principles that template cellular organization. Furthermore, we discover that localization patterns alone are often enough to predict molecular interactions. ‘OpenCell’ is a global proteome-scale resource for human protein localization and interaction at endogenous expression levels. Our analytical methods are open-source and our data set is presented as an advanced interactive website (‘OpenCell’.czbiohub.org) to empower the community with the quantitative cartography of human cellular organization at proteome level.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

SUBMITTER: Mario Oroshi

LAB HEAD: Matthias Mann

PROVIDER: PXD024909 | Pride | 2022-03-14

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

Items per page:

1 - 5 of 10

Similar Datasets

Project description:Background: With the incidence of papillary thyroid carcinoma rising worldwide, the decision about lobectomy versus total thyroidectomy will become relevant for an increasing number of patients. There exists no reliable biomarker for metastatic potential or tendency of recurrence that could assist in the risk stratification of patients. Objective: To develop a gene expression classifier for metastatic potential by measuring RNA expression in the primary tumor at the time of cancer surgery. Further, to investigate the ability of the gene expression classifier to identify metastatic and recurrent cases. Method: Genome-wide expression analyses. The development cohort consisted of freshly frozen tissues from 38 patients collected between the years 1986 and 2009. Validation was performed on a consecutive cohort of formalin fixated paraffin embedded surgical tissue specimens from 183 patients treated at Odense and Aarhus University Hospitals. Results: A 17 gene classifier (ADAMTS1, ANTXR1, C7, CXCL12, EBF1, FBLN2, FOSL2, GGT5, GPR124, JAM3, LRIG1, NDRG1, PRRX1, ROBO1, SORL1, TCF4, and ZEB1) was identified based on the expression values of these genes in the groups with and without metastasis in the development cohort. The 17 gene classifier for regional and/or distant metastasis identified was tested against the clinical status in the validation cohort. Sensitivity was 51.6% (95% CI 41.2%-61.8) and specificity 61.6 % (95% CI 50.5%-71.9%). Further, the Kaplan-Meyer method was used to estimate whether the classifier was useful as a prognostic marker for recurrences. Log-rank testing failed to identify any significance (p=0.32). Conclusion: A 17 gene classifier for metastatic potential was developed, and the results showed a clear biological difference between groups. However, through validation, the prognostic significance of this classifier could not be shown in identifying metastatic cases or in the ability of dichotomizing patients according to risk of recurrence after primary treatment. 38 patients with papillary thyroid carcinoma from a consecutive cohort of patients, no replicates

Project description:Background: Genes upregulated by low oxygen have been suggested as endogenous markers for tumor hypoxia. Yet, most of the genes investigated have shown inconsistent results, which have led to concerns about their ability to be true hypoxia markers. Previous studies have demonstrated that expression of hypoxia induced genes can be affected by extracellular pH (pH e ). Methods: Five different human cell lines (SiHa, FaDu DD, UTSCC5, UTSCC14 and UTSCC15) were exposed to different oxygen concentrations and pH (7.5 or 6.3), and gene expression analyzed with microarray (Affymetrix - Human Genome U133 Plus 2.0 Array). Results: An analysis of two of the cell lines using SAM identified 461 probesets that were able to separate the four groups “ Normal oxygen, normal pH ” , “ Low oxygen, normal pH ” , “ Normal oxygen, low pH ” and “ Low oxygen, low pH ” . From here it was possible to identify a fraction of probesets induced at low oxygen independent of pH in these two cell lines, this fraction included HIG2, NDRG1, PAI1 and RORA. Further verifi cation by qPCR highlighted the necessity of using more cell lines to obtain a robust gene expression profi les. To specifi cally select pH independent hypoxia regulated genes across more cell lines, data for FaDu DD, UTSCC5, UTSCC14 and UTSCC15 were analyzed to identify genes that were induced by hypoxia in each cell line, where the induction was not affected by low pH, and where the gene was not signifi cantly induced by low pH alone. Each cell line had 65 – 122 probesets meeting these criteria. For genes to be considered as target genes (hypoxia inducible pH independent), genes had to be present in three of four cell lines. Conclusion: The result is a robust hypoxia profile unaffected by pH across cell lines consisting of 27 genes. This study demonstrates a way to identify hypoxia markers by microarray, where other factors in the tumor microenvironment are taken into account. Five cell lines, four HNSCC (FaDuDD, UTSCC5, UTSCC14 and UTSCC15) and one cervical (SiHa). Hypoxia was achieved by continually gassing the cells in an airtight chamber for 24 hours with either atmo- spheric air, 5%, 1%, 0.1%, 0.01% or 0% oxygen, sup- plemented with 5% CO 2 and nitrogen, at 37ºC. To simulate acidosis, pH of the medium (without sodium bicarbonate) was buffered with 20 mM tris (Hydroxym- ethyl) aminomethane (TRIS) base (Sigma), 20 mM 2-(N-Morpholino)ethanesulfonic acid (MES) (Sigma) and 0.52 g/l NaHCO 3 (Sigma) and titrated to 6.3 or 7.4. The pH adjusted media was applied immediately before the induction of hypoxia. No replicates.

Project description:Dendritic cells (DCs) in lymphoid tissue comprise conventional DCs (cDCs) and plasmacytoid DCs (pDCs) that develop from common DC progenitors (CDPs). CDPs are Flt3+c-kitintM-CSFR+ and reside in bone marrow. Here we describe a two-step culture system that recapitulates DC development from c-kithiFlt3-/lo multipotent progenitors (MPPs) into CDPs and further into cDC and pDC subsets. MPPs and CDPs are amplified in vitro with Flt3 ligand, stem cell factor, hyper-IL-6 and insulin- like growth factor-1. The four-factor cocktail readily induces self-renewal of MPPs and their progression into CDPs and has no self-renewal activity on CDPs. The amplified CDPs respond to all known DC poietins and generate all lymphoid tissue DCs in vivo and in vitro. Additionally, in vitro CDPs recapitulate the cell surface marker and gene expression profile of in vivo CDPs and possess a DC-primed transcription profile. Transforming growth factor-Î²1 (TGF-Î²1) impacts on CDPs and directs their differentiation towards cDCs. Genome-wide gene expression profiling of TGF-Î²1-induced genes identified transcription factors, such as interferon regulatory factor-4 (IRF-4) and RelB, that are implicated as instructive factors for cDC subset specification. TGF-Î²1 also induced the transcription factor inhibitor of differentiation/DNA binding 2 (Id2) that suppresses pDC development. Thus, TGF-Î²1 directs CDP differentiation into cDC by inducing both cDC instructive factors and pDC inhibitory factors. 20 samples in total. Multipotent progenitor - MPP_1 - MPP_2 Common dendritic cell progenitor - CDP_1 - CDP_2 Plasmacytoid dendritic cell - pDC_1 - pDC_2 Conventional dendritic cell - cDC_1 - cDC_2 In vivo common dendritic cell progenitor - In vivo CDP_1 - In vivo CDP_2 Untreated common dendritic cell progenitor (CDP) - CDP_0h_1 - CDP_0h_2 TGF-beta1 treated (4 hours) CDP - CDP_4h_1 - CDP_4h_2 TGF-beta1 treated (8 hours) CDP - CDP_8h_1 - CDP_8h_2 TGF-beta1 treated (12 hours) CDP - CDP_12h_1 - CDP_12h_2 TGF-beta1 treated (24 hours) CDP - CDP_24h_1 - CDP_24h_2

			Action	DRS
	02-12.zip	Other
	13-23.zip	Other
	24-34.zip	Other
	35-44.zip	Other
	45-56.zip	Other

Dataset Information

OpenCell: Proteome-scale endogenous tagging enables the cartography of human cellular organization

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets