Single cell sequencing of Tri-PyMT cells [10X Genomics]
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE178577 | GEO | 2022/09/01
REPOSITORIES: GEO
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2022-09-01 | GSE178578 | GEO
Project description:Using an EMT lineage-tracing model, we identified tumor cells in the transitioning phases of both EMT and MET. Further analyses revealed that the ribosome biogenesis (RiBi) pathway was specifically elevated in these transitioning cells, regardless of their destined phenotypical fates.
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Project description:To study the contribution of epithelial-to-mesenchymal transition (EMT) in tumor metastasis, we established an EMT lineage tracing model in a multiple-transgenic mouse (MMTV-PyMT/Fsp1-Cre/Rosa26mT/mG, Tri-PyMT). In this model, breast tumor cells that underwent EMT would irreversibly switch their expression of the fluorescent marker from RFP+ to GFP+ due to the mesenchymal specific Cre expression. Surprisingly, we found that metastatic tumor cells did not convert from RFP+ to GFP+. Lung metastases were predominantly composed of RFP+ tumor cells persistently exhibiting epithelial phenotypes. These findings challenge the concept that EMT is required for metastasis, and have attracted vigorous discussion about its true contributions to metastasis. One of the major concerns with the EMT lineage tracing model is that expression of GFP only indicates the complete EMT transition. The partial-EMT programming, a novel concept describing tumor cells exhibiting both epithelial and mesenchymal features, could possibly fail to launch the Fsp1-Cre-mediated fluorescent marker switch. To address this concern, we evaluated the EMT status of Tri-PyMT cells on the single cell level by performing single cell RNA-sequencing (scRNA-seq). We found that the expression of epithelial markers (i.e. Epcam and Krt18) and mesenchymal markers (i.e. Vim and S110a4) were largely confined to RFP+ and GFP+ subpopulations, respectively. Based on the differentially expressed EMT related genes, we observed an EMT spectrum in which the low EMT score end was enriched with RFP+ cells, whereas the high EMT score end was enriched with GFP+ cells. Indeed, the fluorescent marker switch in Tri-PyMT cells predicted their EMT status with high specificity and sensitivity. Tri-PyMT cells which were derived from the primary breast tumor of triple transgenic mouse (MMTV-PyMT:Fsp1-cre:Rosa26mT/mG) were sorted into RFP+ or GFP+ populations by FACS and subjected for Drop-seq analysis.
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Project description:Single cell sequencing of Tri-PyMT cells [10X Genomics]
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Project description:Single-cell RNA sequencing (scRNA-seq) is generally used for profiling transcriptome of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data generated by these two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analyses. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data more. For 10X-based data, we observed higher noise for mRNAs with low expression levels. Approximately 10%-30% of all detected transcripts by both platforms were from non-coding genes, with long non-coding RNAs (lncRNAs) accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected distinct groups of differentially expressed genes between cell clusters, indicating the different characteristics of these technologies. Our study promotes better understanding of these two platforms and offers the basis for an informed choice of these widely used technologies.
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Project description:The linked read sequencing library preparation platform by 10X Genomics produces barcoded sequencing libraries, which are subsequently sequenced using the Illumina short read sequencing technology. In this new approach, long fragments of DNA are partitioned into separate micro-reactions, where the same index sequence is incorporated into each of the sequencing fragment inserts derived from a given long fragment. In this study, we exploited this property by using reads from index sequences associated with a large number of reads, to assemble the chloroplast genome of the Sitka spruce tree (Picea sitchensis). Here we report on the first Sitka spruce chloroplast genome assembled exclusively from P. sitchensis genomic libraries prepared using the 10X Genomics protocol. We show that the resulting 124,049 base pair long genome shares high sequence similarity with the related white spruce and Norway spruce chloroplast genomes, but diverges substantially from a previously published P. sitchensis- P. thunbergii chimeric genome. The use of reads from high-frequency indices enabled separation of the nuclear genome reads from that of the chloroplast, which resulted in the simplification of the de Bruijn graphs used at the various stages of assembly.
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Project description:This file contains a 136-node modular Boolean network model of EMT triggered by biomechanical and growth signaling crosstalk, linked to a published network of epithelial contact inhibition, proliferation, and apoptosis (MODEL2006170001). This model reproduces the ability of the core EMT transcriptional network to maintain distinct epithelial, hybrid E/M and mesenchymal states, as well as EMT driven by mitogens such as EGF on stiff ECM. We also reproduce the observed lack of stepwise MET, in that our model's dynamics does not pass through the hybrid E/M state during MET. We show that in the absence of strong autocrine signals such as TGFβ (not included in this version), cells cannot maintain their mesenchymal state in the absence of mitogens, on softer matrices, or at high cell density.
2023-12-13 | MODEL2208050001 | BioModels
Project description:NMuMG is an epithelial cell line that can be induced into EMT by TGF-β treatment or MET by TGF-β withdrawl. During EMT, several marker genes were downregulated/upregulated, which is consistent with its mesenchymal phenotype. Transcription factors that are regulated during EMT and its reverse process MET are candidate genes for the regulations of the EMT marker genes.
2013-07-26 | GSE48204 | GEO
Project description:This model is an expansion of the Regan2022 - Mechanosensitive EMT model (MODEL2208050001); it includes a TGFβ signaling module and autocrine signaling in mesenchymal cells. The expanded 150-node (630 link) modular model undergoes EMT triggered by biomechanical and growth signaling crosstalk, or by TGFβ. As its predecessor, this model also reproduces the ability of the core EMT transcriptional network to maintain distinct epithelial, hybrid E/M and mesenchymal states, as well as EMT driven by mitogens such as EGF on stiff ECM. We also reproduce the observed lack of stepwise MET, in that our model's dynamics does not pass through the hybrid E/M state during MET. We show that in the absence of strong autocrine signals such as TGFβ (not included in this version), cells cannot maintain their mesenchymal state in the absence of mitogens, on softer matrices, or at high cell density. In contrast, potent autocrine signaling can stabilize the mesenchymal state in all but very dense monolayers on soft ECM. This expanded model also reproduces the inhibitory effects of TGFβ on proliferation and anoikis resistance in mesenchymal cells, as well as its ability to trigger apoptosis on soft ECM vs. EMT on stiff matrices. The model offers several experimentally testable predictions related to the effect of neighbors on partial vs. full EMT, the tug of war between mitosis and the maintenance of migratory hybrid E/M states, as well as cell cycle defects in dynamic, heterogeneous populations of epithelial, hybrid E/M and mesenchymal cells.
2023-12-13 | MODEL2208050002 | BioModels
Project description:Background: The commercially available 10x Genomics protocol to generate droplet-based single cell RNA-seq (scRNA-seq) data is enjoying growing popularity among researchers. Fundamental to the analysis of such scRNA-seq data is the ability to cluster similar or same cells into non-overlapping groups. Many competing methods have been proposed for this task, but there is currently little guidance with regards to which method to use. Methods: Here we use one gold standard 10x Genomics dataset, generated from the mixture of three cell lines, as well as multiple silver standard 10x Genomics datasets generated from peripheral blood mononuclear cells to examine not only the accuracy but also running time and robustness of a dozen methods. Results: We found that Seurat outperformed other methods, although performance seems to be dependent on many factors, including the complexity of the studied system. Furthermore, we found that solutions produced by different methods have little in common with each other. Conclusions: In light of this we conclude that the choice of clustering tool crucially determines interpretation of scRNA-seq data generated by 10x Genomics. Hence practitioners and consumers should remain vigilant about the outcome of 10x Genomics scRNA-seq analysis.
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