| 1. |
- Phuyal, S., et al.
(author)
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Mechanical strain stimulates COPII-dependent secretory trafficking via Rac1
- 2022
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In: Embo Journal. - : EMBO. - 0261-4189 .- 1460-2075. ; 41:18
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Journal article (peer-reviewed)abstract
- Cells are constantly exposed to various chemical and physical stimuli. While much has been learned about the biochemical factors that regulate secretory trafficking from the endoplasmic reticulum (ER), much less is known about whether and how this trafficking is subject to regulation by mechanical signals. Here, we show that subjecting cells to mechanical strain both induces the formation of ER exit sites (ERES) and accelerates ER-to-Golgi trafficking. We found that cells with impaired ERES function were less capable of expanding their surface area when placed under mechanical stress and were more prone to develop plasma membrane defects when subjected to stretching. Thus, coupling of ERES function to mechanotransduction appears to confer resistance of cells to mechanical stress. Furthermore, we show that the coupling of mechanotransduction to ERES formation was mediated via a previously unappreciated ER-localized pool of the small GTPase Rac1. Mechanistically, we show that Rac1 interacts with the small GTPase Sar1 to drive budding of COPII carriers and stimulates ER-to-Golgi transport. This interaction therefore represents an unprecedented link between mechanical strain and export from the ER.
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| 2. |
- Ghannoum, S., et al.
(author)
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DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics
- 2021
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In: International Journal of Molecular Sciences. - : MDPI AG. - 1422-0067. ; 22:3
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Journal article (peer-reviewed)abstract
- The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.
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