3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Valproic acid stimulates myogenesis in pluripotent stem cell-derived mesodermal progenitors in a NOTCH-dependent manner

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Muscular dystrophies are debilitating neuromuscular disorders for which no cure exists. As this disorder affects both cardiac and skeletal muscle, patients would benefit from a cellular therapy that can simultaneously regenerate both tissues. The current protocol to derive bipotent mesodermal progenitors which can differentiate into cardiac and skeletal muscle relies on the spontaneous formation of embryoid bodies, thereby hampering further clinical translation. Additionally, as skeletal muscle is the largest organ in the human body, a high myogenic potential is necessary for successful regeneration. Here, we have optimized a protocol to generate chemically defined human induced pluripotent stem cell-derived mesodermal progenitors (cdMiPs). We demonstrate that these cells contribute to myotube formation and differentiate into cardiomyocytes, both in vitro and in vivo. Furthermore, the addition of valproic acid, a clinically approved small molecule, increases the potential of the cdMiPs to contribute to myotube formation that can be prevented by NOTCH signaling inhibitors. Moreover, valproic acid pre-treated cdMiPs injected in dystrophic muscles increase physical strength and ameliorate the functional performances of transplanted mice. Taken together, these results constitute a novel approach to generate mesodermal progenitors with enhanced myogenic potential using clinically approved reagents.

          Related collections

          Most cited references38

          • Record: found
          • Abstract: found
          • Article: not found

          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Full-length RNA-seq from single cells using Smart-seq2.

            Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

              Abstract Motivation: Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. Results: We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. Availability and Implementation: The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater. Contact: davis@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
                Bookmark

                Author and article information

                Contributors
                maurilio.sampaolesi@kuleuven.be
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                5 July 2021
                5 July 2021
                July 2021
                : 12
                : 7
                : 677
                Affiliations
                [1 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory of Translational Cardiomyology, Department of Development and Regeneration, Stem Cell Research Institute, , KU Leuven, ; 3000 Leuven, Belgium
                [2 ]GRID grid.4708.b, ISNI 0000 0004 1757 2822, Stem Cell Laboratory, Department of Pathophysiology and Transplantation, , Università degli Studi di Milano, Milano, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Centro Dino Ferrari, ; via F. Sforza 35, 20122 Milano, Italy
                [3 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, CARIM School for Cardiovascular Diseases, Department of Cardiology, Maastricht University, 6229 ER Maastricht, the Netherlands; Department of Cardiovascular Sciences, , KU Leuven, ; 3000 Leuven, Belgium
                [4 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Laboratory of Bioengineering and Morphogenesis, Biomechanics Section, Department of Mechanical Engineering, , KU Leuven, ; Leuven, Belgium
                [5 ]GRID grid.410569.f, ISNI 0000 0004 0626 3338, Department of Nuclear Medicine, , University Hospital KU Leuven, ; Leuven, Belgium
                [6 ]GRID grid.8982.b, ISNI 0000 0004 1762 5736, Human Anatomy Unit, Department of Public Health, Experimental and Forensic Medicine, , University of Pavia, ; 27100 Pavia, Italy
                Author information
                http://orcid.org/0000-0002-1701-9109
                http://orcid.org/0000-0002-2422-3757
                Article
                3936
                10.1038/s41419-021-03936-w
                8257578
                34226515
                b384367d-4b4d-44b8-9e93-b5a57321556f
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 February 2021
                : 9 June 2021
                : 10 June 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Cell biology
                cell signalling,molecular biology
                Cell biology
                cell signalling, molecular biology

                Comments

                Comment on this article