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

      RNA aptamers specific for transmembrane p24 trafficking protein 6 and Clusterin for the targeted delivery of imaging reagents and RNA therapeutics to human β cells

      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

          The ability to detect and target β cells in vivo can substantially refine how diabetes is studied and treated. However, the lack of specific probes still hampers a precise characterization of human β cell mass and the delivery of therapeutics in clinical settings. Here, we report the identification of two RNA aptamers that specifically and selectively recognize mouse and human β cells. The putative targets of the two aptamers are transmembrane p24 trafficking protein 6 (TMED6) and clusterin (CLUS). When given systemically in immune deficient mice, these aptamers recognize the human islet graft producing a fluorescent signal proportional to the number of human islets transplanted. These aptamers cross-react with endogenous mouse β cells and allow monitoring the rejection of mouse islet allografts. Finally, once conjugated to saRNA specific for X-linked inhibitor of apoptosis (XIAP), they can efficiently transfect non-dissociated human islets, prevent early graft loss, and improve the efficacy of human islet transplantation in immunodeficient in mice.

          Abstract

          Development of probes specific for human β-cells could aid in delivery of therapeutics and monitoring β-cells mass during diabetes progression or islet transplantation. Here the authors identify two RNA aptamers specific for β-cells that allow efficient transfection of human islets and β-cell quantification of human islet grafts in immunodeficient mice.

          Related collections

          Most cited references110

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor.

            Microarray technology has become a standard molecular biology tool. Experimental data have been generated on a huge number of organisms, tissue types, treatment conditions and disease states. The Gene Expression Omnibus (Barrett et al., 2005), developed by the National Center for Bioinformatics (NCBI) at the National Institutes of Health is a repository of nearly 140,000 gene expression experiments. The BioConductor project (Gentleman et al., 2004) is an open-source and open-development software project built in the R statistical programming environment (R Development core Team, 2005) for the analysis and comprehension of genomic data. The tools contained in the BioConductor project represent many state-of-the-art methods for the analysis of microarray and genomics data. We have developed a software tool that allows access to the wealth of information within GEO directly from BioConductor, eliminating many the formatting and parsing problems that have made such analyses labor-intensive in the past. The software, called GEOquery, effectively establishes a bridge between GEO and BioConductor. Easy access to GEO data from BioConductor will likely lead to new analyses of GEO data using novel and rigorous statistical and bioinformatic tools. Facilitating analyses and meta-analyses of microarray data will increase the efficiency with which biologically important conclusions can be drawn from published genomic data. GEOquery is available as part of the BioConductor project.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Systematic evolution of ligands by exponential enrichment: RNA ligands to bacteriophage T4 DNA polymerase

                Bookmark

                Author and article information

                Contributors
                pserafini@miami.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                5 April 2022
                5 April 2022
                2022
                : 13
                : 1815
                Affiliations
                [1 ]GRID grid.26790.3a, ISNI 0000 0004 1936 8606, Department of Microbiology and Immunology, Miller School of Medicine, , University of Miami, ; Miami, FL USA
                [2 ]GRID grid.26790.3a, ISNI 0000 0004 1936 8606, Diabetes Research Institute, Miller School of Medicine, , University of Miami, ; Miami, FL USA
                [3 ]GRID grid.7548.e, ISNI 0000000121697570, Center for Genome Research, Department of Life Sciences, , University of Modena and Reggio Emilia, ; Modena, Italy
                [4 ]GRID grid.26790.3a, ISNI 0000 0004 1936 8606, Sylvester Comprehensive Cancer Center, Miller School of Medicine, , University of Miami, ; Miami, FL USA
                Author information
                http://orcid.org/0000-0003-3457-761X
                http://orcid.org/0000-0001-5376-0642
                http://orcid.org/0000-0002-9969-5951
                http://orcid.org/0000-0002-6834-4497
                http://orcid.org/0000-0003-2732-8180
                http://orcid.org/0000-0002-6219-0714
                http://orcid.org/0000-0002-4362-2972
                Article
                29377
                10.1038/s41467-022-29377-3
                8983715
                35383192
                14e13ecc-dee6-416c-8c7f-5a3b0601665e
                © The Author(s) 2022

                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
                : 14 January 2020
                : 8 March 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100008871, JDRF;
                Award ID: 2016-173-A-N
                Award ID: SRA-2020-962-S-B
                Award ID: 17-2013-326
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100000062, U.S. Department of Health & Human Services | NIH | National Institute of Diabetes and Digestive and Kidney Diseases (National Institute of Diabetes & Digestive & Kidney Diseases);
                Award ID: UC4 DK 116241
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                type 1 diabetes,nucleic-acid therapeutics
                Uncategorized
                type 1 diabetes, nucleic-acid therapeutics

                Comments

                Comment on this article