13
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Single-nuclei transcriptomes from human adrenal gland reveal distinct cellular identities of low and high-risk neuroblastoma tumors

      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

          Childhood neuroblastoma has a remarkable variability in outcome. Age at diagnosis is one of the most important prognostic factors, with children less than 1 year old having favorable outcomes. Here we study single-cell and single-nuclei transcriptomes of neuroblastoma with different clinical risk groups and stages, including healthy adrenal gland. We compare tumor cell populations with embryonic mouse sympatho-adrenal derivatives, and post-natal human adrenal gland. We provide evidence that low and high-risk neuroblastoma have different cell identities, representing two disease entities. Low-risk neuroblastoma presents a transcriptome that resembles sympatho- and chromaffin cells, whereas malignant cells enriched in high-risk neuroblastoma resembles a subtype of TRKB+ cholinergic progenitor population identified in human post-natal gland. Analyses of these populations reveal different gene expression programs for worst and better survival in correlation with age at diagnosis. Our findings reveal two cellular identities and a composition of human neuroblastoma tumors reflecting clinical heterogeneity and outcome.

          Abstract

          Childhood neuroblastoma can be separated into high and low risk groups, with prognosis depending on age at diagnosis. Here, the authors show that low and high risk neuroblastoma tumours are composed of different cell types with different malignancy potential.

          Related collections

          Most cited references50

          • 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: 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

              Comprehensive Integration of Single-Cell Data

              Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
                Bookmark

                Author and article information

                Contributors
                oscar.bedoya.reina@ki.se
                susanne.schlisio@ki.se
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                7 September 2021
                7 September 2021
                2021
                : 12
                : 5309
                Affiliations
                [1 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Microbiology, Tumor and Cell Biology, , Karolinska Institutet, ; Stockholm, Sweden
                [2 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Physiology and Pharmacology, , Karolinska Institutet, ; Stockholm, Sweden
                [3 ]GRID grid.419520.b, ISNI 0000 0001 2222 4708, Max Planck Institute for Evolutionary Biology, ; Plön, Germany
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Biomedical Informatics, , Harvard Medical School, ; Boston, MA USA
                [5 ]GRID grid.511171.2, Harvard Stem Cell Institute, ; Cambridge, MA USA
                [6 ]GRID grid.1649.a, ISNI 000000009445082X, Department of Pathology and Genetics, University of Gothenburg, , Sahlgrenska University Hospital, ; Gothenburg, Sweden
                [7 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Cell and Molecular Biology, , Karolinska Institutet, ; Stockholm, Sweden
                [8 ]GRID grid.22937.3d, ISNI 0000 0000 9259 8492, Department of Neuroimmunology, Center for Brain Research, , Medical University of Vienna, ; Vienna, Austria
                [9 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Department of Oncology-Pathology, , Karolinska Institutet, ; Stockholm, Sweden
                [10 ]GRID grid.4714.6, ISNI 0000 0004 1937 0626, Women’s and Children’s Health, , Karolinska Institutet, ; Stockholm, Sweden
                Author information
                http://orcid.org/0000-0002-5411-2185
                http://orcid.org/0000-0002-6036-5875
                http://orcid.org/0000-0002-9403-3123
                http://orcid.org/0000-0001-5471-0356
                http://orcid.org/0000-0001-5934-7816
                http://orcid.org/0000-0002-5945-9081
                http://orcid.org/0000-0002-2202-9694
                http://orcid.org/0000-0002-2605-3771
                Article
                24870
                10.1038/s41467-021-24870-7
                8423786
                34493726
                593fcc46-325f-4158-8893-73fe6b5bd49b
                © 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
                : 31 July 2020
                : 8 July 2021
                Categories
                Article
                Custom metadata
                © The Author(s) 2021

                Uncategorized
                cancer genetics,paediatric cancer
                Uncategorized
                cancer genetics, paediatric cancer

                Comments

                Comment on this article