For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
33
views
45
references
 Top references
cited by
105
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      533
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: not found

      Evaluation of variability in human kidney organoids

      research-article

      Read this article at

      ScienceOpenPublisherPMC
          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 utility of human pluripotent stem cell–derived kidney organoids relies implicitly on the robustness and transferability of the protocol. Here we analyze the sources of transcriptional variation in a specific kidney organoid protocol. Although individual organoids within a differentiation batch showed strong transcriptional correlation, we noted significant variation between experimental batches, particularly in genes associated with temporal maturation. Single-cell profiling revealed shifts in nephron patterning and proportions of component cells. Distinct induced pluripotent stem cell clones showed congruent transcriptional programs, with interexperimental and interclonal variation also strongly associated with nephron patterning. Epithelial cells isolated from organoids aligned with total organoids at the same day of differentiation, again implicating relative maturation as a confounder. This understanding of experimental variation facilitated an optimized analysis of organoid-based disease modeling, thereby increasing the utility of kidney organoids for personalized medicine and functional genomics.

          Related collections

          Most cited references45

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

          Fiji: an open-source platform for biological-image analysis.

          Johannes Schindelin, Ignacio Arganda-Carreras, Erwin Frise … (2020)
          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
          Article 0 comments Cited 15609 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            STAR: ultrafast universal RNA-seq aligner.

            Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)
            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/.
            Article 0 comments Cited 15093 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Fitting Linear Mixed-Effects Models Usinglme4

              Steve Walker, Martin Mächler, Ben Bolker … (2022)
              Article 0 comments Cited 12643 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-journal-id): 101215604
                Journal ID (pubmed-jr-id): 32338
                Journal ID (nlm-ta): Nat Methods
                Journal ID (iso-abbrev): Nat. Methods
                Title: Nature methods
                ISSN (Print): 1548-7091
                ISSN (Electronic): 1548-7105
                Publication date Nihms-submitted: 20 June 2019
                Publication date (Electronic): 20 December 2018
                Publication date (Print): January 2019
                Publication date PMC-release: 16 July 2019
                Volume: 16
                Issue: 1
                Pages: 79-87
                Affiliations
                [1 ]Murdoch Children’s Research Institute, Melbourne, Victoria, Australia.
                [2 ]Department of Anatomy and Neuroscience, The University of Melbourne, Melbourne, Victoria, Australia.
                [3 ]Department of Nephrology, Royal Children’s Hospital, Melbourne, Victoria, Australia.
                [4 ]Department of Paediatrics, The University of Melbourne, Melbourne, Victoria, Australia.
                [5 ]School of Biosciences, The University of Melbourne, Melbourne, Victoria, Australia.
                [6 ]Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Queensland, Australia.
                [7 ]Present address: RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
                Author notes

                Author contributions

                B.P. advised on the experimental design, performed all the statistical analysis, and wrote the manuscript. P.X.E., M.T., L.J.H., J.S., T.A.F., and H.-J.Y. performed the differentiation experiments. P.X.E. prepared RNA and analyzed the qPCR data. H.-J.Y. and K.T.L. collected and presented the morphological immunofluorescence data. P.X.E. and A.N.C. performed the isolations and L.Z. performed the initial analysis for single-cell profiling. J.S., T.A.F., and L.J.H. performed the EpCAM + and LTL + MACS sorting, respectively. S.E.H., E.W., and J.S. generated the iPSC cell lines, and S.E.H. performed the CRISPR–Cas9 gene editing. A.O. advised on the experimental design and oversaw the statistical analysis. M.H.L. devised the study, designed and interpreted all the experimental data, and wrote the manuscript. All authors read and approved the final manuscript.

                [* ] Correspondence and requests for materials should be addressed to M.H.L. melissa.little@ 123456mcri.edu.au
                Author information
                http://orcid.org/0000-0002-1711-7454
                http://orcid.org/0000-0003-0380-2263
                Article
                Accession ID: PMC6634992 Pmcid ID: PMC6634992 Pmc-uid ID: 6634992 Manuscript ID: nihpa1034147
                DOI: 10.1038/s41592-018-0253-2
                PMC ID: 6634992
                PubMed ID: 30573816
                SO-VID: b15be9ba-7e80-4994-b242-bb3d68b3b5c5
                License:

                Reprints and permissions information is available at www.nature.com/reprints.

                History
                Categories
                Subject: Article

                Data availability:

                Comments

                Comment on this article

                scite_

                Similar content533

                See all similar

                Cited by105

                See all cited by

                Most referenced authors1,560

                See all reference authors