Single-cell analysis reveals congruence between kidney organoids and human fetal kidney

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

Background

Human kidney organoids hold promise for studying development, disease modelling and drug screening. However, the utility of stem cell-derived kidney tissues will depend on how faithfully these replicate normal fetal development at the level of cellular identity and complexity.

Methods

Here, we present an integrated analysis of single cell datasets from human kidney organoids and human fetal kidney to assess similarities and differences between the component cell types.

Results

Clusters in the combined dataset contained cells from both organoid and fetal kidney with transcriptional congruence for key stromal, endothelial and nephron cell type-specific markers. Organoid enriched neural, glial and muscle progenitor populations were also evident. Major transcriptional differences between organoid and human tissue were likely related to technical artefacts. Cell type-specific comparisons revealed differences in stromal, endothelial and nephron progenitor cell types including expression of WNT2B in the human fetal kidney stroma.

Conclusions

This study supports the fidelity of kidney organoids as models of the developing kidney and affirms their potential in disease modelling and drug screening.

Electronic supplementary material

The online version of this article (10.1186/s13073-019-0615-0) contains supplementary material, which is available to authorized users.

Related collections

Most cited references 37

Record: found
Abstract: found
Article: found

Is Open Access

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

Aaron Lun, Davis J. McCarthy, John Marioni … (2016)

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.

0 comments Cited 618 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis.

Minoru Takasato, Pei X Er, Han S Chiu … (2015)

The human kidney contains up to 2 million epithelial nephrons responsible for blood filtration. Regenerating the kidney requires the induction of the more than 20 distinct cell types required for excretion and the regulation of pH, and electrolyte and fluid balance. We have previously described the simultaneous induction of progenitors for both collecting duct and nephrons via the directed differentiation of human pluripotent stem cells. Paradoxically, although both are of intermediate mesoderm in origin, collecting duct and nephrons have distinct temporospatial origins. Here we identify the developmental mechanism regulating the preferential induction of collecting duct versus kidney mesenchyme progenitors. Using this knowledge, we have generated kidney organoids that contain nephrons associated with a collecting duct network surrounded by renal interstitium and endothelial cells. Within these organoids, individual nephrons segment into distal and proximal tubules, early loops of Henle, and glomeruli containing podocytes elaborating foot processes and undergoing vascularization. When transcription profiles of kidney organoids were compared to human fetal tissues, they showed highest congruence with first trimester human kidney. Furthermore, the proximal tubules endocytose dextran and differentially apoptose in response to cisplatin, a nephrotoxicant. Such kidney organoids represent powerful models of the human organ for future applications, including nephrotoxicity screening, disease modelling and as a source of cells for therapy.

0 comments Cited 516 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Canonical correlation analysis: an overview with application to learning methods.

David R. Hardoon, Sandor Szedmak, John Shawe-Taylor (2004)

We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.

0 comments Cited 412 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Alexander N. Combes: alexander.combes@unimelb.edu.au

Luke Zappia: luke.zappia@mcri.edu.au

Pei Xuan Er: peixuan.er@mcri.edu.au

Alicia Oshlack: alicia.oshlack@mcri.edu.au

Melissa H. Little:

ORCID: http://orcid.org/0000-0003-0380-2263

+61 3 9936 6206 , melissa.little@mcri.edu.au

Journal

Journal ID (nlm-ta): Genome Med

Journal ID (iso-abbrev): Genome Med

Title: Genome Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1756-994X

Publication date (Electronic): 23 January 2019

Publication date PMC-release: 23 January 2019

Publication date Collection: 2019

Volume: 11

Electronic Location Identifier: 3

Affiliations

[1 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Department of Anatomy & Neuroscience, , University of Melbourne, ; Melbourne, VIC Australia

[2 ]ISNI 0000 0000 9442 535X, GRID grid.1058.c, Murdoch Children’s Research Institute, ; Melbourne, VIC Australia

[3 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, School of Biosciences, , The University of Melbourne, ; Melbourne, VIC Australia

[4 ]ISNI 0000 0001 2179 088X, GRID grid.1008.9, Department of Paediatrics, , The University of Melbourne, ; Melbourne, VIC Australia

Author information

Melissa H. Little http://orcid.org/0000-0003-0380-2263

Article

Publisher ID: 615

DOI: 10.1186/s13073-019-0615-0

PMC ID: 6345028

PubMed ID: 30674341

SO-VID: 07783cac-29da-465b-ae7f-18e718dc9b57

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 24 September 2018

Date accepted : 14 January 2019

Funding

Funded by: Australian Research Council (AU)

Award ID: DE150100652

Award Recipient : Alexander N. Combes

Funded by: National Institutes of Health Rebuilding a Kidney consortium

Award ID: DK107344

Award Recipient : Melissa H. Little

Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;

Award ID: 1136085

Award ID: 1156567

Award Recipient : Alexander N. Combes Melissa H. Little

Custom metadata

ScienceOpen disciplines: Molecular medicine

Keywords: single-cell rna sequencing,human kidney organoids,stem cell-derived models,induced pluripotent cells,organoids

Data availability:

ScienceOpen disciplines: Molecular medicine

Keywords: single-cell rna sequencing, human kidney organoids, stem cell-derived models, induced pluripotent cells, organoids

Single-cell analysis reveals congruence between kidney organoids and human fetal kidney

Read this article at

Abstract

Background

Methods

Results

Conclusions

Electronic supplementary material

Related collections

Bacterial extracellular vesicles

Most cited references 37

A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis.

Canonical correlation analysis: an overview with application to learning methods.

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 511

Cited by 72

Most referenced authors 673