The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Cellular extrusion bioprinting improves kidney organoid reproducibility and conformation
Open accessAbstract
Directed differentiation of human pluripotent stem cells to kidney organoids brings the prospect of drug screening, disease modelling and the generation of tissue for renal replacement. Currently, these applications are hampered by organoid variability, nephron immaturity, low throughput and limited scale. Here, we apply extrusion-based three-dimensional cellular bioprinting to deliver rapid and high-throughput generation of kidney organoids with highly reproducible cell number and viability. We demonstrate that manual organoid generation can be replaced by 6- or 96-well organoid bioprinting and evaluate the relative toxicity of aminoglycosides as a proof of concept for drug testing. In addition, three-dimensional bioprinting enables precise manipulation of biophysical properties, including organoid size, cell number and conformation, with modification of organoid conformation substantially increasing nephron yield per starting cell number. This facilitates the manufacture of uniformly patterned kidney tissue sheets with functional proximal tubular segments. Hence, automated extrusion-based bioprinting for kidney organoid production delivers improvements in throughput, quality control, scale and structure, facilitating in vitro and in vivo applications of stem cell-derived human kidney tissue. Extrusion-based bioprinting has been shown to rapidly and reproducibly generate kidney organoids from a cell-only paste, with the number and maturation of functional units within the kidney tissue capable of being further improved by bioprinting tissue sheets.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
scikit-image: image processing in Python
(2014) · doi:10.7717/peerj.453 · Cited by 6,794
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758
Kidney organoids from human iPS cells contain multiple lineages and model human nephrogenesis
Takasato, M., Er, P. X., Chiu, H. S., Maier, B., Baillie, G. J., Ferguson, C., Parton, R. G., Wolvetang, E. J., Roost, M. S., Chuva de Sousa Lopes, S. M., et al. (2015) · Nature · doi:10.1038/nature15695 · Cited by 1,627
Self-citationChemically defined conditions for human iPSC derivation and culture
Chen, G., Gulbranson, D. R., Hou, Z., Bolin, J. M., Ruotti, V., Probasco, M. D., Smuga-Otto, K., Howden, S. E., Diol, N. R., Propson, N. E., et al. (2011) · Nature Methods · doi:10.1038/nmeth.1593 · Cited by 1,447
Self-citationNephron organoids derived from human pluripotent stem cells model kidney development and injury
Morizane, R., Lam, A. Q., Freedman, B. S., Kishi, S., Valerius, M. T., & Bonventre, J. V. (2015) · Nature Biotechnology · doi:10.1038/nbt.3392 · Cited by 927
Taguchi, A., Kaku, Y., Ohmori, T., Sharmin, S., Ogawa, M., Sasaki, H., & Nishinakamura, R. (2014) · Cell Stem Cell · doi:10.1016/j.stem.2013.11.010 · Cited by 865
Freedman, B. S., Brooks, C. R., Lam, A. Q., Fu, H., Morizane, R., Agrawal, V., Saad, A. F., Li, M. K., Hughes, M. R., Werff, R. V., et al. (2015) · Nature Communications · doi:10.1038/ncomms9715 · Cited by 780
Cited by
Natural Hydrogel-Based Bio-Inks for 3D Bioprinting in Tissue Engineering: A Review
Fatimi, A., Okoro, O. V., Podstawczyk, D., Siminska-Stanny, J., & Shavandi, A. (2022) · Gels · doi:10.3390/gels8030179 · Cited by 276
Programmable and contractile materials through cell encapsulation in fibrous hydrogel assemblies
Davidson, M. D., Prendergast, M. E., Ban, E., Xu, K. L., Mickel, G., Mensah, P., Dhand, A., Janmey, P. A., Shenoy, V. B., & Burdick, J. A. (2021) · Science Advances · doi:10.1126/sciadv.abi8157 · Cited by 101
3d organ-on-a-chip: the convergence of microphysiological systems and organoids
Baptista, L. S., Porrini, C., Kronemberger, G. S., Kelly, D. J., & Perrault, C. M. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1043117 · Cited by 90
“In a model of 3D bioprinted kidney organoids, the authors showed that the bioprinting process was accurate and that the organoids had consistent nephron patterning in a large scale tissue (Lawlor et al., 2021).”
BackgroundFunctional genomics and the future of iPSCs in disease modeling
Brooks, I. R., Garrone, C. M., Kerins, C., Kiar, C. S., Syntaka, S., Xu, J. Z., Spagnoli, F. M., & Watt, F. M. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.03.019 · Cited by 54
Zhou, J., Li, Q., Tian, Z., Yao, Q., & Zhang, M. (2023) · Materials Today Bio · doi:10.1016/j.mtbio.2023.100870 · Cited by 53
Metadata
- Authors
- Lawlor, K. T., Vanslambrouck, J. M., Higgins, J. W., Chambon, A., Bishard, K., Arndt, D., Er, P. X., Wilson, S. B., Howden, S. E., Tan, K. S., Li, F., Hale, L. J., Shepherd, B., Pentoney, S., Presnell, S. C., Chen, A. E., & Little, M. H.
- Venue
- Nature Materials
- Published
- 2020-11-23
- Volume
- 20
- Issue
- 2
- Pages
- 260-271
- ISSN
- 1476-1122 (ISSN-L)
- 1476-4660
- OpenAlex
- W3109860230
- Cited by
- 435
- References
- 39
- FWCI
- 15.85
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Cellular extrusion bioprinting improves kidney organoid reproducibility and conformation. https://app.cochat.ai/papers/10.1038/s41563-020-00853-9
- The paper
- Lawlor, K. T., Vanslambrouck, J. M., Higgins, J. W., Chambon, A., Bishard, K., Arndt, D., Er, P. X., Wilson, S. B., Howden, S. E., Tan, K. S., Li, F., Hale, L. J., Shepherd, B., Pentoney, S., Presnell, S. C., Chen, A. E., & Little, M. H. (2021). Cellular extrusion bioprinting improves kidney organoid reproducibility and conformation. Nature Materials. https://doi.org/10.1038/s41563-020-00853-9
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
