Evidence map›Paper›PMID 36056134›Full record

ArticleScientific reports2022

3D proximal tubule-on-chip model derived from kidney organoids with improved drug uptake.

Jeffrey O Aceves, Szilvia Heja, Kenichi Kobayashi, Sanlin S Robinson, Tomoya Miyoshi, Takuya Matsumoto, Olivier J M Schäffers, Ryuji Morizane, Jennifer A Lewis

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.

0numbers the graph read from it
0cells of the map it votes in
56citing papers in PubMed
5.7field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

56 citing papers in PubMed, 74 citations in OpenAlex.

  1. Review
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  7. New developments and applications of human organoids.Nature reviews. Molecular cell biology · 2026
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  14. Vascularization and maturation of kidney organoids: a comprehensive review of technological trends and challenges.Journal of artificial organs : the official journal of the Japanese Society for Artificial Organs · 2026
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Jeffrey O AcevesPaulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Szilvia HejaWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
Kenichi KobayashiNephrology Division, Massachusetts General Hospital, Boston, MA, USA.
Sanlin S RobinsonPaulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Tomoya MiyoshiDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Takuya MatsumotoWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA.
Olivier J M SchäffersDepartment of Medicine, Harvard Medical School, Boston, MA, USA.
Ryuji MorizaneWyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA. rmorizane@mgh.harvard.edu.ORCID 0000-0002-0377-5667
Jennifer A LewisPaulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. jalewis@seas.harvard.edu.ORCID 0000-0002-0280-2774
Brigham and Women's Hospital · USHarvard University · USHarvard University Press · US

Funding

Ex Vivo Generation of Functional Kidney Tissues for TransplantationUC2DK126023 · NIDDK · HARVARD UNIVERSITY · PI LEWIS, JENNIFER A. · 2020 to 2024
$4.0M
Organ Design and Engineering Training Program (ODET Program)T32EB016652 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI BONVENTRE, JOSEPH VINCENT · 2014 to 2023
$3.5M
Kidney Microphysiological Analysis Platforms (MAP) to Optimize Function and Model DiseaseUH3TR002155 · NCATS · BRIGHAM AND WOMEN'S HOSPITAL · PI BONVENTRE, JOSEPH VINCENT, LEE, LUKE P. · 2019 to 2021
$3.3M
Recreating Kidney Organogenesis in vitro with Human Pluripotent Stem CellsDP2DK133821 · NIDDK · MASSACHUSETTS GENERAL HOSPITAL · PI MORIZANE, RYUJI · 2019 to 2019
$2.5M
Kidney Microphysiological Analysis Platforms (MAP) to Optimize Function and Model DiseaseUG3TR002155 · NCATS · BRIGHAM AND WOMEN'S HOSPITAL · PI BONVENTRE, JOSEPH VINCENT, LEE, LUKE P. · 2017 to 2018
$2.0M
NCATS NIH HHS TR002155NCATS NIH HHS UG3 TR002155NCATS NIH HHS UH3 TR002155NIBIB NIH HHS T32 EB016652NIDDK NIH HHS DP2 DK133821NIDDK NIH HHS UC2 DK126023NIDDK NIH HHS UC2DK126023
6 · The paper itself

Abstract

Three-dimensional, organ-on-chip models that recapitulate kidney tissue are needed for drug screening and disease modeling. Here, we report a method for creating a perfusable 3D proximal tubule model composed of epithelial cells isolated from kidney organoids matured under static conditions. These organoid-derived proximal tubule epithelial cells (OPTECs) are seeded in cylindrical channels fully embedded within an extracellular matrix, where they form a confluent monolayer. A second perfusable channel is placed adjacent to each proximal tubule within these reusable multiplexed chips to mimic basolateral drug transport and uptake. Our 3D OPTEC-on-chip model exhibits significant upregulation of organic cation (OCT2) and organic anion (OAT1/3) transporters, which leads to improved drug uptake, compared to control chips based on immortalized proximal tubule epithelial cells. Hence, OPTEC tubules exhibit a higher normalized lactate dehydrogenase (LDH) release, when exposed to known nephrotoxins, cisplatin and aristolochic acid, which are diminished upon adding OCT2 and OAT1/3 transport inhibitors. Our integrated multifluidic platform paves the way for personalized kidney-on-chip models for drug screening and disease modeling.

Indexed as

Kidney Tubules, ProximalOrganoidsBiological TransportEpithelial CellsMembrane Transport ProteinsMembrane Transport Proteins

Identifiers

PMID36056134
PMCPMC9440090
OpenAlexW4294266835

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.