Evidence map›Paper›PMID 36187891›Full record

ArticleMicrosystems & nanoengineering2022

Measurement of oxygen consumption rates of human renal proximal tubule cells in an array of organ-on-chip devices to monitor drug-induced metabolic shifts.

Samuel H Kann, Erin M Shaughnessey, Jonathan R Coppeta, Hesham Azizgolshani, Brett C Isenberg, Else M Vedula, Xin Zhang, Joseph L Charest

Abstract read
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Article in Microsystems & nanoengineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
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

8 citing papers in PubMed.

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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

8 authors.

Samuel H KannDraper Scholar, 555 Technology Square, Cambridge, MA 02139 USA.
Erin M ShaughnesseyDraper Scholar, 555 Technology Square, Cambridge, MA 02139 USA.
Jonathan R CoppetaDraper, 555 Technology Square, Cambridge, MA 02139 USA.
Hesham AzizgolshaniDraper, 555 Technology Square, Cambridge, MA 02139 USA.
Brett C IsenbergDraper, 555 Technology Square, Cambridge, MA 02139 USA.
Else M VedulaDraper, 555 Technology Square, Cambridge, MA 02139 USA.
Xin ZhangDepartment of Mechanical Engineering, Boston University, 110 Cummington Mall, Boston, MA 02215 USA.ORCID 0000-0002-4413-5084
Joseph L CharestDraper, 555 Technology Square, Cambridge, MA 02139 USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Measurement of cell metabolism in moderate-throughput to high-throughput organ-on-chip (OOC) systems would expand the range of data collected for studying drug effects or disease in physiologically relevant tissue models. However, current measurement approaches rely on fluorescent imaging or colorimetric assays that are focused on endpoints, require labels or added substrates, and lack real-time data. Here, we integrated optical-based oxygen sensors in a high-throughput OOC platform and developed an approach for monitoring cell metabolic activity in an array of membrane bilayer devices. Each membrane bilayer device supported a culture of human renal proximal tubule epithelial cells on a porous membrane suspended between two microchannels and exposed to controlled, unidirectional perfusion and physiologically relevant shear stress for several days. For the first time, we measured changes in oxygen in a membrane bilayer format and used a finite element analysis model to estimate cell oxygen consumption rates (OCRs), allowing comparison with OCRs from other cell culture systems. Finally, we demonstrated label-free detection of metabolic shifts in human renal proximal tubule cells following exposure to FCCP, a drug known for increasing cell oxygen consumption, as well as oligomycin and antimycin A, drugs known for decreasing cell oxygen consumption. The capability to measure cell OCRs and detect metabolic shifts in an array of membrane bilayer devices contained within an industry standard microtiter plate format will be valuable for analyzing flow-responsive and physiologically complex tissues during drug development and disease research.

Indexed as

ChemistryEngineering

Identifiers

PMID36187891
PMCPMC9519964

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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.