Evidence map›Paper›PMID 28819645›Full record

ArticleScience advances2017

3D imaging of optically cleared tissue using a simplified CLARITY method and on-chip microscopy.

Yibo Zhang, Yoonjung Shin, Kevin Sung, Sam Yang, Harrison Chen, Hongda Wang, Da Teng, Yair Rivenson, Rajan P Kulkarni, Aydogan Ozcan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Science advances, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
3.0field-weighted citation impact, top 8% 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

12 citing papers in PubMed, 43 citations in OpenAlex.

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  9. Deep learning in holography and coherent imaging.Light, science & applications · 2019
    Article
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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

10 authors at 2 institutions in 1 country.

Yibo ZhangElectrical Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Yoonjung ShinBioengineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Kevin SungBioengineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Sam YangElectrical Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Harrison ChenBioengineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Hongda WangElectrical Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Da TengComputer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Yair RivensonElectrical Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Rajan P KulkarniBioengineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Aydogan OzcanElectrical Engineering Department, University of California, Los Angeles, Los Angeles, CA 90095, USA.
University of California, Los Angeles · USCalifornia NanoSystems Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

High-throughput sectioning and optical imaging of tissue samples using traditional immunohistochemical techniques can be costly and inaccessible in resource-limited areas. We demonstrate three-dimensional (3D) imaging and phenotyping in optically transparent tissue using lens-free holographic on-chip microscopy as a low-cost, simple, and high-throughput alternative to conventional approaches. The tissue sample is passively cleared using a simplified CLARITY method and stained using 3,3'-diaminobenzidine to target cells of interest, enabling bright-field optical imaging and 3D sectioning of thick samples. The lens-free computational microscope uses pixel super-resolution and multi-height phase recovery algorithms to digitally refocus throughout the cleared tissue and obtain a 3D stack of complex-valued images of the sample, containing both phase and amplitude information. We optimized the tissue-clearing and imaging system by finding the optimal illumination wavelength, tissue thickness, sample preparation parameters, and the number of heights of the lens-free image acquisition and implemented a sparsity-based denoising algorithm to maximize the imaging volume and minimize the amount of the acquired data while also preserving the contrast-to-noise ratio of the reconstructed images. As a proof of concept, we achieved 3D imaging of neurons in a 200-μm-thick cleared mouse brain tissue over a wide field of view of 20.5 mm

Identifiers

PMID28819645
PMCPMC5553818
OpenAlexW2744588570

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.