Evidence map›Paper›PMID 36466188›Full record

ArticleNeurophotonics2022

Optimal combinations of fluorescent vessel labeling and tissue clearing methods for three-dimensional visualization of vasculature.

Jingtan Zhu, Yating Deng, Tingting Yu, Xiaomei Liu, Dongyu Li, Dan Zhu

Abstract read
In one paragraph

Article in Neurophotonics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

6 authors.

Jingtan ZhuBritton Chance Center for Biomedical Photonics-MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0000-0001-6596-9566
Yating DengBritton Chance Center for Biomedical Photonics-MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Tingting YuBritton Chance Center for Biomedical Photonics-MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Xiaomei LiuBritton Chance Center for Biomedical Photonics-MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Dongyu LiBritton Chance Center for Biomedical Photonics-MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0000-0002-8793-3238
Dan ZhuBritton Chance Center for Biomedical Photonics-MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, China.ORCID https://orcid.org/0000-0002-3640-4758

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Significance: Visualization of intact vasculatures is crucial to understanding the pathogeneses of different neurological and vascular diseases. Although various fluorescent vessel labeling methods have been used in combination with tissue clearing for three-dimensional (3D) visualization of different vascular networks, little has been done to quantify the labeling effect of each vessel labeling routine, as well as their applicability alongside various clearing protocols, making it difficult to select an optimal combination for finely constructing different vasculatures. Therefore, it is necessary to systematically assess the overall performance of these common vessel labeling methods combined with different tissue-clearing protocols. Aim: A comprehensive evaluation of the labeling quality of various vessel labeling routines in different organs, as well as their applicability alongside various clearing protocols, were performed to find the optimal combinations for 3D reconstruction of vascular networks with high quality. Approach: Four commonly-used vessel labeling techniques and six typical tissue optical clearing approaches were selected as candidates for the systematic evaluation. Results: The vessel labeling efficiency, vessel labeling patterns, and compatibility of each vessel labeling method with different tissue-clearing protocols were quantitatively evaluated and compared. Based on the comprehensive evaluation results, the optimal combinations were selected for 3D reconstructions of vascular networks in several organs, including mouse brain, liver, and kidney. Conclusions: This study provides valuable insight on selecting the proper pipelines for 3D visualization of vascular networks, which may facilitate understanding of the underlying mechanisms of various neurovascular diseases.

Indexed as

intact organsoptical imagingtissue optical clearingvessel labeling

Identifiers

PMID36466188
PMCPMC9709454

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Registered trials

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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.