Evidence map›Paper›PMID 32221334›Full record

ArticleScientific reports2020

Fluorescence microscopy tensor imaging representations for large-scale dataset analysis.

Claudio Vinegoni, Paolo Fumene Feruglio, Gabriel Courties, Stephen Schmidt, Maarten Hulsmans, Sungon Lee, Rui Wang, David Sosnovik, Matthias Nahrendorf, Ralph Weissleder

Abstract read
In one paragraph

Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. 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

10 authors.

Claudio Vinegoni *Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA. cvinegoni@mgh.harvard.edu.ORCID http://orcid.org/0000-0002-9818-3639
Paolo Fumene Feruglio *Center for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Gabriel CourtiesCenter for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Stephen SchmidtCenter for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Maarten HulsmansCenter for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0003-1009-658X
Sungon LeeSchool of Electrical Engineering, Hanyang University, Ansan, Republic of Korea.
Rui WangDepartment of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA, USA.
David SosnovikCardiovascular Research Center, Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Matthias NahrendorfCenter for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Ralph WeisslederCenter for Systems Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.

Funding

Quantitative nanoparticle imaging of macrophagesR01HL131495 · NHLBI · MASSACHUSETTS GENERAL HOSPITAL · PI NAHRENDORF, MATTHIAS, WEISSLEDER, MD, PHD, RALPH · 2017 to 2020
$2.7M
Imaging tumor associated macrophage (TAM) functionR01CA206890 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI PITTET, MIKAEL, WEISSLEDER, MD, PHD, RALPH · 2017 to 2021
$1.9M
NCI NIH HHS R01 CA206890NHLBI NIH HHS R01 HL131495
6 · The paper itself

Abstract

Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail.

Indexed as

AnimalsData AnalysisDatasets as TopicHeartMiceMice, Inbred C57BLMicroscopy, FluorescenceMicrovesselsOptical Imaging

Identifiers

PMID32221334
PMCPMC7101442

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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.