Evidence map›Paper›PMID 35969091›Full record

ArticleJournal of visualized experiments : JoVE2022

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy.

Felix A Kyere, Ian Curtin, Oleh Krupa, Carolyn M McCormick, Mustafa Dere, Sarah Khan, Minjeong Kim, Tzu-Wen Winnie Wang, Qiuhong He, Guorong Wu and 2 more

Open access · greenAbstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 25% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. bioRxiv : the preprint server for biology · 2026
    Article
  2. Article
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

12 authors at 3 institutions in 1 country.

Felix A Kyere *UNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill.
Ian Curtin *UNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill.
Oleh KrupaUNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill.
Carolyn M McCormickUNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill.
Mustafa DereDepartment of Psychiatry, University of North Carolina, Chapel Hill.
Sarah KhanDepartment of Psychiatry, University of North Carolina, Chapel Hill; Department of Computer Science, The University of North Carolina at Greensboro.
Minjeong KimDepartment of Computer Science, The University of North Carolina at Greensboro.
Tzu-Wen Winnie WangCenter for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill; Department of Neurology, The University of North Carolina at Chapel Hill.
Qiuhong HeCenter for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill; Department of Neurology, The University of North Carolina at Chapel Hill.
Guorong WuDepartment of Psychiatry, University of North Carolina, Chapel Hill.
Yen-Yu Ian ShihCenter for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill; Department of Neurology, The University of North Carolina at Chapel Hill.
Jason L SteinUNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill; jason_stein@med.unc.edu.
University of North Carolina at Chapel Hill · USImaging Center · USUniversity of North Carolina at Greensboro · US

Funding

Virology Research Program (Program 4)P30CA016086 · NCI · UNIV OF NORTH CAROLINA CHAPEL HILL · PI David Darr · 1985 to 2026
$201.5M
UNC Neuroscience Center Research Cores: MicroscopyP30NS045892 · NINDS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI ZYLKA, MARK J. · 2003 to 2022
$11.5M
Preclinical CoreP50HD103573 · NICHD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI GABRIEL S DICHTER · 2020 to 2026
$9.7M
NRSA in GeneticsT32GM135128 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Daniel J McKay, JEFF J. SEKELSKY · 2020 to 2026
$5.1M
Circuit Mechanisms Governing the Default Mode NetworkR01MH126518 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Vinod Menon, Yen-Yu Ian Shih · 2021 to 2026
$4.3M
Mechanisms underlying positive and negative BOLD in the striatumRF1MH117053 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SHIH, YEN-YU IAN · 2018 to 2019
$4.0M
Chemogenetic Dissection of Neuronal and Astrocytic Compartment of the BOLD SignalR01MH111429 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SHIH, YEN-YU IAN · 2016 to 2020
$2.7M
The influence of common genetic variation on brain overgrowth pathwaysR01MH120125 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI STEIN, JASON LOUIS · 2019 to 2023
$2.5M
pathQTL: Integrative Multi-Omics Causal Inference of Molecular Mechanisms Leading to Neuropsychiatric IllnessR01MH118349 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI LOVE, MICHAEL ISAIAH, STEIN, JASON LOUIS · 2019 to 2023
$2.4M
Quantifying the developmental trajectory of autism-associated brain overgrowth using 3D cellular resolution imagingR01MH121433 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI STEIN, JASON LOUIS · 2019 to 2023
$2.2M
9.4T Small Animal MRI scanner at UNCS10OD026796 · OD · UNIV OF NORTH CAROLINA CHAPEL HILL · PI SHIH, YEN-YU IAN · 2020 to 2020
$2.0M
A Scalable Platform for Exploring and Analyzing Whole Brain Tissue Cleared ImagesR01NS110791 · NINDS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI WU, GUORONG · 2019 to 2023
$1.9M
NCI NIH HHS P30 CA016086NICHD NIH HHS P50 HD103573NIGMS NIH HHS T32 GM135128NIH HHS S10 OD026796NIMH NIH HHS R01 MH111429NIMH NIH HHS R01 MH118349NIMH NIH HHS R01 MH120125NIMH NIH HHS R01 MH121433NIMH NIH HHS R01 MH126518NIMH NIH HHS RF1 MH117053NIMH NIH HHS S10 MH124745NINDS NIH HHS P30 NS045892NINDS NIH HHS R01 NS110791
6 · The paper itself

Abstract

Tissue clearing followed by light-sheet microscopy (LSFM) enables cellular-resolution imaging of intact brain structure, allowing quantitative analysis of structural changes caused by genetic or environmental perturbations. Whole-brain imaging results in more accurate quantification of cells and the study of region-specific differences that may be missed with commonly used microscopy of physically sectioned tissue. Using light-sheet microscopy to image cleared brains greatly increases acquisition speed as compared to confocal microscopy. Although these images produce very large amounts of brain structural data, most computational tools that perform feature quantification in images of cleared tissue are limited to counting sparse cell populations, rather than all nuclei. Here, we demonstrate NuMorph (Nuclear-Based Morphometry), a group of analysis tools, to quantify all nuclei and nuclear markers within annotated regions of a postnatal day 4 (P4) mouse brain after clearing and imaging on a light-sheet microscope. We describe magnetic resonance imaging (MRI) to measure brain volume prior to shrinkage caused by tissue clearing dehydration steps, tissue clearing using the iDISCO+ method, including immunolabeling, followed by light-sheet microscopy using a commercially available platform to image mouse brains at cellular resolution. We then demonstrate this image analysis pipeline using NuMorph, which is used to correct intensity differences, stitch image tiles, align multiple channels, count nuclei, and annotate brain regions through registration to publicly available atlases. We designed this approach using publicly available protocols and software, allowing any researcher with the necessary microscope and computational resources to perform these techniques. These tissue clearing, imaging, and computational tools allow measurement and quantification of the three-dimensional (3D) organization of cell-types in the cortex and should be widely applicable to any wild-type/knockout mouse study design.

Indexed as

BrainImaging, Three-DimensionalAnimalsAnimals, NewbornMagnetic Resonance ImagingMiceMicroscopy, Confocal

Identifiers

PMID35969091
PMCPMC9912361
OpenAlexW4289340249

What OpenQuestion holds

Textmetadata
LicenceTDM
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.