3D-catFISH: A system for automated quantitative three-dimensional compartmental analysis of temporal gene transcription activity imaged by fluorescence in situ hybridization

Monica K. Chawla, Gang Lin, Kathy Olson, Almira Ivanova Vazdarjanova, Sara N. Burke, Bruce L. McNaughton, Paul F. Worley, John F. Guzowski, Badrinath Roysam, Carol A. Barnes

Research output: Contribution to journalArticle

48 Citations (Scopus)

Abstract

Fluorescence in situ hybridization (FISH) of neural activity-regulated, immediate-early gene (IEG) expression provides a method of functional brain imaging with cellular resolution. This enables the identification, in one brain, of which specific principal neurons were active during each of two distinct behavioral epochs. The unprecedented potential of this differential method for large-scale analysis of functional neural circuits is limited, however, by the time-intensive nature of manual image analysis. A comprehensive software tool for processing three-dimensional, multi-spectral confocal image stacks is described which supports the automation of this analysis. Nuclei counterstained with conventional DNA dyes and FISH signals indicating the sub-cellular distribution of specific, IEG RNA species are imaged using different spectral channels. The DNA channel data are segmented into individual nuclei by a three-dimensional multi-step algorithm that corrects for depth-dependent attenuation, non-isotropic voxels, and imaging noise. Intra-nuclear and cytoplasmic FISH signals are associated spatially with the nuclear segmentation results to generate a detailed tabular/database and graphic representation. Here we present a comprehensive validation of data generated by the automated software against manual quantification by human experts on hippocampal and parietal cortical regions (96.5% concordance with multi-expert consensus). The high degree of reliability and accuracy suggests that the software will generalize well to multiple brain areas and eventually to large-scale brain analysis.

Original languageEnglish (US)
Pages (from-to)13-24
Number of pages12
JournalJournal of Neuroscience Methods
Volume139
Issue number1
DOIs
StatePublished - Oct 15 2004

Fingerprint

Fluorescence In Situ Hybridization
Immediate-Early Genes
Software
Brain
Genes
Parietal Lobe
Functional Neuroimaging
Automation
DNA
Noise
Coloring Agents
Databases
RNA
Gene Expression
Neurons

Keywords

  • Cell counting
  • Cell nuclei
  • Confocal microscopy
  • Image analysis
  • Segmentation
  • catFISH

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

3D-catFISH : A system for automated quantitative three-dimensional compartmental analysis of temporal gene transcription activity imaged by fluorescence in situ hybridization. / Chawla, Monica K.; Lin, Gang; Olson, Kathy; Vazdarjanova, Almira Ivanova; Burke, Sara N.; McNaughton, Bruce L.; Worley, Paul F.; Guzowski, John F.; Roysam, Badrinath; Barnes, Carol A.

In: Journal of Neuroscience Methods, Vol. 139, No. 1, 15.10.2004, p. 13-24.

Research output: Contribution to journalArticle

Chawla, Monica K. ; Lin, Gang ; Olson, Kathy ; Vazdarjanova, Almira Ivanova ; Burke, Sara N. ; McNaughton, Bruce L. ; Worley, Paul F. ; Guzowski, John F. ; Roysam, Badrinath ; Barnes, Carol A. / 3D-catFISH : A system for automated quantitative three-dimensional compartmental analysis of temporal gene transcription activity imaged by fluorescence in situ hybridization. In: Journal of Neuroscience Methods. 2004 ; Vol. 139, No. 1. pp. 13-24.
@article{99e407e681c24a7598a97c5823557023,
title = "3D-catFISH: A system for automated quantitative three-dimensional compartmental analysis of temporal gene transcription activity imaged by fluorescence in situ hybridization",
abstract = "Fluorescence in situ hybridization (FISH) of neural activity-regulated, immediate-early gene (IEG) expression provides a method of functional brain imaging with cellular resolution. This enables the identification, in one brain, of which specific principal neurons were active during each of two distinct behavioral epochs. The unprecedented potential of this differential method for large-scale analysis of functional neural circuits is limited, however, by the time-intensive nature of manual image analysis. A comprehensive software tool for processing three-dimensional, multi-spectral confocal image stacks is described which supports the automation of this analysis. Nuclei counterstained with conventional DNA dyes and FISH signals indicating the sub-cellular distribution of specific, IEG RNA species are imaged using different spectral channels. The DNA channel data are segmented into individual nuclei by a three-dimensional multi-step algorithm that corrects for depth-dependent attenuation, non-isotropic voxels, and imaging noise. Intra-nuclear and cytoplasmic FISH signals are associated spatially with the nuclear segmentation results to generate a detailed tabular/database and graphic representation. Here we present a comprehensive validation of data generated by the automated software against manual quantification by human experts on hippocampal and parietal cortical regions (96.5{\%} concordance with multi-expert consensus). The high degree of reliability and accuracy suggests that the software will generalize well to multiple brain areas and eventually to large-scale brain analysis.",
keywords = "Cell counting, Cell nuclei, Confocal microscopy, Image analysis, Segmentation, catFISH",
author = "Chawla, {Monica K.} and Gang Lin and Kathy Olson and Vazdarjanova, {Almira Ivanova} and Burke, {Sara N.} and McNaughton, {Bruce L.} and Worley, {Paul F.} and Guzowski, {John F.} and Badrinath Roysam and Barnes, {Carol A.}",
year = "2004",
month = "10",
day = "15",
doi = "10.1016/j.jneumeth.2004.04.017",
language = "English (US)",
volume = "139",
pages = "13--24",
journal = "Journal of Neuroscience Methods",
issn = "0165-0270",
publisher = "Elsevier",
number = "1",

}

TY - JOUR

T1 - 3D-catFISH

T2 - A system for automated quantitative three-dimensional compartmental analysis of temporal gene transcription activity imaged by fluorescence in situ hybridization

AU - Chawla, Monica K.

AU - Lin, Gang

AU - Olson, Kathy

AU - Vazdarjanova, Almira Ivanova

AU - Burke, Sara N.

AU - McNaughton, Bruce L.

AU - Worley, Paul F.

AU - Guzowski, John F.

AU - Roysam, Badrinath

AU - Barnes, Carol A.

PY - 2004/10/15

Y1 - 2004/10/15

N2 - Fluorescence in situ hybridization (FISH) of neural activity-regulated, immediate-early gene (IEG) expression provides a method of functional brain imaging with cellular resolution. This enables the identification, in one brain, of which specific principal neurons were active during each of two distinct behavioral epochs. The unprecedented potential of this differential method for large-scale analysis of functional neural circuits is limited, however, by the time-intensive nature of manual image analysis. A comprehensive software tool for processing three-dimensional, multi-spectral confocal image stacks is described which supports the automation of this analysis. Nuclei counterstained with conventional DNA dyes and FISH signals indicating the sub-cellular distribution of specific, IEG RNA species are imaged using different spectral channels. The DNA channel data are segmented into individual nuclei by a three-dimensional multi-step algorithm that corrects for depth-dependent attenuation, non-isotropic voxels, and imaging noise. Intra-nuclear and cytoplasmic FISH signals are associated spatially with the nuclear segmentation results to generate a detailed tabular/database and graphic representation. Here we present a comprehensive validation of data generated by the automated software against manual quantification by human experts on hippocampal and parietal cortical regions (96.5% concordance with multi-expert consensus). The high degree of reliability and accuracy suggests that the software will generalize well to multiple brain areas and eventually to large-scale brain analysis.

AB - Fluorescence in situ hybridization (FISH) of neural activity-regulated, immediate-early gene (IEG) expression provides a method of functional brain imaging with cellular resolution. This enables the identification, in one brain, of which specific principal neurons were active during each of two distinct behavioral epochs. The unprecedented potential of this differential method for large-scale analysis of functional neural circuits is limited, however, by the time-intensive nature of manual image analysis. A comprehensive software tool for processing three-dimensional, multi-spectral confocal image stacks is described which supports the automation of this analysis. Nuclei counterstained with conventional DNA dyes and FISH signals indicating the sub-cellular distribution of specific, IEG RNA species are imaged using different spectral channels. The DNA channel data are segmented into individual nuclei by a three-dimensional multi-step algorithm that corrects for depth-dependent attenuation, non-isotropic voxels, and imaging noise. Intra-nuclear and cytoplasmic FISH signals are associated spatially with the nuclear segmentation results to generate a detailed tabular/database and graphic representation. Here we present a comprehensive validation of data generated by the automated software against manual quantification by human experts on hippocampal and parietal cortical regions (96.5% concordance with multi-expert consensus). The high degree of reliability and accuracy suggests that the software will generalize well to multiple brain areas and eventually to large-scale brain analysis.

KW - Cell counting

KW - Cell nuclei

KW - Confocal microscopy

KW - Image analysis

KW - Segmentation

KW - catFISH

UR - http://www.scopus.com/inward/record.url?scp=4444340185&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=4444340185&partnerID=8YFLogxK

U2 - 10.1016/j.jneumeth.2004.04.017

DO - 10.1016/j.jneumeth.2004.04.017

M3 - Article

C2 - 15351517

AN - SCOPUS:4444340185

VL - 139

SP - 13

EP - 24

JO - Journal of Neuroscience Methods

JF - Journal of Neuroscience Methods

SN - 0165-0270

IS - 1

ER -