Rapid analysis and exploration of fluorescence microscopy images

Benjamin Pavie, Satwik Rajaram, Austin Ouyang, Jason M. Altschuler, Robert J. Steininger, Lani F. Wu, Steven J. Altschuler

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

Original languageEnglish (US)
Article numbere51280
JournalJournal of Visualized Experiments
Issue number85
DOIs
StatePublished - Mar 19 2014

Fingerprint

Workflow
Fluorescence microscopy
Fluorescence Microscopy
Microscopic examination
Quality Control
Image analysis
Quality control
Microscopy
Assays
Throughput
Network protocols
Turnaround time
Learning Curve
Experiments
Biological systems
Cell culture
Screening
Software
Cell Culture Techniques
Tuning

Keywords

  • Basic protocol
  • Fluorescence microscopy
  • High-content analysis
  • High-throughput screening
  • Image analysis
  • Issue 85
  • Open-source
  • PhenoRipper
  • Phenotype

ASJC Scopus subject areas

  • Neuroscience(all)
  • Chemical Engineering(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

Cite this

Pavie, B., Rajaram, S., Ouyang, A., Altschuler, J. M., Steininger, R. J., Wu, L. F., & Altschuler, S. J. (2014). Rapid analysis and exploration of fluorescence microscopy images. Journal of Visualized Experiments, (85), [e51280]. https://doi.org/10.3791/51280

Rapid analysis and exploration of fluorescence microscopy images. / Pavie, Benjamin; Rajaram, Satwik; Ouyang, Austin; Altschuler, Jason M.; Steininger, Robert J.; Wu, Lani F.; Altschuler, Steven J.

In: Journal of Visualized Experiments, No. 85, e51280, 19.03.2014.

Research output: Contribution to journalArticle

Pavie, B, Rajaram, S, Ouyang, A, Altschuler, JM, Steininger, RJ, Wu, LF & Altschuler, SJ 2014, 'Rapid analysis and exploration of fluorescence microscopy images', Journal of Visualized Experiments, no. 85, e51280. https://doi.org/10.3791/51280
Pavie B, Rajaram S, Ouyang A, Altschuler JM, Steininger RJ, Wu LF et al. Rapid analysis and exploration of fluorescence microscopy images. Journal of Visualized Experiments. 2014 Mar 19;(85). e51280. https://doi.org/10.3791/51280
Pavie, Benjamin ; Rajaram, Satwik ; Ouyang, Austin ; Altschuler, Jason M. ; Steininger, Robert J. ; Wu, Lani F. ; Altschuler, Steven J. / Rapid analysis and exploration of fluorescence microscopy images. In: Journal of Visualized Experiments. 2014 ; No. 85.
@article{461db37a30f34cc99759b739d158b88e,
title = "Rapid analysis and exploration of fluorescence microscopy images",
abstract = "Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.",
keywords = "Basic protocol, Fluorescence microscopy, High-content analysis, High-throughput screening, Image analysis, Issue 85, Open-source, PhenoRipper, Phenotype",
author = "Benjamin Pavie and Satwik Rajaram and Austin Ouyang and Altschuler, {Jason M.} and Steininger, {Robert J.} and Wu, {Lani F.} and Altschuler, {Steven J.}",
year = "2014",
month = "3",
day = "19",
doi = "10.3791/51280",
language = "English (US)",
journal = "Journal of Visualized Experiments",
issn = "1940-087X",
publisher = "MYJoVE Corporation",
number = "85",

}

TY - JOUR

T1 - Rapid analysis and exploration of fluorescence microscopy images

AU - Pavie, Benjamin

AU - Rajaram, Satwik

AU - Ouyang, Austin

AU - Altschuler, Jason M.

AU - Steininger, Robert J.

AU - Wu, Lani F.

AU - Altschuler, Steven J.

PY - 2014/3/19

Y1 - 2014/3/19

N2 - Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

AB - Despite rapid advances in high-throughput microscopy, quantitative image-based assays still pose significant challenges. While a variety of specialized image analysis tools are available, most traditional image-analysis-based workflows have steep learning curves (for fine tuning of analysis parameters) and result in long turnaround times between imaging and analysis. In particular, cell segmentation, the process of identifying individual cells in an image, is a major bottleneck in this regard. Here we present an alternate, cell-segmentation-free workflow based on PhenoRipper, an open-source software platform designed for the rapid analysis and exploration of microscopy images. The pipeline presented here is optimized for immunofluorescence microscopy images of cell cultures and requires minimal user intervention. Within half an hour, PhenoRipper can analyze data from a typical 96-well experiment and generate image profiles. Users can then visually explore their data, perform quality control on their experiment, ensure response to perturbations and check reproducibility of replicates. This facilitates a rapid feedback cycle between analysis and experiment, which is crucial during assay optimization. This protocol is useful not just as a first pass analysis for quality control, but also may be used as an end-to-end solution, especially for screening. The workflow described here scales to large data sets such as those generated by high-throughput screens, and has been shown to group experimental conditions by phenotype accurately over a wide range of biological systems. The PhenoBrowser interface provides an intuitive framework to explore the phenotypic space and relate image properties to biological annotations. Taken together, the protocol described here will lower the barriers to adopting quantitative analysis of image based screens.

KW - Basic protocol

KW - Fluorescence microscopy

KW - High-content analysis

KW - High-throughput screening

KW - Image analysis

KW - Issue 85

KW - Open-source

KW - PhenoRipper

KW - Phenotype

UR - http://www.scopus.com/inward/record.url?scp=84897459922&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84897459922&partnerID=8YFLogxK

U2 - 10.3791/51280

DO - 10.3791/51280

M3 - Article

JO - Journal of Visualized Experiments

JF - Journal of Visualized Experiments

SN - 1940-087X

IS - 85

M1 - e51280

ER -