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High-content analysis in neuroscience

Nature Reviews Neuroscience volume 9, pages 779–788 (2008)Cite this article

Abstract

High-content analysis (HCA) combines automated microscopy and automated image analysis to quantify complex cellular anatomy and biochemistry objectively, accurately and quickly. High-content assays that are applicable to neuroscience include those that can quantify various aspects of dendritic trees, protein aggregation, transcription factor translocation, neurotransmitter receptor internalization, neuron and synapse number, cell migration, proliferation and apoptosis. The data that are generated by HCA are rich and multiplexed. HCA thus provides a powerful high-throughput tool for neuroscientists.

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Figure 1: Steps in high-content analysis (HCA).
Figure 2: An example of image processing in high-content analysis (HCA).
Figure 3: An example of a multiplexed assay.
Figure 4: Measuring neurite outgrowth on tissue sections.
Figure 5: Measuring polyglutamine inclusions in cell culture with high-content analysis (HCA).
Figure 6: Measuring astrocyte migration in cell culture with high-content analysis (HCA).

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Acknowledgements

The author would like to thank P. Narayan for her excellent help with establishing and running the HCA in his laboratory, T. Park for the P19 nerve cell images, H. Gibbons for the adult human brain cell cultures, and R. Faull for the human brain material. The HCA platform established by the author was funded by the National Research Centre for Growth and Development, a New Zealand Government Centre of Research Excellence. The research by the author that is cited and described in this article was supported in part by grants from the National Research Centre for Growth and Development, the Health Research Council, the Marsden Fund, the Alzheimer's Trust, the Coker Charitable Trust and the Lynette Sullivan Huntington's Disease Research Fund.

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  1. Mike Dragunow is at the Department of Pharmacology and the National Research Centre for Growth and Development, Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1142, New Zealand. m.dragunow@auckland.ac.nz,

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Supplementary information

Supplementary information S1 (movie)

Measuring neurite outgrowth in situations where there are few cell bodies. The color figure shows calbindin immunostaining of neuritic processes and scattered cell bodies. This image has also been immunostained with an antibody to a nuclear protein giving the intensely stained nuclei. To measure only the neurites (and ignore the other features in this image), the image is first transformed into a gray level image and then a Bottom Hat Morphology filter is applied to the image to segment out the processes. The processes are then detected using thresholding for light objects and measured using fibre length. (AVI 3664 kb)

Supplementary information S2 (figure)

Nuclear translocation measurement. (PDF 1364 kb)

Supplementary information S3 (movie)

Measuring neuron and glial cell number with high content analysis. Cresyl violet stained sections of human pyramidal cells in hippocampal area CA1 were acquired with a Leica microscope at x200 magnification from a neurologically normal human brain. Both pyramidal cells and glial cells are detected by the cresyl violet stain. The color images are first transformed into Gray level images and then 2 different morphology filters are used to segment out the two different cell types – in movie S3 to detect the pyramidal cells and reduce the glial cells we first applied a Dilate (circle, 9 pixels) filter to the images and then applied a Detect Dark Holes filter to the Dilated images; in movie S4 to detect the smaller glial cells and reduce the larger pyramidal cells we used a Bottom Hat (circle, 15 pixels) filter. To count the number of cells we used the Count Nuclei application in Metamorph with the settings appropriate to the size (min/max) and intensity above background (for neurons – 20/20, 15 gray levels; for glia – 5/5, 30 gray levels). This whole process, including logging this data to excel spreadsheets can be automated in Metamorph by writing a Journal comprising these steps. These assays run very quickly depending upon the speed of the computer running the software and we have achieved speeds of 2 sec per image from opening the image to logging the data into excel. (AVI 1926 kb)

Supplementary information S4 (movie)

Measuring neuron and glial cell number with high content analysis. Cresyl violet stained sections of human pyramidal cells in hippocampal area CA1 were acquired with a Leica microscope at x200 magnification from a neurologically normal human brain. Both pyramidal cells and glial cells are detected by the cresyl violet stain. The color images are first transformed into Gray level images and then 2 different morphology filters are used to segment out the two different cell types – in movie S3 to detect the pyramidal cells and reduce the glial cells we first applied a Dilate (circle, 9 pixels) filter to the images and then applied a Detect Dark Holes filter to the Dilated images; in movie S4 to detect the smaller glial cells and reduce the larger pyramidal cells we used a Bottom Hat (circle, 15 pixels) filter. To count the number of cells we used the Count Nuclei application in Metamorph with the settings appropriate to the size (min/max) and intensity above background (for neurons – 20/20, 15 gray levels; for glia – 5/5, 30 gray levels). This whole process, including logging this data to excel spreadsheets can be automated in Metamorph by writing a Journal comprising these steps. These assays run very quickly depending upon the speed of the computer running the software and we have achieved speeds of 2 sec per image from opening the image to logging the data into excel. (AVI 1605 kb)

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FURTHER INFORMATION

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Glossary

Automated image analysis

The use of automation to perform all of the functions of image analysis, including segmentation and data logging into spreadsheets.

Automated microscopy

The use of automation to control a microscope for image acquisition and storage.

High-content analysis

(HCA). A method for analysing complex cellular information (for example, signal intensity and localization) and morphology using automated microscopy and automated image analysis.

High-content screening

Another term for high-content analysis, but one that is generally used to describe high-content analysis that is performed at high throughput for screening purposes.

Image filtering

Removing noise from images.

Image mining

Using software to interface between analysed images and public databases, in order to extract higher-level information (for example, gene function and drug target).

Lab-on-a-chip technology

Miniaturized laboratory hardware that enables quicker and more resource-efficient experimentation and testing.

Multiplexed assay

An assay that extracts more than one piece of information from a data set. Many HCA assays are multiplexed. For example, an assay might count live cells, apoptotic cells and necrotic cells from the same well of a microplate by using different fluorescent channels to identify stages of cell survival and death.

Morphology filters

Mathematical operations for extracting information from images on the basis of size, shape and texture.

Segmentation

The isolation of a particular feature in an image — for example, a cell body if one is counting cells or a neurite if one is analysing outgrowth.

Tissue microarray

A method for patterning multiple samples of tissue onto individual microscope slides to standardize and automate tissue processing (for example, immunohistochemistry), image acquisition and image analysis.

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Dragunow, M. High-content analysis in neuroscience. Nat Rev Neurosci 9, 779–788 (2008). https://doi.org/10.1038/nrn2492

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