Navlakha, S., Suhan, J., Barth, A. L., Bar-Joseph, Z. (July 2013) A high-throughput framework to detect synapses in electron microscopy images. Bioinformatics, 29 (13). i9-i17. ISSN 13674803 (ISSN) (Public Dataset)
Abstract
Motivation: Synaptic connections underlie learning and memory in the brain and are dynamically formed and eliminated during development and in response to stimuli. Quantifying changes in overall density and strength of synapses is an important pre-requisite for studying connectivity and plasticity in these cases or in diseased conditions. Unfortunately, most techniques to detect such changes are either low-throughput (e.g. electrophysiology), prone to error and difficult to automate (e.g. standard electron microscopy) or too coarse (e.g. magnetic resonance imaging) to provide accurate and large-scale measurements.Results: To facilitate high-throughput analyses, we used a 50-year-old experimental technique to selectively stain for synapses in electron microscopy images, and we developed a machine-learning framework to automatically detect synapses in these images. To validate our method, we experimentally imaged brain tissue of the somatosensory cortex in six mice. We detected thousands of synapses in these images and demonstrate the accuracy of our approach using cross-validation with manually labeled data and by comparing against existing algorithms and against tools that process standard electron microscopy images. We also used a semi-supervised algorithm that leverages unlabeled data to overcome sample heterogeneity and improve performance. Our algorithms are highly efficient and scalable and are freely available for others to use. © The Author 2013.
| Item Type: | Paper |
|---|---|
| Subjects: | Investigative techniques and equipment > microscopy > electron microscopy bioinformatics > computational biology > algorithms > machine learning organs, tissues, organelles, cell types and functions > sub-cellular tissues: types and functions > synapse |
| CSHL Authors: | |
| Communities: | CSHL labs > Navlakha lab |
| Depositing User: | Matthew Dunn |
| Date: | 1 July 2013 |
| Date Deposited: | 06 Nov 2019 21:00 |
| Last Modified: | 14 Nov 2023 19:03 |
| PMCID: | PMC3694654 |
| Related URLs: | |
| Dataset ID: |
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| URI: | https://repository.cshl.edu/id/eprint/38690 |
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