SynQuant is a Fiji plugin that automatically quantify synapses from fluorescence microscopy images. If you have any feedback or issue, you are welcome to either post issue in Issues section or send email to yug@vt.edu (Guoqiang Yu at Virginia Tech).

Welcome to SynQuant

SynQuant is a Fiji plugin that automatically quantify synapses from fluorescence microscopy images. Try SynQuant Now!

Any problem? SynQuant does not work on your data? Please open an issue. We are happy to help!

Why SynQuant

Versatile and works with difficult data

Detect and segment synaptic punctum
2D and 3D data
Confocal imaging, two-photon imaging, and array tomograhy
Cultured tissue, brain slice, and in-vivo data
Combine pre-synaptic and post-synaptic chanenls to predict the ground-truth synaptic cleft
With or without neurite comtamination
Puncta may have different sizes, shapes and brighness

Unsupervised, but works as good as (or better than) supervised methods

SynQuant performs better than peer unsupervised methods [5-8] for all the three data sets we tested (3D in-vivo [2], array tomography [3], neuron-astrocyte coculture [4]).
SynQuant is performs similar with the best performing supervised method [10] in terms of F1 score and works better than it in terms of average precision.
SynQuant outperforms other supervised methods [9,11].

Statistical significance for each punctum

SynQuant is based on order statistics, which assign a p-value or z-score to each puncta.
Adjust z-score to trade-off between false positives and false negatives.

Easy to use

User friendly Fiji plug-in
Export detected puncta to ROI manager
For 2D image, SynQuant can trace neurites as well
Coming soon: Have lots of images? It is easy to to call the Java class of SynQuant in your MATLAB code.

Getting started

Downlaod SynQuant.
- If you do not have Fiji yet, get it here.
Put the downloaded jar file to the plugin folder of Fiji.
Open Fiji and load the image.
Open SynQuantVid from the plugins menu.
Check out the user guide.
Check out the Simple Video Demo.

Images and datasets

Example images

Example data for testing SynQuant plugin can be found here.

Synthetic and real data used in the paper

Click here to get the following things related to the experiments in [1]:

Three real data sets and labels
Synthetic data with labels
Description of real and synthetic data
Code to generate synthetic data

Algorithm overview

SynQuant detect synapses through an unsupervised probability principled framework. In this framework, analysis is conducted on salient regions rather than pixels.

All synapse candidates are scored by order statistics which combine the information of size, local contrast and noise level. What’s more, p-value or z-score to determine synapse selection, which provides a statistical evidence of the detected synapse. The parameter used in this framework is only the threshold of p-value or z-score which is statistically meaningful and easy to tune.

The framework of synapse detection algorithm now is based on the idea of component tree which scales well to 3D data.

For more information, check out our paper. For citation, see reference [1].

Updates

Version 1.2.8 [7/30/2020]

Fixed the bug that SynQuant can’t get any result when the input images don’t contain any noise.

Version 1.2.7 [7/3/2020]

When users open SynQuant without any valid image, it will give users a notification..

Version 1.2.6 [6/11/2020]

Add extending distance for the "intersect" channel combination method.

Version 1.2

Allow for the detection of pre-, post-synaptic puncta and synaptic sites.
Add the function of combining pre-, post-synaptic puncta detection results.
Support of puncta detection for both 2D and 3D data in the same plugin.
Add one more noise estimation/stabilization method.
Add two more input parameters for user to tune their targets' shapes.
Add a slider for user to post-process results based on z-score.

Version 1.1

Add the component tree structure for synapse detection suggested by Dr.Petter Ranefall. 3 times faster than before
Output ROI regions overlaid with original synaptic data. Quantification can be done based on detected synapses or synaptic sites.

Reference

Citation

[1] Yizhi Wang*, Congchao Wang*, Petter Ranefall, Gerard Joey Broussard, Yinxue Wang, Guilai Shi, Boyu Lyu, Chiung-Ting Wu, Yue Wang, Lin Tian, Guoqiang Yu. (2020). SynQuant: An Automatic Tool to Quantify Synapses from Microscopy Images, Bioinformatics, 36(5), 1599–1606

Dataset related papers

[2] Bass, C., Helkkula, P., De Paola, V., Clopath, C., & Bharath, A. A. (2017). Detection of axonal synapses in 3d two-photon images. PloS one, 12(9).

[3] Collman, F., Buchanan, J., Phend, K. D., Micheva, K. D., Weinberg, R. J., & Smith, S. J. (2015). Mapping synapses by conjugate light-electron array tomography. Journal of Neuroscience, 35(14), 5792-5807.

[4] Mizuno, G. O., Wang, Y., Shi, G., Wang, Y., Sun, J., Papadopoulos, S., ... & Bhattacharyya, A. (2018). Aberrant calcium signaling in astrocytes inhibits neuronal excitability in a human Down syndrome stem cell model. Cell reports, 24(2), 355-365.

Peer methods: unsupervised

[5] Zhang, B., et al. (2007) Multiscale variance-stabilizing transform for mixed-Poisson-Gaussian processes and its applications in bioimaging. Image Processing, 2007 IEEE International Conference on, VI-233-VI-236.

[6] Rezatofighi, S., et al. (2012) A new approach for spot detection in total internal reflection fluorescence microscopy. Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on, 860-863.

[7] Schmitz, S., et al. "Automated analysis of neuronal morphology, synapse number and synaptic recruitment." Journal of neuroscience methods 195 (2011): 185-193.

[8] Simhal, AK., et al. "Probabilistic fluorescence-based synapse detection." PLoS computational biology 13.4 (2017).

Peer methods: supervised

[9] Bass, C., Helkkula, P., De Paola, V., Clopath, C., & Bharath, A. A. (2017). Detection of axonal synapses in 3d two-photon images. PloS one, 12(9).

[10] Kulikov, V., Guo, S. M., Stone, M., Goodman, A., Carpenter, A., Bathe, M., & Lempitsky, V. (2019). DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images. PLoS computational biology, 15 (5).

[11] Ronneberger, O., et al. "U-net: Convolutional networks for biomedical image segmentation." International Conference on Medical image computing and computer-assisted intervention. Springer, Cham, 2015.

savagedude3 / SynQuant