SynapCountJ: A Validated Tool for Analyzing Synaptic Densities in Neurons

The quantification of synapses is instrumental to measure the evolution of synaptic densities of neurons under the effect of some physiological conditions, neuronal diseases or even drug treatments. However, the manual quantification of synapses is a tedious, error-prone, time-consuming and subjective task; therefore, reliable tools that might automate this process are desirable. In this paper, we present SynapCountJ, an ImageJ plugin, that can measure synaptic density of individual neurons obtained by immunofluorescence techniques, and also can be applied for batch processing of neurons that have been obtained in the same experiment or using the same setting. The procedure to quantify synapses implemented in SynapCountJ is based on the colocalization of three images of the same neuron (the neuron marked with two antibody markers and the structure of the neuron) and is inspired by methods coming from Computational Algebraic Topology. SynapCountJ provides a procedure to semi-automatically quantify the number of synapses of neuron cultures; as a result, the time required for such an analysis is greatly reduced. The computations performed by SynapCountJ have been validated by comparing the results with those of a formally verified algorithm (implemented in a different system).

[1]  Francisco-Jesús Martín-Mateos,et al.  Verifying the bridge between simplicial topology and algebra: the Eilenberg-Zilber algorithm , 2014, Log. J. IGPL.

[2]  Ana Romero,et al.  Discrete Vector Fields and Fundamental Algebraic Topology , 2010, ArXiv.

[3]  Jónathan Heras,et al.  Towards a Certified Computation of Homology Groups for Digital Images , 2012, CTIC.

[4]  Germán Cuesto,et al.  Phosphoinositide-3-Kinase Activation Controls Synaptogenesis and Spinogenesis in Hippocampal Neurons , 2011, The Journal of Neuroscience.

[5]  W. Eric L. Grimson,et al.  Topological Correction of Subcortical Segmentation , 2003, MICCAI.

[6]  Erik H. W. Meijering,et al.  Automatic detection of neurons in high-content microscope images using machine learning approaches , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[7]  Yves Grau,et al.  Shaggy, the Homolog of Glycogen Synthase Kinase 3, Controls Neuromuscular Junction Growth in Drosophila , 2004, The Journal of Neuroscience.

[8]  Kevin W Eliceiri,et al.  NIH Image to ImageJ: 25 years of image analysis , 2012, Nature Methods.

[9]  Jesús Aransay,et al.  A Mechanized Proof of the Basic Perturbation Lemma , 2008, Journal of Automated Reasoning.

[10]  Y. Goda,et al.  Actin-Dependent Regulation of Neurotransmitter Release at Central Synapses , 2000, Neuron.

[11]  James R. Munkres,et al.  Elements of algebraic topology , 1984 .

[12]  E Meijering,et al.  Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images , 2004, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[13]  Nick Benton Machine Obstructed Proof How many months can it take to verify 30 assembly instructions , 2006 .

[14]  L. Niels Cornelisse,et al.  Automated analysis of neuronal morphology, synapse number and synaptic recruitment , 2011, Journal of Neuroscience Methods.

[15]  Eric W. Danielson,et al.  SynPAnal: Software for Rapid Quantification of the Density and Intensity of Protein Puncta from Fluorescence Microscopy Images of Neurons , 2014, PloS one.

[16]  C. Rueden,et al.  Metadata matters: access to image data in the real world , 2010, The Journal of cell biology.

[17]  Rocío González-Díaz,et al.  C V ] 2 3 M ay 2 01 1 On the Cohomology of 3 D Digital Images , 2013 .

[18]  César Domínguez,et al.  Effective homology of bicomplexes, formalized in Coq , 2011, Theor. Comput. Sci..

[19]  Jónathan Heras,et al.  Defining and computing persistent Z-homology in the general case , 2014, ArXiv.

[20]  Ana Romero,et al.  Homotopy groups of suspended classifying spaces: An experimental approach , 2013, Math. Comput..

[21]  Rafael Ayala,et al.  Homotopy in digital spaces , 2003, Discret. Appl. Math..

[22]  Bradley T. Hyman,et al.  Brain Oligomeric &bgr;-Amyloid but Not Total Amyloid Plaque Burden Correlates With Neuronal Loss and Astrocyte Inflammatory Response in Amyloid Precursor Protein/Tau Transgenic Mice , 2011, Journal of neuropathology and experimental neurology.

[23]  Germán Cuesto,et al.  GSK3β Inhibition Promotes Synaptogenesis in Drosophila and Mammalian Neurons , 2015, PloS one.

[24]  Benjamin C. Pierce,et al.  A verified information-flow architecture , 2014, J. Comput. Secur..

[25]  César Domínguez,et al.  A Certified Reduction Strategy for Homological Image Processing , 2014, TOCL.

[26]  Johannes E. Schindelin,et al.  Fiji: an open-source platform for biological-image analysis , 2012, Nature Methods.

[27]  Jónathan Heras,et al.  A Certified Module to Study Digital Images with the Kenzo System , 2011, EUROCAST.

[28]  Francis Sergeraert,et al.  Effective homology , a survey . ∗ , 2005 .

[29]  D. Selkoe Alzheimer's Disease Is a Synaptic Failure , 2002, Science.

[30]  Ana Romero,et al.  Zigzag persistent homology for processing neuronal images , 2015, Pattern Recognit. Lett..