MOTILITY-FLOW AND GROWTH CONE NAVIGATION ANALYSIS DURING IN- VITRO NEURAL DEVELOPMENT BY LONG-TERM BRIGHT-FIELD IMAGING Maya Aviv and Prof. Zeev Zalevsky, Faculty of Engineering, Bar-Ilan University M. Pesce, S. Tilve, E. Chieregatti and Dr. F. Difato, Istituto Italiano di Tecnologia, Department of Neuroscience and Brain Technologies, Genova, Italy Feb 2014 J. Biomed. Opt. 18 (11), (September 20, 2013)

Agenda Motivation Background Incubator-Imaging system Image enhancement and processing results

Motivation Investigate motility flow and grown cone navigation during early stage of neural development in order to learn about the neurons growth mechanism Challenge: Long term imaging – avoid phototoxication, pay with low contrast

Neural structure Soma – central part Dendrites - cellular extensions with many branches Spine - a small part from a neuron's dendrite that receives input Axon - is a finer, cable-like projection. The axon carries nerve signals away from the soma and back. Neurite refers to any projection from the cell body of a neuron, when speaking of immature or developing neurons.

Neural “Wave” Growth cones are the main motile structure located at the tips of the neurite. Image of a fluorescently labeled growth cone extending from an axon Dr. Difato Francesco, Photonic-Neurosurgery lab, Istituto Italiano di Tecnologia Some neurons exhibit periodic recurring growth cone-like structures, referred as "waves” followed by growth bursts.

Incubator-Imaging System The whole micro-incubator

Image Enhancement and Processing Our goal is to develop an image enhancement technique, based of time dependence morphology techniques in order to monitor and measure the growing mechanism over time and overcome poor imaging conditions: -~500 images per movie -Low contrast -Non uniform illumination (space and time) -Minor movements of the system (mechanical and biological)

Time Dependence Morphology – The goal is to identify the "significant" change, at a given a set of images of the same scene, taken at different times – The method is to compare each image to the previous ones. – A key issue is to deliver application (task) specific differential morphology. Since finding the “change mask” is usually the first step into understanding the change, segmentation and classifying changes usually requires particular treatment.

Define “Past” Separate between constants (or slow changes) and quick changes (Time derivative) by derivative with average set of past images

(reminder - edge and open operators) Edge detection - an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation. Open (morphology) - the dilation of the erosion of a set A by a structuring element B:

Mark Significant Change mm

Output 94 images Time gap = 3 min ~5 hours

Results – Actin Waves Bar is 10μm Numbers indicate minutes

Results – Actin Waves Actin wave velocity 3±0.5[μm/min], appearing with time gap of 39±5min

Results – Tip Activity Bar is 10μm Numbers indicate minutes

Results – Tip Activity

Results – Soma Activity Soma area is divided into sector. Sector activity is presented in time (temporal) and spectrogram (45min)

Results – Soma Sctivity PCA images shows short and long neurites are similar in time and tempo, while undeveloped ones and growth cones are different.

Summary Experimental system that represents a simple and non- invasive approach to study neuronal growth Special image processing algorithms were adapted Detection of very small and slowly moving spatial changes, and to inspect low contrast image features characteristic of motion and dynamics of a living cell in a long time frame.

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