Download MOTILITY-FLOW AND GROWTH CONE NAVIGATION ANALYSIS

MOTILITY-FLOW AND GROWTH CONE
NAVIGATION ANALYSIS DURING INVITRO NEURAL DEVELOPMENT BY
LONG-TERM BRIGHT-FIELD IMAGING
J. Biomed. Opt. 18 (11), 111415 (September 20, 2013)
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
Agenda
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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
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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
• Some neurons exhibit periodic
recurring growth cone-like
structures, referred as "waves”
followed by growth bursts.
Dr. Difato Francesco,
Photonic-Neurosurgery lab,
Istituto Italiano di Tecnologia
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:
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~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)
3
2
1
6
4
5
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.
Thank you