Automated Protein Localization using Image Analysis Sankar Venkatraman September 21 st, 2004 AICIP

The Cell Image source

Background DNA (De-oxyribonucleic acid) Information required by a living cell to exist resides inside the nucleus of every cell. These instructions tell the cell what role it is to play in the body. The instructions are in the form of a molecule called DNA that act like a blueprint with a set of instructions. DNA strand is made of letters which form words and which in turn form sentences. Such sentences are genes.

Background contd… GENE They are the instruction manuals for the body. They contain directions for building all the proteins that make our body function. Each DNA fragment is one gene and each gene has a specific instruction to carry so as to produce a protein.

Background contd… PROTEINS Proteins are responsible for every function of a cell and are very small and are usually difficult to see even with the best microscopes around. A specific machinery inside the cell reads a gene and creates an RNA every time there is a need to produce a protein. RNA moves from nucleus to cytoplasm and where the protein manufacturing machinery-Ribosome, reads the message and produces a protein as per the specifications sent out by the gene. Thus to make one protein, we need a number of other highly specialized proteins and thus the humungous number of proteins.

Proteomics…?? A “proteome” is defined as the total set of proteins expressed in a given cell at a given time. Proteomics refers to the science and the process of analyzing and cataloging all the proteins encoded by a genome. A Protein is characterized by  Structure  Sequence  Expression level  Activity  Location – pretty useful to understand its function

Location proteomics Importance…. Protein subcellular location essentially describes the location within a particular cell type where one finds a given protein. The organelle where the protein is located gives a context for it to carry out its role. Each organelle provides a different biochemical environment that influences the associations that a protein may form and the reactions that it may carry out. Thus the knowledge of such data could be invaluable to us.

Motivation to use Image Analysis… The previous systems predicted the protein localization with the help of fractionation, Electron microscopy and Fluorescent microscopy. These are found to be highly biased, time consuming and inconsistent and thus expose the vital need for automated approaches to experimentally determine the sub-cellular localizations.

Motivation contd… Imaging is information rich but has a poor throughput by itself. Automated image analysis can improve the throughput. The motivation is thus to find cells and quantify the image based information. “Location” and “Temporal” proteomics will eventually lead us to functional assignments.

Basic steps for Image Analysis in Proteomics Epi-fluorescence microscopy, confocal microscopy and other fluorescence imaging techniques Correction of uneven illumination, correction for camera response, computational deconvolution and background subtraction Identification of single cells using thresholding and other segmentation procedures Morphological features, texture features and moment-based features Pattern recognition, representative image selection and other analysis using support vector machines and neural networks Image Acquisition Image Restoration Image Processing Feature Extraction Pattern Analysis

Challenges… Segmenting required cells from a given image against heavy background noise and overlapping cells. Defining robust features aiding location proteomics. Classification of subcellular locations using pattern recognition techniques.

Current research standing Robust features that include morphologial, texture and moment, have been defined. Various pattern classifiers have been compared and the apt ones realized. Experiments conducted on CHO and HeLa cells.

Our contribution… Obtain location proteomics with respect to time. Realize better image processing techniques that could suit multi-cell images so as to obtain a better feature set. Use Atomic Force Microscopic (AFM) images in the field of proteomics.

Atomic Force Microscope…  AFM uses a very small cantilever to tap the sample’s surface repeatedly. By shining a laser on this cantilever, a detector can sense how the cantilever is responding to the sample.  It can find how high that point is, and how hard, soft or sticky it is. As the cantilever slowly scans the surface, it feeds all of the data is receives into a computer.  The computer compiles this array of point data into an image that humans can understand. In this way, images with resolutions in a nanometer scale can be obtained, and as the technology matures this resolution will only improve. In contrast, the upper limit for a visible light microscope is 1 micron.

Segmentation… Thresholding algorithms: These caved in for images with high input noise. Were very biased and hardly consistent for different images. Thus the need for a better segmentation technique arises. SNAKES…??!!

Snakes… Active contours, which were first developed by, Kass, Witkin and Terzopoulos [1] are mathematically energy minimizing splines. Splines are mathematical functions used to interpolate or approximate a finite sequence of data values. More intuitively, they are active shapes that respond and move according to energy values in an image and usually deform to fit local minima and thus require appropriate initialization.

Snakes are defined as energy function and to find the best fit between a snake and an object's shape, we minimize the energy.. E internal : Internal spline energy caused by stretching and bending. E image : Measure of the attraction of the image features like contours. E constraint : Measure of external constrains either from higher level shape information or user applied energy.

SNAKES Implicit Embed the snake as a zero level set of a higher dimensional function and to solve the corresponding equation of motion. Due to dimensional formulation are not convenient for shape analysis and user interaction Parametric Consists of an elastic curve that dynamically conforms to object shapes in response to internal (elastic) and external (image and constraint) forces Easier to integrate image data, initial estimate, desired contour properties in a single extraction process. e.g. Dual active contours, Gradient vector fields Classification

Gradient vector field (GVF) snake A traditional snake is a curve X(s) = [x(s), y(s)], s [0, 1], that moves through the spatial domain of the image so as to minimize the energy function A snake that minimizes E must satisfy the Euler equation

GVF contd…. The GVF field is defined as v(x,y) = ( u(x,y),v(x,y) ) that essentially minimizes f is the edge image and alpha is the regularization factor The GVF is implemented by using the following two equations

The all important edges… Since the GVF uses edge image to obtain gradients, we would always prefer an image that would give us “required” edges in a noise free environment. For this, a union of both the images with a spatial threshold set by the user was used to obtain the appropriate edge image. A deblurring filter was applied prior to the Gaussian blur was applied so as to preserve edges and reduce noise respectively.

Sobel edgeCanny edge Union edge Edges… Union

Snakes implementation… Input imageInitialization Snake

Feature Set I know this… It’s a XXX protein!! The goal…!!

Future work… Feature extraction from the segmented images. Multi-cell segmentation. Understand the concept of snakes deeper so as to exploit its usefulness. Devise a better de-noising algorithm so as to get better edges.

Questions…???