Cytokinetics, Inc.

Cytokinentics, Inc.

Eugeni Vaisberg

Cynthia L Adams

Anne M Crompton

James H Sabry

Jinhong Fan

Ge Cong

Daniel A Coleman

Christophe Beraud

Larry C Mattheakis

Jay Trautman

Vadim Kutsyy

Gregg Peter Solar

Aibing Rao

Donald R Oestreicher

Roman Sakowicz

Ming Yu

Kenneth Wood

Cara Ohashi

Ken Wood

Hsien-Hsun Wu

Cell based assays are used to assess the hepatotoxicity of a stimulus. Imaging technologies are used to analyze the effects of a stimulus on hepatocytes. Image analysis may characterize the stimulus on the basis of whether it is hepatotoxic, and if so what type of pathology is exhibited; e.g., apoptosis, necrosis, cholestasis, and/or steatosis.

Predicting hepatotoxicity using cell based assays

A method for generating stimulus response curves (e.g., dose response curves) shows how the phenotype of one or more cells change in response to varying levels of the stimulus. Each 'point' on the curve represents quantitative phenotype for cell(s) at a particular level of stimulus (e.g., dose of a therapeutic). The quantitative phenotypes are multivariate phenotypic representations of the cell(s). They include various features of the cell(s) obtained by image analysis. Such features often include basic parameters obtained from images (e.g., cell shape, nucleus area, Golgi texture) and/or biological characterizations derived from the basic parameters (e.g., cell cycle state, mitotic index, etc.). The stimulus response curves may be compared to allow classification of stimuli and identify subtle differences in related stimuli. To facilitate the comparison, it may be desirable to present the response curves in a principal component space.

Characterizing biological stimuli by response curves

Image analysis methods and apparatus are used for distinguishing live and dead cells. The methods may involve segmenting an image to identify the region(s) occupied by one or more cells and determining the presence of a particular live-dead indicator feature within the region(s). In certain embodiments, the indicator feature is a cytoskeletal component such as tubulin. Prior to producing an image for analysis, cells may be treated with a marker that highlights the live-dead indicator in the image. In the case of tubulin, the marker will co-locate with tubulin and provide a signal that is captured in the image (e.g., a fluorescent emission).

Assay for distinguishing live and dead cells

Methods and apparatus are provided for the analysis of images of cells and extraction biologically-significant shape-related features from the cell images. The extracted features may be correlated with particular conditions induced by biologically-active agents with which cells have been treated, thereby enabling the automated analysis of cells based on cell shape parameters. In particular, the invention provides methods for segmentation of cells in an image using a combination of a reference component image data and cell shape-indicative marker image data in a watershed technique. Further, the invention provides a skeletonization and skeleton analysis technique for extracting biologically-relevant features from cell shapes.

Extracting shape information contained in cell images

A method for calculating distances between stimulus response curves (e.g., dose response curves) allows classification of stimuli. The response curves show how the phenotype of one or more cells changes in response to varying levels of the stimulus. Each 'point' on the curve represents quantitative phenotype or signature for cell(s) at a particular level of stimulus (e.g., dose of a therapeutic). The signatures are multivariate phenotypic representations of the cell(s). They include various features of the cell(s) obtained by image analysis. To facilitate the comparison of stimuli, distances between points on the response curves are calculated. First, the response curves may be aligned on a coordinate representing a separate distance, r, from a common point of negative control (e.g., the point where no stimulus is applied). Integration on r may be used to compute the distance between two response curves. The distance between response curves is used to classify stimuli.

Image analysis methods and apparatus are used for determination of the ploidy of cells. The methods may involve segmenting an image to identify one or more discrete regions occupied by cells or nuclei, determining the presence of a particular ploidy indicator feature within the region(s), and providing a value of the indicator feature to a model that classifies cells' ploidy on the basis of the indicator feature. In some embodiments, the indicator feature is a level of DNA in a cell. In certain embodiments, the method further comprises treating one or more cells with a marker that highlights the ploidy indicator feature. In certain embodiments, the cells are treated prior to producing one or more images of the one or more cells. In certain embodiments, the ploidy indicator feature comprises DNA and the marker co-locates with DNA and provides a signal that is captured in the image. In certain embodiments, the signal comprises a fluorescent emission.

Ploidy classification method

Methods, code and apparatus analyze cell images to automatically identify and characterize the Golgi complex in individual cells. This is accomplished by first locating the cells in the image and defining boundaries of those cells that subsume some or all of the Golgi complex of those cells. The Golgi complex in the images typically have intensity values corresponding to the concentration of a Golgi component in the cell (e.g. a polysaccharide associated with the Golgi complex). The method/system then analyzes the Golgi components of the image (typically on a pixel-by-pixel basis) to mathematically characterize the Golgi complex of individual cells. This mathematical characterization represents phenotypic information about the cells' Golgi complex and can be used to classify cells. From this information, mechanism of action and other important biological information can be deduced.

Image analysis of the golgi complex

Image analysis methods analyze images of cells and place the cells in particular cell cycle phases based upon certain features extracted from the images. The methods can also quantify the total amount of DNA in a cell based on specific features such as fluorescence intensity from fluorescent molecules that bind to DNA. Further, the methods can characterize a cell as mitotic or interphase based on chosen parameters such as the variance in intensity observed in a cell image and/or the size of a region containing DNA. In one example, image analysis methods can classify the cell into one of the following five phases: G, S, G, telophase, and an early stage mitotic phase comprised of prophase, metaphase, and anaphase.

Classifying cells based on information contained in cell images

A system for acquiring knowledge from cellular information. The system has a database comprising a database management module (&ldquo;DBMS&rdquo;). The system also has a variety of modules, including a population module coupled to the DBMS for categorizing and storing a plurality of features (e.g., cell size, distance between cells, cell population, cell type) from an image acquisition device into the database. The system has a translation module coupled to the DBMS for defining a descriptor from a set of selected features from the plurality of features. In a specific embodiment, the descriptor is for a known or unknown compound, e.g., drug. A prediction module is coupled to the DBMS for selecting one of a plurality of a descriptors from known and unknown compounds from the database based upon a selected descriptor from a selected compound. The selected compound may be one that is useful for treatment of human beings or the like.

Database system for predictive cellular bioinformatics

According to the present invention, computer based techniques for using information technology in therapeutics or drug discovery. In an exemplary embodiment, computer based techniques for determining information about the properties of substances based upon information about structure of living or non-living cells exposed to substances are provided. Computer software according to the present invention enables researchers and/or scientists to identify promising candidates in the search for new and better medicines or treatments using, for example, a cellular informatics database.

Growing community of inventors

Foster City, CA, United States of America

Eugeni Vaisberg