Image analysis – Applications – Biomedical applications
Reexamination Certificate
1999-10-12
2004-05-04
Patel, Jayanti K. (Department: 2625)
Image analysis
Applications
Biomedical applications
C422S050000, C422S051000, C422S053000, C422S073000, C435S006120, C435S007200, C435S007230, C702S019000, C702S020000, C702S027000
Reexamination Certificate
active
06731781
ABSTRACT:
BACKGROUND OF THE INVENTION
The field of the present invention relates to the image processing of gene expression microarrays. In particular, the invention relates to automatically processing the information contained in a gene expression microarray.
A cell relies on proteins for a variety of its functions. Producing energy, biosynthesizing all component macromolecules, maintaining cellular architecture, and acting upon intra- and extracellular stimuli are all protein dependent activities. Almost every cell within an organism contains the information necessary to produce the entire repertoire of proteins that that organism can specify. This information is stored as genes within the organism's DNA genome. Different organisms have different numbers of genes to define them. The number of human genes, for example, is estimated to be between 30,000 and 100,000.
Only a portion of the genome is composed of genes, and the set of genes expressed as proteins varies between cell types. Some of the proteins present in a single cell are likely to be present in all cells because they serve functions required in every type of cell. These proteins can be thought of as “housekeeping” proteins. Other proteins serve specialized functions that are only required in particular cell types. Such proteins are generally produced only in limited types of cells. Given that a large part of a cell's specific functionality is determined by the genes that it is expressing, it is logical that transcription, the first step in the process of converting the genetic information stored in an organism's genome into protein, would be highly regulated by the control network that coordinates and directs cellular activity.
The regulation of transcription is readily observed in studies that scrutinize activities evident in cells configuring themselves for a particular function (specialization into a muscle cell) or state (active multiplication or quiescence). As cells alter their state, coordinate transcription of the protein sets required for the change of state can be observed. As a window both on cell status and on the system controlling the cell, detailed, global knowledge of the transcriptional state could provide a broad spectrum of information useful to biologists. For instance, knowledge of when and in what types of cell the protein product of a gene of unknown function is expressed would provide useful clues as to the likely function of that gene. Furthermore, determining gene expression patterns in normal cells could provide detailed knowledge of how the control system achieves the highly coordinated activation and deactivation required to develop and differentiate a single fertilized egg into a mature organism. Also, comparing gene expression patterns in normal and pathological cells could provide useful diagnostic “fingerprints” and help identify aberrant functions that would be reasonable targets for therapeutic intervention.
The ability to perform studies that determine the transcriptional state of a large number of genes has, however, until recently, been severely inhibited by limitations on the ability to survey cells for the presence and abundance of a large number of gene transcripts in a single experiment. A primary limitation has been the small number of identified genes. In humans, only a few thousand of the complete set have been physically purified and characterized to any extent. Another significant limitation has been the cumbersome nature of transcription analysis. Even a large experiment on human cells can track expression of only a dozen genes, clearly an inadequate sampling to make any meaningful inferences about so complex a control system.
Two recent technological advances have provided the means to overcome some of these limitations in examining the patterns and relationships in gene transcription. The cloning of molecules derived from mRNA transcripts in particular tissues, followed by the application of high-throughput sequencing to the DNA ends of the members of these libraries has yielded a catalog of expressed sequence tags (ESTs). M. S. Boguski and G. D. Schuler, “Establishing a Human Transcript Map,”
Nature Genetics
10(4), 369-371 (1995). These signature sequences provide unambiguous identifiers for a large cohort of genes. At present, approximately 40,000 human genes have been “tagged” by this route, and many have been mapped to their genomic location. G. D. Schuler, M. S. Boguski, et al., “A Gene Map of the Human Genome,”
Science
274(5287), 540-546 (1996).
In addition, the clones from which these sequences were derived provide analytical reagents that can be used in the quantitation of transcripts from biological samples. Specifically, the nucleic acid polymers, DNA and RNA, are biologically synthesized in a copying reaction in which one polymer serves as a template for the synthesis of an opposing strand, which is termed its complement. Even after separation from each other, these strands can be induced to pair quite specifically with each other to form a very tight molecular complex in a process called hybridization. This specific binding is the basis of most analytical procedures for quantitating the presence of a particular species of nucleic acid, such as the mRNA specifying a particular protein gene product.
Furthermore, the recent development of microarray technology, a hybridization-based process, has begun to enable the simultaneous quantitation of many nucleic acid species, even genome-wide quantitation. M. Schena, D. Shalon, R. W. Davis, and P. O. Brown, “Quantitative Monitoring of Gene Expression Patterns With a Complementary DNA Microarray,”
Science
270(5235), 467-470, (1995), J. DeRisi, L. Penland, P. O. Brown, M. L. Bittner, P. S. Meltzer, M. Ray, Y. Chen, Y. A. Su, and J. M. Trent, “Use of a cDNA Microarray to Analyze Gene Expression Patterns in Human Cancer,”
Nature Genetics
14(4), 457-460 (1996), M. Schena, D. Shalon, R. Heller, A. Chai, P. O. Brown, and R. W. Davis, “Parallel Human Genome Analysis: Microarray-based Expression Monitoring of 1000 Genes,”
Proc. Nat. Acad. Sci. U.S.A
. 93(20), 10614-10619 (1996). For mRNA expression studies, the goal is to develop microarrays that contain every gene in a genome against which mRNA expression levels can be quantitatively assessed. This technology combines robotic placement (spotting) of small amounts of individual, pure nucleic acid species on a glass slide, hybridization to this array with multiple fluorescently labeled nucleic acids, and traditionally, detection and quantitation of the resulting fluor-tagged hybrids with a scanning confocal fluorescent microscope. When used to detect transcripts, a particular RNA transcript (an mRNA) is copied into DNA (a cDNA) and this copied form of the transcript is immobilized on a glass slide. The entire complement of transcript mRNAs present in a particular cell type is extracted from cells and then a fluor-tagged cDNA representation of the extracted mRNAs is made in vitro by an enzymatic reaction termed reverse transcription. Fluor-tagged representations of mRNA from several cell types, each tagged with a fluor emitting a different color light, are hybridized to the array of cDNAs and then fluorescence at the site of each immobilized cDNA is quantitated.
The various characteristics of this analytic method make it particularly useful for directly comparing the abundance of mRNAs present in two cell types. An example of such a system is presented in FIG.
1
. In this experiment, an array of cDNAs was hybridized with a green fluor-tagged collection of mRNAs extracted from a tumorigenic melanoma cell line (UACC-903) and a red fluor-tagged collection of mRNAs was extracted from a nontumorigenic derivative of the original cell line (UACC-903+6). J. DeRisi, L. Penland, P. O. Brown, M. L. Bittner, P. S. Meltzer, M. Ray, Y. Chen, Y. A. Su, and J. M. Trent, “Use of a cDNA Microarray to Analyze Gene Expression Patterns in Human Cancer,”
Nature Genetics
14(4), 457-460 (1996). Monochrome images of the fluorescent intensity observed for each of the fluors a
Park James Darrell
Shams Soheil
Zhou Yi-Xiong
BioDiscovery, Inc.
Patel Jayanti K.
Tabatabai Abolfazl
Tope - McKay & Associates
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