Ventana Medical Systems, Inc.

Pascal Bamford

Srinivas Chukka

Anindya Sarkar

Olcay Sertel

Ronald T Kurnik

Jim F Martin

Ellen Suzue

Harshal Varangaonkar

Christopher Bieniarz

Michael Otter

Brian Daniel Kelly

Julia Ashworth-Sharpe

Adrian E Murillo

Lou Dietz

Bikash Sabata

Chol Steven Yun

Elizabeth Little

A computer-based specimen analyzer () is configured to detect a level of expression of genes in a cell sample by detecting dots that represent differently stained genes and chromosomes in a cell. The color of the stained genes and the chromosomes is enhanced and filtered to produce a dot mask that defines areas in the image that are genes, chromosomes, or non-genetic material. Metrics are determined for the dots and/or pixels in the image of the cell in areas corresponding to the dots. The metrics are fed to a classifier that separates genes from chromosomes. The results of the classifier are counted to estimate the expression level of genes in the tissue samples.

System and method for detecting gene expression levels in tissue samples

Lab color space silver and red in situ hybridization based techniques for detecting genes in tissue samples

Processing of images acquired via fluorescence microscopy by identifying broadband and other undesired signals from the component signals of a scanned image, and processing selected regions of the image that are known to contain signals of interest, thereby extracting or identifying desired signals while subtracting undesired signals. One or more broadband signals are recognized by their unique signature and ubiquitous dispersion through the image. Regions of the scanned image may be tagged as consisting of predominantly broadband signals and are ignored during a spectral unmixing process. The remaining regions of the image, or selected regions of the image known to contain desired signals, may be unmixed, and the plurality of reference spectra subtracted from the components to extract or identify the target signals. The set of target signals may be refined by eliminating known or obvious sources of noise by, for instance, being compared to known or ideal sets of signals from similar materials.

Spectral unmixing

Embodiments of a system, method, and kit for visualizing a nucleus are disclosed. A tissue sample is pretreated with a protease to permeabilize the nucleus, and then incubated with a nanoparticle/DNA-binding moiety conjugate. The DNA-binding moiety includes at least one DNA-binding molecule. The conjugate binds to DNA within the nucleus, and the nanoparticle is visualized, thereby visualizing the nucleus. Computer and image analysis techniques are used to evaluate nuclear features such as chromosomal distribution, ploidy, shape, size, texture features, and/or contextual features. The method may be used in combination with other multiplexed tests on the tissue sample, including fluorescence in situ hybridization. Kits for performing the method include a protease enzyme composition, a nanoparticle/DNA-binding moiety conjugate, and a reaction buffer.

Application of quantum dots for nuclear staining

A method and system for spectral demultiplexing of fluorescent species, such as quantum dots, conjugated with a biological tissue. The process of demultiplexing involves a non-liner regression based on curve-fitting of estimated spectra of the quantum dots and confidence intervals describing the parameters of such fitting curve for typical quantum dots.

Method and system for spectral unmixing of tissue images

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San Diego, CA, United States of America

Pascal Bamford