Systems and methods for using immunostaining mask to selectively refine ISH analysis results

Abstract

A computer-implemented method of processing image data representing biological units in a tissue sample includes receiving a first image of the tissue sample containing signals from an immunofluorescent (IF) morphological marker, wherein the tissue sample is stained with the IF morphological marker, and receiving a second image of the same tissue sample containing signals from a fluorescent probe, wherein the tissue sample is hybridized in situ with the fluorescent probe. The method further includes classifying each biological unit in the tissue sample into one of at least two classes based on a mean intensity of the signals from the IF morphological marker in the first image, performing a fluorescence in situ hybridization (FISH) analysis of the tissue sample in the second image to obtain results therefrom, and filtering the results of the FISH analysis to produce a subset of the results pertaining to biological units classified in one class.

Description

technical field [0001] In general, the present disclosure relates to protein expression and detection of target nucleic acid sequences in biological samples, and more specifically, to a system for selectively refining the results of in situ hybridization (ISH) analysis using immunostaining masks and methods. Background technique [0002] Protein analysis in tissue sections and other cytological preparations can be performed using fluorescence in situ hybridization (FISH). FISH is a cytogenetic technique used to detect and localize the presence of specific DNA sequences on chromosomes. FISH uses fluorescent probes that bind only those parts of the chromosome to which the fluorescent probe exhibits high sequence complementarity. Fluorescence microscopy can be used to find out where fluorescent probes bind to chromosomes by counting the fluorescent spots present in the image. [0003] For FISH analysis in cancer tumor samples, the measurement of FISH spot counts is only rele...

AI Technical Summary

Problems solved by technology

However, conventional FISH analysis measures the FISH signal from all cells in the field of view because there is a lack of an efficient way to limit the analysis to a defined subset of cells

Without manual intervention, this can lead to many non-epithelial cells (e.g. stromal cells) contributing to FISH spot count statistics, which results in less accurate spot count measurements

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