Applications of quantitative digital image analysis to breast cancer research

Abstract

Our studies of radiogenic carcinogenesis in mouse and human models of breast cancer are based on the view that cell phenotype, microenvironment composition, communication between cells and within the microenvironment are important factors in the development of breast cancer. This is complicated in the mammary gland by its postnatal development, cyclic evolution via pregnancy and involution, and dynamic remodeling of epithelial-stromal interactions, all of which contribute to breast cancer susceptibility. Microscopy is the tool of choice to examine cells in context. Specific features can be defined using probes, antibodies, immunofluorescence, and image analysis to measure protein distribution, cell composition, and genomic instability in human and mouse models of breast cancer. We discuss the integration of image acquisition, analysis, and annotation to efficiently analyze large amounts of image data. In the future, cell and tissue image-based studies will be facilitated by a bioinformatics strategy that generates multidimensional databases of quantitative information derived from molecular, immunological, and morphological probes at multiple resolutions. This approach will facilitate the construction of an in vivo phenotype database necessary for understanding when, where, and how normal cells become cancer.

Hormonally responsive breast cancer cells in a microfluidic co-culture model as a sensor of microenvironmental activity

“…Detection of changes in protein levels is made possible by the quantification of immunofluorescence images acquired under appropriate conditions 38–39 . Additionally, image based quantification permits examination of protein distribution within a population using single-cell analysis techniques.…”

From cells to tissues: Fluorescence confocal microscopy in the study of histological samples

“…Alterations in shape constitute by themselves a potential fingerprint of malignant lesions. Different approaches have been developed on a pure computational base (Lin et al, 2003) or on specific detection of nuclear boundaries by selected bio-molecular targets and subsequent image processing (Ortiz De Solorzano et al, 2002). The efficiency achieved in single-cell recognition by automated procedures makes them a very useful tool in the result quantification not only by saving analysis time, but also gaining the possibility to limit the subjective interpretation that can affect classic histo-pathological evaluation procedures.…”

Remote imaging of single cell 3D morphology with ultrafast coherent phonons and their resonance harmonics

“…Their morphology is a large-scale manifestation of various organizing, competing, and highly regulated biological processes 1–5 . Cell morphological analysis 3,6 thus has long been used in cell biology and physiology for abnormality identification 7,8 , early cancer detection 9,10 , and dynamic change analysis under specific environmental stresses 11,12 . In such investigations, cell biologists usually use cell images provided by optical microscopy to examine cell shape by locating edges, spreading area and so forth, to obtain qualitative and quantitative measurements.…”