Advanced image analysis of stem cells and tumor initiating cells

Abstract

Telomapping combined with advanced spatial recognition approaches allows the identification of adult stem cells within their native niches (unpublished results), which is of great relevance for regenerative medicine and oncology in general. Telomerase activity is a critical and unique aspect of stem cell function, and essential to experimental induction of stem cell characteristics in induced pluripotent stem cells (iPSCs). Telomere length is the most straightforward readout of telomerase activity, and can be measured accurately by image analysis of slides prepared using such a telomapping approach. Studies highlighting the significant lifespan increase in mice through telomerase gene therapy [5] or the rejuvenating effects of telomere elongation [6] have used this approach, which is based on Definiens’ Cognition Network Technology (CNT) image analysis methods [7]. In multiplexed IF images of histological sections of organs, nuclei are segmented based on their DAPI signals. Based on their spatial patterns, hierarchical super-structures such as villi in mouse intestine and Lieberkühn crypts at their bottom are identified, which allow the specific topological assessment of their nuclear sub-objects. Within every nucleus, individual telomere substructures are segmented and telomere length is quantified as a function of the signal intensity of a fluorescently labeled PNA-telomeric probe. The method facilitates not only a binary determination of the stemness of cells in histological sections, but allows a detailed, continuous quantification of telomere length. Cells with longest telomeres characterize most primitive adult stem cells, while shorter telomeres usually mark the more differentiated compartments in a given tissue [8]. The detection and characterization of stem cells in healthy or disease conditions can contribute to a better understanding of treatment response.