Highlights d Targeted chronic 8-oxoG damage at telomeres promotes telomere shortening d Unrepaired telomeric 8-oxoG in OGG1-deficient cells impairs telomere replication d 8-oxoG-induced telomere losses cause dicentric chromosomes and anaphase bridges d Persistent telomeric 8-oxoG drives telomere crisis and global genomic instability
SUMMARY
Histone modifications influence higher-order chromatin structures at individual epigenomic states and chromatin environments to regulate gene expression. However, genome-wide higher-order chromatin structures shaped by different histone modifications remain poorly characterized. With stochastic optical reconstruction microscopy (STORM), we characterized the higher-order chromatin structures at their epigenomic states, categorized into three major types in interphase: histone acetylation marks form spatially segregated nanoclusters, active histone methylation marks form spatially dispersed larger nanodomains, and repressive histone methylation marks form condensed large aggregates. These distinct structural characteristics are also observed in mitotic chromosomes. Furthermore, active histone marks coincide with less compact chromatin and exhibit a higher degree of co-localization with other active marks and RNA polymerase II (RNAP II), while repressive marks coincide with densely packed chromatin and spatially distant from repressive marks and active RNAP II. Taken together, super-resolution imaging reveals three distinct chromatin structures at various epigenomic states, which may be spatially coordinated to impact transcription.
Definitive diagnosis of malignancy is often challenging due to limited availability of human cell or tissue samples and morphological similarity with certain benign conditions. Our recently developed novel technology-spatial-domain low-coherence quantitative phase microscopy (SL-QPM)-overcomes the technical difficulties and enables us to obtain quantitative information about cell nuclear architectural characteristics with nanoscale sensitivity. We explore its ability to improve the identification of malignancy, especially in cytopathologically non-cancerous-appearing cells. We perform proof-of-concept experiments with an animal model of colorectal carcinogenesis-APC(Min) mouse model and human cytology specimens of colorectal cancer. We show the ability of in situ nanoscale nuclear architectural characteristics in identifying cancerous cells, especially in those labeled as "indeterminate or normal" by expert cytopathologists. Our approach is based on the quantitative analysis of the cell nucleus on the original cytology slides without additional processing, which can be readily applied in a conventional clinical setting. Our simple and practical optical microscopy technique may lead to the development of novel methods for early detection of cancer.
Early cancer detection currently relies on screening the entire at-risk population, as with colonoscopy and mammography. Therefore, frequent, invasive surveillance of patients at risk for developing cancer carries financial, physical, and emotional burdens because clinicians lack tools to accurately predict which patients will actually progress into malignancy. Here we present a new method to predict cancer progression risk via nanoscale nuclear architecture mapping (nanoNAM) of unstained tissue sections based on the intrinsic density alteration of nuclear structure rather than the amount of stain uptake. We demonstrate that nanoNAM detects a gradual increase in the density alteration of nuclear architecture during malignant transformation in animal models of colon carcinogenesis and in human patients with ulcerative colitis, even in tissue that appears histologically normal according to pathologists. We evaluated the ability of nanoNAM to predict “future” cancer progression in patients with ulcerative colitis who did and did not develop colon cancer up to 13 years after their initial colonoscopy. NanoNAM of the initial biopsies correctly classified 12 out of 15 patients who eventually developed colon cancer and 15 out of 18 who did not, with an overall accuracy of 85%. Taken together, our findings demonstrate great potential for nanoNAM in predicting cancer progression risk, and suggest that further validation in a multi-center study with larger cohorts may eventually advance this method to become a routine clinical test.
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