Blue intensity matters for cell cycle profiling in fluorescence DAPI-stained images.

In the past decades, there has been an amazing progress in the understanding of the molecular mechanisms of the cell cycle. This has been possible largely due to a better conceptualization of the cycle itself, but also as a consequence of technological advances. Herein, we propose a new fluorescence image-based framework targeted at the identification and segmentation of stained nuclei with the purpose to determine DNA content in distinct cell cycle stages. The method is based on discriminative features, such as total intensity and area, retrieved from in situ stained nuclei by fluorescence microscopy, allowing the determination of the cell cycle phase of both single and sub-population of cells. The analysis framework was built on a modified k-means clustering strategy and refined with a Gaussian mixture model classifier, which enabled the definition of highly accurate classification clusters corresponding to G1, S and G2 phases. Using the information retrieved from area and fluorescence total intensity, the modified k-means (k=3) cluster imaging framework classified 64.7% of the imaged nuclei, as being at G1 phase, 12.0% at G2 phase and 23.2% at S phase. Performance of the imaging framework was ascertained with normal murine mammary gland cells constitutively expressing the Fucci2 technology, exhibiting an overall sensitivity of 94.0%. Further, the results indicate that the imaging framework has a robust capacity to both identify a given DAPI-stained nucleus to its correct cell cycle phase, as well as to determine, with very high probability, true negatives. Importantly, this novel imaging approach is a non-disruptive method that allows an integrative and simultaneous quantitative analysis of molecular and morphological parameters, thus awarding the possibility of cell cycle profiling in cytological and histological samples.

Quantification of topological features in cell meshes to explore E-cadherin dysfunction.

In cancer, defective E-cadherin leads to cell detachment, migration and metastization. Further, alterations mediated by E-cadherin dysfunction affect cell topology and tissue organization. Herein, we propose a novel quantitative approach, based on microscopy images, to analyse abnormal cellular distribution patterns. We generated undirected graphs composed by sets of triangles which accurately reproduce cell positioning and structural organization within each image. Network analysis was developed by exploring triangle geometric features, namely area, edges length and formed angles, as well as their variance, when compared with the respective equilateral triangles. We generated synthetic networks, mimicking the diversity of cell-cell interaction patterns, and evaluated the applicability of the selected metrics to study topological features. Cells expressing wild-type E-cadherin and cancer-related mutants were used to validate our strategy. Specifically, A634V, R749W and P799R cancer-causing mutants present more disorganized spatial distribution when compared with wild-type cells. Moreover, P799R exhibited higher length and angle distortions and abnormal cytoskeletal organization, suggesting the formation of very dynamic and plastic cellular interactions. Hence, topological analysis of cell network diagrams is an effective tool to quantify changes in cell-cell interactions and, importantly, it can be applied to a myriad of processes, namely tissue morphogenesis and cancer.