For a better understanding of the mechanisms behind cellular functions, quantification of the heterogeneity in an organism or cells is essential. Recently, the importance of quantifying temperature has been highlighted, as it correlates with biochemical reaction rates. Several methods for detecting intracellular temperature have recently been established. Here we develop a novel method for sensing temperature in living cells based on the imaging technique of fluorescence of quantum dots. We apply the method to quantify the temperature difference in a human derived neuronal cell line, SH-SY5Y. Our results show that temperatures in the cell body and neurites are different and thus suggest that inhomogeneous heat production and dissipation happen in a cell. We estimate that heterogeneous heat dissipation results from the characteristic shape of neuronal cells, which consist of several compartments formed with different surface-volume ratios. Inhomogeneous heat production is attributable to the localization of specific organelles as the heat source.
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
Spindle orientation defines the plane of cell division and, thereby, the spatial position of all daughter cells. Here, we develop a live cell microscopy-based methodology to extract spindle movements in human epithelial cell lines and study how spindles are brought to a pre-defined orientation. We show that spindles undergo two distinct regimes of movements. Spindles are first actively rotated toward the cells’ long-axis and then maintained along this pre-defined axis. By quantifying spindle movements in cells depleted of LGN, we show that the first regime of rotational movements requires LGN that recruits cortical dynein. In contrast, the second regime of movements that maintains spindle orientation does not require LGN, but is sensitive to 2ME2 that suppresses microtubule dynamics. Our study sheds first insight into spatially defined spindle movement regimes in human cells, and supports the presence of LGN and dynein independent cortical anchors for astral microtubules.
During embryogenesis, cells repeatedly divide and dynamically change their positions in three-dimensional (3D) space. A robust and accurate algorithm to acquire the 3D positions of the cells would help to reveal the mechanisms of embryogenesis. To acquire quantitative criteria of embryogenesis from time-series 3D microscopic images, image processing algorithms such as segmentation have been applied. Because the cells in embryos are considerably crowded, an algorithm to segment individual cells in detail and accurately is needed. To quantify the nuclear region of every cell from a time-series 3D fluorescence microscopic image of living cells, we developed QCANet, a convolutional neural network-based segmentation algorithm for 3D fluorescence bioimages. We demonstrated that QCANet outperformed 3D Mask R-CNN, which is currently considered as the best algorithm of instance segmentation. We showed that QCANet can be applied not only to developing mouse embryos but also to developing embryos of two other model species. Using QCANet, we were able to extract several quantitative criteria of embryogenesis from 11 early mouse embryos. We showed that the extracted criteria could be used to evaluate the differences between individual embryos. This study contributes to the development of fundamental approaches for assessing embryogenesis on the basis of extracted quantitative criteria.
Rcd1, initially identi®ed as a factor essential for the commitment to nitrogen starvation-invoked differentiation in ®ssion yeast, is one of the most conserved proteins found across eukaryotes, and its mammalian homolog is expressed in a variety of differentiating tissues. Here we show that mammalian Rcd1 is a novel transcriptional cofactor and is critically involved in the commitment step in the retinoic acid-induced differentiation of F9 mouse teratocarcinoma cells, at least in part, via forming complexes with retinoic acid receptor and activation transcription factor-2 (ATF-2). In addition, antisense oligonucleotide treatment of embryonic mouse lung explants suggests that Rcd1 also plays a role in retinoic acid-controlled lung development.
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