Abstract
Methods for measuring the properties of individual cells within their native 3D environment will enable a deeper understanding of embryonic development, tissue regeneration, and tumorigenesis. However, current methods for segmenting nuclei in 3D tissues are not designed for situations in which nuclei are densely packed, nonspherical, or heterogeneous in shape, size, or texture, all of which are true of many embryonic and adult tissue types as well as in many cases for cells differentiating in culture. Here, we overcome this bottleneck by devising a novel method based on labelling the nuclear envelope (NE) and automatically distinguishing individual nuclei using a tree-structured ridge-tracing method followed by shape ranking according to a trained classifier. The method is fast and makes it possible to process images that are larger than the computer’s memory. We consistently obtain accurate segmentation rates of >90%, even for challenging images such as mid-gestation embryos or 3D cultures. We provide a 3D editor and inspector for the manual curation of the segmentation results as well as a program to assess the accuracy of the segmentation. We have also generated a live reporter of the NE that can be used to track live cells in 3 dimensions over time. We use this to monitor the history of cell interactions and occurrences of neighbour exchange within cultures of pluripotent cells during differentiation. We provide these tools in an open-access user-friendly format.
Highlights
Studying the properties of individual cells in relation to their neighbours in intact tissues is the first step towards understanding the cell–cell interactions that govern the behaviour of tissues
We developed a nondisruptive method to fluorescently label the nuclear envelope (NE) in live cells and used this to follow the history of cell–cell interactions and to document neighbour exchange as pluripotent cells differentiate in 3D culture
It can become difficult to distinguish individual cells based on DAPI staining when nuclei are densely crowded, for example, during neural differentiation of pluripotent cells (Fig 1A left) or 3D cultures (Fig 1A right) or in densely packed tissues in vivo (Fig 1B)
Summary
Studying the properties of individual cells in relation to their neighbours in intact tissues is the first step towards understanding the cell–cell interactions that govern the behaviour of tissues. Single-cell analysis can reveal heterogeneity in cellular properties that is masked by lowerresolution population-averaging methods. Automated computational image analysis is a attractive approach because it is free from operator bias, provides quantitative data, and reveals subvisual information that would not otherwise be apparent [1,2,3,4]. Automated detection of nuclei to GB (WT100133). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
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