Nessys: a novel method for accurate nuclear segmentation in 3D

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 where nuclei are densely packed, non-spherical, 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 three 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. 2 35 40 45 50 2012; Carpenter et al., 2013; Meijering et al., 2016).Here, we report a new approach to overcome these bottlenecks in quantitative image analysis of individual cells in 3D. Rather than relying on staining for nuclear content (for example DAPI or Hoescht staining), we instead detect the nuclear envelope (NE). This makes it easier to identify individual nuclei that are in close contact with each other and does not suffer from segmentation problems associated with textured nuclear staining, unusually shaped nuclei, or cell debris. Furthermore, the NE of individual nuclei are easily discernable by eye in crowded tissues, and so manual correction of any mis-segmented nuclei becomes easier than is the case for DAPI stained nuclei. We provide a user-friendly 3D-4D editing tool to rapidly correct any

N-cadherin stabilises neural identity by dampening anti-neural signals

“…We measured the inter-nuclear distances of cells ( Fig. 3A) because we are able to perform nuclear segmentation with high accuracy (42), and because pluripotent and early neural cells have very scant cytoplasm, so inter-nuclear distance is a reasonable proxy for intercellular distance. This assay was designed to indirectly capture the consequences of any changes in adhesion or morphology, which could include a relaxation of cell-cell contacts and an increase in migration.…”

“…Quantification of nuclear protein staining: for the quantification of nuclear staining, PickCells software was used. Cells were segmented by nuclear content or nuclear membrane staining (using DAPI or nuclear envelope marker LaminB1 staining, respectively) using the software's inbuilt NESSys nuclear segmentation module(42). Protein expression was then quantified as the mean pixel intensity in any given nucleus.…”

The transcription factor E2A drives neural differentiation in pluripotent cells

“…Immunofluorescence was quantified by nuclear segmentation based on DAPI signal and manual editing of segmentation results using NesSys software, as detailed in (Blin et al, 2018) qRT-PCR Total RNA was isolated from cells using the Absolutely RNA Miniprep Kit (Agilent). cDNA was generated using Maloney murine leukaemia virus reverse transcriptase (MMLV-RT) and random primers.…”

Mapping the Emergent Spatial Organization of Mammalian Cells using Micropatterns and Quantitative Imaging

“…(Figure 4-1), to segment (Figure 4-2,4-4) and curate segmented objects (Figure 4-3,4-5), to compute object features such as coordinates or average intensity (Figure 4-6) and to export the data (Figure 4-7,4-8). Importantly, we developed a robust nuclear segmentation method called Nessys 28 which is particularly suited for dense and heterogeneous populations of cells such as cells grown on micropatterns (Figure 3). Figure 4-2 shows a representative output of the Nessys module where each individual cell is accurately given a unique color identity.…”

“…One limitation is that accurate tracking of individual cells in a 3D dense cell population remains a very challenging task 37 . Our cell detection method uses the nuclear envelope and performs particularly well on dense and overlapping cell populations 28 . Live reporters of the nuclear envelope are readily available 28,38 and one advantage of the micropatterning technique is that it may be used to prevent the cells from moving outside the field of view during long-term imaging.…”