Nessys: A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures

Autor: Daina Sadurska, Rosa Portero Migueles, Sally Lowell, Naiming Chen, Guillaume Blin, Julia A. Watson
Rok vydání: 2019
Předmět:
0301 basic medicine
Embryology
Leaves
Indoles
Sry Box
Cellular differentiation
Plant Science
Manual curation
Biochemistry
Accurate segmentation
Pattern Recognition
Automated
0302 clinical medicine
Image Processing
Computer-Assisted
Morphogenesis
Segmentation
Cell Cycle and Cell Division
Biology (General)
Lamin Type B
General Neuroscience
Plant Anatomy
Methods and Resources
Cell Differentiation
Cell Processes
General Agricultural and Biological Sciences
Algorithms
QH301-705.5
Nuclear Envelope
Imaging Techniques
Biology
Research and Analysis Methods
General Biochemistry
Genetics and Molecular Biology
Bottleneck
03 medical and health sciences
Imaging
Three-Dimensional
Protein Domains
Animals
Humans
Computer Imaging
Fluorescent Dyes
Cell Nucleus
General Immunology and Microbiology
business.industry
Embryos
Biology and Life Sciences
Proteins
Pattern recognition
Cell Biology
Morphogenic Segmentation
Embryonic stem cell
030104 developmental biology
Artificial intelligence
business
Classifier (UML)
030217 neurology & neurosurgery
Computer imaging
Developmental Biology
Zdroj: PLoS Biology
PLoS Biology, Vol 17, Iss 8, p e3000388 (2019)
Blin, G, Sadurska, D, Portero Migueles, M R, Chen, N, Watson, J & Lowell, S 2019, ' Nessys : A new set of tools for the automated detection of nuclei within intact tissues and dense 3D cultures ', PLoS Biology, vol. 17, no. 8, e3000388 . https://doi.org/10.1371/journal.pbio.3000388
ISSN: 1545-7885
DOI: 10.1371/journal.pbio.3000388
Popis: 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.
A new computational method allows researchers to measure the properties of individual nuclei in situations in which cells are tightly packed together, for example, during differentiation of stem cells in culture or during early postimplantation development.
Databáze: OpenAIRE
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