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Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.

ABSTRACT: Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called "-oids" (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells' behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools.

SUBMITTER: Piccinini F

PROVIDER: S-EPMC7303562 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.

Piccinini Filippo F Balassa Tamas T Carbonaro Antonella A Diosdi Akos A Toth Timea T Moshkov Nikita N Tasnadi Ervin A EA Horvath Peter P

Computational and structural biotechnology journal 20200603

Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called "-oids" (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the comple ...[more]

PMID: 32612752

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Project description:BackgroundRecently a series of algorithms have been developed, providing automatic tools for tracing C. elegans embryonic cell lineage. In these algorithms, 3D images collected from a confocal laser scanning microscope were processed, the output of which is cell lineage with cell division history and cell positions with time. However, current image segmentation algorithms suffer from high error rate especially after 350-cell stage because of low signal-noise ratio as well as low resolution along the Z axis (0.5-1 microns). As a result, correction of the errors becomes a huge burden. These errors are mainly produced in the segmentation of nuclei. Thus development of a more accurate image segmentation algorithm will alleviate the hurdle for automated analysis of cell lineage.ResultsThis paper presents a new type of nuclei segmentation method embracing an bi-directional prediction procedure, which can greatly reduce the number of false negative errors, the most common errors in the previous segmentation. In this method, we first use a 2D region growing technique together with the level-set method to generate accurate 2D slices. Then a modified gradient method instead of the existing 3D local maximum method is adopted to detect all the 2D slices located in the nuclei center, each of which corresponds to one nucleus. Finally, the bi-directional prediction method based on the images before and after the current time point is introduced into the system to predict the nuclei in low quality parts of the images. The result of our method shows a notable improvement in the accuracy rate. For each nucleus, its precise location, volume and gene expression value (gray value) is also obtained, all of which will be useful in further downstream analyses.ConclusionsThe result of this research demonstrates the advantages of the bi-directional prediction method in the nuclei segmentation over that of StarryNite/MatLab StarryNite. Several other modifications adopted in our nuclei segmentation system are also discussed.

Project description:UnlabelledIn traditional models ofin vitrobiofilm development, individual bacterial cells seed a surface, multiply, and mature into multicellular, three-dimensional structures. Much research has been devoted to elucidating the mechanisms governing the initial attachment of single cells to surfaces. However, in natural environments and during infection, bacterial cells tend to clump as multicellular aggregates, and biofilms can also slough off aggregates as a part of the dispersal process. This makes it likely that biofilms are often seeded by aggregates and single cells, yet how these aggregates impact biofilm initiation and development is not known. Here we use a combination of experimental and computational approaches to determine the relative fitness of single cells and preformed aggregates during early development ofPseudomonas aeruginosabiofilms. We find that the relative fitness of aggregates depends markedly on the density of surrounding single cells, i.e., the level of competition for growth resources. When competition between aggregates and single cells is low, an aggregate has a growth disadvantage because the aggregate interior has poor access to growth resources. However, if competition is high, aggregates exhibit higher fitness, because extending vertically above the surface gives cells at the top of aggregates better access to growth resources. Other advantages of seeding by aggregates, such as earlier switching to a biofilm-like phenotype and enhanced resilience toward antibiotics and immune response, may add to this ecological benefit. Our findings suggest that current models of biofilm formation should be reconsidered to incorporate the role of aggregates in biofilm initiation.ImportanceDuring the past decades, there has been a consensus around the model of development of a biofilm, involving attachment of single planktonic bacterial cells to a surface and the subsequent development of a mature biofilm. This study presents results that call for a modification of this rigorous model. We show how free floating biofilm aggregates can have a profound local effect on biofilm development when attaching to a surface. Our findings show that an aggregate landing on a surface will eventually outcompete the biofilm population arising from single cells attached around the aggregate and dominate the local biofilm development. These results point to a regime where preformed biofilm aggregates may have a fitness advantage over planktonic cells when it comes to accessing nutrients. Our findings add to the increasingly prominent comprehension that biofilm lifestyle is the default for bacteria and that planktonic single cells may be only a transition state at the most.

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Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.

Publications

Software tools for 3D nuclei segmentation and quantitative analysis in multicellular aggregates.

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