Periodicity of nuclear morphology in human fibroblasts

Seaman, Laura; Meixner, Walter; Snyder, John; Rajapakse, Indika

doi:10.1080/19491034.2015.1095432

Cited by 17 publications

(18 citation statements)

References 19 publications

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“…In order to introduce morphology changes, a part of this collection was subjected to a G0/G1 Serum Starvation Protocol [17]. This protocol is used for cell cycle synchronization and has previously been shown to cause morphology changes in human fibroblasts, affecting nuclear size and shape [31]. As a result, the first collection contains 3D volumetric images of cells in the following phenotypic classes: (1) proliferating fibroblasts (PROLIF), and (2) cell cycle synchronized by the serum-starvation protocol (SS).…”

Section: Dataset Preparation 21 Sample Preparationmentioning

confidence: 99%

3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results

Kalinin

Allyn-Feuer

Ade

et al. 2017

Preprint

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Cell deformation is regulated by complex underlying biological mechanisms associated with spatial and temporal morphological changes in the nucleus. Quantitative analysis of changes in size and shape of nuclear structures in 3D microscopic images is important not only for investigating nuclear organization, but also for detecting and treating pathological conditions such as cancer. Multiple methods have been proposed to classify cell and nuclear morphological phenotypes in 3D, however, there is a lack of publicly available 3D data for the evaluation and comparison of such algorithms. To address this problem, we present a dataset containing a of total of 1,433 segmented nuclear and 3,282 nucleolar binary masks. We also provide a baseline evaluation of a number of popular classification algorithms using voxel-based morphometric measures. Original and derived imaging data are made publicly available for downloading on the project web-page: http://www.socr.umich.edu/ projects/3d-cell-morphometry/data.html.

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Section: Dataset Preparation 21 Sample Preparationmentioning

confidence: 99%

3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results

Kalinin

Allyn-Feuer

Ade

et al. 2017

Preprint

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“…We used images of primary human fibroblast cells that were subjected to a G0/G1 Serum Starvation Protocol for cell cycle synchronization 69 . This protocol has previously been shown to alter nuclear organization, which was reflected in the observed morphology changes such as nuclear size and shape 70 . As a result, this collection contains 962 3D nuclear binary masks in the following phenotypic classes: (1) proliferating fibroblasts (PROLIF), and (2) cell cycle synchronized by the serum-starvation protocol cells (SS).…”

Section: Resultsmentioning

confidence: 95%

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Kalinin

Allyn-Feuer

Ade

et al. 2018

Sci Rep

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Quantitative analysis of morphological changes in a cell nucleus is important for the understanding of nuclear architecture and its relationship with pathological conditions such as cancer. However, dimensionality of imaging data, together with a great variability of nuclear shapes, presents challenges for 3D morphological analysis. Thus, there is a compelling need for robust 3D nuclear morphometric techniques to carry out population-wide analysis. We propose a new approach that combines modeling, analysis, and interpretation of morphometric characteristics of cell nuclei and nucleoli in 3D. We used robust surface reconstruction that allows accurate approximation of 3D object boundary. Then, we computed geometric morphological measures characterizing the form of cell nuclei and nucleoli. Using these features, we compared over 450 nuclei with about 1,000 nucleoli of epithelial and mesenchymal prostate cancer cells, as well as 1,000 nuclei with over 2,000 nucleoli from serum-starved and proliferating fibroblast cells. Classification of sets of 9 and 15 cells achieved accuracy of 95.4% and 98%, respectively, for prostate cancer cells, and 95% and 98% for fibroblast cells. To our knowledge, this is the first attempt to combine these methods for 3D nuclear shape modeling and morphometry into a highly parallel pipeline workflow for morphometric analysis of thousands of nuclei and nucleoli in 3D.

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“…The first cell collection has 176 3D volumetric images of primary human fibroblast cells in two phenotypic states/classes: 1) 64 sub-volumes of proliferating fibroblasts (P ROLIF ), and 2) 112 sub-volumes of cell cycle synchronised by the serum-starvation protocol (SS). SS cells are obtained by subjecting fibroblasts cells to G0/G1 Serum Starvation Protocol which is known to produce changes in nuclear shape or size in human fibroblast cells [18]. The second cell line has 101 3D volumetric images of human prostate cancer cells (PC3) in two states/classes: 1) 50 sub-volumes of epithelial (EP I) state, and 2) 51 sub-volumes of mesenchymal transition (EM T ) state.…”

Section: Data Descriptionmentioning

confidence: 99%

Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation

Rana

Sowmya

Meijering

et al. 2020

Preprint

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Classification and characterisation of cellular morphological states are vital for understanding cell differentiation, development, proliferation and diverse pathological conditions. As the onset of morphological changes transpires following genetic alterations in the chromatin configuration inside the nucleus, the nuclear texture as one of the low-level properties if detected and quantified accurately has the potential to provide insights on nuclear organisation and enable early diagnosis and prognosis. This study presents a three dimensional (3D) nuclear texture description method for cell nucleus classification and variation measurement in chromatin patterns on the transition to another phenotypic state. The proposed approach includes third plane information using hyperplanes into the design of the Sorted Random Projections (SRP) texture feature. The significance of including third plane information for low-resolution volumetric images is investigated by comparing the performance of 3D texture descriptor with its respective pseudo 3D form that ignores the interslice intensity correlations. Following classification, changes in chromatin pattern are estimated by computing the ratio of heterochromatin and euchromatin corresponding to their respective intensities and image gradient obtained by 3D SRP. The proposed method is evaluated on two publicly available 3D image datasets of human fibroblast and human prostate cancer cell lines in two phenotypic states obtained from the public Statistics Online Computational Resource. Experimental results show that 3D SRP and 3D Local Binary Pattern provide better results than other utilised handcrafted descriptors and deep learning features extracted using a pre-trained model. The results also show the advantage of utilising 3D feature descriptor for classification over its corresponding pseudo version. In addition, the proposed method validates that as the cell passes to another phenotypic state, there is a change in intensity and aggregation of heterochromatin.Author SummaryAutomated classification and measurement of cellular phenotypic traits can significantly impact clinical decision making. Early detection of diseases requires an accurate description of low-level cellular features to detect small-scale abnormalities in the few abnormal cells in the tissue microenvironment. The challenge is the development of a computational approach for 3D textural feature description that can capture the heterogeneous information in multiple dimensions and characterise the cells in their ultimate and intermediate phenotypic states effectively. Our work has proposed the method and metrics to measure chromatin condensation pattern and classify the phenotypic states. Experimental evaluation on the 3D image set of human fibroblast and human prostate cancer cell collections validates the proposed method for the classification of cell states. Results also signify the credibility of proposed metrics to characterise the cellular phenotypic states and contributes to studies related to early diagnosis, prognosis and drug resistance.

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Periodicity of nuclear morphology in human fibroblasts

Cited by 17 publications

References 19 publications

3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results

3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results

3D Shape Modeling for Cell Nuclear Morphological Analysis and Classification

Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation

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