{"title":"On the Variability in Cell and Nucleus Shapes.","authors":"Anusha Devulapally, Varun Parekh, Clint Pazhayidam George, Sreenath Balakrishnan","doi":"10.1159/000527825","DOIUrl":null,"url":null,"abstract":"<p><p>Cell morphology is an important regulator of cell function. Many abnormalities in cellular behavior can be discerned from changes in the shape of the cell and its organelles, typically the nucleus. Two major challenges for developing such phenotypic assays are reconstructing 3D surfaces of individual cells and nuclei from confocal images and developing characterizations of these surfaces for comparisons. We demonstrate two algorithms - 3D active contours and 3D condensed-attention UNet - to segment cells and nuclei from confocal images. The cell and nuclear surfaces are then converted into vectors using a reversible, spherical transform - i.e., shapes can be recovered from the vectors. Typical methods for characterizing shapes using size, shape, and image parameters such as area, volume, shape factor, solidity, and pixel intensities are not amenable to such reverse transformation. Our vector representation's principal component analysis shows that the significant modes of variability among cell and nucleus shapes are scaling and flattening. We benchmark these modes using a known mechanical model for nucleus morphology. Subsequent modes alter the eccentricity of the nucleus and translate and rotate it with respect to the cell. Our vector-space representation of cell and nucleus shape helps physically interpret the variability sources. It may further help to guide mechanical models and identify molecular mechanisms driving cell and nuclear shape changes.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1159/000527825","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/10/31 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0
Abstract
Cell morphology is an important regulator of cell function. Many abnormalities in cellular behavior can be discerned from changes in the shape of the cell and its organelles, typically the nucleus. Two major challenges for developing such phenotypic assays are reconstructing 3D surfaces of individual cells and nuclei from confocal images and developing characterizations of these surfaces for comparisons. We demonstrate two algorithms - 3D active contours and 3D condensed-attention UNet - to segment cells and nuclei from confocal images. The cell and nuclear surfaces are then converted into vectors using a reversible, spherical transform - i.e., shapes can be recovered from the vectors. Typical methods for characterizing shapes using size, shape, and image parameters such as area, volume, shape factor, solidity, and pixel intensities are not amenable to such reverse transformation. Our vector representation's principal component analysis shows that the significant modes of variability among cell and nucleus shapes are scaling and flattening. We benchmark these modes using a known mechanical model for nucleus morphology. Subsequent modes alter the eccentricity of the nucleus and translate and rotate it with respect to the cell. Our vector-space representation of cell and nucleus shape helps physically interpret the variability sources. It may further help to guide mechanical models and identify molecular mechanisms driving cell and nuclear shape changes.
细胞形态是细胞功能的重要调节因素。细胞行为的许多异常都可以从细胞及其细胞器(通常是细胞核)形状的变化中辨别出来。开发此类表型检测的两大挑战是:从共聚焦图像中重建单个细胞和细胞核的三维表面,以及对这些表面进行特征描述以进行比较。我们展示了从共聚焦图像中分割细胞和细胞核的两种算法--3D Active Contours 和 3D Condensed-Attention UNet。然后使用可逆球面变换将细胞和细胞核表面转换为矢量,即可以从矢量中恢复形状。使用大小、形状和图像参数(如面积、体积、形状系数、实体度和像素强度)来描述形状的典型方法不适合这种反向变换。我们的矢量表征主成分分析(PCA)显示,细胞和细胞核形状的主要变化模式是缩放和扁平化。我们使用已知的细胞核形态机械模型对这些模式进行了基准测试。随后的模式会改变细胞核的偏心率,并使其相对于细胞进行平移和旋转。我们对细胞和细胞核形状的矢量空间表示有助于从物理角度解释变异源。它还有助于指导机械模型,并确定驱动细胞和细胞核形状变化的分子机制。本文使用的源代码和数据可在以下网址获取:https://github.com/iitgoa-ml/3d-cells-nuclei-segmentation。