Department of Biomedical Engineering, Yale University, New Haven, CT, 06510, USA.
Bioinformatics. 2021 Aug 4;37(14):2042–2052. doi: 10.1093/bioinformatics/btab053. Epub 2021 Feb 1.
Cancer cell heterogeneity can manifest genetically and phenotypically. Bioinformatics methods have been used to analyze complex genomics and transcriptomics data, but have not been well-established for analyzing biophysical data of phenotypically heterogeneous tumor cells. Here, we take an informatics approach to analyze the biophysical data of MDA-MB-231 cells, a widely used breast cancer cell line, during their spontaneous migration through confined environments. Experimentally, we vary the constriction microchannel geometries (wide channel, short constriction, and long constriction) and apply drug treatments. We find that cells in the short constriction are similar in morphology to the cells in the wide channel. However, their fluorescence profiles are comparable to those in the long constriction. We demonstrate that the cell migratory phenotype is correlated more to mitochondria in a non-confined environment and more to actin in a confined environment. We demonstrate that the cells' migratory phenotypes are altered by ciliobrevin D, a dynein inhibitor, in both confined and non-confined environments. Overall, our approach elucidates phenotypic heterogeneity in cancer cells under confined microenvironments at single-cell resolution.
Here, we apply a bioinformatics approach to a single cell invasion assay. We demonstrate that this method can determine distinctions in morphology, cytoskeletal activities, and mitochondrial activities under various geometric constraints and for cells of different speeds. Our approach can be readily adapted to various heterogeneity studies for different types of input biophysical data. In addition, this approach can be applied to studies related to biophysical changes due to differences in external stimuli, such as treatment effects on cellular and subcellular activities, at single-cell resolution. Finally, as similar bioinformatics methods have been widely applied in studies of genetic heterogeneity, biophysical information extracted using this approach can be analyzed together with the genetic data to relate genetic and phenotypic heterogeneity.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Supplementary data are available at Bioinformatics online.
癌细胞异质性可以在遗传和表型上表现出来。生物信息学方法已被用于分析复杂的基因组学和转录组学数据，但尚未很好地建立用于分析表型异质肿瘤细胞的生物物理数据。在这里，我们采用信息学方法分析 MDA-MB-231 细胞的生物物理数据，这是一种广泛使用的乳腺癌细胞系，在它们自发通过受限环境迁移时。在实验中，我们改变了约束微通道的几何形状（宽通道、短约束和长约束）并进行了药物处理。我们发现短约束中的细胞在形态上与宽通道中的细胞相似。然而，它们的荧光谱与长约束中的细胞相似。我们证明细胞迁移表型与非受限环境中的线粒体更相关，与受限环境中的肌动蛋白更相关。我们证明细胞的迁移表型在受限和非受限环境中均受动力蛋白抑制剂 ciliobrevin D 的改变。总体而言，我们的方法以单细胞分辨率阐明了受限微环境中癌细胞的表型异质性。
在这里，我们将生物信息学方法应用于单细胞侵袭测定。我们证明该方法可以确定在各种几何约束和不同速度的细胞下形态、细胞骨架活性和线粒体活性的差异。我们的方法可以很容易地适应各种不同类型输入生物物理数据的异质性研究。此外，该方法可应用于与由于外部刺激（如细胞和亚细胞活性的治疗效果）的差异而导致的生物物理变化相关的研究，以单细胞分辨率。最后，由于类似的生物信息学方法已广泛应用于遗传异质性的研究，因此使用该方法提取的生物物理信息可以与遗传数据一起分析，以将遗传和表型异质性联系起来。
支持这项研究结果的数据可应合理要求向通讯作者索取。
补充数据可在《生物信息学》在线获取。
