Computational Methods for Single-Cell Imaging and Omics Data Integration - Watson, Ebony Rose; Taherian Fard, Atefeh; Mar, Jessica Cara (Frontiers in Molecular Biosciences, 2022)
Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been observing phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunities to integrate these data becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing and analysis of single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomenon like ageing. We include an emphasis on machine learning algorithms, for their ability to identify complex patterns in large multidimensional data.