Abstract
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 to observe 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 opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.
Highlights
Advances in high-throughput techniques have made it possible to collect largescale data from different types of regulatory information that controls a single cell
This review provides a comprehensive overview of the advanced technologies used for single cell imaging and omics sequencing, and the opportunities that exist to integrate these two types of data
We describe key advances in technologies and outline the major steps that are important for working with these two data types
Summary
Advances in high-throughput techniques have made it possible to collect largescale data from different types of regulatory information that controls a single cell. The recent advances that have made single cell sequencing possible include improvements in single cell isolation, genome amplification, and barcoding which collectively have provided a platform to source information from different cellular and molecular levels without having to pool starting material. There are several technologies that are mainly based on the applications of fluorescence-activated cell sorting (FACS), Western blotting, metal-tagged antibodies followed by mass cytometry to sort, qualify phenotypes and high-multiplexing protein analysis (He et al, 2020) These methods are able to capture and analyse cell surface, cytoplasmic and secreted proteins (Labib and Kelley, 2020). ECCITE-seq is an extension of the CITE-seq method which provides a range of multi-modal information including transcriptome, protein, clonotype, and CRISPR perturbation data at the single cell level (Mimitou et al, 2019; Lee et al, 2020). At this stage, being able to capture single-cell level data for proteins is still only for smaller numbers of molecules at a time
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