Abstract
Simple SummarySingle-cell sequencing technologies are growing, advancing, and supporting new opportunities to better understand cancer. A variety of technologies are available that analyze the human transcriptome, genome, epigenome, and proteome, enabling integrated datasets. As a result, these integrated datasets contribute to new mechanistic insights and areas with therapeutic potential. This review summarizes the various single-cell sequencing techniques and provides examples of recent high-impact findings from the utilization of these technologies. Additionally, the translational relevance of these technologies and their use in clinical trials is described, along with the future potential for novel findings using these innovative methods.Single-cell sequencing encompasses a variety of technologies that evaluate cells at the genomic, transcriptomic, epigenomic, and proteomic levels. Each of these levels can be split into additional techniques that enable specific and optimized sequencing for a specialized purpose. At the transcriptomic level, single-cell sequencing has been used to understand immune-malignant cell networks, as well as differences between primary versus metastatic tumors. At the genomic and epigenomic levels, single-cell sequencing technology has been used to study genetic mutations involved in tumor evolution or the reprogramming of regulatory elements present in metastasized disease, respectively. Lastly, at the proteomic level, single-cell sequencing has been used to identify biomarkers important for predicting patient prognosis, as well as biomarkers essential for evaluating optimal treatment strategies. Integrated databases and atlases, as a result of large sequencing experiments, provide a vast array of information that can be applied to various studies and accessed by researchers to further answer scientific questions. This review summarizes recent, high-impact literature covering these aspects, as well as single-cell sequencing in the translational setting. Specifically, we review the potential that single-cell sequencing has in the clinic and its implementation in current clinical studies.
Highlights
Single-cell sequencing evaluates heterogeneity in cellular populations at the transcriptomic, genomic, epigenomic, and proteomic levels [1,2]
There are a variety of current technologies that help understand individual profiling or multiomics profiling of single cells [3] that we briefly summarize in this review
Despite the success and recent expansion of single-cell technology that allows us to seek answers to many previously unknown questions, the field of single-cell data science still faces challenges. These range from generating the best data possible to analyze, and consistency and integration with other datasets uploaded to databases
Summary
Single-cell sequencing evaluates heterogeneity in cellular populations at the transcriptomic, genomic, epigenomic, and proteomic levels [1,2] This methodology is frequently used to understand changes that occur in disease states and is especially helpful for analyzing tumors that exhibit various morphological and phenotypic profiles. Another study used four scRNA-seq datasets to gain insight into normal liver architecture and gene expression and to generate a portal [31] This portal contains updated information and allows for the compilation of a comprehensive array of data available to any scientist investigating liver diseases. Has single-cell transcriptome profiling been used to study a single organ at a time, it has been implemented to identify the characteristics of 15 different human organs [36] This helps better understand the mechanisms behind disease in multiple tissues. This table includes the data sets that are discussed in this review
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