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Technical advancements in single-cell genomics have improved our understanding of molecular and genetic regulation. All cell types in the human body can now be characterized using single-cell multi-omics analyses, which help uncover the complex genetic and epigenetic regulatory mechanisms and indicate cellular interactions within tissues. Now, as single-cell research moves toward clinical implementation, it is being incorporated in diagnostic and therapeutic measures for precision medicine. This special issue in single-cell genomics provides a comprehensive view of the current technological status and the future perspectives and applications of single-cell analysis.
Profiling the human body at the single cell resolution will reveal a map to enable researchers to compare cell types that are found in healthy and disease tissues at a much finer resolution. Large international projects such as FANTOM (Functional ANnoTation Of the Mammalian genome) and GTEx (Genotype-Tissue Expression) have profiled gene expression in hundreds of human cell types. However, the data were obtained from ‘bulk’ samples. The possibility of sequencing large numbers of single cells at a reasonable cost offers the opportunity to create a much more detailed human cell reference map. Jay Shin, at the RIKEN Institute, Yokohama, Japan, and colleagues review rapidly growing single cell genomics consortia and highlight the advantages of initiating national collaborations to harmonize procedures and to obtain a more accurate representation of regional ethnic diversity.
Combining data from different single-cell sequencing techniques could greatly improve understanding of the molecular profiles associated with disease. Sequencing studies provide valuable insights into diseased and healthy states at a single-cell level, for example the evolutionary paths of brain tumors and cancerous mutations. Ayako Suzuki at the University of Tokyo in Chiba, Japan, and co-workers examined the challenges of integrating data from various experimental and computational single-cell sequencing methods. These methods usually determine the genomic, epigenomic (DNA modifications) or transcriptomic (messenger RNAs) state of a cell, and can be combined to create a detailed picture. Other ‘multiomics’ techniques provide multilayered information from the same cell. The researchers recommend detailed analysis of individual data layers prior to integration, and highlight emerging techniques that analyze larger tissue sections, thus retaining the temporal and spatial information around a cell.
The expansion of single-cell profiling technologies will provide unprecedented insights into the molecular mechanisms inherent in disease. Novel technologies known collectively as ‘single-cell multiomics’ enable systematic, high-resolution profiling of DNA, RNA and proteins in individual cells. This provides valuable data about gene regulation and molecular populations, and cellular processes during disease development and progression. Daehee Hwang and co-workers at Seoul National University, Seoul, South Korea, reviewed existing single-cell multiomics technologies and highlighted ways to integrate the data generated. Analytical features of multiomics allow scientists to isolate, sequence and label (or ‘barcode’) multiple molecules in single cells. Different sequencing techniques can be used for different purposes, such as exploring gene mutation coverage or measuring RNA transcripts. Combining these sequencing data will help identify links between significant features during disease.
The characterization of cells into more precise groups will improve the chances of selecting the best types of cells to use for regenerative medicine. Traditionally, cell types are defined by physiological and morphological markers and molecular properties. However, sequencing technologies are enabling researchers to classify cells into increasingly distinct subgroups. Wataru Fujibuchi at Kyoto University, Japan, and co-workers reviewed progress in cell-type identification to help guide regenerative medicine. They examined the insights gained from the Human Cell Atlas project, an international collaboration of over 1000 institutes across 71 countries. Participants in this project are sequencing cellular RNA and categorizing hundreds of thousands of individual cells, demonstrating that many assumptions about cells are too simplistic. Such data will inform regenerative therapies, for example, by selecting the “best” platelet-producing stem cells for blood donation.
By analyzing gene expression patterns in individual tumor cells, researchers can gain patient-specific insights that might inform more effective cancer treatment. Tumors are highly dynamic and heterogeneous collections of cells. Single-cell transcriptomics techniques can offer a valuable window into that complexity but only if the appropriate computational tools are used to analyze the data. Jean Fan of Harvard University, Cambridge, USA, and colleagues have reviewed some of these computational strategies and how they can be employed in cancer research. Single-cell analysis algorithms, for example, can reveal characteristics that distinguish healthy cells from cancerous cells, or indicate how the cells within the tumor may be communicating with each other to promote malignant growth. These are still new technologies, however, and the authors highlight the limitations of the conclusions that can currently be drawn from such analyses.