New frontiers in single-cell genomics
- Nicholas E. Navin1,2,3,
- Orit Rozenblatt-Rosen4 and
- Nancy R. Zhang5
- 1Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA;
- 2Graduate School of Biomedical Sciences, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA;
- 3Department of Bioinformatics, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA;
- 4Genentech, Inc., South San Francisco, California 94080, USA;
- 5Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
This extract was created in the absence of an abstract.
The burgeoning field of single-cell genomics has undergone enormous progress since its inception more than a decade ago. The rapid growth of the field has been fueled by the development of innovative technologies, novel computational analysis methods, and a growing number of diverse applications across many fields of biology and biomedicine. Advances in single-cell profiling technologies now allow us to chart cells at unprecedented resolution. Today, through the development of high-throughput methods including droplets, nanowells, and combinatorial indexing methods, we can profile thousands of cells in parallel. Single-cell and single-nucleus RNA-seq are now used for data generation by many laboratories. Genomic methods for measuring chromatin states and for multimodal profiling are also maturing and there is a growing number of spatial technologies for profiling cells in the tissue context. Much progress has been made in the analysis and annotation of single-cell data and its integration with spatial data, and many algorithms, analysis pipelines, and tools are now accessible as open-source software. These experimental and computational methods are now an invaluable part of the molecular biologist's toolkit.
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