From Microscopes to Multi-omics: The Evolution of Single Cell Genomic Technologies
Dave Gennet
Dave Gennet,
Graduate Student



Throughout the age of genetics, researchers have largely been limited by either the number of genetic loci they could study, the number of cells that could be analyzed simultaneously, or both. Fluorescence-activated cell sorting (FACS) 1, invented in the 1960s, allows researchers to quantify protein abundance in a huge number of single cells. However, the number of distinct labels that can be used on a single cell is very limited. Through the 1980s, microscopy still reigned as the most information-rich single- cell assay. Some studies combined microscopy with in situ probe hybridization to resolve the spatial distribution of specific sequences, but those traded throughput for spatial and sequence resolution 2,3 .

Several papers kicked off single-cell transcriptome analysis in 1990, using Southern and Northern blot readout 4,5 . Importantly, these studies found that amplifying a single cell’s genetic material was a crucial step for sufficient detection of transcripts. For almost all subsequent advances in sequencing technology, the single-cell version of each method came after first figuring out how to preserve the data quality of the original method in amplified single cells. The first giant leap in the capabilities of single-cell genomics came with the advent of the microarray for transcriptomics6, giving the first insights into coregulatory genetic networks in single cells on a genome-wide scale 7. Microarrays came with shortcomings, though; only known genes or sequences could be assayed, and very little isoform information could be gathered from each short probe sequence.

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Everything changed with the rise of high-throughput sequencing. The problems of number of cells assayed at once, a priori knowledge of transcripts for quantification, and base pair-level resolution were all solved. The first single-cell RNA-sequencing analysis came out in 20098 , and the first single-cell DNA-sequencing method followed in 20119 .

Building upon these, the next generation of methods enrich for subsets of the genome or transcriptome to provide epigenetic or epitranscriptomic information. The global methylation profile of single-cell genomes can be resolved by RRBS 10, chromatin organization by single-cell Hi-C 11 , chromatin accessibility by single-cell DNaseI hypersensitivity sequencing 12 or ATAC-seq13,14 , and RNA modifications by several methods15.

To round out the central dogma of single-cell genomic technologies, mass cytometry16 has given the tried-and-true method of FACS a face-lift. Although not unbiased detection, mass cytometry increases the number of protein species detected in a single cell from around a dozen to upwards of one hundred. With an eye towards today and the future, current trends in single-cell sequencing technology development push the boundaries of what is possible in two directions: throughput and multi-omic measurement.

Researchers increasingly turn their attention to very heterogeneous tissues or rare cell populations for single-cell analysis. Many sequencing methods approach the theoretical limit of their detection sensitivity, so gains in statistical power rely on gains in the number of cells profiled. To fill this need, microfluidic drops have emerged as an efficient way to prepare huge numbers of sequencing libraries. RNA-seq methods such as inDrop17 and Drop-seq18 showed promise in the lab, and commercial entities such as

10X Genomics have fulfilled this promise and now provide similar methods as a service. Alternatively, combinatorial indexing uses the split-and-pool approach to molecularly barcode individual cells for pooled sequencing 13,19 .

The combination of multiple technologies to simultaneously measure different species from the same cell may currently be the most active frontier in the field. All in the past couple years, we have seen combined genome/transcriptome20 , methylome/transcriptome21 , proteome/transcriptome22 , chromatin accessibility/TCR sequence23 , genome editing screens/transcriptome24 ; the list goes on and on and is constantly expanding. The power to correlate changes in one form of genetic information with changes in other forms can be incredibly informative, as we gain a fuller understanding of intracellular processes and regulatory networks.

References

  1. Bonner, W. A., Hulett, H. R., Sweet, R. G. & Herzenberg, L. A. Fluorescence Activated Cell Sorting. Rev. Sci. Instrum. 43, 404 (1972).
  2. Pardue, M. L. & Gall, J. G. Chromosomal Localization of Mouse Satellite DNA. Science (80-. ). 168, 1356–1358 (1970).
  3. Langer-Safer, P. R., Levine, M. & Ward, D. C. Immunological method for mapping genes on Drosophila polytene chromosomes. Proc. Natl. Acad. Sci. U. S. A. 79, 4381–5 (1982).
  4. Brady, G., Barbara, M. & Iscove, N. N. Representative in Vitro cDNA Amplification From Individual Hemopoietic Cells and Colonies. METHODS IN MOLECULAR AND CELLULAR BIOLOGY 2, (1990).
  5. Van Gelder, R. N. et al. Amplified RNA synthesized from limited quantities of heterogeneous cDNA (cerebelium/guanine nucleotide-binding protein/T7 RNA polymerase/Purkinje ceil). Proc. Nati. Acad. Sci. USA 87, (1990).
  6. Schena, M., Shalon, D., Davis, R. W. & Brown, P. O. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science 270, 467–70 (1995).
  7. Kurimoto, K. et al. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34, e42 (2006).
  8. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–82 (2009).
  9. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–4 (2011).
  10. Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–35 (2013).
  11. Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).
  12. Jin, W. et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–6 (2015).
  13. Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–4 (2015).
  14. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
  15. Li, X., Xiong, X. & Yi, C. Epitranscriptome sequencing technologies: decoding RNA modifications. Nat. Methods 14, 23–31 (2017).
  16. Bendall, S. C. et al. Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum. Science (80-. ). 332, 687–696 (2011).
  17. Klein, A. M. et al. Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells. Cell 161, 1187–1201 (2015).
  18. Macosko, E. Z. et al. Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell 161, 1202–1214 (2015).
  19. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).
  20. Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).
  21. Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).
  22. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).
  23. Satpathy, A. T. et al. Transcript-indexed ATAC-seq for precision immune profiling. Nat. Med. 24, 580–590 (2018).
  24. Dixit, A. et al. Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. Cell 167, 1853–1866.e17 (2016).

Author Bio

Dave Gennet

Graduate Student, Stanford University School of Medicine, Department of Genetics Laboratory of Professor Howard Y. Chang, Stanford University School of Medicine, Department of Dermatology. Lab website:  http://changlab.stanford.edu/   Dave received his B.S. in Biology from Tufts University and is currently pursuing his Ph.D. in Genetics at Stanford University.  His research has explored transcriptional and epigenetic changes associated with immune cell function and dysfunction, with an eye towards single-cell sequencing technologies.  He is excited about communicating with the public about science, as a host for the science news podcast Goggles Optional and a guest blogger for Meenta.