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Stratifying tissue heterogeneity with scalable single-cell assays

Nature Methods volume 14, pages 238–239 (2017)Cite this article

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Three methods dramatically improve the scale of single-cell genomic sequencing and structural assays, allowing for the hierarchical partitioning of cell populations within a tissue.

Genomic alterations are important sources of functional heterogeneity, in cancers and even in adult tissues such as the brain. Taking two different approaches to tackle heterogeneity, Zahn et al.1 and Vitak et al.2 analyzed copy-number variation (CNV) in hundreds to thousands of single cells. Conceptually, the direct library preparation (DLP) approach of Zahn et al.1 is similar to the method pioneered by Quake and colleagues4 and commercialized as the Fluidigm C1, which confines single cells in individual microfluidic reactors for a series of enzymatic reactions. However, DLP-generated sequences are far more uniform, which translates to better CNV-calling sensitivity, especially for smaller genomic intervals or copy-number changes. To achieve this, Zahn et al.1 eliminated whole-genome amplification, a primary source of technical variability, before sequencing library preparation. This allowed them to count copy numbers across many random genomic intervals with unique end sequences by collapsing sequencing reads generated by amplification during subsequent library preparation. Amplification-free library preparation was made possible by 'tagmentation', the use of Tn5 transposase to insert adaptor sequences.

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Figure 1: Routes for scaling up single-cell assays.

Marina Corral Spence/Springer Nature

References

  1. Zahn, H. et al. Nat. Methods 14, 167–173 (2017).

    Article  CAS  Google Scholar 

  2. Vitak, S.A. et al. Nat. Methods 14, 302–308 (2017).

    Article  CAS  Google Scholar 

  3. Ramani, V. et al. Nat. Methods 14, 263–266 (2017).

    Article  CAS  Google Scholar 

  4. Marcy, Y. et al. PLoS Genet. 3, e155 (2007).

    Article  Google Scholar 

  5. Adey, A. et al. Genome Biol. 11, R119 (2010).

    Article  CAS  Google Scholar 

  6. Amini, S. et al. Nat. Genet. 46, 1343–1349 (2014).

    Article  CAS  Google Scholar 

  7. Adey, A. et al. Genome Res. 24, 2041–2049 (2014).

    Article  CAS  Google Scholar 

  8. Cusanovich, D.A. et al. Science 348, 910–914 (2015).

    Article  CAS  Google Scholar 

  9. Nagano, T. et al. Nature 502, 59–64 (2013).

    Article  CAS  Google Scholar 

Download references

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Authors and Affiliations

  1. Kun Zhang is in the Department of Bioengineering, University of California at San Diego, La Jolla, California, USA.,

    Kun Zhang

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  1. Kun Zhang
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Correspondence to Kun Zhang.

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Zhang, K. Stratifying tissue heterogeneity with scalable single-cell assays. Nat Methods 14, 238–239 (2017). https://doi.org/10.1038/nmeth.4209

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