Genome-wide copy number analysis of single cells

Journal name:
Nature Protocols
Volume:
7,
Pages:
1024–1041
Year published:
DOI:
doi:10.1038/nprot.2012.039
Published online
Corrected online

Abstract

Copy number variation (CNV) is increasingly recognized as an important contributor to phenotypic variation in health and disease. Most methods for determining CNV rely on admixtures of cells in which information regarding genetic heterogeneity is lost. Here we present a protocol that allows for the genome-wide copy number analysis of single nuclei isolated from mixed populations of cells. Single-nucleus sequencing (SNS), combines flow sorting of single nuclei on the basis of DNA content and whole-genome amplification (WGA); this is followed by next-generation sequencing to quantize genomic intervals in a genome-wide manner. Multiplexing of single cells is discussed. In addition, we outline informatic approaches that correct for biases inherent in the WGA procedure and allow for accurate determination of copy number profiles. All together, the protocol takes ~3 d from flow cytometry to sequence-ready DNA libraries.

At a glance

Figures

  1. Schematic of the experimental workflow of SNS.
    Figure 1: Schematic of the experimental workflow of SNS.

    Step numbering corresponds to the Steps of the PROCEDURE. The FACSAria image is courtesy of Becton, Dickinson and Company; reprinted with permission. HiSeq2000 image is courtesy of Illumina.

  2. Flow sorting of single nuclei on the basis of DNA content.
    Figure 2: Flow sorting of single nuclei on the basis of DNA content.

    (a) Dot plot view of DAPI-stained nuclei. Gate, drawn on the diagonal, excludes cellular debris and doublets and captures single nuclei. The black dots represent cellular debris and doublets, whereas the green and red dots represent diploid and nondiploid fractions from the single-nuclei gate, respectively. The dot plots are drawn with DAPI-H and DAPI-W to allow for enhanced precision in distinguishing subpopulations. (b,c) Examples of histograms drawn from single-nuclei gates illustrating a diploid profile (b) and an aneuploidy profile (c).

  3. WGA amplification profiles of single-cell DNA from four different single cells.
    Figure 3: WGA amplification profiles of single-cell DNA from four different single cells.

    (a) WGA DNA spreads (100–1,000 bp) of single-cell genomes as measured on the Bioanalyzer. S1-S2-S3-S4 refers to four different single cell–amplified products. (b) An example histogram of DNA spread from cell S1 as measured by the Bioanalyzer. FU, fluorescence units; L, ladder.

  4. Sonication profiles of WGA DNA using different sonication programs on the Covaris E210 instrument.
    Figure 4: Sonication profiles of WGA DNA using different sonication programs on the Covaris E210 instrument.

    (a) Sonication profiles as measured on the Bioanalyzer. S1: nonsonicated WGA DNA. S1 400 / S1 300 / S1 200: WGA DNA sonicated using 400±, 300± and 200± Covaris E210 programs, respectively. Profiles represent size distributions of DNA molecules (in bp) of the samples. The choice of which sonication program to use is dependent on the desired sequencing library length and the type of sequencing that will be implemented. We generally use the 300± program when sequencing 76-bp reads on the Illumina platform. (b) Histograms illustrating sonication profiles as measured by the Bioanalyzer. L, ladder.

  5. Sequencing library size profiles following AMPure bead purification using different volumes of beads.
    Figure 5: Sequencing library size profiles following AMPure bead purification using different volumes of beads.

    The amount of beads indicated was added to a ligation reaction of 75 μl volume (50–40–30–20 μl of beads). Replicas are shown. Profiles illustrate the differing sequence library size profiles obtained when using different volumes of AMPure beads. We generally perform library purification using 30 μl of beads and sequence 76 bp on the Illumina platform. L, ladder.

  6. Schematic of the informatics workflow of SNS.
    Figure 6: Schematic of the informatics workflow of SNS.

    Blue numbers refer to the Steps of the PROCEDURE.

  7. Genome plot of normalized bin counts and segmentation illustrating the genome-wide copy number profile of the single-cell example SRR054616.
    Figure 7: Genome plot of normalized bin counts and segmentation illustrating the genome-wide copy number profile of the single-cell example SRR054616.

    The blue line shows the seg.mean values from the CBS.

  8. Density plot of segment value differences.
    Figure 8: Density plot of segment value differences.

    The density plot shows the Gaussian kernel smoothed density of differences in seg.mean values for differences between all segments called by the segmentor weighted by segment length. The second peak represents the mode of the seg.mean difference between segments one copy number apart.

  9. A close-up view of a region on chromosome 4 illustrating normalized bin count for each bin on the chromosomal segment.
    Figure 9: A close-up view of a region on chromosome 4 illustrating normalized bin count for each bin on the chromosomal segment.

    The blue line is the seg.mean as called by the CBS algorithm. The red line is the estimated copy number.

  10. Representative illustration of genome sector loss (GSL), where large homozygous deletions and patterns consistent with chromosomal 'shredding' are evident.
    Figure 10: Representative illustration of genome sector loss (GSL), where large homozygous deletions and patterns consistent with chromosomal 'shredding' are evident.

    (a) Whole-genome view of a single cell collected out of a 'normal' diploid flow sorting gate. (b) A close-up view of the profile of the same cell on chromosomes 7 and 8.

Accession codes

Referenced accessions

Sequence Read Archive

Change history

Corrected online 24 February 2016

In the version of this article initially published, the units for the concentration of NaCl in the NST buffer described in the Reagent Setup section were incorrect. The correct unit should be mM. The error has been corrected in the HTML and PDF versions of the article.

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Author information

Affiliations

  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA.

    • Timour Baslan,
    • Jude Kendall,
    • Linda Rodgers,
    • Hilary Cox,
    • Mike Riggs,
    • Asya Stepansky,
    • Jennifer Troge,
    • Kandasamy Ravi,
    • Diane Esposito,
    • Michael Wigler &
    • James Hicks
  2. Molecular and Cellular Biology Program, Stony Brook University, Stony Brook, New York, USA.

    • Timour Baslan
  3. Ontario Institute for Cancer Research, Toronto, Ontario, Canada.

    • B Lakshmi
  4. Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Nicholas Navin
  5. Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.

    • Nicholas Navin

Contributions

T.B., J.K., N.N., J.H. and M.W. designed methods and analyzed data. T.B., L.R., H.C., M.R., A.S. and J.T. performed experiments. K.R., D.E. and B.L. contributed to the technology development. J.K., N.N., T.B., J.H. and M.W. developed informatic analysis. T.B., J.K. and J.H. wrote the paper.

Competing financial interests

The authors declare no competing financial interests.

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Supplementary information

PostScript documents

  1. Supplementary Figure 1 (1 MB)

    Outline of the informatics section. Provide a concise outline of all the steps with input-program-output labels.

Zip files

  1. Supplementary Data (78 MB)

    Input/output data for the informatics procedure of the protocol required to reproduce copy number profiles from single cell sequencing data. Supplementary Data Legend outlines all the data with descriptions.

  2. Supplementary Methods (107 KB)

    Contains all programs required to process single cell sequencing data for copy number determination, from genome preparation to sequence mapping and copy number estimation. Supplementary Methods Legend outlines all the programs and their utilities.

Text files

  1. Supplementary Note (2 KB)

    Descriptions of data and program files. Text providing a brief overview of the data and program files included in the Supplementary Methods and Supplementary Data.

Additional data