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Simul-seq: combined DNA and RNA sequencing for whole-genome and transcriptome profiling

Abstract

Paired DNA and RNA profiling is increasingly employed in genomics research to uncover molecular mechanisms of disease and to explore personal genotype and phenotype correlations. Here, we introduce Simul-seq, a technique for the production of high-quality whole-genome and transcriptome sequencing libraries from small quantities of cells or tissues. We apply the method to laser-capture-microdissected esophageal adenocarcinoma tissue, revealing a highly aneuploid tumor genome with extensive blocks of increased homozygosity and corresponding increases in allele-specific expression. Among this widespread allele-specific expression, we identify germline polymorphisms that are associated with response to cancer therapies. We further leverage this integrative data to uncover expressed mutations in several known cancer genes as well as a recurrent mutation in the motor domain of KIF3B that significantly affects kinesin–microtubule interactions. Simul-seq provides a new streamlined approach for generating comprehensive genome and transcriptome profiles from limited quantities of clinically relevant samples.

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Figure 1: Simultaneous, single-tube sequencing of DNA and RNA.
Figure 2: Characterization of Simul-seq whole-genome data.
Figure 3: Characterization of Simul-seq transcriptome data.
Figure 4: Comprehensive genome and transcriptome profiling of esophageal adenocarcinoma (EAC).
Figure 5: Identification and biochemical characterization of a recurrent mutation in KIF3B.

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Acknowledgements

We thank C. Araya, C. Cenik, P. Dumesic, D. Phanstiel and D. Webster for many helpful discussions and input regarding the manuscript and analyses. We acknowledge J. Churko from the laboratory of J. Wu at Stanford University for providing the fibroblasts as well as the work of both the sequencing core at the Stanford Center for Genomics and Personalized Medicine and the Genetics Bioinformatics Service Center, with special thanks to G. Euskirchen, L. Ramirez, C. Eastman, N. Watson and N. Hammond. Finally, we would like to thank H. Chen from Bina Technologies.

Author information

Authors and Affiliations

Authors

Contributions

J.A.R., D.V.S. and M.P.S. conceived the project, designed experiments and wrote the manuscript. J.A.R. and D.V.S. performed analyses and experiments. R.K.P. provided pathology expertise and formalin-fixed paraffin-embedded esophageal adenocarcinoma specimens. Work in the Snyder lab is supported by NIH grants to M.P.S. (1P50HG00773501 and 8U54DK10255602). J.A.R. was supported by the Damon Runyon Cancer Research Foundation, and D.V.S. was supported by an NIH T32 fellowship (HG000044) and a Genentech Graduate Fellowship.

Corresponding author

Correspondence to Michael P Snyder.

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Competing interests

M.P.S. is a cofounder of Personalis and a member of the scientific advisory boards of Personalis and Genapsys.

Integrated supplementary information

Supplementary Figure 1 Simul-seq library preparation and quality control.

(a) Histogram of incubation times for parallel Tru-seq DNA and RNA library preparation as well as Simul-seq. (b) High-sensitivity DNA bioanalyzer trace for a yeast/human mixed Simul-seq library. Note, this trace is representative of an average Simul-seq library. (c-d) Representative droplet digital PCR (ddPCR) raw fluorescence amplitude data (left) and assay design (right) for quantification of DNA (c) and RNA (d) constituents of Simul-seq libraries.

Supplementary Figure 2 Comparison of variant calls between Simul-seq genome and DNA-seq replicates.

(a-b) Venn diagrams comparing SNV (a) and indel (b) calls between the Simul-seq genome and two DNA-seq control genomes derived from different tissues of the same individual13.

Supplementary Figure 3 Distribution of coding and noncoding genes in Simul-seq transcriptome.

Bar graph of all Ensembl biotype annotations for genes with FPKM values greater than or equal to 5.

Supplementary Figure 4 Simul-seq and RNA-seq replicates are well correlated.

Scatter plots of Log10(FPKM+1) gene measurements for Simul-seq and RNA-seq replicates. Spearman’s ρ correlation values for each comparison are shown.

Supplementary Figure 5 DNA and RNA sequencing data for 50,000 (50K) fibroblast replicates.

(a) Coverage distributions for Simul-seq libraries of the same individual. (b) Venn diagrams comparing SNV calls between the Simul-seq replicates. (c) Scatter plots of Log10(FPKM+1) gene measurements for 50K Simul-seq replicates (Spearman’s ρ=0.97). (d) Correlation between External RNA Controls Consortium (ERCC) spike-in control Log10 RNA concentrations versus the average Log10(RPKM+1) for Simul-seq (blue; Spearman’s ρ=0.97) and 50K Simul-seq (orange; Spearman’s ρ=0.96) fibroblast replicates (n=2/group). Note, zero values have been shifted to 1, and all ERCC transcripts are shown.

Supplementary Figure 6 Simul-seq replicate and tumor RNA quality control analysis.

(a) Distribution of normalized transcript coverage for RNA-seq and Simul-seq replicates performed on fibroblasts as well as Simul-seq data obtained for esophageal adenocarcinoma tissue isolated using laser capture microscopy (Simul-seq EAC). (b) Strand specificity of Simul-seq and RNA-seq samples. (c) The fraction of reads mapping to various genomic annotations for Simul-seq and RNA-seq samples. Note, an increased intronic read fraction combined with a similar intergenic read fraction in the Simul-seq EAC sample likely indicates increased intron retention and/or a higher proportion of unspliced RNA in this specimen.

Supplementary Figure 7 Targeted resequencing of KIF3B locus in esophageal adenocarcinoma patient samples.

(a) Histogram of the unique and unmapped Bowtie aligned reads obtained for 76 FFPE samples (50 tumors and 26 normals). The original sample (02-28923-C9) that was subjected to the Simul-seq protocol was included as a positive control. A single tumor-normal pair (00-18224-A2) displayed a substantially higher number of variant calls yet a lower number of uniquely mapped reads, suggesting that these samples harbored increased rates of PCR errors induced by low quality genomic DNA. Therefore, these samples were not included in somatic mutation analysis. (b) Validation of variant calls using pyrophosphate sequencing.

Supplementary Figure 8 Purification of recombinant wild-type and R293W mutant motor domains.

(a) Schematic of KIF3B protein, with motor domain and ATP binding region highlighted in blue and red, respectively. For biochemical assays, a region spanning the motor domain of KIF3B (amino acids 1-365) was cloned and recombinantly expressed with an N-terminal 6x-Histidine tag (bottom). (b) Coomassie stained gel of recombinant proteins pre- and post-induction with Isopropyl β-D-1-thiogalactopyranoside (IPTG) as well as after Ni2+ affinity purification.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–8 and Supplementary Note. (PDF 1853 kb)

Supplementary Table 1

Simul-seq and control library read counts and mapping rates. (XLSX 12 kb)

Supplementary Table 2

Somatic SVs for Simul-seq EAC tumor genome (XLSX 35 kb)

Supplementary Table 3

Somatic, expressed gene fusions in Simul-seq EAC tumor genome (XLSX 9 kb)

Supplementary Table 4

VCF of somatic SNVs for Simul-seq EAC tumor genome (XLSX 2379 kb)

Supplementary Table 5

VCF of somatic indels for Simul-seq EAC tumor genome (XLSX 474 kb)

Supplementary Table 6

EAC tumor ASE analysis at heterozygous SNV positions in the normal genome (XLSX 7583 kb)

Supplementary Table 7

ASE of annotated tumor supressor genes harboring damaging germline variants (XLSX 12 kb)

Supplementary Table 8

Simul-seq RNA and RNA-seq ERCC spike-in transcript quantification (XLSX 16 kb)

Supplementary Table 9

Genomic regions of KIF3B locus targeted for resequencing (XLSX 8 kb)

Supplementary Table 10

Primer sets used in KIF3B targeted resequencing (XLSX 11 kb)

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Reuter, J., Spacek, D., Pai, R. et al. Simul-seq: combined DNA and RNA sequencing for whole-genome and transcriptome profiling. Nat Methods 13, 953–958 (2016). https://doi.org/10.1038/nmeth.4028

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