Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI)

Abstract

Single-cell genomics is important for biology and medicine. However, current whole-genome amplification (WGA) methods are limited by low accuracy of copy-number variation (CNV) detection and low amplification fidelity. Here we report an improved single-cell WGA method, Linear Amplification via Transposon Insertion (LIANTI), which outperforms existing methods, enabling micro-CNV detection with kilobase resolution. This allowed direct observation of stochastic firing of DNA replication origins, which differs from cell to cell. We also show that the predominant cytosine-to-thymine mutations observed in single-cell genomics often arise from the artifact of cytosine deamination upon cell lysis. However, identifying single-nucleotide variations (SNVs) can be accomplished by sequencing kindred cells. We determined the spectrum of SNVs in a single human cell after ultraviolet radiation, revealing their nonrandom genome-wide distribution.

Fig. 1

LIANTI single-cell whole genome amplification scheme and amplification uniformity. (A) Comparison of exponential and linear amplification. Assuming the DNA fragments A and B have replication yields of 100% and 70% per round, respectively. For a final amplification factor of ~10,000 of fragment A, exponential amplification results in a ratio of 8 : 1, hampering the accuracy of CNV detection. In contrast, linear amplification exhibits a much smaller ratio of 1 : 0.7. Linear amplification is also superior to exponential amplification in fidelity. In exponential amplification, a polymerase of the highest fidelity (10⁻⁷) replicating the human genome (3 × 10⁹ bp) in the first cycle would give ~300 errors, which will be propagated permanently in the next replication cycles, leading to false positive SNVs. In contrast, in linear amplification, the errors would appear randomly at different locations in the amplicons and can be easily filtered out. (B) LIANTI transposon and transposome. LIANTI transposon consists of a 19-bp double-stranded transposase binding site and a single-stranded T7 promoter loop. Equal molar of LIANTI transposon and Tn5 transposase are mixed and dimerized to form LIANTI transposome. (C) LIANTI scheme. Genomic DNA from a single cell is randomly fragmented and tagged by LIANTI transposon, followed by DNA polymerase gap extension to convert single-stranded T7 promoter loops into double-stranded T7 promoters on both ends of each fragment. In vitro transcription overnight is performed to linearly amplify the genomic DNA fragments into genomic RNAs which are capable of self-priming on the 3' end. After reverse transcription, RNase digestion and second strand synthesis, double-stranded LIANTI amplicons tagged with unique molecular barcodes are formed, representing the amplified product of the original genomic DNA from a single cell, and ready for DNA library preparation and next generation sequencing. (D) Read depths across the genome with 1-Mb bin size, and a zoom in to a 10-Mb region (Chr1:60,000,000-70,000,000) with 10-Kb bin size. The MALBAC data is normalized by the average of two other MALBAC cells to remove the sequence-dependent bias reproducible from cell to cell. (E) Coefficient of variation for read depths along the genome as a function of bin sizes from 1 bp to 100 Mb, showing amplification noise on all scales for single-cell WGA methods, including DOP-PCR, MDA, MALBAC, and LIANTI. The normalized MALBAC data (dashed) is shown together with the unnormalized MALBAC data. Only the unnormalized data of the other methods are shown as no significant improvement by normalization were observed. Poisson curve is the expected coefficient of variation for read depth assuming only Poisson noise. LINATI exhibits a much improved amplification uniformity over the previous methods on all scales.

Fig. 2

Genome-wide detection of micro-CNVs and replication origin firing events in single BJ cells. (A) Principle for the inference of fragment numbers by LIANTI. Single-cell LIANTI amplicons mapped to the same starting and ending coordinates on the reference genome are grouped as originated from one fragment of the genomic DNA. This allows for the correction of the different amplification efficiency, often size dependent, for each fragment. The digital counting of the inferred fragment number across the genome is shown here for 2-to-1 copy number loss. (B) Example of a 57-Kb 2-to-1 micro-CNV detected in a single BJ cell, plotted with 100-bp bin size. Top panel is the read depth from unamplified bulk sequencing showing the existence of the micro-CNV. Middle panel is the read depth of the single-cell LIANTI amplicons, which obscures the micro-CNV due to amplification noise at this resolution. Bottom panel shows the inferred fragment number by LIANTI digital-counting analysis, which recovers the micro-CNV in the single cell. (C) Genome-wide detection of replication origin firing and replicon formation based on the copy number gain in 11 single cells with 10-Kb bin size (~250 Mb Chr1 shown in the plot). (D) Correlation plots of single-cell replicon copy numbers with the bulk readouts of the Repli-Seq assay and the DNase I hypersensitive assay using 100-Kb bin size. (E) Correlation plots of replicon copy numbers between pairs of single cells close in replication progress in S-phase using 100-Kb bin size. The diagonal signal represents replicon copy numbers shared by both cells, and the off-diagonal signal suggests stochastic origin firing and replicon formation, which is different from cell to cell.

Fig. 3

Detection of SNVs in single BJ cells. (A) False positive rates of SNV detection in a single BJ cell. The error bars were calculated from three different BJ cells. (B) Spectra of SNV false positives in unamplified bulk, single-cell LIANTI, single-cell MDA and single-cell UDG-treated LIANTI samples. The number of false positives is shown in the bracket for each sample. Both LIANTI and MDA results exhibit predominant C→T false positives not seen in the unamplified bulk. Similar C→T SNVs have been reported in previous single-cell MDA studies and attributed to de novo mutations (26). We attribute the phenomenon to the spontaneous C→U deamination upon cell lysis, which is often seen in ancient DNA bulk samples. We prove that such C→T deamination accounts for the observed SNV false positives by WGA of the cell lysate treated with uracil-DNA glycosylase (UDG), which eliminates cytosine-deaminated uracil bases and hence recovers the reduced C→T false positive fraction in the bulk.

Fig. 4

Genome-wide profiling of UV-induced mutations in single BJ cells. (A) Experimental design. BJ cells cultivated in dishes are exposed to UV radiation at a dose of 5, 15 and 30 J/m², respectively. Single cells that survived cell cycle arrest and apoptosis were picked and allowed to divide into multiple kindred cells (Fig. S11), among which a pair of kindred cells are picked for LIANTI. (B) Spectra of UV-induced SNVs in a representative cell exposed to 15 J/m² UV radiation. (C) Depletion of UV-induced SNVs within transcribed regions, DNase I hypersensitive sites and early-replicating regions. "Expected" column is the percentage of SNVs simulated assuming random distribution along the genome. "Observed" column is the percentage of SNVs observed in UV-radiated samples, with the error bars calculated from four kindred pairs. (D) Overlay of the density of UV-induced SNVs (red) and the minus Repli-Seq signal (blue) reflecting the replicated genomic regions, as well as the minus DNase I hypersensitive signal (blue) throughout the genome (~250 Mb Chr1 shown in the plot). Both signals were calculated in 2-Mb moving windows with 100-kb increments. (E) Non-template-to-template ratio of UV-induced C→T and T→A mutations within transcribed regions, and the sequence context of such mutations. "Expected" column is the ratio simulated assuming random distribution of SNVs on both strands. "Observed" column is the ratio observed in UV-radiated samples, with the error bars calculated from four kindred pairs. Sequence context is plotted based on the frequency of each base next to the corresponding type of mutation.