IGHA 3′ UTR contains novel splice junction.

<p>a) Full length cDNA of single B cells was generated using the C1 autoprep system, fragmented using Nextera XT, and sequenced using Illumina sequencers (RNAseq). Additionally, RNA was extracted from bulk B cells and the IGH transcripts were analyzed using the PacBio sequencer (IGH-Seq) or conventional Sanger sequencing after gel isolation b) RNAseq reads of two B cells were aligned to the IGH locus. Coverage density is shown as a histogram for both IGHA1 and IGHM exons for both B cells. Coverage density in the IGHA1 expressing cell indicated a splicing event in the canonical IGHA membrane exon. c) PacBio single molecule sequencing reads were mapped to the IGHA1 locus. Reads containing the whole VDJ region as well as either S or M exons were grouped and quantified. This confirmed the presence of a splice site in the canonical IGHA1 membrane exon resulting in two exons (named IGHA1 M1 and M2). d) Gel separation of amplicons generated from bulk B cell RNA using primers specific for exon J4 and putative exon IGHA1 M2 on the left. Schematic representation of isoform splice structure on the right. The longer band confirmed the IGHA1 M1 to M2 splicing event, the shorter band represents a novel isoform of IGHA1.</p

Novel Exons and Splice Variants in the Human Antibody Heavy Chain Identified by Single Cell and Single Molecule Sequencing

<div><p>Antibody heavy chains contain a variable and a constant region. The constant region of the antibody heavy chain is encoded by multiple groups of exons which define the isotype and therefore many functional characteristics of the antibody. We performed both single B cell RNAseq and long read single molecule sequencing of antibody heavy chain transcripts and were able to identify novel exons for IGHA1 and IGHA2 as well as novel isoforms for IGHM antibody heavy chain.</p></div

Alternative splicing of IGHM transcripts.

<p>a) RNA was extracted from bulk B cells and the IGH transcripts were analyzed using the PacBio sequencer (IGH-Seq) and mapped to the IGHM locus. Reads containing the whole VDJ region as well as either S or M exons were grouped and quantified. Several reads represented abundant novel short IGHM isoforms lacking structural exons. b) Gel separation of amplicons generated from bulk B cell RNA using primers specific for exon J4 and putative exon IGHA M2 is shown on theleft. Schematic representation of isoform splice structure as validated by Sanger sequencing is shown on the right. The bands of several sizes confirmed the presence of short IGHM isoforms found by single molecule sequencing. c) Mutation rates of different IGH isoforms derived from PacBio reads shown as boxplots. The low mutation rates of the novel isotypes strongly indicate that their expression is limited to naïve B cells.</p

CNV resolution.

<p>Size of resolvable duplications (W, minimum width of a sliding window average filter that gives rise to a relative genome mapping density smaller than 2 across all positions in the genome) versus gain. Filled symbols signify bulk experiments, open symbols single-cell experiments.</p

A Quantitative Comparison of Single-Cell Whole Genome Amplification Methods

<div><p>Single-cell sequencing is emerging as an important tool for studies of genomic heterogeneity. Whole genome amplification (WGA) is a key step in single-cell sequencing workflows and a multitude of methods have been introduced. Here, we compare three state-of-the-art methods on both bulk and single-cell samples of <i>E. coli</i> DNA: Multiple Displacement Amplification (MDA), Multiple Annealing and Looping Based Amplification Cycles (MALBAC), and the PicoPLEX single-cell WGA kit (NEB-WGA). We considered the effects of reaction gain on coverage uniformity, error rates and the level of background contamination. We compared the suitability of the different WGA methods for the detection of copy-number variations, for the detection of single-nucleotide polymorphisms and for <i>de-novo</i> genome assembly. No single method performed best across all criteria and significant differences in characteristics were observed; the choice of which amplifier to use will depend strongly on the details of the type of question being asked in any given experiment.</p></div

Design of experiments.

<p>Overview of experiments, where n denotes the number of experiments of a given type. In the “<i>E. coli</i> single cells” column, the box that straddles both the “Microfluidic” and the “Tube” fields corresponds to the method of carrying out a first round of amplification in a microfluidic chamber and then a second round of amplification in a test tube. This method will be denoted by “microfluidic+tube” in subsequent figure captions.</p

Amplification bias and uniformity.

<p>(A) Local mapping density from properly mapped reads (at fixed 5x sampling depth), normalized to average 1, as a function of position along the reference sequence, for single-cell MDA (microfluidic in red, microfluidic+tube in orange). (B) Same as panel A, but for single-cell MALBAC and bulk NEB-WGA. (C) Fractional genome coverage from properly mapped read pairs, plotted as a function of gain. Here, each set of properly mapped read pairs was randomly down-sampled to 5x depth. Experiments that did not generate this many properly mapped reads were not included in the figure. (D) Fraction of the genome covered by mapped read pairs when the set of <i>raw</i> read pairs was down-sampled to a fixed depth of 20x, plotted as a function of gain. Filled symbols signify bulk experiments, open symbols single-cell experiments.</p

Sequence read classification.

<p>(A) Breakdown of read pairs in each experiment according to type of mapping achieved. (B) Breakdown of unmapped reads by organism of origin, expressed as a fraction of the total number of reads.</p