Combined ultra-low input mRNA and whole-genome sequencing of human embryonic stem cells

Abstract

Background: Next Generation Sequencing has proven to be an exceptionally powerful tool in the field of genomics and transcriptomics. With recent development it is nowadays possible to analyze ultra-low input sample material down to single cells. Nevertheless, investigating such sample material often limits the analysis to either the genome or transcriptome. We describe here a combined analysis of both types of nucleic acids from the same sample material.

Methods: The method described enables the combined preparation of amplified cDNA as well as amplified whole-genome DNA from an ultra-low input sample material derived from a sub-colony of in-vitro cultivated human embryonic stem cells. cDNA is prepared by the application of oligo-dT coupled magnetic beads for mRNA capture, first strand synthesis and 3'-tailing followed by PCR. Whole-genome amplified DNA is prepared by Phi29 mediated amplification. Illumina sequencing is applied to short fragment libraries prepared from the amplified samples.

Results: We developed a protocol which enables the combined analysis of the genome as well as the transcriptome by Next Generation Sequencing from ultra-low input samples. The protocol was evaluated by sequencing sub-colony structures from human embryonic stem cells containing 150 to 200 cells. The method can be adapted to any available sequencing system.

Conclusions: To our knowledge, this is the first report where sub-colonies of human embryonic stem cells have been analyzed both at the genomic as well as transcriptome level. The method of this proof of concept study may find useful practical applications for cases where only a limited number of cells are available, e.g. for tissues samples from biopsies, tumor spheres, circulating tumor cells and cells from early embryonic development. The results we present demonstrate that a combined analysis of genomic DNA and messenger RNA from ultra-low input samples is feasible and can readily be applied to other cellular systems with limited material available.

Fig. 1

Method overview for combined sequencing of mRNA and whole genome DNA. a Schematics for sequencing of ultra-low input DNA and mRNA from a single cell-colony sample. b OCT3/4, NANOG and DAPI staining of cultured human embryonic stem cells. Rectangular cuts indicating size of sample applied (150–200 cells each). Scale bar =200 μm

Fig. 2

Comparison of RNAseq data in terms of transcript identification. a Read coverage across transcripts with coverage for transcripts of sizes 0 kb-1 kb, 1 kb-2 kb, 2 kb-3 kb, 3 kb-4 kb, 4 kb-5 kb and 5 kb-15 kb. Transcripts were divided into 40 equally sized bins and coverage was averaged over all transcripts of the dedicated size interval. Mean values of mRNA sample1 and sample2 are displayed, error bars denoting standard deviation of samples. b Boxplot of total length of sequenced Refseq genes for RNAseq sample 1 and sample 2. c Correlation plot for RNAseq sample 1 and sample 2 in terms of FPKM values for expressed Refseq genes

Fig. 3

Overlap between microarray and sequencing measurements. a.1 Venn diagram displaying overlap of significant genes found in the Illumina microarray and next-generation sequencing (NGS) experiments. Genes expressed with Illumina detection p-value < 0.05 in the microarray experiment or FPKM > 0.5 in the NGS experiments were considered significant. Genes were compared via HGNC gene symbol annotation. a.2 Venn diagram displaying overlap of significant CPDB categories found in the Illumina microarray and deep sequencing experiments. Significant genes from microarray and NGS experiments were analysed with the CPDB functional annotation tool and categories with a q-value <0.05 were considered significant. b Pluripotency associated genes from microarray experiment and NGS experiments of sample1 and sample2 were compared. Logarithmic values (base 2) of Illumina average signals and FPKM values from expressed genes (detection p-value < 0.05, FPKM > 0.5) were quantile normalized and subjected to cluster analysis via R heatmap2 function using Euclidean distance as distance measure

Fig. 4

Read coverage of genome sequencing and RNA-seq data. a Read coverage of genomic DNA is visualized in a Manhattan plot showing coverage of 10 kb regions over chromosomes. b Example of mapped reads for selected stem cell marker genes (NANOG, POU5F1, SOX2,) and housekeeping gene (ACTB) for both RNA-seq samples (light grey: RNA-seq sample1 and medium grey: RNA-seq sample2) and WGA DNA sample (dark grey). The aligned RNA-seq reads resemble the exon structure depicted below