Partner Profile

Next-generation sequencing (NGS) of single cells is of high importance to understand complex biological processes, such as the heterogeneity during disease progression in cancer. At Fraunhofer ITEM in Regensburg, these methods are used to identify hallmarks of cancer cells at defined stages of systemic cancer progression, thereby opening new entry points for therapeutic interventions.
Isolated single cells are prepared for subsequent amplification of the genome and/or transcriptome of single cells. Specially developed quality control assays are applied after amplification to assess the quality of the amplified material. This ensures that only high-quality amplified single-cell samples yielding reliable data are forwarded to downstream NGS applications. NGS quality workflows provide a set of quality parameters independent of the assay and sequencing technology. Adapted open-source tools and in-house developed scripts enable NGS data analysis tailored to the special requirements of single-cell amplification technologies. The Fraunhofer team in Regensburg has extensive experience in developing and optimizing workflows from single cells to adapted NGS workflows and bioinformatics evaluation.
Although considerable progress has been made in recent years, single-cell NGS technologies are still struggling with two major technical challenges: (1) homogeneous and representative amplification of genomic DNA and/or mRNA in amounts sufficient for downstream applications and (2) reliable identification of errors introduced into single-cell-derived nucleic acids by amplification. The Fraunhofer ITEM scientists have addressed both needs by developing approaches generating sufficient quantities of high-quality amplified material allowing for test repetitions and biobanking of amplified samples and, moreover, implementing barcoding strategies in the WGA approach comprising the elimination of sequence errors originating from WGA or sequencing.
For reliable assessment of biomarkers with a high sensitivity and specificity for a certain disease and high reproducibility under different experimental settings, we make use of innovative combined single-cell DNA and RNA analysis. Merging information from the genome (mutations) and the transcriptome (expression) allows robust and reliable evaluation of previously described biomarkers as well as description of novel ones.
Moreover, miniaturization and automation of single cell applications enables cost effective library preparation and lowering input from precious samples in high-throughput applications.