SUMMARY Sequencing studies of breast tumor cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumors. Here, we developed a whole-genome and exome single cell sequencing approach called Nuc-Seq that utilizes G2/M nuclei to achieve 91% mean coverage breadth. We applied this method to sequence single normal and tumor nuclei from an estrogen-receptor positive breast cancer and a triple-negative ductal carcinoma. In parallel, we performed single nuclei copy number profiling. Our data show that aneuploid rearrangements occurred early in tumor evolution and remained highly stable as the tumor masses clonally expanded. In contrast, point mutations evolved gradually, generating extensive clonal diversity. Many of the diverse mutations were shown to occur at low frequencies (<10%) in the tumor mass by targeted single-molecule sequencing. Using mathematical modeling we found that the triple-negative tumor cells had an increased mutation rate (13.3X) while the ER+ tumor cells did not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer.

Single cell sequencing (SCS) has emerged as a powerful new set of technologies for studying rare cells and delineating complex populations. Over the past 5 years, SCS methods for DNA and RNA have had a broad impact on many diverse fields of biology, including microbiology, neurobiology, development, tissue mosaicism, immunology and cancer research. In this review, we will discuss SCS technologies and applications, as well as translational applications in the clinic.

Current variant callers are not suitable for single-cell DNA sequencing (SCS) as they do not account for allelic dropout, false-positive errors, and coverage non-uniformity. We developed Monovar, a novel statistical method for detecting and genotyping single nucleotide variants in SCS data. Evaluation based on an isogenic fibroblast cell line and three different human tumor datasets showed substantial improvement of Monovar over standard algorithms for identifying driver mutations and delineating clonal substructure.

Single-cell genome sequencing methods are challenged by poor physical coverage and high error rates, making it difficult to distinguish real biological variants from technical artifacts. To address this problem, we developed a method called SNES that combines flow-sorting of single G1/0 or G2/M nuclei, time-limited multiple-displacement-amplification, exome capture, and next-generation sequencing to generate high coverage (96%) data from single human cells. We validated our method in a fibroblast cell line, and show low allelic dropout and false-positive error rates, resulting in high detection efficiencies for single nucleotide variants (92%) and indels (85%) in single cells.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-015-0616-2) contains supplementary material, which is available to authorized users.

Single-cell DNA sequencing methods are challenged by poor physical coverage, high technical error rates and low throughput. To address these issues, we developed a single-cell DNA sequencing protocol that combines flow-sorting of single nuclei, time-limited multiple-displacement amplification (MDA), low-input library preparation, DNA barcoding, targeted capture and next-generation sequencing (NGS). This approach represents a major improvement over our previous single nucleus sequencing (SNS) Nature Protocols paper in terms of generating higher-coverage data (>90%), thereby enabling the detection of genome-wide variants in single mammalian cells at base-pair resolution. Furthermore, by pooling 48–96 single-cell libraries together for targeted capture, this approach can be used to sequence many single-cell libraries in parallel in a single reaction. This protocol greatly reduces the cost of single-cell DNA sequencing, and it can be completed in 5–6 d by advanced users. This single-cell DNA sequencing protocol has broad applications for studying rare cells and complex populations in diverse fields of biological research and medicine.