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Cystic fibrosis (CF) is one of the most common genetic diseases worldwide with high carrier frequencies across different ethnicities. Next generation sequencing of the cystic fibrosis transmembrane conductance regulator (CFTR) gene has proven to be an effective screening tool to determine carrier status with high detection rates. Here, we evaluate the performance of the Swift Biosciences Accel-Amplicon CFTR Capture Panel using CFTR-positive DNA samples. This assay is a one-day protocol that allows for one-tube reaction of 87 amplicons that span all coding regions, 5′ and 3′UTR, as well as four intronic regions. In this study, we provide the FASTQ, BAM, and VCF files on seven unique CFTR-positive samples and one normal control sample (14 samples processed including repeated samples). This method generated sequencing data with high coverage and near 100% on-target reads. We found that coverage depth was correlated with the GC content of each exon. This dataset is instrumental for clinical laboratories that are evaluating this technology as part of their carrier screening program., Measurement(s)amplicon sequencingTechnology Type(s)DNA sequencingFactor Type(s)CFTR mutationsSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.10060244

Metastasis is a complex biological process that has been difficult to delineate in human colorectal cancer (CRC) patients. A major obstacle in understanding metastatic lineages is the extensive intra-tumor heterogeneity at the primary and metastatic tumor sites. To address this problem, we developed a highly multiplexed single-cell DNA sequencing approach to trace the metastatic lineages of two CRC patients with matched liver metastases. Single-cell copy number or mutational profiling was performed, in addition to bulk exome and targeted deep-sequencing. In the first patient, we observed monoclonal seeding, in which a single clone evolved a large number of mutations prior to migrating to the liver to establish the metastatic tumor. In the second patient, we observed polyclonal seeding, in which two independent clones seeded the metastatic liver tumor after having diverged at different time points from the primary tumor lineage. The single-cell data also revealed an unexpected independent tumor lineage that did not metastasize, and early progenitor clones with the “first hit” mutation in APC that subsequently gave rise to both the primary and metastatic tumors. Collectively, these data reveal a late-dissemination model of metastasis in two CRC patients and provide an unprecedented view of metastasis at single-cell genomic resolution.

A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline, Sleeping Beauty (SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumor’s major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.

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.

Sequencing studies of breast tumour cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumours. Here we developed a whole-genome and exome single cell sequencing approach called nuc-seq that uses G2/M nuclei to achieve 91% mean coverage breadth. We applied this method to sequence single normal and tumour nuclei from an oestrogen-receptor-positive (ER(+)) 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 tumour evolution and remained highly stable as the tumour masses clonally expanded. In contrast, point mutations evolved gradually, generating extensive clonal diversity. Using targeted single-molecule sequencing, many of the diverse mutations were shown to occur at low frequencies (10%) in the tumour mass. Using mathematical modelling we found that the triple-negative tumour cells had an increased mutation rate (13.3×), whereas the ER(+) tumour cells did not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer.

Metastatic colorectal cancer (mCRC) is a devastating disease with only 11% 5-year survival rate for stage IV patients. The genomic basis of metastasis has been difficult to study, in part due to the extensive intratumor heterogeneity at both the primary and metastatic tumor sites. Previous studies have applied bulk next-generation sequencing (NGS) methods, which have limited ability to resolve intratumor heterogeneity. To address this problem, we developed a highly-multiplexed single cell DNA sequencing method that combines flow-sorting of single nuclei, multiple-displacement-amplification, low-input library preparation, library barcoding, targeted capture and NGS to generate high-coverage data from single tumor cells. To increase throughput, we performed targeted single-cell DNA sequencing using a panel of 201-cancer genes to analyze single cells from primary tumors and liver metastases from two mCRC patients. In each patient, we sequenced 90 single tumor cells from the primary and metastatic tumor, to delineate the clonal architecture of the tumor and reconstruct their phylogenetic lineages. In addition, we sequenced populations of tumor cells at high coverage depth (200X). Our data identified a large number of nonsynonymous mutations that evolved in the root nodes during the earliest stages of primary tumor evolution and were maintained in all single cells during the clonal expansion of the tumor mass. We also identified a small number of mutations that were specific to the liver metastases, which are likely to play an important role in metastatic dissemination. Using the single cell data, we constructed phylogenetic trees, which revealed branched evolution at both organ sites. Collectively, our data suggest that both mCRC patients are consistent with a late-dissemination model, in which the primary tumors evolved for a long period of time prior to the dissemination of clones to distant organ sites. This model has important clinical implications, by suggesting that surgical intervention or therapeutic treatment of the primary CRC tumor can prevent metastasis, since single tumor cells do not disseminate at the earliest stages of tumor growth Citation Format: Marco L. Leung, Anna Casasent, Yong Wang, Emi Sei, Nicholas Navin. Single-cell DNA sequencing identifies a late dissemination model in metastatic colorectal cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 157.

Human breast cancers often display intratumor genomic heterogeneity. This clonal diversity confounds the clinical diagnosis and basic research of cancer, because single samples may not represent that tumor as a whole. Sequencing breast tumor cohorts en masse has identified many prevalent mutations, but has limited ability for resolving subclonal diversity. Here, we developed a whole-genome and exome single-cell sequencing approach (Nuc-Seq) using G2/M cells. To validate our method, we applied Nuc-Seq to sequence the whole genomes of two single cells from a genetically monoclonal breast cancer cell line (SK-BR-3) at high coverage depth (61X ± 5 sem, n=2) and breadth (83.70% ± 3.40 sem, n=2) to detect somatic mutations. Our analysis suggests that Nuc-Seq generates low allelic dropout rates (9.73% ± 2.19%) and low false positive error rates for point mutations (FPR = 1.24e-6). We then applied this method to sequence single normal and tumor cells from an estrogen-receptor positive breast cancer and a triple-negative ductal carcinoma at base-pair resolution. In parallel, we performed single cell copy number profiling. In both tumors, we observed a large number of rare variants that were not detected by sequencing the bulk tumor en masse. In contrast, we find that single cell copy number profiles are highly similar. Our data suggest that aneuploid rearrangements occurred early in tumor evolution and remained highly stable as the tumor mass expanded. In contrast we find that point mutations evolved gradually, generating extensive clonal diversity. Many of the diverse mutations were shown to occur at low frequencies (0.03 -10%) in the tumor mass by targeted duplex sequencing. Mathematical modeling suggests that the triple-negative tumor cells have an increased mutation rate (13.3X), while the ER+ tumor cells do not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer. Citation Format: Yong Wang, Nicholas Navin, Jill Waters, Marco Leung, Anna Unruh, Xiuqing Shi, Whijae Roh, Ken Chen, Paul Scheet, Selina Vattathil, Han Liang, Asha Multani, Hong Zhang, Funda Meric-Bernstam, Franziska Michor, Rui Zhao. Single cell genome sequencing reveals clonal stability and diversity in breast cancer. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-310. doi:10.1158/1538-7445.AM2014-LB-310

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 material The online version of this article (doi:10.1186/s13059-015-0616-2) contains supplementary material, which is available to authorized users.