- Genome-wide segregation of single nucleotide and structural variants into single cancer cells. - Background: Single-cell genome sequencing provides high-resolution details of the clonal genomic modifications that occur during cancer initiation, progression, and ongoing evolution as patients undergo treatment. - One limitation of current single-cell sequencing strategies is a suboptimal capacity to detect all classes of single- nucleotide and structural variants in the same cells.. - This method can reconstruct the clonal architecture and mutational history of a malignancy using all classes and sizes of somatic variants, providing more complete details of the temporal changes in mutational classes and processes that led to the development of a malignant neoplasm. - Using this approach, we interrogated cells from a patient with leukemia, determining that processes producing structural variation preceded single nucleotide changes in the development of that malignancy.. - Keywords: Single-cell genomics, cancer evolution, acute lymphoblastic leukemia. - By bringing genomics to the cellular level, we have begun to segregate mutations to distinct cellular populations, enabling us to define the population genetic diversity and clonal structures of complex tissues.. - the mutations and mutational processes that resulted in the formation of a malignancy [4, 5].. - Contemporary strategies for amplifying and interrogating the genomes of single cells have resulted in the ability to segregate single nucleotide or copy number variants (CNV) into single cells. - However, due to the tradeoffs in genome coverage and uniformity using current amplifi- cation strategies, we have had limited success compre- hensively detecting both variant classes in the same cells [6]. - Isothermal methods that provide sufficient breadth of genomic coverage to identify most single nucleotide variants (SNV) but much less uniformity in coverage depth have a very limited capacity to detect. - Full list of author information is available at the end of the article. - 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0. - The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.. - Quantitative PCR-based methods have been developed to detect both CNV and SNV in the same cell, but they are only able to interrogate a small num- ber of variants due to limitations in the multiplexing of fluorophores, which hampers the investigator’s ability to accurately determine the clonal structures [8, 9].. - In the present study, we report a strategy for segregat- ing hundreds of any variant type to individual cells in an accurate, cost-effective, and efficient manner. - We then amplify the genomes of the single cells using multiple displace- ment amplification for maximal genome recovery from each cell [10]. - This is followed by the detection of SNVs and all classes of SV detected in the bulk sample using amplicon-based resequencing (Fig. - To maximize effi- ciency while reducing reagents costs, we generate the amplicons for single-cell variant calling in the microfluidic devices controlled by the Access Array System, executing thousands of parallel reactions in extremely small volumes in an automated manner. - Finally, we use the single cell mutation profile to determine the relationships between cells and infer the clonal structure and mutational history of that malignancy [5, 12].. - As an example to show the capability of this approach to identify all types of variants in the same cells, we. - We first performed whole gen- ome, exome, and RNA sequencing to characterize the somatic variants in that sample as part of the Pediatric Cancer Genome Project [13, 14]. - To minimize effort and cost, generation of amplicons for the confirm- ation of mutations in the bulk sample was done in the same microfluidic chip with the single cells, with condi- tions described in detail below.. - Using this approach, we were able to cover 96% of the 91 target sites at 10X coverage depth in the bulk samples.. - The genome amplification consistently resulted in the generation of 120-150 ng of product that was harvested in 13 μl of. - Microfluidic Single Cell Isolation and Whole Genome. - Whole Genome Sequencing To Identify Putative SNVs and SVs. - Confirmation and Single Cell Evaluations. - Single Cell SV Calls. - Single Cell SNV/Indel Calls. - Single Nucleotide Variants Structural Variants. - a Whole genome sequencing is first performed to determine the comprehensive mutation profile of the sample, followed by variant confirmation using targeted resequencing. - Single cells are then isolated, followed by amplification of the variant sites, variant calling, and binary matrix construction to determine the clonal structure. - We then used 3.75 μl of WGA product to perform amplicon-based resequencing of the amplified single cell genomes. - To accomplish this, we designed primers to target all putative SNV sites identi- fied in the bulk sequencing using BatchPrimer3 (https://. - As detailed in the Access Array manual, we added common sequences ACACTGACGACATGGTTCTACA and TACGGTAGCAGAGACTTGGTCT to the 5′ end of the forward and reverse primers, respectively. - Instruc- tions detailed in the C1 for DNA sequencing and Access Array manuals (https://www.fluidigm.com/support) were followed for chip loading, PCR reagents used, and thermocycling conditions. - Custom bash scripts then required concordance of the location and base change between the whole genome and confirmation data, as well as a minimum of 3 reads comprising more than 1%. - of all reads at that position to support the variant call in the single cell. - For SV calling, custom bash scripts were used to identify and quantify SV breakpoints in the raw reads. - To call an SV breakpoint in a given cell, greater than 40 reads had to be an exact match of the 30 bp that spanned the breakpoint.. - 2, after creating a binary matrix of all cells and mutation calls, we then retraced the mutational history of the tumor by grouping cells and mutations into clusters using a mixture model of multivariate Bernoulli distributions. - We used our previously published approach, with the addition of SVs in the same binary matrix (https://github.com/lianchye/Clonal_Analysis). - We also considered SNVs and SVs to be equivalent contribu- tors to the clonal evolution. - However, this is likely an overestimate of the number of double cells, as chambers where the amplification started with two genome copies are more likely to produce high quality. - Closer exam- ination of the mutations in the clusters showed a shared ancestral cluster that had only acquired SV, followed by separate mutation clusters of SNVs and a smaller number of SVs being acquired as the two clones evolved (Fig. - Structural Variant Single Nucleotide Variant. - The relative contribution of single nucleotide and structural variation to each clone are also represented by the pie charts adjacent to each clone. - In this study, we present a new method for detecting hundreds of genomic variants in hundreds of single cells in a cost-effective and efficient manner. - Consistent with this, the ancestral clone that only harbored SV had rear- rangements of most of the RAG target immune receptor genes. - However, that variation was insufficient to produce malignant transformation, which required the APOBEC-mediated SNVs that drove later evolution of the leukemic cell genomes. - Thus, with our new method, we are not only able to order the sequences of genetic events, but also the underlying mutational processes that drove malignant transformation of the disease as those normal fetal hematopoietic precursors evolved into leukemia over several years [17]. - This allows us to infer the trunk of the clonal structure, but does not provide insights into the intra-clonal evolution and mutational diversity.. - ideally with high throughput single-cell whole genome or exome sequencing that is executed in an efficient, accurate, and cost-effective manner.. - Single-cell sequencing is a powerful tool for deconvoluting the mutational histories of tumors. - By acquiring information on all subtypes and sizes of both variant types in the same cells, we have provided a strategy for determining the order in which those events occurred, and potentially, how they cooperate in the development of human cancer. - Samples from this patient were obtained as part of the ongoing St. - banking protocol that has been approved by the Institu- tional Review Board after acquiring written informed consent of the parent if the child was under 16 or the adolescent if they were 16 years or older in accordance with the Declaration of Helsinki. - Investigators are blinded to the identity of the participant in this study.. - Single-cell isolation and WGA. - The cells were washed four additional times using Flui- digm wash buffer according to the manufacturer. - The cells were then loaded into a small Fluidigm C1 DNA sequencing microfluidic chip, followed by WGA according to the manufacturer’s instructions.. - Single cell WGA products underwent targeted sequen- cing using the Access Array System according to the manufacturer’s instructions as previously reported (Fluidigm) [5]. - The sequences of the primers are listed in Additional file 4: Table S4). - SVs were first confirmed in the bulk sample by determining the number of reads that had an exact match to the 30 bp sequence that. - Variants were considered present if greater than 40 reads were an exact match of the 30 bp that spanned the breakpoint. - This file provides a list of structural variants confirmed in the bulk patient sample, as well as the location, quality, and breakpoint sequence data output by CREST. - List of Somatic Single-Nucleotide Variants Identified in this Sample. - This file provides a list of single-nucleotide variants confirmed in the bulk patient sample, as well as the location and number of supporting reads output by VarScan. - This file provides an overview of the number of cells captured and included in the analyses after surpassing quality control criteria, as well as. - This file provides a list of the primers used for interrogating the bulk and single cell samples for single-nucleotide and structural variants. - SNV: single-nucleotide variant. - WGA: whole genome amplification. - In addition, the authors would like to thank the members of the Pediatric Cancer Genome Project, especially Charles Mullighan and Ching-Hon Pui who lead the Hematological Malignancies Program.. - The raw fastq files are available in the short read archive at NCBI under bioproject PRJNA413094.. - Samples from this patient were obtained following approval of the St. - Jude Children ’ s Research Hospital Institutional Review Board and acquiring written informed consent of the patient or their legal guardian in accordance with the Declaration of Helsinki.. - A copy of the written consent is available for review by the Editor of this journal.. - Single-cell genome sequencing: current state of the science. - doi:10.1038/nrg.. - doi:10.1126/science.1243472.. - Single cell sequencing reveals low levels of aneuploidy across mammalian tissues. - doi:10.1073/pnas . - doi:10.1038/nature13600.. - Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. - Single-cell whole-genome amplification and sequencing: methodology and applications. - doi:10.1146/annurev-genom . - Genome-wide detection of single- nucleotide and copy-number variations of a single human cell. - doi:10.1126/science.1229164.. - Single-cell mutational profiling and clonal phylogeny in cancer. - doi:10.1101/gr . - doi:10.1038/leu.2014.322.. - A quantitative comparison of single-cell whole genome amplification methods. - doi:10.1371/journal.pone.. - doi:10.1038/nmeth.1628.. - Analysis pipeline for dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell.. - doi:10.1038/ng.2287.. - doi:10.1038/. - doi:10.1038/ng.2874.. - doi:10.1038/nrc1164.
Xem thử không khả dụng, vui lòng xem tại trang nguồn hoặc xem
Tóm tắt