Preparing Single-cell DNA Library Using Nextera for Detection of CNV.
Ontology highlight
ABSTRACT:
SUBMITTER: Xi L
PROVIDER: S-EPMC7854077 | biostudies-literature | 2019 Feb
REPOSITORIES: biostudies-literature
Publications
Preparing Single-cell DNA Library Using Nextera for Detection of CNV.
Xi Larry L Leong Patrick P Mihajlovic Aleksandar A
Bio-protocol 20190220 4
Single-cell DNA sequencing is a powerful tool to evaluate the state of heterogeneity of heterogeneous tissues like cancer in a quantitative manner that bulk sequencing can never achieve. DOP-PCR (Degenerate Oligonucleotide-Primed Polymerase Chain Reaction), MDA (Multiple Displacement Amplification), MALBAC (Multiple Annealing and Looping-Based Amplification Cycles), LIANTI (Linear Amplification via Transposon Insertion) and TnBC (Transposon Barcoded) have been the primary choices to prepare sing ...[more]
Similar Datasets
Project description:The Nextera protocol, which utilises a transposome based approach to create libraries for Illumina sequencing, requires pure DNA template, an accurate assessment of input concentration and a column clean-up that limits its applicability for high-throughput sample preparation. We addressed the identified limitations to develop a robust workflow that supports both rapid and high-throughput projects also reducing reagent costs.We show that an initial bead-based normalisation step can remove the need for quantification and improves sample purity. A 75% cost reduction was achieved with a low-volume modified protocol which was tested over genomes with different GC content to demonstrate its robustness. Finally we developed a custom set of index tags and primers which increase the number of samples that can simultaneously be sequenced on a single lane of an Illumina instrument.We addressed the bottlenecks of Nextera library construction to produce a modified protocol which harnesses the full power of the Nextera kit and allows the reproducible construction of libraries on a high-throughput scale reducing the associated cost of the kit.
| S-EPMC4222894 | biostudies-literature
Project description:Background Copy number variation is crucial in deciphering the mechanism and cure of complex disorders and cancers. The recent advancement of scDNA sequencing technology sheds light upon addressing intratumor heterogeneity, detecting rare subclones, and reconstructing tumor evolution lineages at single-cell resolution. Nevertheless, the current circular binary segmentation based approach proves to fail to efficiently and effectively identify copy number shifts on some exceptional trails. Results Here, we propose SCYN, a CNV segmentation method powered with dynamic programming. SCYN resolves the precise segmentation on in silico dataset. Then we verified SCYN manifested accurate copy number inferring on triple negative breast cancer scDNA data, with array comparative genomic hybridization results of purified bulk samples as ground truth validation. We tested SCYN on two datasets of the newly emerged 10x Genomics CNV solution. SCYN successfully recognizes gastric cancer cells from 1% and 10% spike-ins 10x datasets. Moreover, SCYN is about 150 times faster than state of the art tool when dealing with the datasets of approximately 2000 cells. Conclusions SCYN robustly and efficiently detects segmentations and infers copy number profiles on single cell DNA sequencing data. It serves to reveal the tumor intra-heterogeneity. The source code of SCYN can be accessed in https://github.com/xikanfeng2/SCYN. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-021-07941-3).
| S-EPMC8596905 | biostudies-literature
Project description:We developed a low-cost method for the production of Illumina-compatible sequencing libraries that allows up to 14 times more libraries for high-throughput Illumina sequencing to be generated for the same cost. We call this new method Hackflex. The quality of library preparation was tested by constructing libraries from Escherichia coli MG1655 genomic DNA using either Hackflex, standard Nextera Flex (recently renamed as Illumina DNA Prep) or a variation of standard Nextera Flex in which the bead-linked transposase is diluted prior to use. In order to test the library quality for genomes with a higher and a lower G+C content, library construction methods were also tested on Pseudomonas aeruginosa PAO1 and Staphylococcus aureus ATCC 25923, respectively. We demonstrated that Hackflex can produce high-quality libraries and yields a highly uniform coverage, equivalent to the standard Nextera Flex kit. We show that strongly size-selected libraries produce sufficient yield and complexity to support de novo microbial genome assembly, and that assemblies of the large-insert libraries can be much more contiguous than standard libraries without strong size selection. We introduce a new set of sample barcodes that are distinct from standard Illumina barcodes, enabling Hackflex samples to be multiplexed with samples barcoded using standard Illumina kits. Using Hackflex, we were able to achieve a per-sample reagent cost for library prep of A$7.22 (Australian dollars) (US $5.60; UK £3.87, £1=A$1.87), which is 9.87 times lower than the standard Nextera Flex protocol at advertised retail price. An additional simple modification and further simplification of the protocol by omitting the wash step enables a further price reduction to reach an overall 14-fold cost saving. This method will allow researchers to construct more libraries within a given budget, thereby yielding more data and facilitating research programmes where sequencing large numbers of libraries is beneficial.
| S-EPMC8914357 | biostudies-literature
Project description:Improved Workflows for High Throughput Library Preparation using the Transposome-Based Nextera System
| PRJEB4315 | ENA
Project description:DNA library preparation for high-throughput sequencing of genomic DNA usually involves ligation of adapters to double-stranded DNA fragments. However, for highly degraded DNA, especially ancient DNA, library preparation has been found to be more efficient if each of the two DNA strands are converted into library molecules separately. We present a new method for single-stranded library preparation, ssDNA2.0, which is based on single-stranded DNA ligation with T4 DNA ligase utilizing a splinter oligonucleotide with a stretch of random bases hybridized to a 3? biotinylated donor oligonucleotide. A thorough evaluation of this ligation scheme shows that single-stranded DNA can be ligated to adapter oligonucleotides in higher concentration than with CircLigase (an RNA ligase that was previously chosen for end-to-end ligation in single-stranded library preparation) and that biases in ligation can be minimized when choosing splinters with 7 or 8 random nucleotides. We show that ssDNA2.0 tolerates higher quantities of input DNA than CircLigase-based library preparation, is less costly and better compatible with automation. We also provide an in-depth comparison of library preparation methods on degraded DNA from various sources. Most strikingly, we find that single-stranded library preparation increases library yields from tissues stored in formalin for many years by several orders of magnitude.
| S-EPMC5449542 | biostudies-literature
Project description:BackgroundAdvances in single-cell sequencing provide unprecedented opportunities for clinical examination of circulating tumor cells, cancer stem cells, and other rare cells responsible for disease progression and drug resistance. On the genomic level, single-cell whole exome sequencing (scWES) started to gain popularity with its unique potentials in characterizing mutational landscapes at a single-cell level. Currently, there is little known about the performance of different exome capture kits in scWES. Nextera rapid capture (NXT; Illumina, Inc.) has been the only exome capture kit recommended for scWES by Fluidigm C1, a widely accessed system in single-cell preparation.ResultsIn this study, we compared the performance of NXT following Fluidigm's protocol with Agilent SureSelectXT Target Enrichment System (AGL), another exome capture kit widely used for bulk sequencing. We created DNA libraries of 192 single cells isolated from spheres grown from a melanoma specimen using Fluidigm C1. Twelve high-yield cells were selected to perform dual-exome capture and sequencing using AGL and NXT in parallel. After mapping and coverage analysis, AGL outperformed NXT in coverage uniformity, mapping rates of reads, exome capture rates, and low PCR duplicate rates. For germline variant calling, AGL achieved better performance in overlap with known variants in dbSNP and transition-transversion ratios. Using calls from high coverage bulk sequencing from blood DNA as the golden standard, AGL-based scWES demonstrated high positive predictive values, and medium to high sensitivity. Lastly, we evaluated somatic mutation calling by comparing single-cell data with the matched blood sequence as control. On average, 300 mutations were identified in each cell. In 10 of 12 cells, higher numbers of mutations were identified using AGL than NXT, probably caused by coverage depth. When mutations are adequately covered in both AGL and NXT data, the two methods showed very high concordance (93-100% per cell).ConclusionsOur results suggest that AGL can also be used for scWES when there is sufficient DNA, and it yields better data quality than the current Fluidigm's protocol using NXT.
| S-EPMC7868962 | biostudies-literature
Project description:While methods for detecting SNVs and indels in circulating tumor DNA (ctDNA) with hybridization capture-based next-generation sequencing (NGS) have been available, copy number variations (CNVs) detection is more challenging. Here, we present a method enabling CNV detection from a 150-gene panel using a very low amount of ctDNA. First, a read depth-based CNV estimation method without a paired blood sample was developed and cfDNA sequencing data from healthy people were used to build a panel of normal (PoN) model. Then, in silico and in vitro simulations were performed to define the limit of detection (LOD) for EGFR, ERBB2, and MET. Compared to the WES results of the 48 samples, the concordance rate for EGFR, ERBB2, and MET CNVs was 78%, 89.6%, and 92.4%, respectively. In another cohort profiled with the 150-gene panel from 5980 lung cancer ctDNA samples, we detected the three genes' amplification with comparable population frequency with other cohorts. One lung adenocarcinoma patient with MET amplification detected by our method reached partial response to crizotinib. These findings show that our ctDNA CNV detection pipeline can detect CNVs with high specificity and concordance, which enables CNV calling in a non-invasive way for cancer patients when tissues are not available.
| S-EPMC6895974 | biostudies-literature
Project description:MotivationSeveral recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data (<0.05× per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing.ResultsWe developed Single Cell Data Tumor Clusterer (SECEDO, lat. 'to separate'), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7250 cells and eight tumor subclones from a single patient and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to five real single-cell sequencing datasets from a breast cancer patient, each consisting of ≈2000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03×, achieving an Adjusted Rand Index (ARI) score of ≈0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of ≈0, even after merging cells to create higher coverage data (factor 10 increase), and was only able to match SECEDOs performance when pooling data from all five datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants.Availability and implementationSECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo. Instructions to download the data and the evaluation code to reproduce the findings in this paper are available at: https://github.com/ratschlab/secedo-evaluation. The code and data of the submitted version are archived at: https://doi.org/10.5281/zenodo.6516955.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC9477524 | biostudies-literature
Project description:Genetic engineering of cellular function holds much promise for the treatment of a variety of diseases including gene deficiencies and cancer. However, engineering the full complement of cellular functions can be a daunting genetic exercise since many molecular triggers need to be activated to achieve complete function. In the case of T cells, genes encoding chimeric antigen receptors (CARs) covalently linking antibodies to cytoplasmic signaling domains can trigger some, but not all, cellular functions against cancer cells. To date, relatively few CAR formats have been investigated using a candidate molecule approach, in which rationally chosen molecules were trialed one by one. Therefore, to expedite this arduous process we developed an innovative screening method to screen many thousands of CAR formats to identify genes able to enhance the anticancer ability of T cells. We used a directional in-frame library of randomly assembled signaling domains in a CAR specific for the tumor associated antigen erbB2. Several new and original CARs were identified, one of which had an enhanced ability to lyse cancer cells and inhibit tumor growth in mice. This study highlights novel technology that could be used to screen a variety of molecules for their capacity to induce diverse functions in cells.
| S-EPMC3646939 | biostudies-literature