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Digital DNA detection based on a compact optofluidic laser with ultra-low sample consumption.

ABSTRACT: DNA lasers self-amplify optical signals from a DNA analyte as well as thermodynamic differences between sequences, allowing quasi-digital DNA detection. However, these systems have drawbacks, such as relatively large sample consumption and complicated dye labelling. Moreover, although the lasing signal can detect the target DNA, it is superimposed on an unintended fluorescence background, which persists for non-target DNA samples as well. From an optical point of view, it is thus not truly digital detection and requires spectral analysis to identify the target. In this work, we propose and demonstrate an optofluidic laser that has a single layer of DNA molecules as the gain material. A target DNA produces intensive laser emission comparable to existing DNA lasers, while any unnecessary fluorescence background is successfully suppressed. As a result, the target DNA can be detected with a single laser pulse, in a truly digital manner. Since the DNA molecules cover only a single layer on the surface of the laser microcavity, the DNA sample consumption is a few orders of magnitude lower than that of existing DNA lasers. Furthermore, the DNA molecules are stained by simply immersing the microcavity in the intercalating dye solution, and thus the proposed DNA laser is free of any complex dye-labelling process prior to analysis.

SUBMITTER: Lee W

PROVIDER: S-EPMC5137248 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Digital DNA detection based on a compact optofluidic laser with ultra-low sample consumption.

Lee Wonsuk W Chen Qiushu Q Fan Xudong X Yoon Dong Ki DK

Lab on a chip 20161101 24

DNA lasers self-amplify optical signals from a DNA analyte as well as thermodynamic differences between sequences, allowing quasi-digital DNA detection. However, these systems have drawbacks, such as relatively large sample consumption and complicated dye labelling. Moreover, although the lasing signal can detect the target DNA, it is superimposed on an unintended fluorescence background, which persists for non-target DNA samples as well. From an optical point of view, it is thus not truly digit ...[more]

PMID: 27868127

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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.

Dataset Information

Digital DNA detection based on a compact optofluidic laser with ultra-low sample consumption.

Publications

Digital DNA detection based on a compact optofluidic laser with ultra-low sample consumption.

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