Serge Saxonov
Bio-Rad Laboratories
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Featured researches published by Serge Saxonov.
Analytical Chemistry | 2011
Benjamin J. Hindson; Kevin Ness; Donald A. Masquelier; Phillip Belgrader; Nicholas J. Heredia; Anthony J. Makarewicz; Isaac J. Bright; Michael Y. Lucero; Amy L. Hiddessen; Tina C. Legler; Tyler K. Kitano; Michael R. Hodel; Jonathan Petersen; Paul Wyatt; Erin Steenblock; Pallavi Shah; Luc J. Bousse; Camille Troup; Jeffrey Clark Mellen; Dean K. Wittmann; Nicholas G. Erndt; Thomas H. Cauley; Ryan Koehler; Austin P. So; Simant Dube; Klint A. Rose; Luz Montesclaros; Shenglong Wang; David P. Stumbo; Shawn Hodges
Digital PCR enables the absolute quantitation of nucleic acids in a sample. The lack of scalable and practical technologies for digital PCR implementation has hampered the widespread adoption of this inherently powerful technique. Here we describe a high-throughput droplet digital PCR (ddPCR) system that enables processing of ∼2 million PCR reactions using conventional TaqMan assays with a 96-well plate workflow. Three applications demonstrate that the massive partitioning afforded by our ddPCR system provides orders of magnitude more precision and sensitivity than real-time PCR. First, we show the accurate measurement of germline copy number variation. Second, for rare alleles, we show sensitive detection of mutant DNA in a 100 000-fold excess of wildtype background. Third, we demonstrate absolute quantitation of circulating fetal and maternal DNA from cell-free plasma. We anticipate this ddPCR system will allow researchers to explore complex genetic landscapes, discover and validate new disease associations, and define a new era of molecular diagnostics.
Nature Biotechnology | 2016
Grace X Y Zheng; Billy Lau; Michael Schnall-Levin; Mirna Jarosz; John M. Bell; Christopher M Hindson; Sofia Kyriazopoulou-Panagiotopoulou; Donald A Masquelier; Landon Merrill; Jessica M Terry; Patrice A Mudivarti; Paul W Wyatt; Rajiv Bharadwaj; Anthony J Makarewicz; Yuan Li; Phillip Belgrader; Andrew D Price; Adam J Lowe; Patrick Marks; Gerard M Vurens; Paul Hardenbol; Luz Montesclaros; Melissa Luo; Lawrence Greenfield; Alexander Wong; David E Birch; Steven W Short; Keith P Bjornson; Pranav Patel; Erik S. Hopmans
Haplotyping of human chromosomes is a prerequisite for cataloguing the full repertoire of genetic variation. We present a microfluidics-based, linked-read sequencing technology that can phase and haplotype germline and cancer genomes using nanograms of input DNA. This high-throughput platform prepares barcoded libraries for short-read sequencing and computationally reconstructs long-range haplotype and structural variant information. We generate haplotype blocks in a nuclear trio that are concordant with expected inheritance patterns and phase a set of structural variants. We also resolve the structure of the EML4-ALK gene fusion in the NCI-H2228 cancer cell line using phased exome sequencing. Finally, we assign genetic aberrations to specific megabase-scale haplotypes generated from whole-genome sequencing of a primary colorectal adenocarcinoma. This approach resolves haplotype information using up to 100 times less genomic DNA than some methods and enables the accurate detection of structural variants.
Translational Medicine | 2012
Lincoln D. Nadauld; John F. Regan; Laura Miotke; Reet K. Pai; Teri A. Longacre; Shirley S. Kwok; Serge Saxonov; James M. Ford; Hanlee P. Ji
For the analysis of cancer, there is great interest in rapid and accurate detection of cancer genome amplifications containing oncogenes that are potential therapeutic targets. The vast majority of cancer tissue samples are formalin fixed and paraffin embedded (FFPE) which enables histopathological examination and long term archiving. However, FFPE cancer genomic DNA is oftentimes degraded and generally a poor substrate for many molecular biology assays. To overcome the issues of poor DNA quality from FFPE samples and detect oncogenic copy number amplifications with high accuracy and sensitivity, we developed a novel approach. Our assay requires nanogram amounts of genomic DNA, thus facilitating study of small amounts of clinical samples. Using droplet digital PCR (ddPCR), we can determine the relative copy number of specific genomic loci even in the presence of intermingled normal tissue. We used a control dilution series to determine the limits of detection for the ddPCR assay and report its improved sensitivity on minimal amounts of DNA compared to standard real-time PCR. To develop this approach, we designed an assay for the fibroblast growth factor receptor 2 gene (FGFR2) that is amplified in a gastric and breast cancers as well as others. We successfully utilized ddPCR to ascertain FGFR2 amplifications from FFPE-preserved gastrointestinal adenocarcinomas.
Cancer Research | 2012
Benjamin J. Hindson; Austin P. So; Ryan Koehler; Camille Troup; Nick Heredia; George Karlin-Neumann; Serge Saxonov; Helen E. White
Molecular tests for genetic mutations play an important role in the diagnosis of cancer. Somatic mutations that drive the pathological features of most tumors have increasing promise as biomarkers for cancer prognosis and therapeutic efficacy. The detection of somatic mutations poses an analytical challenge due to the heterogeneous nature of most samples, where a gene carrying a mutation may differ from the highly abundant wild type sequence by only a single nucleotide. Although a variety of methods exist for mutation analysis, many have poor selectivity and fail to detect mutant sequence below 1 in 100 wildtype sequences. Methods that provide better discrimination and quantitation of somatic mutations are desirable. Here we present a simple strategy using droplet digital™ PCR (ddPCR™) for the detection of somatic mutations with high selectivity and sensitivity. Based on the simple principle of sample partitioning into water-in-oil microdroplets, this ddPCR method increases the abundance of a mutant DNA sequence up to 20,000 times compared to an equivalent bulk PCR reaction. Using conventional TaqMan chemistries and workflow, selectivities of up to 1/100,000 can readily be achieved in any laboratory. Here we present results on the use of ddPCR for the detection and quantitation of several clinically important mutations, including KRAS, c-KIT D816V and JAK2 from clinical samples such as bone marrow aspirates and FFPE. Results from ddPCR are compared to those of conventional approaches including allele specific real-time PCR and sequencing. This ddPCR method may play an important role in the earlier detection of cancer, monitoring the progress of disease and response to therapeutics. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4859. doi:1538-7445.AM2012-4859
Cancer Research | 2012
Austin P. So; Benjamin J. Hindson; Ryan Koehler; Serge Saxonov; George Karlin-Neumann; Nolan G. Ericson; Jason H. Bielas
Detection and quantitation of specific mutations in circulating plasma holds promise for earlier and less invasive diagnosis of disease. This presents significant analytical challenges, particularly as the biomarker may differ from its highly abundant wildtype by only a single nucleotide. Conventional methods have poor selectivity and fail to detect mutant sequence below 1 in 100 wildtype sequences. Compounding this, the amount of circulating nucleic acid in plasma is low. Here we present a simple strategy using droplet digital™ PCR (ddPCR™) for the detection of somatic mutations with high selectivity and sensitivity. Based on the simple principle of sample partitioning into water-in-oil microdroplets, this ddPCR method increases the abundance of a mutant DNA sequence up to 20,000 times compared to an equivalent bulk PCR reaction. Using conventional TaqMan chemistries and workflow, selectivities of up to 1/100,000 can readily be achieved in any laboratory. We evaluated ddPCR for the detection and quantitation of several clinically important mutations in the EGFR and KRAS loci from clinical samples derived from normal and tumor plasma samples. We also demonstrate the feasibility of multiplexing of Kras and EGFR assays to improve sample processing efficiency. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3399. doi:1538-7445.AM2012-3399
Cancer Research | 2011
Serge Saxonov; Nick Heredia; Phil Belgrader; Ben Hindson
Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL Accurate detection and quantification of molecular targets is a challenge that arises across a range of oncology applications. In particular, existing techniques are faced with difficulties when trying to quantify copy number alterations or when attempting to detect single point mutations in the presence of high background of wildtype DNA. This work assesses the performance of a new droplet digitial PCR (ddPCRTM) platform in addressing these challenges. The ddPCR system partitions the sample into an emulsion of 20,000 stable mono-dispersed nanoliter droplets, such that some droplets have the target of interest and some do not. The emulsion is then thermocycled where each droplet serves as an independent reactor for PCR. After PCR, every droplet is read with a two-color fluorescence detector to assess whether the targets were amplified. The number of positive and negative droplets is used to compute an absolute concentration of the target in the sample with high precision and accuracy. The simple workflow uses standard 96-well plate processing enabling the system to generate data for millions of PCR replicates within a matter of hours. Many genomic regions undergo copy number alterations in human cancers and are associated with clinical features. Existing methods (including FISH, CGH, real-time PCR) suffer from various shortcomings, such as cumbersome workflow, limited sensitivity, and inability to discriminate better than 1.5 to 2 fold differences. Here we show that ddPCR provides accurate and reproducible copy number estimates with precision better than 10%. Specifically, using several systems of germline copy number variation we show CNV estimates that are highly reproducible, cluster tightly near integer values, and span 0 to 13 copies – so that a sample with 13 copies can be reliably distinguished from one with 12. Detection and quantification of rare mutations in the presence of highly homologous wild-type background is important in many oncology applications including analyses of heterogeneous tumors, monitoring response to therapy, and the analyses of cell free DNA in plasma. Conventional sequencing and real time PCR typically fail to detect the mutant when its present at less than 10% frequency relative to the wildtype background. Here, we use a spike-in series with EGFR mutations to show that ddPCR can readily quantify targets down to the level of 0.01% mutant relative to the wild-type DNA. The system can be used for single molecule detection in the presence of tens of thousands of highly homologous molecules. In sum, we show that droplet digital PCR system produces highly accurate and reproducible quantification of molecular targets, with orders of magnitude performance improvements over conventional techniques for copy number estimation and rare event detection. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 4892. doi:10.1158/1538-7445.AM2011-4892
Methods | 2013
Nicholas J. Heredia; Phillip Belgrader; Shenglong Wang; Ryan Koehler; Jack Regan; Angela M. Cosman; Serge Saxonov; Benjamin J. Hindson; Stephanie Tanner; Alexandra Brown; George Karlin-Neumann
Archive | 2010
Benajamin Hindson; Serge Saxonov; Phillip Belgrader; Kevin D. Ness; Michael Y. Lucero; Billy W. Colston
Archive | 2012
Serge Saxonov
Archive | 2014
Benjamin J. Hindson; Christopher M. Hindson; Michael Schnall-Levin; Kevin Ness; Mirna Jarosz; Donald A. Masquelier; Serge Saxonov; Landon Merrill; Andrew D. Price; Paul Hardenbol; Yuan Li