Publications

Apostle technologies have been discussed or cited in the following publications:


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  1. Apostle COVID-19 RNA Extraction System Applied in the Effective Detection of SARS-CoV-2. Horner S. Apostle Inc. Application Note. Oct 2020.

    The current coronavirus disease 2019 (COVID-19) pandemic started in late 2019. COVID-19 is the result of severe acute respiratory syndrome 2 (SARS-CoV-2) virus contraction. COVID-19 is often accompanied by a wide range of symptoms including fever, cough, and shortness of breath. The SARS-CoV-2 virus consists of a ~30 kb RNA genome encoding for 15 proteins, including the spike protein that enables the virus to enter host cells. The current gold standard qualitative detection method, qRT-PCR, reverse transcribes the viral RNA into cDNA, which is subsequently amplified and quantitated. This application note illustrates the effective detection of SARS-CoV-2 using the Apostle COVID-19 Viral RNA Isolation Automation System and qRT-PCR in clinical lab settings. This system uses efficient magnetic nanoparticle technology for fast extraction and purification of viral nucleic acids from various types of biological samples collected in transport media. The proficient and consistent systems provide reliable test results to individuals that contribute to COVID-19 pandemic relief.
    The use of Apostle COVID-19 Viral RNA Isolation Automation System has proven to be an effective process as a part in detecting SARS-CoV-2 from more than two million samples carried out by our clients in clinical lab settings.

  2. Electronic cigarettes induce mitochondrial DNA damage and trigger toll-like receptor 9-mediated atherosclerosis Jieliang Li, Do Luong Huynh, Moon-Shong Tang, Hannah Simborio, Jing Huang, Beata Kosmider, Michael B. Steinberg, Le Thu Thi Le, Kien Pham, Chen Liu, He Wang bioRxiv. August 15, 2020. https://doi.org/10.1101/2020.08.15.252494

    Objective Both electronic cigarette (e-cig) use and toll-like receptor 9 (TLR9) activation have been implicated in promoting atherosclerosis. In this study we aimed to investigate the causative relationship of e-cig exposure on TLR9 activation and atherosclerosis development.
    Approach and Results Eight-week-old ApoE-/- mice fed normal chow diet were exposed to e-cig vapor (ECV) for 2 h/day, 5 days/week for 16 weeks. We found that ECV exposure significantly induced atherosclerotic lesions as examined by Oil Red O staining of aortic root and greatly upregulated TLR9 expression in classical monocytes and in the atherosclerotic plaques, which the latter was corroborated by upregulated TLR9 expression in human femoral artery atherosclerotic plaques in e-cig smokers. Intriguingly, we found a significant increase of damaged mitochondria DNA level in the circulating blood of ECV exposed mice. Furthermore, administration of TLR9 antagonist prior to ECV exposure not only alleviated atherosclerotic lesion and the upregulation of TLR9 in plaques, but also attenuated the increase of plasma levels of inflammatory cytokines, reduced the accumulation of lipid and macrophages, and decreased the frequency of blood CCR2+ classical monocytes. Surprisingly, we found that the cytoplasmic mtDNA isolated from ECV extract-treated cells can greatly enhance the expression of TLR9 in reporter cells.
    Conclusion E-cig induces mtDNA damage and the mtDNA in circulating blood stimulates the expression of TLR9, which elevate the expression of proinflammatory cytokines in monocyte/macrophage and consequently lead to atherosclerosis. Our results raise the possibility that intervention of TLR9 activation is a potential pharmacologic target of ECV-related inflammation and cardiovascular diseases.

  3. High-resolution DNA size enrichment using a magnetic nano-platform and application in non-invasive prenatal testing. Bo Zhang, Shuting Zhao, Hao Wan, Ying Liu, Fei Zhang, Xin Guo, Wenqi Zeng, Haiyan Zhang, Linghua Zeng, Jiale Qu, Ben-Qing Wu, Xinhong Wan, Charles R. Cantor and Dongliang Ge Analyst. July 2020, 145, 5733-5739

    Precise DNA sizing can boost sequencing efficiency, reduce cost, improve data quality, and even allow sequencing of low-input samples, while current pervasive DNA sizing approaches are incapable of differentiating DNA fragments under 200 bp with high resolution (<20 bp). In non-invasive prenatal testing (NIPT), the size distribution of cell-free fetal DNA in maternal plasma (main peak at 143 bp) is significantly different from that of maternal cell-free DNA (main peak at 166 bp). The current pervasive workflow of NIPT and DNA sizing is unable to take advantage of this 20 bp difference, resulting in sample rejection, test inaccuracy, and restricted clinical utility. Here we report a simple, automatable, high-resolution DNA size enrichment workflow, named MiniEnrich, on a magnetic nano-platform to exploit this 20 bp size difference and to enrich fetal DNA fragments from maternal blood. Two types of magnetic nanoparticles were developed, with one able to filter high-molecular-weight DNA with high resolution and the other able to recover the remaining DNA fragments under the size threshold of interest with >95% yield. Using this method, the average fetal fraction was increased from 13% to 20% after the enrichment, as measured by plasma DNA sequencing. This approach provides a new tool for high-resolution DNA size enrichment under 200 bp, which may improve NIPT accuracy by rescuing rejected non-reportable clinical samples, and enable NIPT earlier in pregnancy. It also has the potential to improve non-invasive screening for fetal monogenic disorders, differentiate tumor-related DNA in liquid biopsy and find more applications in autoimmune disease diagnosis.Two types of magnetic nanoparticles were developed, with one able to filter high-molecular-weight DNA with high resolution and the other able to recover the remaining DNA fragments under the size threshold of interest with >95% yield. Using this method, the average fetal fraction was increased from 13% to 20% after the enrichment, as measured by plasma DNA sequencing. This approach provides a new tool for high-resolution DNA size enrichment under 200 bp, which may improve NIPT accuracy by rescuing rejected non-reportable clinical samples, and enable NIPT earlier in pregnancy. It also has the potential to improve non-invasive screening for fetal monogenic disorders, differentiate tumor-related DNA in liquid biopsy and find more applications in autoimmune disease diagnosis.

  4. Improved conversion in extraction, library construction, and capture improve sensitivity for variants in liquid biopsy samples. Nicole Roseman, Shilpa Parakh, Hsiao-Yun Huang, Kevin Lai, Timothy Barnes, Lyn Lewis, Ushati Das Chakravarty, Anastasia Potts, Alisa Jackson, Amy Yoder, Jessica Sheu, Tzu-Chun Chen. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5863.

    Demonstrate performance of a complete automation and reagent workflow for analysis of cfDNA from bodily fluids. The efficient extraction of cfDNA from bodily fluids is a unique challenge due to the very low concentrations of nucleic acid. The extraction process along with library preparation is a laborious workflow, where human variability can lead to increased variability in the downstream analysis. Integrated DNA Technology (IDT) and Beckman Coulter (BC) have teamed up to provide a complete automation and reagent workflow for analysis of low frequency variants in cfDNA. The Apostle MiniMax™ High Efficiency Isolation Kit from BC provides complex, utilized magnetic nanoparticles to effectively capture cfDNA. IDT's library prep kit utilizes novel chemistry to maximize conversion, suppress adapter-dimer formation, reduce chimera rates, and facilitate double strand consensus analysis to call ultra-low frequency variants. Finally, IDT's xGen™ hybrid capture products maintain high library diversity and on-target rates to enable low frequency variant calling regardless of panel size. The Biomek i5 and i7 Hybrid workstations bring out the best performance from these reagents. The Biomek NGS workstations protocol is written with a modular design with safe stop points, making it customizable for each lab. The automated protocol uses Beckman's Demonstrated Method Interface tools which include: Biomek Method Launcher to run the method without going into Biomek software, Method Options Selector to choose the run parameters with a user friendly interface, Guided labware Setup to set the deck with labware based on the run parameters, DeckOptix Final Check software to help reduce deck setup errors. We demonstrate the performance of this complete workflow with a range of plasma inputs (4-8 mL). Using control samples with known variant frequencies, the workflow yields high library complexity, 100% positive predictive value, and reliable detection of <0.5% mutant allele frequency variants. With real cfDNA, the workflow demonstrates both high cfDNA and sequencing library yields along with high library complexity. The combination of these reagents on the Biomek workstations provides a robust and reproducible solution for the analysis of cfDNA.

  5. A complete automation and reagent workflow for analysis of cfDNA: from plasma to variants. Roseman N, Parakh S, Lai K, Sheu J, Wei H, Niccum B, Chen T, Huang H, Barnes T, Lewis L, Chakravarty UD, and Potts A. Advances in Genome Biology and Technology (AGBT). (abstract #511). Marco Island, FL. Feb 23-26, 2020

    As cost of sequencing continues to drop, the throughput and complexity of NGS assays have risen precipitously. At the same time, the types of samples being used for these assays have expanded as researchers find value in studying low input, degraded biological samples, such as cell free DNA (cfDNA). However, efficient extraction of cfDNA from bodily fluids is challenging due to the very low concentrations of nucleic acid. Manual protocols involved in the NGS workflow – DNA extraction, library preparation, and target enrichment – are often laborious and require significant hands on time, which can increase variability in the downstream analysis. Thus, complete automation workflows can increase throughput, reduce hands-on time, and minimize error. Integrated DNA Technologies (IDT) and Beckman Coulter (BC) have teamed up to provide a complete automation and reagent workflow for analysis of low frequency variants in cfDNA. The Apostle MiniMaxTM High Efficiency Isolation Kit from BC is a magnetic nanoparticle based kit that extracts cfDNA from all bodily fluids. IDT’s xGenTM Prism DNA library prep kit provides novel chemistry to maximize conversion, suppress adapter-dimer formation, reduce chimera rates, and facilitate UMI-based error correction to call ultra-low frequency variants. IDT’s xGenTM target enrichment products maintain high library diversity and on-target rates to enable low frequency variant calling regardless of panel size. The combination of these reagents on the Biomek workstations provides a robust and reproducible solution for the analysis of cfDNA.
    Conclusion
    • Apostle Minimax, xGen Prism, and xGen hybridization capture provide a complete automated solution from plasma to sequencing on the Beckman Coulter Biomek platforms
    • Apostle Minimax provides higher quantity and quality cfDNA extraction compared to a competitor kit
    • xGen Prism is specifically designed for low input, degraded samples, such as cfDNA and generates higher coverage and complexity compared to competitor kits
    • UMI-based error correction can be used to eliminate nearly all false positive calls and enables accurate low frequency variant calling

  6. G3viz: an R package to interactively visualize genetic mutation data using a lollipop-diagram. Xin G, Bo Z, Wenqi Z, et al. Bioinformatics. 2020; 36(3):928–929

    The lollipop-diagram is one of the widely used graphical representations to visualize and explore translational effects of genetic mutations in cancer genomics. However, an easy-to-use lollipop-diagram tool with full functionality is still lacking. Here, we introduce g3viz, an R package that enables researchers to explore genetic mutation data using a lollipop-diagram in a web browser. With a few lines of R code, users can interactively visualize data details, annotate findings and export resultant diagrams in high-quality figures. Because of usefulness and usability, g3viz can be generally exploited by researchers with different levels of bioinformatics skills and programming experience.

  7. Correlation between mutations found in FFPE tumor tissue and paired cfDNA samples. Niccum B., Heath C., Saunders L., Hur A.,Patel A. Association for Molecular Pathology (AMP). (abstract #ST103). Baltimore, MD. November 7-9, 2019

    Liquid biopsies represent a promising area of facilitating cancer research as blood collection is less invasive than tumor biopsies. Cell free DNA (cfDNA) consists of small (150 – 500 bp) DNA fragments that circulate in the blood. cfDNA levels tend to be low in healthy, non-pregnant patients, and increase in patients with cancer, pregnancy, or extensive damage to tissue. cfDNA is believed to be derive mostly from apoptotic cells for which biomarkers for a variety of diseases have been found in cfDNA.
    FFPE tissue is often used to look for cancer-associated mutations despite invasiveness; however it does not always correlate with the mutations seen in cfDNA. In this poster we present a comparison of matched FFPE and plasma samples to determine how many mutation are seen in both tissues. We also look at where the mutational mismatches appear in the chromosome. Different chromosomal regions can have different mismatch rates, and we use this to draw conclusions about the best chromosomal locations for biomarkers. We automated from extraction through sequencing in collaboration with Swift biosciences.
    As cfDNA is extracted from blood, it is a non-invasive way to detect disease; however, there is some concern that cfDNA does not contain the same biomarkers as tumor tissue. Tumor tissue is typically removed and stored as formalin-fixed, paraffinembedded tissue, a process that preserves the morphological structures well but chemically modifies and degrades the nucleic acids.
    Here we show:
    • Sequencing of cfDNA captures the majority of variants that found in sequenced FFPE DNA
    • More indels are identified using cfDNA than with FFPE DNA, especially with breast tissue
    • Distribution of variants across the genome differs when sequencing FFPE DNA
    • More previously identified clinically relevant variants, as identified by the ClinVar database were found when sequencing FFPE DNA
    This study is small and further work should be done using larger data sets to gain more conclusive information.

  8. Dynamics of Plasma EGFR T790M Mutation in Advanced NSCLC: A Multicenter Study. Yang et al. Targeted Oncology. 2019;14:719-728. Published: 06 November 2019.

    Background Droplet digital polymerase chain reaction (ddPCR) is an emerging technology for quantitative cell-free DNA oncology applications. However, a ddPCR assay for the epidermal growth factor receptor (EGFR) p.Thr790Met (T790M) mutation suitable for clinical use remains to be established with analytical and clinical validations. Objective We aimed to develop and validate a new ddPCR assay to quantify the T790M mutation in plasma for monitoring and predicting the progression of advanced non-small-cell lung cancer (NSCLC). Methods Specificity of the ddPCR assay was evaluated with genomic DNA samples from healthy individuals. The inter- and intraday variations of the assay were evaluated using mixtures of plasmid DNA containing wild-type EGFR and T790M mutation sequences. We assessed the clinical utility of the T790M assay in a multicenter prospective study in patients with advanced NSCLC receiving tyrosine kinase inhibitor (TKI) treatment by analyzing longitudinal plasma DNA samples. Results We set the criteria for a positive call when the following conditions were satisfied: (1) T790M mutation frequency > 0.098% (3 standard deviations above the background signal); (2) at least two positive droplets in duplicate ddPCR reactions. Among the 62 patients with advanced NSCLC exhibiting resistance to TKI treatment, 15 had one or more serial plasma samples that tested positive for T790M. T790M mutation was detected in the plasma as early as 205 days (median 95 days) before disease progression, determined by imaging analysis. Plasma T790M concentrations also correlated with intervention after disease progression. Conclusions We developed a ddPCR assay to quantify the T790M mutation in plasma. Quantification of longitudinal plasma T790M mutation may allow noninvasive assessment of drug resistance and guide follow-up treatment in TKI-treated patients with NSCLC. Trial Registration Clinical Trials.gov identifier: NCT02804100.

  9. Correlation between mutations found in FFPE tumor tissue and paired cfDNA samples. (n=8) Niccum B., Saunders L., Hur A.,Patel A. The American Society of Human Genetics (ASHG). (abstract #1766). Houston, TX. Oct 15, 2019

    Liquid biopsies represent a promising area of facilitating cancer research as taking blood is less invasive than tumor biopsies. Cell-free DNA (cfDNA) consists of small (150 – 500 bp) DNA fragments that circulate in the blood. Levels of cfDNA tend to be low in healthy, non-pregnant patients and increased in patients with cancer, pregnancy, or extensive tissue damage. cfDNA is believed to be derived mostly from apoptotic cells and a source for biomarkers for a variety of diseases.
    As a non-invasive way to detect disease cfDNA is extracted from blood; however, there is some concern that cfDNA does not contain the same biomarkers as tumor tissue. Tumor tissue is typically removed and stored as formalin-fixed, paraffin-embedded tissue, a process that preserves the morphological structures well but chemically modifies and degrades the nucleic acids.
    Despite the difficulties, FFPE tissue (shown on the left) is often used to look for cancer-associated mutations; however it does not always correlate with the mutations seen in cfDNA. In this poster we present a comparison of matched FFPE and plasma samples to determine how many mutation are seen in both tissues. We also look at where the mutational mismatches appear in the chromosome. We found chromosomal regions have different mismatch rates, and we use this to draw conclusions about the best chromosomal locations for biomarkers. We also look at the different results that can come from using multiple different panels.
    Here we show that sequencing of cfDNA captures the majority of variants that are found when sequencing FFPE DNA. One result shown here is that more indels are identified using cfDNA than with FFPE DNA. This is especially shown here with breast tissue, while this is inconclusive with lung tissue. More samples should be tested before any conclusions can be derived.
    Another interesting finding is the distribution of variants across the genome. There is some indication that variants sequenced using cfDNA is correlated better to the variants found with either cfDNA or FFPE. This result could be used to better understand if there is bias that occurs when sequencing FFPE DNA and where this bias could be from such as cross-linking could be more apparent in parts of chromosomes.

  10. Comparison between mutation profiles of paired whole blood and cfDNA samples. Patel A., Saunders L., Hur A. The American Society of Human Genetics (ASHG). (abstract #1767). Houston, TX. Oct 15, 2019

    Liquid biopsies are increasingly becoming a tool of choice for researching cancer detection and monitoring. Cell-free DNA or cfDNA is simply small fragments of DNA circulating in bodily fluids. It is also known as circulating cell-free DNA (ccfDNA), circulating tumor DNA (ctDNA) and cell free-fetal DNA (cffDNA). Next-Gen Sequencing of cfDNA is coming into maturity as a non-invasive method to identify mutational profiles in many cancer types.
    A big question is how do you separate a germ line variant from a tumor variant. Understanding the difference in a patient sample can give a more thorough understanding of a variant that can be used to study a cancer type. An easy solution is to compare germ line variants from whole blood genomic DNA (gDNA). This would couple easily with plasma sample collection as plasma can be directly separated from a single sample point.
    Here we describe a simple method to isolate both gDNA and cfDNA from a donor blood sample and discuss the automation of both extractions. We show the efficacy of cfDNA as reliable biomarker analysis tool by comparing mutations in cfDNA vs whole blood. The study determines if the difference between tumor and germ line mutations can be established and the limitations.
    Due to larger volumes necessary to extract sufficient concentrations of cfDNA, automation can assist in the extraction. The Apostle MiniMax™ High Efficiency Cell-Free DNA (cfDNA) extraction kit was automated on the Biomek i-Series. It provides equal recovery of cfDNA as a manual extraction with much less hands on-time. The kit used in the study to extract whole blood, GenFind V3, has also been automated on the Biomek i-Series; allowing for reduced hands with the same quality results as a manual extraction.
    Conclusions
    • Variants found only in cfDNA could be used as an initial screen for ctDNA analysis
    • Apostle MiniMax™ and GenFind V3 can be used together to get a picture of germ line variants and cfDNA, potential ctDNA, variants
    • These results show that a holistic view of a cancer subject can be gained by using one sample source, whole blood

  11. Isolation of cell-free DNA (cfDNA) from plasma using Apostle MiniMaxTM High Efficiency cfDNA Isolation kit—comparison of fully automated, semi-automated and manual workflow processing. Brittany Niccum, PhD., Randy Pares and Antonia Hur. Beckman Coulter Life Sciences. Application Note. Sept 2019.

    Cell-free DNA is present in plasma, urine, and other bodily fluids. Typically cfDNA is at low concentration and comprises double-stranded DNA fragments that are overwhelmingly short (140 to 180 base pairs). Its small size and low quantities present a challenge for sequencing and other downstream applications due to insufficient cfDNA yield. Subsequently, resulting in a need to extract from more substantial volumes of bodily fluid; yet higher input volumes can be more challenging to manage for high-throughput sample processing.
    This application note compares workflows and yield for the extraction of cfDNA using Apostle MiniMaxTM High Efficiency cfDNA Isolation Kit, following the manual protocol, automating the extraction using the Biomek i7 Hybrid Workstation, and semi-automating the extraction using the KingFisherTM Duo Prime Sample Purification System. These potential solutions help mitigate some of the challenges of processing large volume samples. Automating the chemistry can also reduce the risk of human error and reduce hands-on time, therefore giving the user the ability to run more samples in a day.

  12. Cell-Free DNA Isolation Kit. Science. 17 May 2019:Vol. 364, Issue 6441, pp. 696. DOI: 10.1126/science.364.6441.696-a. (Featured in New Products section)

    Surpassing industry standards for yield and purity of cell-free DNA (cfDNA), Apostle MiniMax from Beckman Coulter is a magnetic nanoparticle–based kit that extracts cfDNA from plasma using manual or automated workflows. Due to limited cfDNA concentration, sufficient yield for NGS or other downstream applications can require higher volumes of plasma. As the volume increases, issues such as recovery efficiency and workflow complexity often arise. Plasma contains a host of contaminants that are problematic at this scale and can reduce assay sensitivity. Apostle MiniMax technology performs reliably across a range of volume inputs, consistently recovering high quantities of cfDNA while effectively removing contaminants.

  13. A workflow for medium-throughput isolation of cfDNA from plasma samples using Apostle MiniMaxTM on the KingFisherTM Technology. Brittany Niccum, PhD. Beckman Coulter Life Sciences. Application Note. 2019.

    Cell-free DNA (cfDNA) is found in various bodily fluids. cfDNA has a characteristic size of approximately 175 bp. Due to its small size and low quantity the main challenge is to get enough cfDNA for sequencing. Subsequently, this results in a need to extract from larger volumes of bodily fluid, yet higher input volumes can be more difficult to manage for high-throughput sample processing.
    This application note demonstrates the use of Apostle MiniMaxTM High Efficiency cfDNA Isolation Kit, in conjunction with the KingFisher Duo Prime automated protein purification system. This potential solution that mitigates some of the challenges of processing large volume samples. Automating the chemistry can also reduce the risk of human error, reduce hands-on time and total time; therefore giving the user the ability to run more samples in a day.

  14. Correlation between mutations found in FFPE tumor tissue and paired cfDNA samples. (n=3) Saunders L and Patel A. American Association for Cancer Research (AACR). (abstract #2233). Atlanta, GA. April 2, 2019

    Liquid biopsies represent a promising area of facilitating cancer research as taking blood is less invasive than tumor biopsies. The cell free DNA (cfDNA) present in the blood includes DNA derived from cancer cells and cancer biomarkers can be detected in the extracted cfDNA. However, cfDNA is a less direct view of what is happening in the tumor, and can have a different genetic profile than the tumor tissue itself.
    Tumor tissue is typically removed and stored as formalin-fixed, paraffin-embedded tissue, a process that preserves the morphological structures well but chemically modifies and degrades the nucleic acids. This tissue is often used to look for cancer- associated mutations despite these difficulties; however, it does not always correlate with the mutations seen in cfDNA.
    In this poster we present a comparison of matched FFPE and plasma samples to determine how many mutations are seen in both tissues. We also look at where the mutational mismatches appear in the chromosome. Different chromosomal regions can have different mismatch rates, and we use this to draw conclusions about the best chromosomal locations for biomarkers.
    Conclusions:
    Both FFPE and cfDNA detect at least two thirds of the observed indels and at least 90% of the observed SNVs. As SNVs are more likely to be found in both tissue types, they are more suitable for biomarker use if looking across different tissues. This was true for all three cancers tested.
    Different regions of the chromosome have different rates of mismatches in mutation detection between plasma and FFPE tissue. The first 10-30%, 40-50%, 60-80%, and 90-100% of the chromosome have the lowest rates of mismatch and provide the best locations for biomarkers detectible in both tissues. Future work will focus in increasing the sample size to further narrow down the areas of the chromosome with the highest likelihood of good mutation detection in both plasma and FFPE.

  15. A new Scalable and automatable method for the extration of cfDNA. Saunders LP, Hur A, Niccum B, and Patel A. Advances in Genome Biology and Technology (AGBT). (abstract #419). Marco Island, FL. Feb 28, 2019

    Liquid biopsies represent a promising area of cancer testing as taking blood is less invasive than tumor biopsies. The cell free DNA (cfDNA) present in the blood includes DNA derived from cancer cells and cancer biomarkers can be detected in the extracted cfDNA.
    Whole blood also contains genomic DNA, and can be removed by centrifugation, resulting in plasma. cfDNA is present in very small amounts in blood or plasma, and thus larger amounts of plasma are required for many applications. Larger extractions are more challenging to automate, as they require additional pipetting steps.
    Here we present a novel cfDNA extraction kit and show its compatibility with extractions from 200 μl – 5 mL. We discuss the optimization of the method and demonstrate automation on a KingFisher Duo. This workflow can also be automated on a Biomek i7 Automated Workstation. We demonstrate that this kit can be used for NGS and produce results comparable to other commercial kits.
    Conclusions:
    • DNA can be extracted from 200 μL to 5 mL of plasma
    • The Apostle MiniMax kit removes the PCR inhibitors present in plasma
    • Genomic contamination is not present in the extracted cfDNA
    • Extraction of 1 mL plasma can be automated on a KingFisher instrument with yields similar to manual extraction
    • Similar numbers of mutations were found in cancer plasma with the Apostle MiniMax kit and another commercial kit.
    The Apostle MiniMax kit is a versatile new cfDNA kit that can extract from a wide range of sample amounts and be run either manually or on a variety of automation systems.

  16. cfDNA Extraction from Plasma for Liquid Biopsy: Apostle MiniMaxTM High Efficiency cfDNA Isolation Kit. Beckman Coulter Life Sciences, Data Sheet. 2019.

    Apostle MiniMaxTM High Efficiency cfDNA Isolation Kit, Apostle MiniMax is a cell–free DNA (cfDNA) isolation reagent kit, built on magnetic bead–based technology. Apostle MiniMax has been demonstrated to purify cfDNA from human plasma in both manual and automated workflows.
    • Data representative of results of cfDNA extracted from 1–5mL of plasma
    • Demonstrated compatibility with a variety of collection tubes
    • cfDNA purity shown to be suitable for downstream PCR based assays

A list of Documentation provided by Beckman Coulter Life Sciences:


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Team's track record of publications


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D. Ge

  1. Ge D*, Fellay J*, Thompson AJ*, Simon JS*, Shianna KV, Urban TJ, Heinzen EL, Qiu P, Bertelsen AH, Muir AJ, Sulkowski M, McHutchison JG, Goldstein DB. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 2009:461, 399-401.
    • Featured in Nature: news & views: Genomics: Hepatitis C virus gets personal. Nature 2009; 461 (357-358).
    Nature: Outlook: Pharmacogenomics: playing the odds. Nature. 2011;474(7350):S9-10.
    • Licensed to: LabCorp Inc.; Quest Diagnostics Inc.
    • Citations: 3900+
    • Ranked No. 7 across all scientific areas published by Nature (Google Scholar Metrics, 2015)
  2. Fellay J*, Thompson AJ*, Ge D*, Gumbs CE, Urban TJ, Shianna KV, et al. ITPA gene variants protect against anaemia in patients treated for chronic hepatitis C. Nature. 2010;464(7287):405-8. (* Equal co-authors)
  3. Thomas DL, Thio CL, Martin MP, Qi Y, Ge D, O'hUigin C, Kidd J, Kidd K, Khakoo SI, Alexander G, Goedert JJ, Kirk GD, Donfield SM, Rosen HR, Tobler LH, Busch MP, McHutchison JG, Goldstein DB, Carrington M. Genetic variation in IL28B and spontaneous clearance of hepatitis C virus. Nature 2009; 461, 798-801.
  4. Stefansson H, Ophoff RA, Steinberg S, Andreassen OA, Cichon S, Rujescu D, Werge T, Pietilainen OP, Mors O, Mortensen PB, Sigurdsson E, Gustafsson O, Nyegaard M, Tuulio-Henriksson A, Ingason A, Hansen T, Suvisaari J, Lonnqvist J, Paunio T, Borglum AD, Hartmann A, Fink-Jensen A, Nordentoft M, Hougaard D, Norgaard-Pedersen B, Bottcher Y, Olesen J, Breuer R, Moller HJ, Giegling I, Rasmussen HB, Timm S, Mattheisen M, Bitter I, Rethelyi JM, Magnusdottir BB, Sigmundsson T, Olason P, Masson G, Gulcher JR, Haraldsson M, Fossdal R, Thorgeirsson TE, Thorsteinsdottir U, Ruggeri M, Tosato S, Franke B, Strengman E, Kiemeney LA, Group, Melle I, Djurovic S, Abramova L, Kaleda V, Sanjuan J, de Frutos R, Bramon E, Vassos E, Fraser G, Ettinger U, Picchioni M, Walker N, Toulopoulou T, Need AC, Ge D, Lim Yoon J, Shianna KV, Freimer NB, Cantor RM, Murray R, Kong A, Golimbet V, Carracedo A, Arango C, Costas J, Jonsson EG, Terenius L, Agartz I, Petursson H, Nothen MM, Rietschel M, Matthews PM, Muglia P, Peltonen L, St Clair D, Goldstein DB, Stefansson K, Collier DA, Kahn RS, Linszen DH, van Os J, Wiersma D, Bruggeman R, Cahn W, de Haan L, Krabbendam L, Myin-Germeys I. Common variants conferring risk of schizophrenia. Nature 2009:460(7256):744-7
  5. Stefansson H, Rujescu D, Cichon S, Pietilainen OP, Ingason A, Steinberg S, Fossdal R, Sigurdsson E, Sigmundsson T, Buizer-Voskamp JE, Hansen T, Jakobsen KD, Muglia P, Francks C, Matthews PM, Gylfason A, Halldorsson BV, Gudbjartsson D, Thorgeirsson TE, Sigurdsson A, Jonasdottir A, Jonasdottir A, Bjornsson A, Mattiasdottir S, Blondal T, Haraldsson M, Magnusdottir BB, Giegling I, Moller HJ, Hartmann A, Shianna KV, Ge D , Need AC, Crombie C, Fraser G, Walker N, Lonnqvist J, Suvisaari J, Tuulio-Henriksson A, Paunio T, Toulopoulou T, Bramon E, Di Forti M, Murray R, Ruggeri M, Vassos E, Tosato S, Walshe M, Li T, Vasilescu C, Muhleisen TW, Wang AG, Ullum H, Djurovic S, Melle I, Olesen J, Kiemeney LA, Franke B, Kahn RS, Linszen D, van Os J, Wiersma D, Bruggeman R, Cahn W, Germeys I, de Haan L, Krabbendam L, Sabatti C, Freimer NB, Gulcher JR, Thorsteinsdottir U, Kong A, Andreassen OA, Ophoff RA, Georgi A, Rietschel M, Werge T, Petursson H, Goldstein DB, Nothen MM, Peltonen L, Collier DA, St Clair D, Stefansson K. Large recurrent microdeletions associated with schizophrenia. Nature . 2008;455(7210):232-6.
  6. Fellay J, Shianna KV *, Ge D *, Colombo S *, Ledergerber B *, Weale M *, Zhang K, Gumbs C, Castagna A, Cossarizza A, Cozzi-Lepri A, De Luca A, Easterbrook P, Francioli P, Mallal S, Martinez-Picado J, Miro JM, Obel N, Smith JP, Wyniger J, Descombes P, Antonarakis SE, Letvin NL, McMichael AJ, Haynes BF, Telenti A, Goldstein DB. A whole-genome association study of major determinants for host control of HIV-1. Science . 2007;317(5840):944-7. (* Equal authors) (Citations: 390)
  7. Thomas R, Apps R, Qi Y, Gao X, Male V, O'HUigin C, O'Connor G, Ge D, et al. HLA-C cell surface expression and control of HIV/AIDS correlate with a variant upstream of HLA-C. Nat Genet. 2009;41(12):1290-4.
  8. Ge D, Ruzzo EK, Shianna KV, He M, Pelak K, Heinzen EL, Need AC, Cirulli ET, Maia JM, Zhu M, Singh A, Allen AS, Goldstein DB. SVA: Software for Annotating and Visualizing Sequenced Human Genomes. Bioinformatics, 2011;27(14):1998-2000. (software: http://www.svaproject.org )
  9. Zhu Q *, Ge D*, Maia JM, Zhu M, Petrovski S, Dickson SP, Heinzen EL, Shianna KV, Goldstein DB. A Genome-Wide Comparison of the Functional Properties of Rare and Common Genetic Variants in Humans. American Journal of Human Genetics. 2011;88(4):458-68.
  10. W. J. Sandborn, B. R. Bhandari, R. Fogel, J. Onken, E. Yen, X. Zhao, Z. Jiang, D. Ge, Y. Xin, Z. Ye, D. French, J. A. Silverman, B. Kanwar, G. M. Subramanian, J. G. McHutchison, S. D. Lee, L. M. Shackelton, R. K. Pai, B. G. Levesque & B. G. Feagan. Randomised clinical trial: a phase 1, dose-ranging study of the anti-matrix metalloproteinase-9 monoclonal antibody GS-5745 versus placebo for ulcerative colitis. Alimentary Pharmacology and Therapeutics. 2016 (In press)
  11. Charuworn P, Hengen PN, Aguilar Schall R, Dinh P, Ge D, Corsa A, Reesink HW, Zoulim F, Kitrinos KM. Baseline interpatient hepatitis B viral diversity differentiates HBsAg outcomes in patients treated with tenofovir disoproxil fumarate. J Hepatol. 2015 May;62(5):1033-9.
  12. Nelson D, Yoshida EM, Paulson MS, Hengen MS, Hengen PN, Ge D, Kanwar B, McNally J, Pang PS, Subramanian GM, McHutchison JG, Urbanek P, Lawitz E, Urban TJ. Genome-wide association study to characterize serum bilirubin elevations in patients with HCV treated with GS-9256, an HCV NS3 serine protease inhibitor. Antiviral Therapy. 2014 Feb 6. doi: 10.3851/IMP2747.
  13. Pelak K*, Shianna KV*, Ge D*, Maia JM, Zhu M, Smith JP, et al. The characterization of twenty sequenced human genomes. PLoS Genet. 2010;6(9): e1001111.
  14. Sobreira NLM, Cirulli ET, Avramopoulos D, Wohler E, Oswald GL, Stevens EL, Ge D, et al. Whole-Genome Sequencing of a Single Proband Together with Linkage Analysis Identifies a Mendelian Disease Gene. PLoS Genet 2010;6(6):e1000991.
  15. Todd J, Goldstein DB, Ge D, Christie J, Palmer, SM. The State of Genome-Wide Association Studies in Pulmonary Disease: A New Perspective. American Journal of Respiratory and Critical Care Medicine. 2011.
  16. Thompson AJ, Muir AJ, Sulkowski MS, Patel K, Tillmann HL, Clark PJ, Naggie S, Fellay J, Ge D, McCarthy JJ et al: Hepatitis C trials that combine investigational agents with pegylated interferon should be stratified by interleukin-28B genotype. Hepatology 2010, 52(6):2243-2244.
  17. Thompson AJ, Clark PJ, Singh A, Ge D, et al. Genome-wide association study of interferon-related cytopenia in chronic hepatitis C patients. J Hepatol. 2011.
  18. Thompson AJ, Fellay J, Patel K, Tillmann HL, Naggie S, Ge D, et al. Variants in the ITPA Gene Protect Against Ribavirin-Induced Hemolytic Anemia and Decrease the Need for Ribavirin Dose Reduction. Gastroenterology. 2010;139(4):1181-9.
  19. Thompson AJ, Muir AJ, Sulkowski MS, Ge D, et al. Interleukin-28B polymorphism improves viral kinetics and is the strongest pretreatment predictor of sustained virologic response in genotype 1 hepatitis C virus. Gastroenterology. 2010;139(1):120-9 e18.
  20. Walley NM, Julg B, Dickson SP, Fellay J, Ge D, Walker BD, Carrington M, Cohen MS, de Bakker PI, Goldstein DB, Shianna KV, Haynes BF, Letvin NL, McMichael AJ, Michael NL, Weintrob AC. The Duffy antigen receptor for chemokines null promoter variant does not influence HIV-1 acquisition or disease progression. Cell Host Microbe 2009; 5 (5) : 408-10.
  21. Need AC, Attix DK, McEvoy JM, Cirulli ET, Linney KL, Hunt P, Ge D, Heinzen EL, Maia JM, Shianna KV, Weale ME, Cherkas LF, Clement G, Spector TD, Gibson G, Goldstein DB. A Genome-wide Study of Common SNPs and CNVs in Cognitive Performance in the CANTAB battery. Hum Mol Genet 2009.
  22. Pillai SG, Ge D *, Zhu G*, Kong X*, Shianna KV, Need AC, S. F, Hersh CP, Bakkgators, Rennard SI, Lomas D, Silverman EK, Goldstein DB. A Genome-wide Association Study in Chronic Obstructive Pulmonary Disease (COPD): Identification of two Major Susceptibility Loci. PLoS Genet . 2009; 5(3): e1000421. doi:10.1371/journal.pgen.1000421 (* Equal authors)
  23. Need AC *, Ge D * , Maia J, Shianna KV, Feng S, Strittmatter WJ, McEvoy JP, Keefe RSE, St Jean PL, Giegling I, Hartmann AM, M?ller H, Ruppert A, Fraser G, Crombie C, Francks C, St.Clair D, Roses AD, Muglia P, Rujescu D, Goldstein DB. A Genome-Wide Investigation of SNPs and CNVs in Schizophrenia. PLoS Genet. 2009; 5(2):e1000373.(* Equal authors)
  24. Heinzen E *, Ge D *, Cronin KD, Maia J, Shianna KV, Gabriel W, Welsh-Bohmer KA, Hulette CM, Denny T, Goldstein DB. Tissue specific genetic control of gene expression and alternative splicing: Implications for the study of human complex traits. PLoS Biol . 2008; 6(12): e1000001. (* Equal authors)
  25. Ge D , Zhang K, Need AC, Martin O, Fellay J, Urban TJ, Telenti A, Goldstein DB. WGAViewer: Software for genomic annotation of whole genome association studies. Genome Research. 2008;18(4):640-3. (citations: 87)
  26. Price AL, Weale ME, Patterson N, Myers SR, Need AC, Shianna KV, Ge D , Rotter JI, Torres E, Taylor KD, Goldstein DB, Reich D. Long-range LD can confound genome scans in admixed populations. Am J Hum Genet 2008 . 83(1):132-5.
  27. Cavalleri GL, Weale ME, Shianna KV, Singh R, Lynch JM, Grinton B, Szoeke C, Murphy K, Kinirons P, O'Rourke D, Ge D , Depondt C, Claeys KG, Pandolfo M, Gumbs C, Walley N, McNamara J, Mulley JC, Linney KN, Sheffield LJ, Radtke RA, Tate SK, Chissoe SL, Gibson RA, Hosford D, Stanton A, Graves TD, Hanna MG, Eriksson K, Kantanen AM, Kalviainen R, O'Brien TJ, Sander JW, Duncan JS, Scheffer IE, Berkovic SF, Wood NW, Doherty CP, Delanty N, Sisodiya SM, Goldstein DB. Multicentre search for genetic susceptibility loci in sporadic epilepsy syndrome and seizure types: a case-control study. Lancet Neurol. 2007;6(11):970-80.
  28. Valdes AM, Loughlin J, Timms KM, van Meurs JJ, Southam L, Wilson SG, Doherty S, Lories RJ, Luyten FP, Gutin A, Abkevich V, Ge D, Hofman A, Uitterlinden AG, Hart DJ, Zhang F, Zhai G, Egli RJ, Doherty M, Lanchbury J, Spector TD. Genome-wide association scan identifies a prostaglandin-endoperoxide synthase 2 variant involved in risk of knee osteoarthritis. Am J Hum Genet 2008;82(6):1231-40.
  29. Cronin KD, Ge D, Manninger P, Linnertz C, Rossoshek A, Orrison BM, Bernard DJ, El-Agnaf OM, Schlossmacher MG, Nussbaum RL, Chiba-Falek O. Expansion of the Parkinson Disease-Associated SNCA-Rep1 Allele Up-Regulates Human {alpha}-Synuclein in Transgenic Mouse Brain. Hum Mol Genet 2009.
  30. Need AC, Keefe RS, Ge D, Grossman I, Dickson S, McEvoy JP, Goldstein DB. Pharmacogenetics of antipsychotic response in the CATIE trial: a candidate gene analysis. Eur J Hum Genet 2009; 17 (7) : 946-57 .
  31. Ge D , Su S, Zhu H, Dong Y, Wang X, Harshfield GA, Treiber FA, Snieder H. Stress-Induced Sodium Excretion. A New Intermediate Phenotype to Study the Early Genetic Etiology of Hypertension? Hypertension 2009;53:262-269
  32. Ge D , Gooljar SB, Kyriakou T, Collins LJ, Swaminathan R, Snieder H, Spector TD, O'Dell SD. Association of Common JAK2 Variants With Body Fat, Insulin Sensitivity and Lipid Profile. Obesity (Silver Spring) 2008;16(2) : 492-6.
  33. Ge D, Zhu H, Huang Y, Treiber FA, Harshfield GA, Snieder H, Dong Y. Multilocus analyses of Renin-Angiotensin-aldosterone system gene variants on blood pressure at rest and during behavioral stress in young normotensive subjects. Hypertension . 2007 ;49(1):107-12.
  34. Ge D, Young TW, Wang X, Kapuku GK, Treiber FA, Snieder H. Heritability of arterial stiffness in black and white American youth and young adults. Am J Hypertens 2007;20(10) : 1065-72.
  35. Kapuku GK, Ge D, Vemulapalli S, Harshfield GA, Treiber FA, Snieder H. Change of genetic determinants of left ventricular structure in adolescence: longitudinal evidence from the Georgia cardiovascular twin study. Am J Hypertens 2008;21(7):799-805.
  36. Oberg S, Ge D, Cnattingius S, Svensson A, Treiber FA, Snieder H, Iliadou A. Ethnic differences in the association of birth weight and blood pressure the georgia cardiovascular twin study. Am J Hypertens 2007;20(12):1235-41.
  37. Zhu H, Yan W, Ge D , Treiber FA, Harshfield GA, Kapuku G, Snieder H, Dong Y. Relationships of cardiovascular phenotypes with healthy weight, at risk of overweight, and overweight in US youths. Pediatrics 2008;121(1) : 115-22.
  38. Wang L, Li B, Lu X, Zhao Q, Li Y, Ge D, Li H, Zhang P, Chen S, Chen R, Qiang B, Gu D. A functional intronic variant in tyrosine hydroxylase (TH) gene confers risk of essential hypertension in northern Chinese Han population. Clin Sci (Lond) 2008.
  39. Dalageorgou C, Ge D , Jamshidi Y, Nolte IM, Riese H, Savelieva I, Carter ND, Spector TD, Snieder H. Heritability of QT Interval: How Much Is Explained by Genes for Resting Heart Rate? J Cardiovasc Electrophysiol 2007.
  40. Morell RJ, Brewer CC, Ge D , Snieder H, Zalewski CK, King KA, Drayna D, Friedman TB. A twin study of auditory processing indicates that dichotic listening ability is a strongly heritable trait. Hum Genet 2007;122(1):103-11.
  41. Zhu H, Yan W, Ge D, Treiber FA, Harshfield GA, Kapuku G, Snieder H, Dong Y. Cardiovascular characteristics in American youth with prehypertension. Am J Hypertens 2007;20(10):1051-7.
  42. Jamshidi Y, Snieder H, Ge D , Spector TD, O'Dell SD. The SH2B gene is associated with serum leptin and body fat in normal female twins. Obesity (Silver Spring) . 2007;15(1):5-9.
  43. Healey PR, Mitchell P, Gilbert CE, Lee AJ, Ge D , Snieder H, Spector TD, Hammond CJ. The inheritance of peripapillary atrophy. Invest Ophthalmol Vis Sci 2007;48(6):2529-34.
  44. Weili Y, He B, Yao H, Dai J, Cui J, Ge D , Zheng Y, Li L, Guo Y, Xiao K, Fu X, Ma D. Waist-to-height ratio is an accurate and easier index for evaluating obesity in children and adolescents. Obesity (Silver Spring) 2007;15(3):748-52.
  45. Jamshidi Y, Gooljar SB, Snieder H, Wang X, Ge D , Swaminathan R, Spector TD, O'Dell SD. SHP-2 and PI3-kinase genes PTPN11 and PIK3R1 may influence serum apoB and LDL cholesterol levels in normal women. Atherosclerosis 2007; 194 (2) : e26-33
  46. Ge D, Dong Y, Wang X, Treiber FA, Snieder H. The Georgia Cardiovascular Twin Study: influence of genetic predisposition and chronic stress on risk for cardiovascular disease and type 2 diabetes. Twin Res Hum Genet . 2006;9(6):965-70.
  47. Spencer-Jones NJ*, Ge D *, Snieder H, Perks U, Swaminathan R, Spector TD, Carter ND, O'Dell SD. AMP-kinase alpha2 subunit gene PRKAA2 variants are associated with total cholesterol, low-density lipoprotein-cholesterol and high-density lipoprotein-cholesterol in normal women. Journal of Medical Genetics. 2006 ;43(12):936-42. (* Joint authors.)
  48. Gu D, Ge D, Snieder H, He J, Chen S, Huang J, Li B, Chen R, Qiang B. Association of alpha-1A adrenergic receptor gene variants on chromosome 8p21 with human stage-2 hypertension. Journal of Hypertension , 2006;24(6):1057-1064.
  49. Kupper N, Ge D, Treiber FA, Snieder H . Tracking of blood pressure and underlying hemodynamics in European and African American adolescents. Stable heritabilities and expression of new genes. Hypertension , 2006 47(5):948-54 .
  50. Gu D, Su S, Ge D, Chen S, Huang J, Li B, Chen R, Qiang B. An Association Study with 33 SNPs in 11 Candidate Genes for Hypertension in Chinese. Hypertension , 2006;47(6):1147-54.
  51. Herold SE, Young TW, Ge D, Snieder H, Lovrekovic GZ. Sleep Disordered Breathing in Pediatric Patients with Tetralogy of Fallot. Pediatric Cardiology , 2006;27(2):243-9.
  52. de Lange M, Andrew T, Snieder H, Ge D, Futers TS, Standeven K, Spector TD, Grant PJ and Ariens RAS. Joint linkage and association of 6 single nucleotide polymorphisms in the factor XIII-A subunit gene point to V34L as the main functional locus. Arteriosclerosis, Thrombosis, and Vascular Biology . 2006 Aug;26(8):1914-9.
  53. Yang W, Huang J, Yao C, Su S, Liu D, Ge D , Gu D. Linkage and linkage disequilibrium analysis of the lipoprotein lipase gene with lipid profiles in Chinese hypertensive families. Clinical Science (Lond). 2005;108(2):137-142.
  54. Ge D, Huang J, Yang W, Zhao J, Shen Y, Qiang B, Gu D. Linkage analysis of chromosome 1 with essential hypertension and blood pressure quantitative traits in Chinese families. Annals of Human Genetics . 2005;69(Pt 1):45-54.
  55. Ge D , Huang J, He J, Li B, Duan X, Chen R, Gu D. beta2-Adrenergic receptor gene variations associated with stage-2 hypertension in northern Han Chinese. Annals of Human Genetics . 2005;69(Pt 1):36-44.
  56. Li B, Ge D , Wang Y, Zhao W, Zhou X, Gu D, Chen R. G Protein beta3 Subunit Gene Variants and Essential Hypertension in the Northern Chinese Han Population. Annals of Human Genetics . 2005;69(Pt 4):468-473.
  57. Chen S, Yan W, Huang J, Ge D, Yao Z, Gu D. Association analysis of the variant in the regulatory subunit of phosphoinositide 3-kinase (p85alpha) with Type 2 diabetes mellitus and hypertension in the Chinese Han population. Diabetic Medicine . 2005;22(6):737-743.
  58. Yan W, Yang X, Zheng Y, Ge D , Zhang Y, Shan Z , Simu H , Sukerobai M , Wang R . The Metabolic Syndrome in Uygur and Kazak Population. Diabetes Care . 28(10):2554-5, 2005.
  59. Zhou X, Huang J, Chen J, Zhao J, Ge D , Yang W, Gu D. Genetic association analysis of myocardial infarction with thrombospondin-1 N700S variant in a Chinese population. Thrombosis Research . 2004;113(3-4):181-186.
  60. Gu D, Ge D, He J, Li B, Chen J, Liu D, Chen J, Chen R. Haplotypic analyses of the aldosterone synthase gene CYP11B2 associated with stage-2 hypertension in northern Han Chinese. Clinical Genetics . 2004;66(5):409-416.
  61. Li B, Ge D, Wang Y, Zhao W, Zhou X, Gu D, Chen R. Lipoprotein lipase gene polymorphisms and blood pressure levels in the Northern Chinese Han population. Hypertension Research . 2004;27(6):373-378.
  62. Gu F, Ge D, Huang J, Chen J, Yang W, Gu D. Genetic susceptibility loci for essential hypertension and blood pressure on chromosome 17 in 147 Chinese pedigrees. Journal of Hypertension . 2004;22(8):1511-1518.
  63. Gu D, Ge D. The New Genetics in Hypertension: Asia Pacific Perspective. Paper presented at: 3rd Asian-Pacific Congress of Hypertension; April 3-7, 2004; Singapore.

B. Zhang

  1. Bo Zhang (1st and Corresponding author), Benjamin A. Pinsky, Jeyarama S. Ananta, Su Zhao, Shylaja Arulkumar, Hao Wan, Malaya K. Sahoo, Janaki Abeynayake, Jesse J. Waggoner, Clay Hopes, Meijie Tang and Hongjie Dai. “Diagnosis of Zika virus infection on a nanotechnology platform” Nature Medicine, 23, 548, 2017.
  2. Christelle Pomares, Bo Zhang (co-1st and Corresponding author), Shylaja Arulkumar, Geraldine Gonfrier, Pierre Marty, Su Zhao, Steven Cheng, Meijie Tang, Hongjie Dai and Jose Montoya. “Validation of IgG, IgM multiplex plasmonic gold platform in French clinical cohorts for the serodiagnosis and follow up of Toxoplasma gondii infection” Diagnostic Microbiology & Infectious Disease, 87, 213, 2017.
  3. Xiaohua Chen, Odgerel Oidovsambuu, Ping Liu, Rosslyn Grosely, Menashe Elazar, Virginia D Winn, Benjamin Fram, Zhang Bo, Hongjie Dai, Bekhbold Dashtseren, Dahgwahdorj Yagaanbuyant, Zulkhuu Genden, Naranbaatar Dashdorj, Andreas Bungert, Naranjargal Dashdorj, Jeffrey S Glenn. “A novel quantitative microarray antibody capture (Q‐MAC) assay identifies an extremely high HDV prevalence amongst HBV infected Mongolians” Hepatology, DOI: 10.1002/hep.28957, 2016.
  4. Shoujun Zhu, Qinglai Yang, Alexander Antaris, Jingying Yue, Zhuoran Ma, Huasen Wang, Wei Huang, Hao Wan, Joy Wang, Shuo Diao, Bo Zhang, Xiaoyang Li, Yeteng Zhong, Kuai Yu, Guosong Hong, Jian Luo, Yongye Liang and Hongjie Dai. “Molecular imaging of biological systems with a clickable dye in the broad 800- to 1700-nm near-infrared window” Proceedings of the National Academy of Sciences, 114, 962, 2016.
  5. Bin Liu, Yaling Li, Hao Wan, Lin Wang, Wei Xu, Shoujun Zhu, Yongye Liang, Bo Zhang, Jiatao Lou, Hongjie Dai and Kun Qian. “High performance, multiplexed lung cancer biomarker detection on a plasmonic gold chip” Advanced Functional Materials, 26, 7994, 2016.
  6. Byumseok Koh, Xiaoyang Li, Bo Zhang, Bing Yuan, Yi Lin, Alexander L. Antaris, Hao Wan, Ming Gong, Jiang Yang, Xiaodong Zhang, Yongye Liang, and Dai. "Visible to Near-Infrared Fluorescence Enhanced Cellular Imaging on Plasmonic Gold Chips" Small, 12, 457, 2016.
  7. Alexander L Antaris, Hao Chen, Kai Cheng, Yao Sun, Guosong Hong, Chunrong Qu, Shuo Diao, Zixin Deng, Xianming Hu, Bo Zhang, Xiaodong Zhang, Omar K Yaghi, Zita R Alamparambil, Xuechuan Hong, Zhen Cheng and Hongjie Dai. “A small-molecule dye for NIR-II imaging” Nature materials, 15, 235, 2016.
  8. Shuo Diao, Jeffrey L Blackburn, Guosong Hong, Alexander L Antaris, Junlei Chang, Justin Z Wu, Bo Zhang, Kai Cheng, Calvin J Kuo and Hongjie Dai. “Fluorescence Imaging In Vivo at Wavelengths beyond 1500 nm” Angewandte Chemie, 127, 14971, 2015.
  9. Valentina Manfe, Jan Fleckner, Peder Norby Lisby, Bo Zhang, Hongjie Dai and Pernille Keller. “Cytokine detection and simultaneous assessment of rheumatoid factor interference in human serum and synovial fluid using high-sensitivity protein arrays on plasmonic gold chips” BMC Biotechnology, 15, 73, 2015.
  10. Changxin Chen, Justin Zachary Wu, Kai Tak Lam, Guosong Hong, Ming Gong, Bo Zhang, Yang Lu, Alexander L. Antaris, Shuo Diao, Jing Guo, and Hongjie Dai. "Graphene Nanoribbons Under Mechanical Strain" Advanced Materials, 27, 303-309, 2015.
  11. Alexander L. Antaris, Omar K. Yaghi, Guosong Hong, Shuo Diao, Bo Zhang, Jiang Yang, Leila Chew, and Hongjie Dai. “Single chirality (6,4) single-walled carbon nanotubes for fluorescence imaging with silicon detectors” Small, 11, 6325-6330, 2015.
  12. Bo Zhang, Rajiv Kumar, Hongjie Dai and Brian Feldman. “A plasmonic Chip for Biomarker Discovery and Diagnosis of Type-1 Diabetes.” Nature Medicine, 20, 948, 2014.
  13. Bo Zhang, Jiang Yang, Yingping Zou, Ming Gong, Hui Chen, Guosong Hong, Alexander L. Antaris, Xiaoyang Li, Chien-Liang Liu, Changxin Chen and Hongjie Dai. “Plasmonic micro-beads for fluorescence enhanced, multiplexed protein detection with flow cytometry.” Chemical Science, 5, 4070, 2014.
  14. Brian Feldman, Rajiv Kumar, Bo Zhang, and Hongjie Dai. “A novel plasmonic chip for biomarker discovery and point-of-care diagnosis of Type-1 Diabetes.” Endocrine Reviews, 35, 3, 2014.
  15. Guosong Hong, Shuo Diao, Junlei Chang, Alexander L. Antaris, Changxin Chen, Bo Zhang, Su Zhao, Dmitriy N. Atochin, Paul L. Huang, Katrin I. Andreasson, Calvin J. Kuo, and Hongjie Dai. “Through-skull fluorescence imaging of the brain in a new near-infrared window.” Nature Photonics, 8, 723, 2014.
  16. Ming Gong, Wu Zhou, Mon-Che Tsai, Jigang Zhou, Mingyun Guan, Meng-Chang Lin, Bo Zhang, Yongfeng Hu, Di-Yan Wang, Jiang Yang, Stephen J. Pennycook, Bing-Joe Hwang and Hongjie Dai. “Nanoscale nickel oxide/nickel heterostructures for active hydrogen evolution electrocatalysis.” Nature Communications, 5, 4695, 2014.
  17. Guosong Hong, Yingping Zou, Alexander L. Antaris, Shuo Diao, Di Wu, Kai Cheng, Xiaodong Zhang, Changxin Chen, Bo Liu, Yuehui He, Justin Z. Wu, Jun Yuan, Bo Zhang, Zhimin Tao, Chihiro Fukunaga and Hongjie Dai. “Ultrafast fluorescence imaging in vivo with conjugated polymer fluorophores in the second near-infrared window.” Nature Communications, 5, 4206, 2014.
  18. Xiaobin Tang, Bo Zhang, Justin A. Jarrell, Jordan V. Price, Hongjie Dai, Paul J Utz and Sam Strober. “Ly108 expression distinguishes subsets of invariant NKT cells that help autoantibody production and secrete IL-21 from those that secrete IL-17 in lupus prone NZB/W mice.” Journal of Autoimmunity, 50, 87, 2014.
  19. Ming Gong, Yanguang Li, Hongbo Zhang, Bo Zhang, Wu Zhou, Ju Feng, Hailiang Wang, Yongye Liang, Zhuangjun Fan, Jie Liu and Hongjie Dai. “Ultrafast high-capacity NiZn battery with NiAlCo-layered double hydroxide.” Energy and Environmental Science, 7, 2025, 2014.
  20. Zhimin Tao, Guosong Hong, Chihiro Shinji, Changxin Chen, Shuo Diao, Alexander L. Antaris, Bo Zhang, Yingping Zou and Hongjie Dai. “Biological imaging using nanoparticles of small organic molecules with fluorescence at wavelengths longer than 1000 nm.” Angewante Chemie, 125 (49), 13240, 2013.
  21. Bo Zhang, Justin A. Jarrell, Jordan V. Price, Scott M. Tabakman, Yanguang Li, Ming, Gong, Guosong Hong, Ju Feng, Paul J. Utz and Hongjie Dai. “An integrated peptide-antigen microarray on plasmonic gold films for sensitive human antibody profiling.” Plos ONE, 8, e71043, 2013.
  22. Yanguang Li, Ming Gong, Yongye Liang, Ju Feng, Ji-Eun Kim, Hailiang Wang, Guosong Hong, Bo Zhang and Hongjie Dai. “Advanced zinc-air batteries based on high-performance hybrid electrocatalysts.” Nature Communications, 4, 1805, 2013.
  23. Bo Zhang, Jordan Price, Guosong Hong, Scott M. Tabakman, Hailiang Wang, Justin A. Jarrell, Ju Feng, Paul J. Utz and Hongjie Dai. “Multiplexed cytokine detection on plasmonic gold substrates with enhanced near-infrared fluorescence.” Nano Research, 6, 113, 2013.
  24. Ju Feng, Yongye Liang, Hailiang Wang, Yanguang Li, Bo Zhang, Jigang Zhou, Jian Wang, Tom Regier and Hongjie Dai. “Engineering manganese oxide/nanocarbon hybrid materials for oxygen reduction electrocatalysis.” Nano Research, 5 (10), 718, 2012.
  25. Joshua T. Robinson, Guosong Hong, Yongye Liang, Bo Zhang, Omar K. Yaghi and Hongjie Dai. “In vivo fluorescence imaging in the second near-infrared window with long circulating carbon nanotubes capable of ultrahigh tumor uptake.” Journal of the American Chemical Society, 134 (25), 10664, 2012.
  26. Guosong Hong, Justin Z. Wu, Joshua T. Robinson, Hailiang Wang, Bo Zhang and Hongjie Dai. “Three-dimensional imaging of single nanotube molecule endocytosis on plasmonic substrates.” Nature Communications, 3, 700, 2012.
  27. Scott M. Tabakman, Lana Lau, Joshua T. Robinson, Jordan Price, Sarah P. Sherlock, Hailiang Wang, Bo Zhang, Zhuo Chen, Stephanie Tangsombatvisit, Justin A. Jarrell, Paul J. Utz and Hongjie Dai. “Plasmonic substrates for multiplexed protein microarrays with femtomolar sensitivity and broad dynamic range.” Nature Communications, 2, 466, 2011.
  28. Guosong Hong, Scott M. Tabakman, Kevin Welsher, Zhuo Chen, Joshua T. Robinson, Hailiang Wang, Bo Zhang and Hongjie Dai. “Near-infrared-fluorescence-enhanced molecular imaging of live cells on gold substrates.” Angewante Chemie, 50, 4644, 2011.
  29. Guo Hong, Bo Zhang, Banghua Peng, Xiaojun Xian, Jin Zhang, WonMook Choi, Jae-Young Choi, Jong Min Kim and Zhongfan Liu. “Direct growth of semiconducting single-walled carbon nanotube array.” Journal of the American Chemical Society, 131, 14642, 2009.
  30. Bo Zhang, Guo Hong, Banghua Peng, Jin Zhang, WonMook Choi, Jong Min Kim, Jae- Young Choi, and Zhongfan Liu. “Grow Single-Walled Carbon Nanotubes Cross-Bar in One Batch.” J. Phys. Chem. C, 113, 5341, 2009. (Cover Art)

X. Guo

  1. X Guo, A Bernard, DA Orlando, SB Haase, & AJ Hartemink. (2013). “Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program”, Proceedings of the National Academy of Sciences (PNAS), 110, E968-E977.
  2. MB Mayhew, X Guo, SB Haase, & AJ Hartemink. (2012). “Close encounters of the collaborative kind”, IEEE Computer, 45, pp. 24-30. (cover feature)
  3. X Guo, ML Bulyk, & AJ Hartemink. (2012). “Intrinsic disorder within and flanking the DNA-binding domains of human transcription factors”, In Pac Symp Biocomput 2012 (PSB12), pp. 104-115.
  4. X Guo, & AJ Hartemink. (2009). “Domain-oriented edge-based alignment of protein interaction networks”, Bioinformatics, 25, pp. i240-246.

W. Zeng

  1. Biotin-responsive basal ganglia disease maps to 2q36.3 and is due to mutations in SLC19A3. Zeng WQ, Al-Yamani E, Acierno JS Jr, Slaugenhaupt S, Gillis T, MacDonald ME, Ozand PT, Gusella JF. Am J Hum Genet. 2005 Jul;77(1):16-26. Impact Factor 12. 649
  2. Congenital lethal motor neuron disease with a novel defect in ribosome biogenesis. Butterfield RJ1, Stevenson TJ, Xing L, Newcomb TM, Nelson B, Zeng W, Li X, Lu HM, Lu H, Farwell Gonzalez KD, Wei JP, Chao EC, Prior TW, Snyder PJ,Bonkowsky JL, Swoboda KJ. Neurology. 2014 Apr 15;82(15):1322-30. Epub 2014 Mar 19. Impact Factor 8.3
  3. Enhanced utility of family-centered diagnostic exome sequencing with inheritance model-based analysis: results from 500 unselected families with undiagnosed genetic conditions. Farwell KD1, Shahmirzadi L1, El-Khechen D1, Powis Z1, Chao EC2, Davis BT1, Baxter RM1, Zeng W1, Mroske C1, Parra MC1, Gandomi SK1, Lu I1, Li X1, Lu H1, Lu HM1, Salvador D1, Ruble D1, Lao M1, Fischbach S1, Wen J1, Lee S1, Elliott A1, Dunlop CL1, Tang S1. Genetics in Medicine Impact Factor 6.44
  4. ELP2 is a novel gene implicated in neurodevelopmental disabilities. Cohen JS1, Srivastava S, Farwell KD, Lu HM, Zeng W, Lu H, Chao EC, Fatemi A. Am J Med Genet A. 2015 Apr 2.
  5. Diagnostic Exome Sequencing and Tailored Bioinformatics of the Parents of a Deceased Child with Cobalamin Deficiency Suggests Digenic Inheritance of the MTR and LMBRD1 Genes Farwell Gonzalez KD1, Li X, Lu HM, Lu H, Pellegrino JE, Miller RT, Zeng W, Chao EC. JIMD Rep. 2015;15:29-37. Epub 2014 Mar 25.
  6. Expanding the clinical and mutational spectrum of Kaufman oculocerebrofacial syndrome with biallelic UBE3B mutations. Basel-Vanagaite L1, Yilmaz R, Tang S, Reuter MS, Rahner N, Grange DK, Mortenson M, Koty P, Feenstra H, Farwell Gonzalez KD, Sticht H, Boddaert N,Désir J, Anyane-Yeboa K, Zweier C, Reis A, Kubisch C, Jewett T, Zeng W, Borck G. Hum Genet. 2014 Jul;133(7):939-49. Epub 2014 Mar 11.
  7. Xiang, B Xu, F., Zeng WQ, Dan Zi, Ma DQ, Navigating Web-Based Resources for Genetic Testing of Chromosome Abnormalities, CNVs and Gene Mutations. N A J Med Sci. 2014;7(4):163-170. DOI: 10.7156/najms. 2014.0704163
  8. Diagnostic Exome Sequencing Identifies Two Novel IQSEC2 Mutations Associated with X-Linked Intellectual Disability with Seizures: Implications for Genetic Counseling and Clinical Diagnosis. Gandomi SK, Farwell Gonzalez KD, Parra M, Shahmirzadi L, Mancuso J, Pichurin P, Temme R, Dugan S, Zeng W, Tang S. J Genet Couns. 2013.
  9. A human de novo mutation in MYH10 phenocopies the loss of function mutation in mice Rare Diseases 1. Tuzovic L, Yu L, Zeng W, Li X, Lu H, Lu HM, Gonzalez K, Chung WK. Rare Dis. 2013 Aug 14;1:e26144.
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  13. Yan J, Noltner K, Feng J, Li W, Schroer R, Skinner C, Zeng W, Schwartz CE, Sommer SS. Neurexin 1alpha structural variants associated with autism. Neurosci Lett. 2008; 438(3):368-70. Epub 2008 Apr 25 IF 2.085.
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  17. Zeng W The Social Culture Explanation for Symptoms of Mental Disorders. Medicine and Philosophy医学与哲学,1994;15(11):5-8.