Gigapixel digital PCR in Giant Unilamellar Vesicles
Gigapixel digital PCR in Giant Unilamellar Vesicles
批准号:
10197925
负责人:
Adam R. Abate
金额:
$62.1万
依托单位国家:
美国
项目类别:
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-09-30 至 2023-03-31
关键词:
AntibioticsBacterial InfectionsBacterial MeningitisBiological AssayBiological MarkersBiological SciencesBiopsy SpecimenCerebrospinal FluidClinicClinicalCollaborationsCommunicable DiseasesComplexDNADataDiagnosisDiagnosticDisease OutbreaksDoctor of PhilosophyDrug resistanceEmulsionsEquipmentFamilyFlaviviridaeFlavivirus InfectionsGenomeGenomicsGenotypeGoalsHepatitis C virusHumanInfectionInfectious AgentInterventionLibrariesMedicalMeningitisMethodsMicrobeMicrofluidicsMorbidity - disease rateMycobacterium tuberculosisNucleic AcidsOilsOrganismPathogen detectionPathogenicityPatientsPhysiciansPlasmaPreparationPrognosisProtocols documentationPublishingResearchResistanceRifampinSamplingSpeedSymptomsTechnologyTherapeuticTimeTranslationsVesicleVirulence FactorsVirusWaterWest Nile virusZika Virusaccurate diagnosisbacterial genome sequencingbasebioinformatics toolclinical Diagnosisclinically actionableclinically relevantcommunicable disease diagnosiscomputerized toolscostdetection methoddigitaldrug resistant pathogeneffective therapygenome sequencingin silicoinnovationinstrumentliquid biopsymosquito-bornenew technologynext generation sequencingnovelnovel strategiespathogenpathogen genomepathogenic bacteriapathogenic microbepathogenic virusrapid testresistance generespiratorysingle cell sequencingunilamellar vesiclewhole genome
中文摘要
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英文摘要
ABSTRACT
Infectious disease remains a major cause of morbidity across the globe. Successful medical intervention
requires accurate diagnosis, which can be challenging due to symptoms shared among infections. Moreover,
increasing drug resistance makes it important to identify and characterize pathogenic microbes and choose
effective treatments in a timely manner. In this competing renewal, we will extend technology developed under
our previous R01 to enable rapid, sensitive, and information-rich infectious disease diagnosis. Our new
technology, which we dub Gigapixel NGS, extends our Gigapixel digital PCR (dPCR) technology from the
previous R01 by incorporating a powerful next-generation sequencing capability. The core innovation of
Gigapixel dPCR was to perform dPCR assays in double emulsion vesicles, rather than conventional water-in-
oil droplets utilized by commercial dPCR instruments (Bio-Rad, RainDance). Vesicles obviate the need for
specialized droplet analyzers and allow common flow cytometers to be used for quantification. Flow cytometers
are ubiquitous in research and clinical labs, with superior analysis capabilities (i.e. speed, sensitivity, and
multiplexing) in comparison to specialized droplet analyzers. This allows Gigapixel dPCR to increase the
number of compartments that can be analyzed to over 100 million, providing a 100-fold increase in sensitivity
of quantification. Moreover, flow cytometers can sort out vesicles in which PCR has detected a DNA target, a
functionality not found in existing droplet analyzers.
In this competing renewal, we will leverage the capability of Gigapixel NGS to detect, isolate and sequence
infectious pathogen genomes directly from patient samples. The proposed workflow will increase the efficiency
and sensitivity of pathogen sequencing, yielding higher-quality draft genomes at far lower cost. In collaboration
with Dr. Charles Chiu, an infectious disease physician at UCSF specializing in clinical sample sequencing for
pathogen detection, we will develop bioinformatic tools to interrogate the recovered genomes for relevant
biomarker sequences, such as virulence factors and drug resistance genes. Through further collaboration with
Fluent Biosciences, we will adapt Gigapixel NGS to their commercial platform, allowing the protocol to be
implemented by research and clinical labs as a commercially available kit.
The Specific Aims of our proposal are to:
1) Extend Gigapixel PCR into a clinically relevant platform, in the form of Gigapixel NGS.
2) Demonstrate integrated quantification and accurate genome sequencing of bacterial infections in
meningitis.
3) Apply the technology for in silico identification and diagnosis of flavivirus infections from (liquid) biopsy
samples such as plasma and cerebrospinal fluid.
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