A kit for massively parallel single cell gene expression analysis
A kit for massively parallel single cell gene expression analysis
批准号:
8832632
负责人:
Christina Fan
金额:
$151.24万
依托单位:
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:
2014-12-10 至 2015-11-30
关键词:
AdoptionBase SequenceBiologicalBiological AssayBiologyBloodCellsClinicalCollaborationsCommunitiesComputer SystemsComputer softwareDataDevicesDiseaseGene ExpressionGene Expression ProfileGene Expression ProfilingGenesGoalsHealthHumanIndividualKnowledgeLabelLearningLibrariesMeasurementMeasuresMedicineMethodsMolecularMonitorPharmaceutical PreparationsPhasePreparationReagentResearch PersonnelResolutionSamplingScreening for cancerSequence AnalysisSpecimenSystemTechniquesTechnologyTestingTimeTissuesVaccinesWorkabstractingbasecancer therapycell typeclinical applicationcomplex biological systemscostdesigndigitalgene panelimprovedinstrumentinterestnext generation sequencingprototypepublic health relevanceresponsesingle cell analysissingle molecule
中文摘要
描述(申请人提供):用于大规模并行单细胞基因表达分析的试剂盒摘要利用下一代测序(NGS)的能力进行单细胞基因表达分析最近在学术界获得了势头。虽然测序已经变得更便宜,但在单细胞水平上测量基因表达谱的能力受到从单细胞制备测序库的技术的限制。目前可用的样品制备技术需要昂贵的仪器,而且是蛮力和低产量的。如果单细胞基因的表达能力可以扩大到一次检查数万个细胞,并跨越许多基因,那么单细胞基因的表达将会强大得多。这项技术不仅将提高我们在基础生物学和医学方面的知识,而且还具有许多潜在的临床应用。我们最近开发了一种低成本、高分辨率的大规模并行方法,从大量单个细胞中制备测序文库,用于基因表达分析。该方法基于随机标记的概念,在单细胞和单分子水平上执行。我们已经成功地对每个样本进行了近1000个单细胞的~100个基因的常规表达分析,并展示了我们的系统能够对人类血液等异质细胞混合物中的主要细胞类型进行分类。我们技术的可扩展性、吞吐量和经济性远远超过现有的商业平台。对于这个拟议的第二阶段项目,我们计划进一步扩大我们的技术规模,使每个样本能够对10,000个细胞中的数百个基因进行常规分析,并将当前正在工作的原型转换为可出口产品,其中包括试剂盒、简单的试剂加载设备以及支持的分析设计和分析软件。
英文摘要
DESCRIPTION (provided by applicant): A kit for massively parallel single cell gene expression analysis Abstract Interest in single cell gene expression analysis harnessing the capability of next- generation sequencing (NGS) has recently gained momentum in the academic community. Although sequencing has become cheaper, the ability to measure gene expression profile at the single cell level is extremely constrained by the limitation of technolog for preparing sequencing libraries from single cells. Currently available sample preparation techniques require expensive instruments and are brute-force and low-throughput. Single cell gene expression would be much more powerful if it can be scaled to examine tens of thousands of cells at a time and across many genes. Not only will such technology advance our knowledge in basic biology and medicine, it also has many potential clinical applications. We have recently developed a low-cost and high-resolution massively parallel method to prepare sequencing libraries from large number of single cells for gene expression analysis. The method is based on the concept of stochastic labeling, executed at the single cell and the single molecule level. We have successfully conducted expression analysis of ~100 genes of close to 1000 single cells per sample routinely, and have demonstrated the ability of our system to classify major cell types in heterogeneous cell mixtures such as human blood. The scalability, throughput, and economy of our technology far exceed existing commercial platforms. For this proposed Phase II project, we plan to further scale our technology to enable routine analysis of hundreds of genes across 10,000 cells per sample, and to convert the current working prototype into an exportable product that includes a reagent kit, a simple reagent-loading device, and supporting assay design and analysis software.
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批准号:10592373
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项目类别:
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资助金额:$69.92万
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财政年份:2022
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负责人:Christina Fan
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依托单位:
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