Link phenotypic characteristics with gene expression profile of single cells at high throughput for drug discovery and cell therapy development
Link phenotypic characteristics with gene expression profile of single cells at high throughput for drug discovery and cell therapy development
批准号:
10482180
负责人:
Yuchao Chen
金额:
$24.68万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-08-15 至 2024-02-14
关键词:
AddressAntibodiesBar CodesBiologicalBiological AssayCell LineCell TherapyCellsCellular MorphologyCharacteristicsCollectionComplementary DNAComputer softwareDataData AnalysesDevelopmentDevicesDimensionsDiseaseGene ExpressionGene Expression ProfileGene Expression ProfilingGenesGenetic TranscriptionGoalsHeterogeneityImageIndividualInjectionsInvestigationLabelLinkManualsMetabolicMicrofluidic MicrochipsMorphologyNamesOligonucleotidesOutcomePerformancePharmaceutical PreparationsPhasePhenotypePopulationPreparationProteinsProteomicsRNARecoveryResearch PersonnelRunningSamplingSignal TransductionSmall Business Innovation Research GrantStainsTechniquesTechnologyTimeVariantbasebiomarker signaturecellular imagingdata acquisitiondesigndrug developmentdrug discoveryflexibilityfluorescence imagingimprovedinnovationinsightinterestmassive parallel processingmicroscopic imagingmultimodalitymultiple omicsnew technologynoveloperationparallel processingphenomicsscreeningsingle cell analysissingle-cell RNA sequencingsynthetic biologytherapy developmenttooltranscriptome sequencingtranscriptomics
中文摘要
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英文摘要
PROJECT SUMMARY / ABSTRACT
Researchers have shown substantial interest in the integration of phenomics and transcriptomics data for novel
biological insights since phenotypic characteristics are highly correlated with gene expression patterns. However,
there is a lack of an efficient approach that could link gene expression profiles to cellular phenotypes (e.g.,
protein abundance and localization, cellular morphology, enzymatic and metabolic activity) at single-cell level in
a high-throughput manner. To bridge this gap, WellSIM proposes to develop a platform which can extract
phenotypic characteristics from thousands of individual cells via high-content imaging and link these features
with their transcriptomic profiles. This technology will not only achieve multi-dimensional single cell studies, but
also improve the performance of single-cell RNA sequencing through automatic sample preparation on a
microfluidic device without using barcoded beads.
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