Highly Multiplexed Single-cell Transcript Analysis Using DNA-barcoded Nanowells

使用 DNA 条形码纳米孔进行高度多重单细胞转录本分析

• 批准号: 8413936
• 负责人: John Christopher Love
• 金额: $ 18.37万
• 依托单位: MASSACHUSETTS INSTITUTE OF TECHNOLOGY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8413936
• 关键词: Address; B-Lymphocytes; Back; Bar Codes; Biological; Biology; Cell Communication; Cell Count; Cell secretion; Cells; Cellular biology; Complementary DNA; Custom; Cytolysis; DNA; Data; Data Set; Gene Expression Profile; Genetic Transcription; Immunology; Individual; Label; Magnetism; Maps; Measurement; Measures; Medical; Messenger RNA; Methodology; Methods; Monitor; Neurobiology; Oligonucleotide Microarrays; Oligonucleotides; Phenotype; Poly T; Population; Procedures; Process; Proteomics; Protocols documentation; RNA-Directed DNA Polymerase; Reaction; Research; Reverse Transcription; Solutions; Surface; System; T-Cell Receptor; Tail; Techniques; Technology; Transcript; Work; cDNA Arrays; cancer stem cell; cell type; data integration; design; drug discovery; nanolitre; nanoparticle; next generation; novel; particle; receptor; seal; single cell analysis; tool

DESCRIPTION (provided by applicant): Single cell transcriptional analysis has already demonstrated its ability to identify novel cell subsets but is currently limited by the number of cells and analytes that can be measured in parallel. We plan to increase both the number of cells and analytes that can be monitored by one to two orders of magnitude by using DNA-bar coded nanoliter well (nanowell) arrays to label each individual transcript with a DNA-encoded address. Next generation sequencing will identify both the transcript identity and the attached barcode, thereby tracing each sequenced transcript back to a single cell. This will be accomplished by transferring a million DNA barcodes synthesized on the surface of a microarray to primer-conjugated nanoparticles in the nanowells through asymmetric PCR while the nanowells are sealed by the microarray surface. The PCR reaction will also add a poly(dT) tail to each barcode. Single cells will then be sealed into the bar coded nanowells. Following lysis of the cells, the poly(dT) probe will capture the mRNA and reverse transcriptase will extend the poly(dT) sequence, thereby fusing the barcode with each transcript. The bar coded cDNA will be amplified and integrated into next generation sequencing workflows. The technique will be validated by comparing the measured transcript levels to the levels measured in the same cell population by single cell qPCR using the Fluidigm platform. We will also demonstrate that the barcodes identify individual cell transcripts by sequencing B and T cell receptor transcripts and demonstrating that each unique BCR or TCR transcript has a unique barcode fused to it and the barcode maps back to a well that originally contained the correct cell type. Furthermore, the single cell transcript data will be integrated with single cell secretion data from the same cells obtained prior to cell lysis through our previously described microengraving methodology, thereby establishing the first platform that can create highly multiplexed single cell transcript ad proteomic data from the same population of single cells. Application of this technology will greatly accelerate our understanding of single cell biology and heterogeneous cell populations. PUBLIC HEALTH RELEVANCE: Many important medical processes involve the interaction of a large number of distinct cell types that act together to produce an observed biological state. Traditional research approaches that average measurements over the entire population cannot determine the cell-to- cell interactions that are occurring between unique cell subsets within the population, which is critical for understanding the system as a whole and misses an opportunity for focusing drug discovery efforts on very specific cell types that are critical for the process. ur proposed method for highly parallel single cell transcriptome analysis will open the door to defining and understanding the biology of rare subsets of cells in heterogenous populations and thereby accelerate our understanding in fields as far ranging as immunology, cancer, stem cells and neurobiology.

描述（由申请人提供）：单细胞转录分析已经证明了其识别新细胞子集的能力，但目前受到可以并行测量的细胞和分析物的限制。我们计划通过使用DNA-bar编码的纳米级井（Nanowell）阵列来增加一到两个数量级的细胞数量和分析物，以用DNA编码的地址标记每个单独的转录物。下一代测序将同时识别转录本身份和附件的条形码，从而将每个测序的转录物追溯到单个单元格。这将通过将微阵列表面合成的100万个DNA条形码转移到纳米维尔中的引物偶联的纳米颗粒中，通过非对称PCR来实现，而纳米线则通过微阵列表面密封。 PCR反应还将在每个条形码中添加一个聚（DT）尾巴。然后将单细胞密封到编码的纳米线中。细胞裂解后，聚（DT）探针将捕获mRNA，逆转录酶将扩展聚（DT）序列，从而将条形码与每个转录物融合。条编码的cDNA将被放大并集成到下一代测序工作流程中。该技术将通过使用流体平台将所测得的转录水平与单个细胞qPCR在同一细胞中测量的水平进行比较来验证。我们还将证明条形码通过测序B和T细胞受体转录物来识别单个细胞转录物，并证明每个独特的BCR或TCR转录本都具有与之融合的独特条形码，并且条形码映射回到最初包含正确单元类型的井。此外，通过我们先前描述的微型涂层方法，将与来自相同细胞获得的单细胞分泌数据集成在一起，从而建立了第一个平台，该平台可以创建来自同一单元群体的高度多重单细胞转录AD蛋白质组数据。该技术的应用将大大加速我们对单细胞生物学和异质细胞群体的理解。 公共卫生相关性：许多重要的医学过程涉及大量不同的细胞类型的相互作用，这些细胞类型共同起作用以产生观察到的生物学状态。传统的研究方法是，整个人群的平均测量方法无法确定人群中独特的细胞子集之间发生的细胞间相互作用，这对于理解整个系统至关重要，而错过了将药物发现工作集中在非常具体的细胞类型上的机会，这对于该过程至关重要。 UR提出的用于高度平行的单细胞转录组分析的方法将为定义和理解异质种群中稀有细胞子集的生物学打开大门，从而加速我们在范围内的理解，范围与免疫学，癌症，干细胞和神经生物学相比。