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(申请人提供)：单细胞转录分析已经证明了其识别新细胞亚组的能力，但目前受到可平行测量的细胞和分析物数量的限制。我们计划通过使用DNA条形码纳升井(Nanowell)阵列来标记每个转录本的DNA编码地址，从而将可以监测的细胞和分析物的数量增加一到两个数量级。下一代测序将识别转录本的身份和所附的条形码，从而将每个测序的转录本追溯到单个细胞。这将通过不对称聚合酶链式反应将在微阵列表面合成的一百万个DNA条形码转移到纳米孔中的底物连接的纳米颗粒上来实现，同时纳米孔被微阵列表面密封。该聚合酶链式反应还将在每个条形码上添加一条聚(DT)尾巴。然后，单个细胞将被密封到条形码纳米孔中。在细胞裂解后，聚(DT)探针将捕获mRNA，逆转录酶将延长聚(DT)序列，从而将条形码与每个转录物融合。条形码的cDNA将被放大，并整合到下一代测序工作流程中。这项技术将通过将测量的转录本水平与使用Fluidigm平台的单细胞定量聚合酶链式反应在同一细胞群体中测量的水平进行比较来验证。我们还将演示条形码通过对B和T细胞受体转录本进行测序来识别单个细胞转录本，并演示每个唯一的BCR或TCR转录本都有一个与其融合的唯一条形码，并且条形码映射回最初包含正确细胞类型的井。此外，单细胞转录本数据将通过我们前面描述的显微雕刻方法与在细胞裂解前获得的相同细胞的单细胞分泌数据整合在一起，从而建立第一个可以从相同的单细胞群体中创建高度多元化的单细胞转录本和蛋白质组数据的平台。这项技术的应用将极大地促进我们对单细胞生物学和异质细胞群体的了解。 公共卫生相关性：许多重要的医学过程涉及大量不同类型细胞的相互作用，这些细胞类型共同作用，产生观察到的生物状态。传统的研究方法是对整个群体的测量进行平均，无法确定群体内独特的细胞子集之间发生的细胞间相互作用，这对于了解整个系统至关重要，并且错过了将药物发现工作集中在对这一过程至关重要的非常特定的细胞类型上的机会。我们提出的高度平行的单细胞转录组分析方法将为定义和理解异种群体中稀有细胞亚群的生物学打开大门，从而促进我们在免疫学、癌症、干细胞和神经生物学等领域的理解。