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鉴定新细胞亚群的能力，但目前受到可以平行测量的细胞和分析物数量的限制。我们计划增加细胞和分析物的数量，可以通过一个到两个数量级的监测，通过使用DNA条形码纳升井（nanoliter well）阵列标记每个单独的转录与DNA编码的地址。下一代测序将鉴定转录物身份和连接的条形码，从而将每个测序的转录物追溯到单个细胞。这将通过在微阵列表面密封微阵列细胞的同时，通过不对称PCR将在微阵列表面上合成的一百万个DNA条形码转移到微阵列细胞中的引物缀合的纳米颗粒来实现。PCR反应还将向每个条形码添加聚（dT）尾。然后将单个细胞密封到条形码微室中。细胞裂解后，聚（dT）探针将捕获mRNA，逆转录酶将延伸聚（dT）序列，从而将条形码与每个转录物融合。条形码cDNA将被扩增并整合到下一代测序工作流程中。将通过使用Fluidigm平台通过单细胞qPCR比较测量的转录物水平与在相同细胞群中测量的水平来验证该技术。我们还将通过对B和T细胞受体转录物进行测序并证明每个独特的BCR或TCR转录物具有与其融合的独特条形码并且条形码映射回最初包含正确细胞类型的孔来证明条形码识别个体细胞转录物。此外，单细胞转录物数据将与通过我们先前描述的微雕刻方法在细胞裂解之前获得的来自相同细胞的单细胞分泌数据整合，从而建立可以从相同的单细胞群体产生高度多重的单细胞转录物和蛋白质组学数据的第一平台。这项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的理解。 公共卫生关系：许多重要的医学过程涉及大量不同细胞类型的相互作用，这些细胞类型共同作用以产生观察到的生物状态。传统的研究方法对整个群体进行平均测量，无法确定群体中独特细胞亚群之间发生的细胞与细胞之间的相互作用，这对于理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该过程至关重要的特定细胞类型上的机会。我们提出的用于高度平行的单细胞转录组分析的方法将打开定义和理解异源群体中罕见细胞亚群的生物学的大门，从而加速我们在免疫学、癌症、干细胞和神经生物学等领域的理解。