MULTIseq: multiplexing massively parallel single cell transcriptional analysis across time, space, and conditions

摘要 许多人类疾病--包括自身免疫、癌症和衰老--都与免疫能力的下降有关。 不同的细胞类型来协调它们在整个组织中的行为和决定。协调是通过 各种机制，包括细胞间交换的分子、机械和电信号。 这些信号通过信号通路整合，并最终影响许多转录因子的转录。 基因.在组织的每种细胞类型之间协调转录的多细胞调节网络可以 通过扰乱单个基因或细胞类型的转录状态，然后测量每个基因或细胞类型如何被激活， 细胞类型在动态和空间上都松弛到新的稳定状态。然而，系统地应用 这种方法将需要新的实验工具。因此，我们将建立在我们实验室开发的新方法上 - MULTISEQ -允许使用现有的单克隆测序技术同时对多个样品进行转录分析。 Cell RNAseq流水线（例如DropSeq、10 X、SeqWell等）。MULTISEQ可实现全新的 类单细胞实验。该方法具有显著提高吞吐量的额外好处 以及减少诸如双重峰和批次效应的伪像。因此，MULTISEQ允许研究人员 收集更丰富的单细胞信息，样品制备成本降低5至100倍。我们提出了三 目标是使MULTISEQ的应用能够及时分析动态生物过程;异质 太空中的生物过程;以及复杂组织对数百或数千个基因， 化学或微环境扰动。最后，我们建议将联合收割机MULTISEQ与以下方法结合， 将测序成本降低10倍，同时从低丰度 成绩单我们目标的完成将为科学界提供一个强大的新工具， 使用简单且经济的方案应用于任何细胞类型。

Abstract Many human diseases – including autoimmunity, cancer, and aging – are linked to a breakdown in the ability of different cell types to coordinate their behaviors and decisions across a tissue. Coordination occurs through a variety of mechanisms, including molecular, mechanical, and electrical signals that are exchanged among cells. These signals are integrated through signaling pathways, and ultimately, impact the transcription of numerous genes. The multicellular regulatory networks that coordinate transcription among each cell type of a tissue can be deduced by perturbing the transcriptional state of individual genes or cell types, then measuring how each cell type relaxes to a new steady state, both dynamically and spatially. However, the systematic application of this approach will require new experimental tools. We will therefore build on a new method developed in our lab – MULTIseq – that allows the simultaneous transcriptional analysis of numerous samples using existing single cell RNAseq pipelines (e.g. DropSeq, 10X, SeqWell, etc.). MULTIseq enables the implementation of entirely new classes of single cell experiments. The method has the additional benefits of dramatically increasing throughput and decreasing artifacts such as doublets and batch effects. As a consequence, MULTIseq allows researchers to gather richer single cell information at a 5- to 100-fold reduction in sample preparation costs. We present three aims that will enable application of MULTIseq to analyze dynamic biological processes in time; heterogeneous biological processes in space; and the response of complex tissues to hundreds or thousands of genetic, chemical, or microenvironmental perturbations. Finally, we propose to combine MULTIseq with methods to reduce sequencing costs by 10-fold while simultaneously increasing the information content from low-abundance transcripts. Completion of our goals will provide a powerful new tool to the scientific community that can be applied to any cell type using a simple and economical protocol.