Image-Seq: A high-density microfluidic trap array for single cell transcriptome analysis coupled with image based phenotyping

摘要 以大规模并行格式将单细胞RNA测序和基于图像的表型分析相结合的能力 将使单个细胞的功能和基因表达之间能够直接相关。 然而，现有技术的根本局限性阻碍了这一目标在 高通量自动化平台。在这里，我们建议通过创建一个 高密度陷阱阵列，包含打印在陷阱阵列中已知地址的唯一DNA条形码。这个 提出的技术，我们称之为图像序列，将涉及以高密度组织单个细胞 阵列，以多个波长以高分辨率成像每个单元/井，最后准备单个细胞用于 使用本地打印的DNA条形码作为可在NGS数据集中追踪的蜂窝识别码的RNA-seq 回到特定的活细胞图像。为了实现这一目标，杜克大学团队将与应用微阵列公司合作，后者 将在唯一的空间地址打印一组DNA条形码。目标1将专注于证明DNA 条形码可以高保真地打印出来，并用作细胞mRNA反转录的模板。 目标2将展示实现单细胞高通量捕获、自动化成像和 直接在微流控芯片内裂解单个细胞。目标3将展示获得两个现场直播的能力 人和小鼠混合细胞系中每个单个细胞的细胞图像和转录组图谱 分离的组织样本。该项目在基础研究和后续研究中都有许多潜在的应用。 在临床上的应用。其中一个应用是更好地利用有限的样本，其中只有数百到 从一个组织中可以获得数以千计的分离的单个细胞，这是目前其他组织无法实现的 单细胞分析工作流程。另一个潜在的应用是药物敏感性测试，这需要时间- Lapse成像以量化单细胞生长率，并将其与那些 相同的细胞，以牵涉到耐药细胞激活的信号通路。

ABSTRACT The ability to combine single cell RNA sequencing and image-based phenotyping in a massively parallel format would enable direct correlations to be made between the function and gene expression of single cells. However, the fundamental limitations of existing technologies have prevented the realization of this goal in a high-throughput automated platform. Here we propose to solve this systems design challenge by creating a high-density trap array that contains unique DNA barcodes printed at known addresses in the trap array. The proposed technology, which we refer to as Image-Seq, will involve organizing single cells in a high-density array, imaging each cell/well at high resolution at multiple wavelengths, and finally preparing the single cells for RNA-seq using the locally printed DNA barcodes as cellular identifiers that can be traced in NGS datasets back to specific live cell images. To achieve this goal, the Duke team will partner with Applied Microarray, who will print an array of DNA barcodes at unique spatial addresses. Aim 1 will focus on demonstrating that DNA barcodes can be printed with high fidelity and used as templates for reverse transcription of cellular mRNA. Aim 2 will demonstrate the ability to achieve high throughput trapping of single cells, automated imaging, and lysis of single cells directly inside the microfluidic chips. Aim 3 will demonstrate the ability to obtain both a live cell image and a transcriptome profile of each single cell in mixed human and mouse cell lines and also in dissociated tissue samples. This project has many potential applications both in basic research and in follow- on clinical applications. One application is in making better use of limited samples where only hundreds to thousands of dissociated single cells can be obtained from a tissue, which is not currently possible by other single cell analysis workflows. Another potential application is in drug sensitivity testing, which involves time- lapse imaging to quantify single cell growth rates and compare these to the gene expression analysis of those same cells in order to implicate the signaling pathways invoked by drug resistant cells.