dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues
dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues
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DOI：
10.1101/781559
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发表时间：
2019-09
影响因子：
14.9
通讯作者：
K. Maynard;M. Tippani;Y. Takahashi;BaDoi N. Phan;T. Hyde;A. Jaffe;K. Martinowich
中科院分区：
文献类型：
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作者：
K. Maynard;M. Tippani;Y. Takahashi;BaDoi N. Phan;T. Hyde;A. Jaffe;K. Martinowich
多重单分子荧光原位杂交(SmFISH)是验证RNA测序和新出现的空间转录数据的有效方法，但量化仍然是一个计算挑战。我们提出了一个在复杂组织中产生和分析smFISH数据的框架，同时克服了自体荧光并增加了多路复用能力。我们开发了点点(https://github.com/LieberInstitute/dotdotdot)作为相应的软件包，用于对单个核中的RNA转录进行定量和差异表达分析。我们首先通过量化活性调节基因的差异表达来证明我们的平台在单个小鼠神经元中的稳健性。然后，我们使用光谱成像和点点来掩蔽脂褐素自发荧光来量化人类背外侧前额叶皮质(DLPFC)中的空间基因表达。最后，我们应用机器学习来预测细胞类型，并进行下游细胞类型的特定表达分析。总之，我们提供了实验工作流程、成像采集和分析策略，用于对复杂组织中的smFISH数据进行量化和生物学解释。
Multiplex single-molecule fluorescent in situ hybridization (smFISH) is a powerful method for validating RNA sequencing and emerging spatial transcriptomic data, but quantification remains a computational challenge. We present a framework for generating and analyzing smFISH data in complex tissues while overcoming autofluorescence and increasing multiplexing capacity. We developed dotdotdot (https://github.com/LieberInstitute/dotdotdot) as a corresponding software package to quantify RNA transcripts in single nuclei and perform differential expression analysis. We first demonstrate robustness of our platform in single mouse neurons by quantifying differential expression of activity-regulated genes. We then quantify spatial gene expression in human dorsolateral prefrontal cortex (DLPFC) using spectral imaging and dotdotdot to mask lipofuscin autofluorescence. We lastly apply machine learning to predict cell types and perform downstream cell type-specific expression analysis. In summary, we provide experimental workflows, imaging acquisition and analytic strategies for quantification and biological interpretation of smFISH data in complex tissues.