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首页> 外文期刊>Nucleic acids research >dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues
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dotdotdot: an automated approach to quantify multiplex single molecule fluorescent in situ hybridization (smFISH) images in complex tissues

机译：DOTDOTDOT：一种在复杂组织中定量多重单分子荧光（SMFID）图像的自动化方法

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摘要

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.
机译：原位杂交（SMFISH）的多重单分子荧光是用于验证RNA测序和新出现的空间转录组数据的强大方法，但量化仍然是计算挑战。我们介绍了一种用于在复杂组织中产生和分析SMFISH数据的框架，同时克服自发荧光并增加复用容量。我们开发了dotdotdot（https://github.com/lieberinstitute/dotdotdot）作为相应的软件包，以定量单一核中的RNA转录物并进行差异表达分析。我们首先通过量化活性调节基因的差异表达，展示我们平台在单小鼠神经元中的鲁棒性。然后，使用光谱成像和DOTDOTDOT来定量人背面前额甲基质皮层（DLPFC）中的空间基因表达，以掩盖脂血清荧光荧光荧光。我们最后应用机器学习来预测细胞类型并执行下游细胞类型特异性表达分析。总之，我们提供了复杂组织中SM鱼数据的定量和生物学解释的实验工作流，成像和解策略。

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