Genomics techniques are currently being adapted to provide spatially resolved omics profiling. However, the adaptation of each new method typically requires the setup of specific detection strategies or specialized instrumentation. A generic approach to spatially resolve different types of high throughput data is missing. Here, we describe an imaging-free framework to localize high throughput readouts within a tissue by combining compressive sampling and image reconstruction. We implemented this framework to transform a low-input RNA sequencing protocol into an imaging-free spatial transcriptomics technique (STRP-seq) and validated this method with a transcriptome profiling of the murine brain. To verify the broad applicability of STRP-seq, we applied the technique on the brain of the Australian bearded dragon Pogona vitticeps. Our results reveal the molecular anatomy of the telencephalon of this lizard, providing evidence for a marked regionalization of the reptilian pallium and subpallium. Overall, the proposed framework constitutes a new approach that allows upgrading in a generic fashion conventional genomic assays to spatially resolved techniques.

中文翻译：

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通过无成像分子层析成像对空间组织进行分析

目前，基因组学技术正在适应提供空间分辨的组学分析。但是，每种新方法的改编通常都需要设置特定的检测策略或专用仪器。缺少在空间上解析不同类型的高吞吐量数据的通用方法。在这里，我们描述了一种无图像的框架，通过结合压缩采样和图像重建来定位组织内的高通量读数。我们实施了此框架，以将低输入RNA测序方案转化为无成像的空间转录组学技术（STRP-seq），并通过对鼠脑进行转录组分析来验证了该方法。为了验证STRP-seq的广泛适用性，我们将该技术应用于了澳大利亚大胡子龙Pogona vitticeps的大脑。我们的研究结果揭示了该蜥蜴端脑的分子解剖结构，为爬行动物的皮层和皮下层的明显区域化提供了证据。总体而言，提出的框架构成了一种新方法，该方法允许以通用方式将常规基因组测定升级为空间分辨技术。