Spatially resolved single-cell deconvolution of bulk transcriptomes using Bulk2Space

We developed a spatially resolved RNA-seq protocol based on laser capture microdissection (LCM) and spatial barcoding strategy termed Spatial-seq. Here, we provide a demo transcriptomic data derived from mouse hypothalamus for an application of our newly proposed spatial deconvolution algorithm termed Bulk2Space. We want to reveal the spatial heterogeneity and cellular diversity within the bulk brain region. We performed laser capture microdissection to isolate the hypothalamus region from mouse brain sections at sagittal direction. The brain sections were first registered to the Allen Brain Atlas, and each main region was annotated. The annotated region outline was then imported into LCM. The hypothalamus region was cut by LCM and followed with RNA-seq to obtain the gene expression matrix.本研究开发了一种基于激光捕获显微切割（Laser Capture Microdissection, LCM）与空间条形码策略的空间分辨RNA测序实验方案，命名为Spatial-seq。本研究提供一套源自小鼠下丘脑的示范性转录组数据，用于我们新近提出的空间反卷积算法Bulk2Space的应用验证，旨在揭示整块脑组织区域内的空间异质性与细胞多样性。实验过程中，我们通过激光捕获显微切割技术从矢状位小鼠脑组织切片中分离下丘脑区域：首先将脑组织切片配准至艾伦脑图谱（Allen Brain Atlas），并对各主要脑区进行注释；随后将已注释的脑区轮廓导入激光捕获显微切割系统，通过LCM切割下丘脑区域，再进行RNA测序以获取基因表达矩阵。