Domanskyi Sergii, Srivastava Anuj, Kaster Jessica, Li Haiyin, Herlyn Meenhard, Rubinstein Jill C, Chuang Jeffrey H
bioRxiv. 2023 Jul 30:2023.07.27.550727. doi: 10.1101/2023.07.27.550727.
We have developed an automated data processing pipeline to quantify mouse and human data from patient-derived xenograft samples assayed by Visium spatial transcriptomics with matched hematoxylin and eosin (H&E) stained image. We enable deconvolution of reads with Xenome, quantification of spatial gene expression from host and graft species with Space Ranger, extraction of B-allele frequencies, and splicing quantification with Velocyto. In the H&E image processing sub-workflow, we generate morphometric and deep learning-derived feature quantifications complementary to the Visium spots, enabling multi-modal H&E/expression comparisons. We have wrapped the pipeline into Nextflow DSL2 in a scalable, portable, and easy-to-use framework.
We designed a Nextflow DSL2-based pipeline, Spatial Transcriptomics Quantification (STQ), for simultaneous processing of 10x Genomics Visium spatial transcriptomics data and a matched hematoxylin and eosin (H&E)-stained whole slide image (WSI), optimized for Patient-Derived Xenograft (PDX) cancer specimens. Our pipeline enables the classification of sequenced transcripts for deconvolving the mouse and human species and mapping the transcripts to reference transcriptomes. We align the H&E WSI with the spatial layout of the Visium slide and generate imaging and quantitative morphology features for each Visium spot. The pipeline design enables multiple analysis workflows, including single or dual reference genomes input and stand-alone image analysis. We showed the utility of our pipeline on a dataset from Visium profiling of four melanoma PDX samples. The clustering of Visium spots and clustering of imaging features of H&E data reveal similar patterns arising from the two data modalities.
我们开发了一种自动化数据处理流程，用于对通过Visium空间转录组学分析的患者来源异种移植样本中的小鼠和人类数据进行定量分析，并匹配苏木精和伊红（H&E）染色图像。我们使用Xenome对reads进行反卷积，使用Space Ranger对宿主和移植物种的空间基因表达进行定量，提取B等位基因频率，并使用Velocyto进行剪接定量。在H&E图像处理子工作流程中，我们生成与Visium斑点互补的形态计量学和深度学习衍生特征定量，从而实现多模态H&E/表达比较。我们已将该流程包装到Nextflow DSL2中，形成一个可扩展、便携且易于使用的框架。
我们设计了一个基于Nextflow DSL2的流程，即空间转录组学定量（STQ），用于同时处理10x Genomics Visium空间转录组学数据和匹配的苏木精和伊红（H&E）染色全玻片图像（WSI），该流程针对患者来源异种移植（PDX）癌症标本进行了优化。我们的流程能够对测序转录本进行分类，以反卷积小鼠和人类物种，并将转录本映射到参考转录组。我们将H&E WSI与Visium玻片的空间布局对齐，并为每个Visium斑点生成成像和定量形态特征。该流程设计支持多种分析工作流程，包括单参考基因组或双参考基因组输入以及独立图像分析。我们在来自四个黑色素瘤PDX样本的Visium分析数据集上展示了我们流程的实用性。Visium斑点的聚类和H&E数据的成像特征聚类揭示了两种数据模式产生的相似模式。
