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SPOTlight:Seeded NMF regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes
bioRxiv - Bioinformatics Pub Date : 2020-06-04 , DOI: 10.1101/2020.06.03.131334
Marc Elosua , Paula Nieto , Elisabetta Mereu , Ivo Gut , Holger Heyn

The integration of orthogonal data modalities greatly supports the interpretation of transcriptomic landscapes in complex tissues. In particular, spatially resolved gene expression profiles are key to understand tissue organization and function. However, spatial transcriptomics (ST) profiling techniques lack single-cell resolution and require a combination with single-cell RNA sequencing (scRNA-seq) information to deconvolute the spatially indexed datasets. Leveraging the strengths of both data types, we developed SPOTlight, a computational tool that enables the integration of ST with scRNA-seq data to infer the location of cell types and states within a complex tissue. SPOTlight is centered around a seeded non-negative matrix factorization (NMF) regression, initialized using cell-type marker genes, and non-negative least squares (NNLS) to subsequently deconvolute ST capture locations (spots). Using synthetic spots, simulating varying reference quantities and qualities, we confirmed high prediction accuracy also with shallowly sequenced or small-sized scRNA-seq reference datasets. We trained the NMF regression model with sample-matched or external datasets, resulting in accurate and sensitive spatial predictions. SPOTlight deconvolution of the mouse brain correctly mapped subtle neuronal cell states of the cortical layers and the defined architecture of the hippocampus. In human pancreatic cancer, we successfully segmented patient sections into healthy and cancerous areas, and further fine-mapped normal and neoplastic cell states. Trained on an external pancreatic tumor immune reference, we charted the localization of clinical-relevant and tumor-specific immune cell states. Using SPOTlight to detect regional enrichment of immune cells and their co-localization with tumor and adjacent stroma provides an illustrative example in its flexible application spectrum and future potential in digital pathology.

中文翻译:

SPOTlight:使用单细胞转录组将NMF回归播种以反卷积空间转录组学斑点

正交数据模态的集成极大地支持了复杂组织中转录组景观的解释。特别地,空间分辨的基因表达谱是了解组织的组织和功能的关键。但是,空间转录组学(ST)分析技术缺乏单细胞分辨率,需要与单细胞RNA测序(scRNA-seq)信息结合使用以对空间索引数据集进行反卷积。利用这两种数据类型的优势,我们开发了SPOTlight,这是一种计算工具,可将ST与scRNA-seq数据集成,以推断复杂组织内细胞类型和状态的位置。SPOTlight围绕着播种的非负矩阵分解(NMF)回归,该回归使用细胞类型标记基因进行初始化,非负最小二乘(NNLS)来反卷积ST捕获位置(斑点)。使用合成斑点,模拟变化的参考量和质量,我们证实了浅序列或小型scRNA-seq参考数据集的预测准确性也很高。我们使用样本匹配或外部数据集训练了NMF回归模型,从而得出了准确而敏感的空间预测。小鼠大脑的SPOTlight解卷积可正确映射皮层的微妙神经元细胞状态和海马的定义结构。在人类胰腺癌中,我们成功地将患者切片分为健康和癌变区域,并进一步细分了正常和赘生性细胞状态。接受外部胰腺肿瘤免疫参考培训,我们绘制了临床相关和肿瘤特异性免疫细胞状态的定位图。使用SPOTlight检测免疫细胞的区域富集及其与肿瘤和邻近基质的共定位,可提供其灵活的应用范围和数字病理学的未来潜力的例证。
更新日期:2020-06-04
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