Streamlining single-cell spatial transcriptomics for human kidney tissue

Single-cell spatial technologies have emerged in recent years, enabling characterization of tissue architecture and organization at unprecedented resolution. However, computational analysis of spatial transcriptomics data remains a bottleneck for scientific discoveries in the absence of dedicated bioinformatics expertise. Here, we describe a novel cell area normalization method and workflow to annotate 15 kidney cell types from a dataset generated using NanoString’s CosMx single-cell-resolution spatial transcriptomic platform. This approach enabled a comparison between two healthy kidney biopsies and two diseased samples. We validated our pipeline’s accuracy using gene expression analysis, demonstrating increased sensitivity compared with other normalization methods and consistency with pathological changes observed in biopsy-proven diabetic kidney disease (DKD). Using precise cell type annotation, we observed significant changes in the proportions of podocytes and immune cells in DKD, with regional enrichment of immune cells and differential gene expression. Injured proximal tubules showed the expected upregulation of HAVCR1 and VCAM1, as well as other genes associated with diabetes, including IL18, ITGA3, and ITGB1. The workflow, now fully integrated into the BioTuring SpatialX (Lens V2.0), is available as a platform designed for users with no formal bioinformatics training, providing accessible web-based tools for spatial data analysis.
Kidney biopsy samples from two healthy patients and two diabetic kidney disease patients were analyzed using CosMx single-cell spatial transcriptomics. We developed a cell annotation and analysis pipeline using a novel cell area based normalization technique. Our workflow is fully integrated into the BioTuring SpatialX (Lens V2.0) platform, offering web-based analysis tools that provide a low barrier to entry for spatial data analysis.

Developed a cell area normalization for CosMx single-cell spatial transcriptomics integrated in a web-based platform.
Achieved higher sensitivity in gene expression analysis compared with existing normalization approaches.
Annotation of 15 kidney cell types using NanoString’s 1000-plex CosMx platform combined with a custom panel of 29 kidney-specific genes.
Comparative spatial analysis between healthy and diabetic kidney disease tissues identifies DKD-associated gene changes and regional immune enrichment at single-cell resolution.

Developed a cell area normalization for CosMx single-cell spatial transcriptomics integrated in a web-based platform.
Achieved higher sensitivity in gene expression analysis compared with existing normalization approaches.
Annotation of 15 kidney cell types using NanoString’s 1000-plex CosMx platform combined with a custom panel of 29 kidney-specific genes.
Comparative spatial analysis between healthy and diabetic kidney disease tissues identifies DKD-associated gene changes and regional immune enrichment at single-cell resolution.

近年来，单细胞空间技术蓬勃发展，使得研究人员能够以前所未有的分辨率刻画组织的结构与排布特征。然而，在缺乏专业生物信息学技术储备的前提下，空间转录组（single-cell spatial transcriptomics）数据的计算分析仍是制约科学发现的关键瓶颈。本研究介绍了一种全新的细胞面积归一化方法与分析流程，可用于注释使用NanoString公司CosMx单细胞分辨率空间转录组平台生成的数据集内的15种肾脏细胞类型。该流程支持对2份健康肾脏活检样本与2份病变样本开展对比分析。我们通过基因表达实验验证了该流程的准确性，结果显示其相较于其他归一化方法具备更高的检测灵敏度，且与活检证实的糖尿病肾病（diabetic kidney disease, DKD）的病理变化高度吻合。借助精准的细胞类型注释，我们观察到糖尿病肾病样本中足细胞与免疫细胞的比例发生显著改变，免疫细胞呈现区域富集特征，同时伴随差异基因表达现象。受损的近端肾小管可见HAVCR1、VCAM1以及其他与糖尿病相关的基因（包括IL18、ITGA3与ITGB1）的预期上调表达。本分析流程已完全集成至BioTuring SpatialX (Lens V2.0)平台，专为未接受过正规生物信息学培训的用户设计，提供便捷易用的基于网页的空间数据分析工具。

本研究对2名健康受试者与2名糖尿病肾病患者的肾脏活检样本进行了CosMx单细胞空间转录组分析。我们基于新型细胞面积归一化技术开发了一套细胞注释与分析流程。该流程完全集成于BioTuring SpatialX (Lens V2.0)平台，提供基于网页的分析工具，有效降低了空间数据分析的入门门槛。

1. 开发了适配CosMx单细胞空间转录组的细胞面积归一化方法，并集成至网页分析平台中。
2. 相较于现有归一化方法，在基因表达分析中展现出更高的检测灵敏度。
3. 结合NanoString的1000-plex CosMx平台与定制的29种肾脏特异性基因组合，实现了15种肾脏细胞类型的精准注释。
4. 通过健康与糖尿病肾病组织的对比空间分析，在单细胞分辨率下鉴定出与DKD相关的基因表达变化以及区域免疫富集现象。

1. 开发了适配CosMx单细胞空间转录组的细胞面积归一化方法，并集成至网页分析平台中。
2. 相较于现有归一化方法，在基因表达分析中展现出更高的检测灵敏度。
3. 结合NanoString的1000-plex CosMx平台与定制的29种肾脏特异性基因组合，实现了15种肾脏细胞类型的精准注释。
4. 通过健康与糖尿病肾病组织的对比空间分析，在单细胞分辨率下鉴定出与DKD相关的基因表达变化以及区域免疫富集现象。