Journal of Agricultural Science and Technology ›› 2024, Vol. 26 ›› Issue (11): 23-31.DOI: 10.13304/j.nykjdb.2024.0609
• BIOTECHNOLOGY & LIFE SCIENCE • Previous Articles Next Articles
Yongzi CHEN1,2(), Hua WANG1,3,4, Weixuan WANG5,6(
)
Received:
2024-07-30
Accepted:
2024-08-28
Online:
2024-11-15
Published:
2024-11-19
Contact:
Weixuan WANG
通讯作者:
王维轩
作者简介:
陈永孜 E-mail: yzchen@tmu.edu.cn;
基金资助:
CLC Number:
Yongzi CHEN, Hua WANG, Weixuan WANG. Development of Spatial Transcriptomics and Its Applications[J]. Journal of Agricultural Science and Technology, 2024, 26(11): 23-31.
陈永孜, 王化, 王维轩. 空间转录组学的发展及其应用进展[J]. 中国农业科技导报, 2024, 26(11): 23-31.
技术 Technique | 原理 Principle | 特点 Characteristics | 局限性 Limitation |
---|---|---|---|
原位杂交 In situ hybridization (ISH) | 互补核酸探针结合检测RNA Complementary nucleic acid probes for RNA detection | 细胞水平的空间信息,适用于特定基因的定位 Spatial information at the cellular level, suitable for locating specific genes | 有限的多路复用能力,可能灵敏度较低 Limited multiplexing capability, which may result in lower sensitivity |
空间转录组技术 Spatial transcriptomics (ST) | 带有空间坐标的基质或芯片 Matrix or chip with spatial coordinates | 提供整个组织区域的转录组数据,揭示基因的空间分布模式 Provides transcriptomic data across entire tissue regions, revealing spatial distribution | 可能存在可伸缩性问题和潜在的空间分辨率限制 Potential scalability issues and possible limitations in spatial resolution |
原位测序 In situ sequencing (ISS) | 合成测序在组织中进行 Synthetic sequencing conducted within tissues | 具有单细胞分辨率和多路复用的能力,适用于复杂样本 Offers single-cell resolution and multiplexing capabilities, suitable for complex samples | 与测序错误和优化相关的技术难题 Technical challenges related to sequencing errors and optimization |
带有空间条形码的单细胞RNA测序 scRNA-seq with spatial barcoding | 单细胞RNA测序与带有空间条形码的技术相结合 Single-cell RNA sequencing combined with spatial barcoding technologies | 同时获得单细胞和空间信息,揭示细胞异质性的空间分布 Acquiring single-cell and spatial data to reveal cellular heterogeneity | 数据集成的复杂性和潜在的空间分辨率损失 Complexity in data integration and potential loss of spatial resolution |
带条形码的原位杂交 FISH with barcoding | FISH与条形码技术相结合 Combines FISH with barcoding technology | 提供直观的多目标RNA成像,适用于研究RNA在空间上的关系 Provides intuitive multi-target RNA imaging for studying spatial relationships of RNA | 可能存在可伸缩性问题和潜在的光漂白问题 May face scalability issues and potential photobleaching problems |
Slide-seq 技术 Slide-seq | RNA捕获的微滴和空间坐标标签 RNA capture with microdroplets and spatial coordinate labels | 对整个组织切片进行高通量的转录组学分析,提供细胞类型和空间信息 High-throughput transcriptomics of tissue sections, providing cell type and spatial data | 对数据分析和解释的计算需求较高 High computational demands for data analysis and interpretation |
Nanostring GeoMx 数字空间分析仪 Nanostring GeoMx digital spatial profiler | 基于芯片的数字化空间分析技术 Chip-based digital spatial analysis technology | 提供高度精准的定量信息,并允许在组织中进行多目标的高通量分析 Provides highly accurate quantitative information and enables multi-target high-throughput analysis in tissues | 有关芯片设计和性能的 限制 Constraints related to chip design and performance |
激光捕获微切片 (LCM) 结合 RNA 测序 Laser capture microdissection (LCM) combined with RNA sequencing | 激光捕获微切片中的特定细胞,然后进行RNA测序 Laser capture microdissection of specific cells followed by RNA sequencing | 可以选择性地获取特定细胞群体的转录组信息 Allows selective acquisition of transcriptomic information from specific cell populations | 样本处理可能导致RNA降解或污染 Sample handling may lead to RNA degradation or contamination |
Table 1 Characteristics and limitation of common technique in spatial transcriptomics
技术 Technique | 原理 Principle | 特点 Characteristics | 局限性 Limitation |
---|---|---|---|
原位杂交 In situ hybridization (ISH) | 互补核酸探针结合检测RNA Complementary nucleic acid probes for RNA detection | 细胞水平的空间信息,适用于特定基因的定位 Spatial information at the cellular level, suitable for locating specific genes | 有限的多路复用能力,可能灵敏度较低 Limited multiplexing capability, which may result in lower sensitivity |
空间转录组技术 Spatial transcriptomics (ST) | 带有空间坐标的基质或芯片 Matrix or chip with spatial coordinates | 提供整个组织区域的转录组数据,揭示基因的空间分布模式 Provides transcriptomic data across entire tissue regions, revealing spatial distribution | 可能存在可伸缩性问题和潜在的空间分辨率限制 Potential scalability issues and possible limitations in spatial resolution |
原位测序 In situ sequencing (ISS) | 合成测序在组织中进行 Synthetic sequencing conducted within tissues | 具有单细胞分辨率和多路复用的能力,适用于复杂样本 Offers single-cell resolution and multiplexing capabilities, suitable for complex samples | 与测序错误和优化相关的技术难题 Technical challenges related to sequencing errors and optimization |
带有空间条形码的单细胞RNA测序 scRNA-seq with spatial barcoding | 单细胞RNA测序与带有空间条形码的技术相结合 Single-cell RNA sequencing combined with spatial barcoding technologies | 同时获得单细胞和空间信息,揭示细胞异质性的空间分布 Acquiring single-cell and spatial data to reveal cellular heterogeneity | 数据集成的复杂性和潜在的空间分辨率损失 Complexity in data integration and potential loss of spatial resolution |
带条形码的原位杂交 FISH with barcoding | FISH与条形码技术相结合 Combines FISH with barcoding technology | 提供直观的多目标RNA成像,适用于研究RNA在空间上的关系 Provides intuitive multi-target RNA imaging for studying spatial relationships of RNA | 可能存在可伸缩性问题和潜在的光漂白问题 May face scalability issues and potential photobleaching problems |
Slide-seq 技术 Slide-seq | RNA捕获的微滴和空间坐标标签 RNA capture with microdroplets and spatial coordinate labels | 对整个组织切片进行高通量的转录组学分析,提供细胞类型和空间信息 High-throughput transcriptomics of tissue sections, providing cell type and spatial data | 对数据分析和解释的计算需求较高 High computational demands for data analysis and interpretation |
Nanostring GeoMx 数字空间分析仪 Nanostring GeoMx digital spatial profiler | 基于芯片的数字化空间分析技术 Chip-based digital spatial analysis technology | 提供高度精准的定量信息,并允许在组织中进行多目标的高通量分析 Provides highly accurate quantitative information and enables multi-target high-throughput analysis in tissues | 有关芯片设计和性能的 限制 Constraints related to chip design and performance |
激光捕获微切片 (LCM) 结合 RNA 测序 Laser capture microdissection (LCM) combined with RNA sequencing | 激光捕获微切片中的特定细胞,然后进行RNA测序 Laser capture microdissection of specific cells followed by RNA sequencing | 可以选择性地获取特定细胞群体的转录组信息 Allows selective acquisition of transcriptomic information from specific cell populations | 样本处理可能导致RNA降解或污染 Sample handling may lead to RNA degradation or contamination |
工具 Tool | 开发语言 Development language | 步骤 Procedure | 链接 Link |
---|---|---|---|
Seurat[ | R | 批量效应校正;降维与聚类;空间细胞类型注释;空间可变基因 Batch effect correction; dimensionality reduction and clustering; spatial cell type annotation; and spatially variable genes | https://github.com/satijalab/seurat |
Harmony[ | R | 批量效应校正 Batch effect correction | https://github.com/immunogenomics/harmony |
stLearn | Python | 降维与聚类;空间可变基因;空间轨迹 Dimensionality reduction and clustering; spatially variable genes; spatial trajectory | https://github.com/BiomedicalMachineLearning/stLearn |
cell2location[ | Python | 空间细胞类型注释;空间区域定位 Spatial cell type annotation; spatial region localization | https://github.com/BayraktarLab/cell2location/ |
Scanorama[ | Python | 批量效应校正 Batch effect correction | https://github.com/brianhie/scanorama |
scHOT[ | R | 基因-基因相互作用 Gene-gene interaction | https://bioconductor.org/packages/release/bioc/html/scHOT.html |
STUtility[ | R | 降维与聚类;空间可变基因;三维模型构建 Dimensionality reduction and clustering; spatially variable genes; 3D model construction | https://github.com/jbergenstrahle/STUtility |
Table 2 Tools commonly used for spatial transcriptomic data analysis
工具 Tool | 开发语言 Development language | 步骤 Procedure | 链接 Link |
---|---|---|---|
Seurat[ | R | 批量效应校正;降维与聚类;空间细胞类型注释;空间可变基因 Batch effect correction; dimensionality reduction and clustering; spatial cell type annotation; and spatially variable genes | https://github.com/satijalab/seurat |
Harmony[ | R | 批量效应校正 Batch effect correction | https://github.com/immunogenomics/harmony |
stLearn | Python | 降维与聚类;空间可变基因;空间轨迹 Dimensionality reduction and clustering; spatially variable genes; spatial trajectory | https://github.com/BiomedicalMachineLearning/stLearn |
cell2location[ | Python | 空间细胞类型注释;空间区域定位 Spatial cell type annotation; spatial region localization | https://github.com/BayraktarLab/cell2location/ |
Scanorama[ | Python | 批量效应校正 Batch effect correction | https://github.com/brianhie/scanorama |
scHOT[ | R | 基因-基因相互作用 Gene-gene interaction | https://bioconductor.org/packages/release/bioc/html/scHOT.html |
STUtility[ | R | 降维与聚类;空间可变基因;三维模型构建 Dimensionality reduction and clustering; spatially variable genes; 3D model construction | https://github.com/jbergenstrahle/STUtility |
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