时空组学技术新进展<sup>*</sup>

doi:10.13523/j.cb.2312103

中国生物工程杂志

2024, Vol. 44

Issue (1): 19-31 DOI: 10.13523/j.cb.2312103

生物经济核心产业专题

时空组学技术新进展^*

姜宇佳¹,荆泽华¹,冯静¹,徐讯^1,^2,^**(

)

1 杭州华大生命科学研究院杭州 310030
2 深圳华大生命科学研究院深圳 518083

Recent Advances in Spatiotemporal Omics Technology

Yujia JIANG¹,Zehua JING¹,Jing FENG¹,Xun XU^1,^2,^**(

)

1 BGI Research, Hangzhou 310030, China
2 BGI Research, Shenzhen 518083, China

全文: PDF(1703 KB) HTML

摘要：

时空组学技术(spatiotemporal omics technology)是对组织或细胞在连续的时间和空间维度内观测或检测生命多组学表达和调控特征的技术统称,可以在亚细胞、细胞、组织、器官、个体、群体和进化水平解析由遗传分子决定的生命现象本质。多组学主要包括基因组学、表观组学、转录组学和蛋白质组学。其中,空间转录组学(spatial transcriptomics)近年来发展迅猛,应用较为广泛。对时空组学技术的发展历程、技术原理以及物理极限进行介绍,概述其整体分析流程和经典时空算法,总结时空组学为发育生物学、复杂疾病、神经科学、植物学领域带来的重大突破,同时提出时空组学的技术迭代、分析方法开发、实验设计环节面临的挑战以及对未来发展的展望。

关键词： 时空组学; 空间转录组; 发育生物学; 复杂疾病; 神经科学; 植物学

Abstract:

Spatiotemporal omics technology is a collective term for technologies that observe or detect the multi-omics expression and regulatory features of life in tissues or cells in successive temporal and spatial dimensions, which enables analysis of the nature of life phenomena determined by genetic molecules at the subcellular, cellular, tissue, organ, individual, population, and evolutionary levels. Multi-omics mainly include genomics, epigenomics, transcriptomics and proteomics. Among them, spatial transcriptomics has been developing rapidly in recent years and is more widely used. Based on the principles, spatiotemporal omics techniques can be categorized into imaging-based techniques and sequencing-based techniques. Imaging-based technologies include spatial in situ hybridization technologies(SISH), such as smFISH, seqFISH and MERFISH, and spatial in situ sequencing technologies(SISS), such as ISS, FISSEQ and STARmap. Sequencing-based technologies include spatial in situ microsection(SISM) technologies such as tomo-seq, Geo-seq and DSP, and spatial in situ barcoding(SISB) technologies such as ST, Stereo-seq and seq-Scope technologies. The performance difference between different technologies is mainly reflected in the number of captured genes, spatial resolution, capture area, etc. Imaging-based technologies generally have higher spatial resolution, which can reach the level of cellular and subcellular resolution. However, because of the physical limitations brought by optical crowding and experimental complexity, the number and types of genes captured by the target are limited and difficult to improve. Sequencing-based technologies use polyT to specifically capture polyA, which enables unbiased capture at the whole transcriptome level and greatly improves the variety and number of captured genes. However, due to the mechanical limitations received from microsection and capture barcode planting, their spatial resolution generally falls short of single-cell resolution and is mostly a multicellular region with a mixture of multiple cells. Currently, benefiting from the nanoscale realization of nucleic acid binding site spacing on the surface of sequencing microarrays, Stereo-seq and seq-Scope have for the first time achieved nucleic acid capture at subcellular resolution and cell segmentation through integration with cellular staining profiles, which can truly achieve spatial single-cell resolution. The rapid development of spatial transcriptome technology has laid the foundation for the development of spatiotemporal multi-omics technology. Based on the existing experimental flow of spatial transcriptome or capture chip, through the transformation of the capture target and the supporting experimental flow, spatial genome, epigenome and proteome technologies have appeared one after another, and are gradually developing towards the ability to detect multiple histologists simultaneously in a single slice. The wide application of spatiotemporal genomics technologies has brought many challenges and targeted solutions for data analysis. Some of the more important current analysis methods include cell segmentation, spatial domain identification, and cell interactions. Significant challenges remain in the future for de-batching and integrative analysis of large amounts of data. The spatial information of nucleic acids and cells provided by spatiotemporal genomics can be constructed to detect macroscopic life activities and identify microscopic regulatory information at subcellular, cellular, tissue, organ, and holistic levels in both temporal and spatial dimensions. This has led to breakthroughs in important life science research areas such as developmental biology, complex diseases, neuroscience, and botany. Here, the development history of spatiotemporal omics technology, the characteristics of technical principles and physical limits are summarized, and the overall analysis process of spatiotemporal omics and classical spatiotemporal algorithms are outlined. The breakthroughs brought by spatiotemporal omics to the fields of developmental biology, complex diseases, neuroscience, and botany are summarized. The challenges of spatiotemporal omics in terms of technology iteration, analytical method development, and experimental design are also presented, as well as the outlook for the future development of spatiotemporal omics.

Key words: Spatiotemporal omics Spatial transcriptomics Developmental biology Complex diseases Neurosciences Botany

收稿日期: 2023-12-29 出版日期: 2024-02-04

ZTFLH:

Q819

基金资助: *国家重点研发计划(2022YFC3400400)

通讯作者: ** 电子信箱:xuxun@genomics.cn

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引用本文:

姜宇佳, 荆泽华, 冯静, 徐讯. 时空组学技术新进展^*[J]. 中国生物工程杂志, 2024, 44(1): 19-31.

Yujia JIANG, Zehua JING, Jing FENG, Xun XU. Recent Advances in Spatiotemporal Omics Technology. China Biotechnology, 2024, 44(1): 19-31.

链接本文:

https://manu60.magtech.com.cn/biotech/CN/10.13523/j.cb.2312103 或 https://manu60.magtech.com.cn/biotech/CN/Y2024/V44/I1/19

图1 时空组学技术发展时间线

图2 时空组学技术原理

表1 空间多组学技术

分析方法	领域/目标	算法类型	发表期刊及发表年份
StarDist	高分辨率空间组学,细胞分割	神经网络	CVPR, 2018
DeepCell	高分辨率空间组学,细胞分割	神经网络	Nature Methods, 2021
CellPose	高分辨率空间组学,细胞分割	神经网络	Nature Methods, 2021
Baysor	高分辨率空间组学,细胞分割	空间几何	Nature Biotechnology, 2021
SSAM	高分辨率空间组学,细胞分割	空间几何	Nature Communication, 2021
ClusterMap	高分辨率空间组学,细胞分割	空间几何	Nature Communication, 2021
SCS	高分辨率空间组学,细胞分割	神经网络	Nature Methods, 2023
Cell2location	低分辨率空间组学,解卷积	贝叶斯模型	Nature Biotechnology, 2022
Spotlight	低分辨率空间组学,解卷积	非负矩阵分解	Nucleic Acids Research, 2021
Tangram	低分辨率空间组学,解卷积	神经网络	Nature Methods, 2021
CARD	低分辨率空间组学,解卷积	自回归模型	Nature Biotechnology, 2022
Lovain	聚类分析,空间域识别	社区聚类算法	Journal of Statistical Mechanics-Theory and Experiment, 2008
Leiden	聚类分析,空间域识别	社区聚类算法	Scientific Reports, 2019
SpatialDE	空间特征基因识别	空间统计学模型	Nature Methods, 2018
SPARK-X	空间特征基因识别	空间统计学模型	Genome Biology, 2021
nnSVG	空间特征基因识别	空间统计学模型	Nature Communication, 2023
HotSpot	空间特征基因,空间共表达识别	空间统计学模型	Cell Systems, 2021
SpaGCN	聚类分析,空间域识别	神经网络	Nature Methods, 2021
STAGATE	聚类分析,空间域识别	神经网络	Nature Communication, 2022
SpatialPCA	降维、聚类分析,空间域识别	空间统计学模型	Nature Communication, 2022
BayesSpace	聚类分析,空间域识别	空间统计学模型	Nature Biotechnology, 2021
MISTy	空间细胞互作	空间统计学模型	Genome Biology, 2022
DIALOGUE	空间细胞互作	空间统计学模型	Nature Biotechnology, 2022
NCEM	空间细胞互作	空间统计学模型	Nature Biotechnology, 2022
COMMOT	空间细胞互作	最优传输模型	Nature Methods, 2023

表2 时空组学算法

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