Journal of Genetics and Genomics

Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges

Mengnan Cheng, Yujia Jiang, Jiangshan Xu, Alexios-Fotios A. Mentis, Shuai Wang, Huiwen Zheng, Sunil Kumar Sahu, Longqi Liu, Xun Xu

2023, 50(9): 625-640. doi: 10.1016/j.jgg.2023.03.011

Abstract (677) PDF (65)

Abstract:
The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology, from the invention of the microscope 350 years ago to the recent emergence of single-cell sequencing, by which the scientific community has been able to visualize life at an unprecedented resolution. Most recently, the Spatially Resolved Transcriptomics (SRT) technologies have filled the gap in probing the spatial or even three-dimensional organization of the molecular foundation behind the molecular mysteries of life, including the origin of different cellular populations developed from totipotent cells and human diseases. In this review, we introduce recent progresses and challenges on SRT from the perspectives of technologies and bioinformatic tools, as well as the representative SRT applications. With the currently fast-moving progress of the SRT technologies and promising results from early adopted research projects, we can foresee the bright future of such new tools in understanding life at the most profound analytical level.

Laser capture microdissection for biomedical research: towards high-throughput, multi-omics, and single-cell resolution

Wenbo Guo, Yining Hu, Jingyang Qian, Lidan Zhu, Junyun Cheng, Jie Liao, Xiaohui Fan

2023, 50(9): 641-651. doi: 10.1016/j.jgg.2023.07.011

Abstract (397) PDF (28)

Abstract:
Spatial omics technologies have become powerful methods to provide valuable insights into cells and tissues within a complex context, significantly enhancing our understanding of the intricate and multifaceted biological system. With an increasing focus on spatial heterogeneity, there is a growing need for unbiased, spatially resolved omics technologies. Laser capture microdissection (LCM) is a cutting-edge method for acquiring spatial information that can quickly collect regions of interest (ROIs) from heterogeneous tissues, with resolutions ranging from single cells to cell populations. Thus, LCM has been widely used for studying the cellular and molecular mechanisms of diseases. This review focuses on the differences among four types of commonly used LCM technologies and their applications in omics and disease research. Key attributes of application cases are also highlighted, such as throughput and spatial resolution. In addition, we comprehensively discuss the existing challenges and the great potential of LCM in biomedical research, disease diagnosis, and targeted therapy from the perspective of high-throughput, multi-omics, and single-cell resolution.

Improved in situ sequencing for high-resolution targeted spatial transcriptomic analysis in tissue sections

Xinbin Tang, Jiayu Chen, Xinya Zhang, Xuzhu Liu, Zhaoxiang Xie, Kaipeng Wei, Jianlong Qiu, Weiyan Ma, Chen Lin, Rongqin Ke

2023, 50(9): 652-660. doi: 10.1016/j.jgg.2023.02.004

Abstract (432) PDF (26)

Abstract:
Spatial transcriptomics enables the study of localization-indexed gene expression activity in tissues, providing the transcriptional landscape that in turn indicates the potential regulatory networks of gene expression. In situ sequencing (ISS) is a targeted spatial transcriptomic technique, based on padlock probe and rolling circle amplification combined with next-generation sequencing chemistry, for highly multiplexed in situ gene expression profiling. Here, we present improved in situ sequencing (IISS) that exploits a new probing and barcoding approach, combined with advanced image analysis pipelines for high-resolution targeted spatial gene expression profiling. We develop an improved combinatorial probe anchor ligation chemistry using a 2-base encoding strategy for barcode interrogation. The new encoding strategy results in higher signal intensity as well as improved specificity for in situ sequencing, while maintaining a streamlined analysis pipeline for targeted spatial transcriptomics. We show that IISS can be applied to both fresh frozen tissue and formalin-fixed paraffin-embedded tissue sections for single-cell level spatial gene expression analysis, based on which the developmental trajectory and cell-cell communication networks can also be constructed.

Divergent expression of Neurl3 from hemogenic endothelial cells to hematopoietic stem progenitor cells during development

Xiaowei Ning, Junjie Du, Yandong Gong, Yingpeng Yao, Zhijie Bai, Yanli Ni, Yanyan Li, Zongcheng Li, Haixin Zhao, Jie Zhou, Bing Liu, Yu Lan, Siyuan Hou

2023, 50(9): 661-675. doi: 10.1016/j.jgg.2023.05.006

Abstract (319) PDF (27)

Abstract:
Prior to the generation of hematopoietic stem cells (HSCs) from the hemogenic endothelial cells (HECs) mainly in the dorsal aorta in midgestational mouse embryos, multiple hematopoietic progenitors including erythro-myeloid progenitors and lymphoid progenitors are generated from yolk sac HECs. These HSC-independent hematopoietic progenitors have recently been identified as major contributors to functional blood cell production until birth. However, little is known about yolk sac HECs. Here, combining integrative analyses of multiple single-cell RNA-sequencing datasets and functional assays, we reveal that Neurl3-EGFP, in addition to marking the continuum throughout the ontogeny of HSCs from HECs, can also serve as a single enrichment marker for yolk sac HECs. Moreover, while yolk sac HECs have much weaker arterial characteristics than either arterial endothelial cells in the yolk sac or HECs within the embryo proper, the lymphoid potential of yolk sac HECs is largely confined to the arterial-biased subpopulation featured by the Unc5b expression. Interestingly, the B lymphoid potential of hematopoietic progenitors, but not for myeloid potentials, is exclusively detected in Neurl3-negative subpopulations in midgestational embryos. Taken together, these findings enhance our understanding of blood birth from yolk sac HECs and provide theoretical basis and candidate reporters for monitoring step-wise hematopoietic differentiation.

Single-cell RNA-Seq reveals transcriptional regulatory networks directing the development of mouse maxillary prominence

Jian Sun, Yijun Lin, Nayoung Ha, Jianfei Zhang, Weiqi Wang, Xudong Wang, Qian Bian

2023, 50(9): 676-687. doi: 10.1016/j.jgg.2023.02.008

Abstract (339) PDF (22)

Abstract:
During vertebrate embryonic development, neural crest-derived ectomesenchyme within the maxillary prominences undergoes precisely coordinated proliferation and differentiation to give rise to diverse craniofacial structures, such as tooth and palate. However, the transcriptional regulatory networks underpinning such an intricate process have not been fully elucidated. Here, we perform single-cell RNA-Seq to comprehensively characterize the transcriptional dynamics during mouse maxillary development from embryonic day (E) 10.5–E14.5. Our single-cell transcriptome atlas of ~28,000 cells uncovers mesenchymal cell populations representing distinct differentiating states and reveals their developmental trajectory, suggesting that the segregation of dental from the palatal mesenchyme occurs at E11.5. Moreover, we identify a series of key transcription factors (TFs) associated with mesenchymal fate transitions and deduce the gene regulatory networks directed by these TFs. Collectively, our study provides important resources and insights for achieving a systems-level understanding of craniofacial morphogenesis and abnormality.

Microbiota-mediated shaping of mouse spleen structure and immune function characterized by scRNA-seq and Stereo-seq

Yin Zhang, Juan Shen, Wei Cheng, Bhaskar Roy, Ruizhen Zhao, Tailiang Chai, Yifei Sheng, Zhao Zhang, Xueting Chen, Weiming Liang, Weining Hu, Qijun Liao, Shanshan Pan, Wen Zhuang, Yangrui Zhang, Rouxi Chen, Junpu Mei, Hong Wei, Xiaodong Fang

2023, 50(9): 688-701. doi: 10.1016/j.jgg.2023.04.012

Abstract (550) PDF (58)

Abstract:
Gut microbes exhibit complex interactions with their hosts and shape an organism's immune system throughout its lifespan. As the largest secondary lymphoid organ, the spleen has a wide range of immunological functions. To explore the role of microbiota in regulating and shaping the spleen, we employ scRNA-seq and Stereo-seq technologies based on germ-free (GF) mice to detect differences in tissue size, anatomical structure, cell types, functions, and spatial molecular characteristics. We identify 18 cell types, 9 subtypes of T cells, and 7 subtypes of B cells. Gene differential expression analysis reveals that the absence of microorganisms results in alterations in erythropoiesis within the red pulp region and congenital immune deficiency in the white pulp region. Stereo-seq results demonstrate a clear hierarchy of immune cells in the spleen, including marginal zone (MZ) macrophages, MZ B cells, follicular B cells and T cells, distributed in a well-defined pattern from outside to inside. However, this hierarchical structure is disturbed in GF mice. Ccr7 and Cxcl13 chemokines are specifically expressed in the spatial locations of T cells and B cells, respectively. We speculate that the microbiota may mediate the structural composition or partitioning of spleen immune cells by modulating the expression levels of chemokines.

Single-cell transcriptomic analysis identifies a highly replicating Cd168⁺ skeletal stem/progenitor cell population in mouse long bones

Rui-Cong Hao, Zhi-Ling Li, Fei-Yan Wang, Jie Tang, Pei-Lin Li, Bo-Feng Yin, Xiao-Tong Li, Meng-Yue Han, Ning Mao, Bing Liu, Li Ding, Heng Zhu

2023, 50(9): 702-712. doi: 10.1016/j.jgg.2023.04.004

Abstract (500) PDF (24)

Abstract:
Skeletal stem/progenitor cells (SSPCs) are tissue-specific stem/progenitor cells localized within skeletons and contribute to bone development, homeostasis, and regeneration. However, the heterogeneity of SSPC populations in mouse long bones and their respective regenerative capacity remain to be further clarified. In this study, we perform integrated analysis using single-cell RNA sequencing (scRNA-seq) datasets of mouse hindlimb buds, postnatal long bones, and fractured long bones. Our analyses reveal the heterogeneity of osteochondrogenic lineage cells and recapitulate the developmental trajectories during mouse long bone growth. In addition, we identify a novel Cd168⁺ SSPC population with highly replicating capacity and osteochondrogenic potential in embryonic and postnatal long bones. Moreover, the Cd168⁺ SSPCs can contribute to newly formed skeletal tissues during fracture healing. Furthermore, the results of multicolor immunofluorescence show that Cd168⁺ SSPCs reside in the superficial zone of articular cartilage as well as in growth plates of postnatal mouse long bones. In summary, we identify a novel Cd168⁺ SSPC population with regenerative potential in mouse long bones, which adds to the knowledge of the tissue-specific stem cells in skeletons.

VT3D: a visualization toolbox for 3D transcriptomic data

Lidong Guo, Yao Li, Yanwei Qi, Zhi Huang, Kai Han, Xiaobin Liu, Xin Liu, Mengyang Xu, Guangyi Fan

2023, 50(9): 713-719. doi: 10.1016/j.jgg.2023.04.001

Abstract (461) PDF (18)

Abstract:
Data visualization empowers researchers to communicate their results that support scientific reasoning in an intuitive way. Three-dimension (3D) spatially resolved transcriptomic atlases constructed from multi-view and high-dimensional data have rapidly emerged as a powerful tool to unravel spatial gene expression patterns and cell type distribution in biological samples, revolutionizing the understanding of gene regulatory interactions and cell niches. However, limited accessible tools for data visualization impede the potential impact and application of this technology. Here we introduce VT3D, a visualization toolbox that allows users to explore 3D transcriptomic data, enabling gene expression projection to any 2D plane of interest, 2D virtual slice creation and visualization, and interactive 3D data browsing with surface model plots. In addition, it can either work on personal devices in standalone mode or be hosted as a web-based server. We apply VT3D to multiple datasets produced by the most popular techniques, including both sequencing-based approaches (Stereo-seq, spatial transcriptomics, and Slide-seq) and imaging-based approaches (MERFISH and STARMap), and successfully build a 3D atlas database that allows interactive data browsing. We demonstrate that VT3D bridges the gap between researchers and spatially resolved transcriptomics, thus accelerating related studies such as embryogenesis and organogenesis processes. The source code of VT3D is available at https://github.com/BGI-Qingdao/VT3D, and the modeled atlas database is available at http://www.bgiocean.com/vt3d_example.

Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network

Yuying Huo, Yilang Guo, Jiakang Wang, Huijie Xue, Yujuan Feng, Weizheng Chen, Xiangyu Li

2023, 50(9): 720-733. doi: 10.1016/j.jgg.2023.06.005

Abstract (308) PDF (16)

Abstract:
Recent advances in spatially resolved transcriptomic technologies have enabled unprecedented opportunities to elucidate tissue architecture and function in situ. Spatial transcriptomics can provide multimodal and complementary information simultaneously, including gene expression profiles, spatial locations, and histology images. However, most existing methods have limitations in efficiently utilizing spatial information and matched high-resolution histology images. To fully leverage the multi-modal information, we propose a SPAtially embedded Deep Attentional graph Clustering (SpaDAC) method to identify spatial domains while reconstructing denoised gene expression profiles. This method can efficiently learn the low-dimensional embeddings for spatial transcriptomics data by constructing multi-view graph modules to capture both spatial location connectives and morphological connectives. Benchmark results demonstrate that SpaDAC outperforms other algorithms on several recent spatial transcriptomics datasets. SpaDAC is a valuable tool for spatial domain detection, facilitating the comprehension of tissue architecture and cellular microenvironment. The source code of SpaDAC is freely available at Github (https://github.com/huoyuying/SpaDAC.git).

Current Articles 2023, Volume 50, Issue 9

Current Articles
2023, Volume 50, Issue 9