1
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Xu K, Lu Y, Hou S, Liu K, Du Y, Huang M, Feng H, Wu H, Sun X. Detecting anomalous anatomic regions in spatial transcriptomics with STANDS. Nat Commun 2024; 15:8223. [PMID: 39300113 PMCID: PMC11413068 DOI: 10.1038/s41467-024-52445-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 09/06/2024] [Indexed: 09/22/2024] Open
Abstract
Detection and Dissection of Anomalous Tissue Domains (DDATD) from multi-sample spatial transcriptomics (ST) data provides unprecedented opportunities to characterize anomalous tissue domains (ATDs), revealing both population-level and individual-specific pathogenic factors for understanding pathogenic heterogeneities behind diseases. However, no current methods can perform de novo DDATD from ST data, especially in the multi-sample context. Here, we introduce STANDS, an innovative framework based on Generative Adversarial Networks which integrates three core tasks in multi-sample DDATD: detecting, aligning, and subtyping ATDs. STANDS incorporates multimodal-learning, transfer-learning, and style-transfer techniques to effectively address major challenges in multi-sample DDATD, including complications caused by unalignable ATDs, under-utilization of multimodal information, and scarcity of normal ST datasets necessary for comparative analysis. Extensive benchmarks from diverse datasets demonstrate STAND's superiority in identifying both common and individual-specific ATDs and further dissecting them into biologically distinct subdomains. STANDS also provides clues to developing ATDs visually indistinguishable from surrounding normal tissues.
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Affiliation(s)
- Kaichen Xu
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China
| | - Yan Lu
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China
| | - Suyang Hou
- School of Information Engineering, Zhongnan University of Economics and Law, Wuhan, 430073, China
| | - Kainan Liu
- Information Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 510000, China
| | - Yihang Du
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China
| | - Mengqian Huang
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China
| | - Hao Feng
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Hao Wu
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Shenzhen, 518055, China
- Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Science, Shenzhen, 518055, China
| | - Xiaobo Sun
- School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, 430073, China.
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2
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Adema K, Schon MA, Nodine MD, Kohlen W. Lost in space: what single-cell RNA sequencing cannot tell you. TRENDS IN PLANT SCIENCE 2024; 29:1018-1028. [PMID: 38570278 DOI: 10.1016/j.tplants.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/21/2024] [Accepted: 03/11/2024] [Indexed: 04/05/2024]
Abstract
Plant scientists are rapidly integrating single-cell RNA sequencing (scRNA-seq) into their workflows. Maximizing the potential of scRNA-seq requires a proper understanding of the spatiotemporal context of cells. However, positional information is inherently lost during scRNA-seq, limiting its potential to characterize complex biological systems. In this review we highlight how current single-cell analysis pipelines cannot completely recover spatial information, which confounds biological interpretation. Various strategies exist to identify the location of RNA, from classical RNA in situ hybridization to spatial transcriptomics. Herein we discuss the possibility of utilizing this spatial information to supervise single-cell analyses. An integrative approach will maximize the potential of each technology, and lead to insights which go beyond the capability of each individual technology.
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Affiliation(s)
- Kelvin Adema
- Laboratory of Cell and Developmental Biology, Cluster of Plant Developmental Biology, Department of Plant Sciences, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Michael A Schon
- Laboratory of Cell and Developmental Biology, Cluster of Plant Developmental Biology, Department of Plant Sciences, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands; Laboratory of Molecular Biology, Cluster of Plant Developmental Biology, Department of Plant Sciences, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Michael D Nodine
- Laboratory of Molecular Biology, Cluster of Plant Developmental Biology, Department of Plant Sciences, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands
| | - Wouter Kohlen
- Laboratory of Cell and Developmental Biology, Cluster of Plant Developmental Biology, Department of Plant Sciences, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands; Laboratory of Molecular Biology, Cluster of Plant Developmental Biology, Department of Plant Sciences, Wageningen University, Droevendaalsesteeg 1, 6708 PB, Wageningen, The Netherlands.
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3
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Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell 2024; 187:4488-4519. [PMID: 39178830 DOI: 10.1016/j.cell.2024.07.040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 07/05/2024] [Accepted: 07/23/2024] [Indexed: 08/26/2024]
Abstract
The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.
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Affiliation(s)
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | | | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China.
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4
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Sui Z, Li Z, Sun W. Exploit Spatially Resolved Transcriptomic Data to Infer Cellular Features from Pathology Imaging Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.05.606654. [PMID: 39149252 PMCID: PMC11326158 DOI: 10.1101/2024.08.05.606654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Digital pathology is a rapidly advancing field where deep learning methods can be employed to extract meaningful imaging features. However, the efficacy of training deep learning models is often hindered by the scarcity of annotated pathology images, particularly images with detailed annotations for small image patches or tiles. To overcome this challenge, we propose an innovative approach that leverages paired spatially resolved transcriptomic data to annotate pathology images. We demonstrate the feasibility of this approach and introduce a novel transfer-learning neural network model, STpath (Spatial Transcriptomics and pathology images), designed to predict cell type proportions or classify tumor microenvironments. Our findings reveal that the features from pre-trained deep learning models are associated with cell type identities in pathology image patches. Evaluating STpath using three distinct breast cancer datasets, we observe its promising performance despite the limited training data. STpath excels in samples with variable cell type proportions and high-resolution pathology images. As the influx of spatially resolved transcriptomic data continues, we anticipate ongoing updates to STpath, evolving it into an invaluable AI tool for assisting pathologists in various diagnostic tasks.
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Affiliation(s)
- Zhining Sui
- Department of Biostatistics and Computational Biology, University of Rochester, 265 Crittenden Blvd. Rochester, 14642, NY, USA
| | - Ziyi Li
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 7007 Bertner Avenue, 77030, TX, USA
| | - Wei Sun
- Biostatistics Program, Fred Hutchinson Cancer Center, 1100 Fairview Ave N, 98109, WA, USA
- Department of Biostatistics, University of Washington, 3980 15th Avenue NE, 98195, WA, USA
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5
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Lian X, Zhong L, Bai Y, Guang X, Tang S, Guo X, Wei T, Yang F, Zhang Y, Huang G, Zhang J, Shao L, Lei G, Li Z, Sahu SK, Zhang S, Liu H, Hu F. Spatiotemporal transcriptomic atlas of rhizome formation in Oryza longistaminata. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:1652-1668. [PMID: 38345936 PMCID: PMC11123419 DOI: 10.1111/pbi.14294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 01/09/2024] [Accepted: 01/10/2024] [Indexed: 02/22/2024]
Abstract
Rhizomes are modified stems that grow underground and produce new individuals genetically identical to the mother plant. Recently, a breakthrough has been made in efforts to convert annual grains into perennial ones by utilizing wild rhizomatous species as donors, yet the developmental biology of this organ is rarely studied. Oryza longistaminata, a wild rice species featuring strong rhizomes, provides a valuable model for exploration of rhizome development. Here, we first assembled a double-haplotype genome of O. longistaminata, which displays a 48-fold improvement in contiguity compared to the previously published assembly. Furthermore, spatiotemporal transcriptomics was performed to obtain the expression profiles of different tissues in O. longistaminata rhizomes and tillers. Two spatially reciprocal cell clusters, the vascular bundle 2 cluster and the parenchyma 2 cluster, were determined to be the primary distinctions between the rhizomes and tillers. We also captured meristem initiation cells in the sunken area of parenchyma located at the base of internodes, which is the starting point for rhizome initiation. Trajectory analysis further indicated that the rhizome is regenerated through de novo generation. Collectively, these analyses revealed a spatiotemporal transcriptional transition underlying the rhizome initiation, providing a valuable resource for future perennial crop breeding.
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Affiliation(s)
- Xiaoping Lian
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Liyuan Zhong
- State Key Laboratory of Agricultural GenomicsBGI‐ShenzhenShenzhenGuangdongChina
| | - Yixuan Bai
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Xuanmin Guang
- State Key Laboratory of Agricultural GenomicsBGI‐ShenzhenShenzhenGuangdongChina
| | - Sijia Tang
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Xing Guo
- State Key Laboratory of Agricultural GenomicsBGI‐ShenzhenShenzhenGuangdongChina
| | - Tong Wei
- State Key Laboratory of Agricultural GenomicsBGI‐ShenzhenShenzhenGuangdongChina
| | - Feng Yang
- State Key Laboratory of Agricultural GenomicsBGI‐ShenzhenShenzhenGuangdongChina
| | - Yujiao Zhang
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Guangfu Huang
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Jing Zhang
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Lin Shao
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Guijie Lei
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Zheng Li
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Sunil Kumar Sahu
- State Key Laboratory of Agricultural GenomicsBGI‐ShenzhenShenzhenGuangdongChina
| | - Shilai Zhang
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
| | - Huan Liu
- State Key Laboratory of Agricultural GenomicsBGI‐ShenzhenShenzhenGuangdongChina
| | - Fengyi Hu
- New Cornerstone Science Laboratory, State Key Laboratory for Conservation and Utilization of Bio‐Resources in Yunnan, Key Laboratory of Biology and Germplasm Innovation of Perennial rice (Co‐construction by Ministry and Province) of Ministry of Agriculture and Rural Affairs, Center of Innovation for Perennial Rice Technology in Yunnan, School of AgricultureYunnan UniversityKunmingChina
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6
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Zhang L, Xiong Z, Xiao M. A Review of the Application of Spatial Transcriptomics in Neuroscience. Interdiscip Sci 2024; 16:243-260. [PMID: 38374297 DOI: 10.1007/s12539-024-00603-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 12/29/2023] [Accepted: 01/02/2024] [Indexed: 02/21/2024]
Abstract
Since spatial transcriptomics can locate and distinguish the gene expression of functional genes in special regions and tissue, it is important for us to investigate the brain development, the development mechanism of brain diseases, and the relationship between brain structure and function in Neuroscience (or Brain science). While previous studies have introduced the crucial spatial transcriptomic techniques and data analysis methods, there are few studies to comprehensively overview the key methods, data resources, and technological applications of spatial transcriptomics in Neuroscience. For these reasons, we first investigate several common spatial transcriptomic data analysis approaches and data resources. Second, we introduce the applications of the spatial transcriptomic data analysis approaches in Neuroscience. Third, we summarize the integrating spatial transcriptomics with other technologies in Neuroscience. Finally, we discuss the challenges and future research directions of spatial transcriptomics in Neuroscience.
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Affiliation(s)
- Le Zhang
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Zhenqi Xiong
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Ming Xiao
- College of Computer Science, Sichuan University, Chengdu, 610065, China.
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7
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Tao Q, Xu Y, He Y, Luo T, Li X, Han L. Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data. Brief Bioinform 2024; 25:bbae250. [PMID: 38796691 PMCID: PMC11128029 DOI: 10.1093/bib/bbae250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/17/2024] [Accepted: 05/08/2024] [Indexed: 05/28/2024] Open
Abstract
Limited gene capture efficiency and spot size of spatial transcriptome (ST) data pose significant challenges in cell-type characterization. The heterogeneity and complexity of cell composition in the mammalian brain make it more challenging to accurately annotate ST data from brain. Many algorithms attempt to characterize subtypes of neuron by integrating ST data with single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing. However, assessing the accuracy of these algorithms on Stereo-seq ST data remains unresolved. Here, we benchmarked 9 mapping algorithms using 10 ST datasets from four mouse brain regions in two different resolutions and 24 pseudo-ST datasets from snRNA-seq. Both actual ST data and pseudo-ST data were mapped using snRNA-seq datasets from the corresponding brain regions as reference data. After comparing the performance across different areas and resolutions of the mouse brain, we have reached the conclusion that both robust cell-type decomposition and SpatialDWLS demonstrated superior robustness and accuracy in cell-type annotation. Testing with publicly available snRNA-seq data from another sequencing platform in the cortex region further validated our conclusions. Altogether, we developed a workflow for assessing suitability of mapping algorithm that fits for ST datasets, which can improve the efficiency and accuracy of spatial data annotation.
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Affiliation(s)
- Quyuan Tao
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Hangzhou 310012, China
| | - Yiheng Xu
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Center of Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Youzhe He
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Hangzhou 310012, China
| | - Ting Luo
- BGI Research, Hangzhou 310012, China
- BGI Research, Shenzhen 518103, China
| | - Xiaoming Li
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Center of Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
- Research Units for Emotion and Emotion disorders, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Lei Han
- BGI Research, Hangzhou 310012, China
- BGI Research, Shenzhen 518103, China
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8
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Lei L, Han K, Wang Z, Shi C, Wang Z, Dai R, Zhang Z, Wang M, Guo Q. Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics. Brief Bioinform 2024; 25:bbae173. [PMID: 38627939 PMCID: PMC11021349 DOI: 10.1093/bib/bbae173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 03/03/2024] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.
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Affiliation(s)
- Lixin Lei
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Kaitai Han
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zijun Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Chaojing Shi
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zhenghui Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Ruoyan Dai
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Zhiwei Zhang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Mengqiu Wang
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
| | - Qianjin Guo
- Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China
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9
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Zhai Y, Chen L, Deng M. scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data. Brief Bioinform 2024; 25:bbae188. [PMID: 38678389 PMCID: PMC11056022 DOI: 10.1093/bib/bbae188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/11/2024] [Accepted: 04/14/2024] [Indexed: 04/30/2024] Open
Abstract
MOTIVATION Over the past decade, single-cell transcriptomic technologies have experienced remarkable advancements, enabling the simultaneous profiling of gene expressions across thousands of individual cells. Cell type identification plays an essential role in exploring tissue heterogeneity and characterizing cell state differences. With more and more well-annotated reference data becoming available, massive automatic identification methods have sprung up to simplify the annotation process on unlabeled target data by transferring the cell type knowledge. However, in practice, the target data often include some novel cell types that are not in the reference data. Most existing works usually classify these private cells as one generic 'unassigned' group and learn the features of known and novel cell types in a coupled way. They are susceptible to the potential batch effects and fail to explore the fine-grained semantic knowledge of novel cell types, thus hurting the model's discrimination ability. Additionally, emerging spatial transcriptomic technologies, such as in situ hybridization, sequencing and multiplexed imaging, present a novel challenge to current cell type identification strategies that predominantly neglect spatial organization. Consequently, it is imperative to develop a versatile method that can proficiently annotate single-cell transcriptomics data, encompassing both spatial and non-spatial dimensions. RESULTS To address these issues, we propose a new, challenging yet realistic task called universal cell type identification for single-cell and spatial transcriptomics data. In this task, we aim to give semantic labels to target cells from known cell types and cluster labels to those from novel ones. To tackle this problem, instead of designing a suboptimal two-stage approach, we propose an end-to-end algorithm called scBOL from the perspective of Bipartite prototype alignment. Firstly, we identify the mutual nearest clusters in reference and target data as their potential common cell types. On this basis, we mine the cycle-consistent semantic anchor cells to build the intrinsic structure association between two data. Secondly, we design a neighbor-aware prototypical learning paradigm to strengthen the inter-cluster separability and intra-cluster compactness within each data, thereby inspiring the discriminative feature representations. Thirdly, driven by the semantic-aware prototypical learning framework, we can align the known cell types and separate the private cell types from them among reference and target data. Such an algorithm can be seamlessly applied to various data types modeled by different foundation models that can generate the embedding features for cells. Specifically, for non-spatial single-cell transcriptomics data, we use the autoencoder neural network to learn latent low-dimensional cell representations, and for spatial single-cell transcriptomics data, we apply the graph convolution network to capture molecular and spatial similarities of cells jointly. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scBOL over various state-of-the-art cell type identification methods. To our knowledge, we are the pioneers in presenting this pragmatic annotation task, as well as in devising a comprehensive algorithmic framework aimed at resolving this challenge across varied types of single-cell data. Finally, scBOL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scBOL.
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Affiliation(s)
- Yuyao Zhai
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Liang Chen
- Huawei Technologies Co., Ltd., Beijing, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, China
- Center for Statistical Science, Peking University, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing, China
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10
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Wang L, Hu Y, Gao L. Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics. Brief Bioinform 2024; 25:bbae063. [PMID: 38426323 PMCID: PMC10939420 DOI: 10.1093/bib/bbae063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/10/2024] [Accepted: 02/01/2024] [Indexed: 03/02/2024] Open
Abstract
Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.
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Affiliation(s)
- Lanying Wang
- School of Computer Science and Technology, Xidian University, Xi’an 710100, China
| | - Yuxuan Hu
- School of Computer Science and Technology, Xidian University, Xi’an 710100, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi’an 710100, China
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11
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Yuan Z. MENDER: fast and scalable tissue structure identification in spatial omics data. Nat Commun 2024; 15:207. [PMID: 38182575 PMCID: PMC10770058 DOI: 10.1038/s41467-023-44367-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 12/11/2023] [Indexed: 01/07/2024] Open
Abstract
Tissue structure identification is a crucial task in spatial omics data analysis, for which increasingly complex models, such as Graph Neural Networks and Bayesian networks, are employed. However, whether increased model complexity can effectively lead to improved performance is a notable question in the field. Inspired by the consistent observation of cellular neighborhood structures across various spatial technologies, we propose Multi-range cEll coNtext DEciphereR (MENDER), for tissue structure identification. Applied on datasets of 3 brain regions and a whole-brain atlas, MENDER, with biology-driven design, offers substantial improvements over modern complex models while automatically aligning labels across slices, despite using much less running time than the second-fastest. MENDER's identification power allows the uncovering of previously overlooked spatial domains that exhibit strong associations with brain aging. MENDER's scalability makes it freely appliable on a million-level brain spatial atlas. MENDER's discriminative power enables the differentiation of breast cancer patient subtypes obscured by single-cell analysis.
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, 200433, China.
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12
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Zhang C, Liu L, Zhang Y, Li M, Fang S, Kang Q, Chen A, Xu X, Zhang Y, Li Y. spatiAlign: an unsupervised contrastive learning model for data integration of spatially resolved transcriptomics. Gigascience 2024; 13:giae042. [PMID: 39028588 PMCID: PMC11258913 DOI: 10.1093/gigascience/giae042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/08/2024] [Accepted: 06/21/2024] [Indexed: 07/21/2024] Open
Abstract
BACKGROUND Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. FINDINGS We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. CONCLUSIONS In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.
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Affiliation(s)
| | - Lin Liu
- BGI Research, Shenzhen 518083, China
| | | | - Mei Li
- BGI Research, Shenzhen 518083, China
| | - Shuangsang Fang
- BGI Research, Shenzhen 518083, China
- BGI Research, Beijing 102601, China
| | | | - Ao Chen
- BGI Research, Shenzhen 518083, China
- BGI Research, Chongqing 401329, China
| | - Xun Xu
- BGI Research, Wuhan 430074, China
| | - Yong Zhang
- BGI Research, Shenzhen 518083, China
- BGI Research, Wuhan 430074, China
- Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen 518083, China
| | - Yuxiang Li
- BGI Research, Shenzhen 518083, China
- BGI Research, Wuhan 430074, China
- Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen 518083, China
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13
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Lv T, Zhang Y, Li M, Kang Q, Fang S, Zhang Y, Brix S, Xu X. EAGS: efficient and adaptive Gaussian smoothing applied to high-resolved spatial transcriptomics. Gigascience 2024; 13:giad097. [PMID: 38373746 PMCID: PMC10939424 DOI: 10.1093/gigascience/giad097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 09/12/2023] [Accepted: 10/13/2023] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND The emergence of high-resolved spatial transcriptomics (ST) has facilitated the research of novel methods to investigate biological development, organism growth, and other complex biological processes. However, high-resolved and whole transcriptomics ST datasets require customized imputation methods to improve the signal-to-noise ratio and the data quality. FINDINGS We propose an efficient and adaptive Gaussian smoothing (EAGS) imputation method for high-resolved ST. The adaptive 2-factor smoothing of EAGS creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, and then utilizes the weights to restore the gene expression profiles. We assessed the performance and efficiency of EAGS using simulated and high-resolved ST datasets of mouse brain and olfactory bulb. CONCLUSIONS Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretations, and significantly reduced computational consumption.
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Affiliation(s)
- Tongxuan Lv
- BGI Research, Shenzhen 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Mei Li
- BGI Research, Shenzhen 518083, China
- Department of Biotechnology and Biomedicine, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | | | - Shuangsang Fang
- BGI Research, Shenzhen 518083, China
- BGI Research, Beijing 102601, China
| | | | | | - Xun Xu
- BGI Research, Shenzhen 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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14
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Cao L, Yang C, Hu L, Jiang W, Ren Y, Xia T, Xu M, Ji Y, Li M, Xu X, Li Y, Zhang Y, Fang S. Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder. Gigascience 2024; 13:giae003. [PMID: 38373745 PMCID: PMC10939418 DOI: 10.1093/gigascience/giae003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/26/2023] [Accepted: 01/10/2024] [Indexed: 02/21/2024] Open
Abstract
BACKGROUND Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance. RESULTS To address this issue, we proposed SGAE, a novel framework for spatial domain identification, incorporating the power of the Siamese graph autoencoder. SGAE mitigates the information correlation at both sample and feature levels, thus improving the representation discrimination. We adapted this framework to ST analysis by constructing a graph based on both gene expression and spatial information. SGAE outperformed alternative methods by its effectiveness in capturing spatial patterns and generating high-quality clusters, as evaluated by the Adjusted Rand Index, Normalized Mutual Information, and Fowlkes-Mallows Index. Moreover, the clustering results derived from SGAE can be further utilized in the identification of 3-dimensional (3D) Drosophila embryonic structure with enhanced accuracy. CONCLUSIONS Benchmarking results from various ST datasets generated by diverse platforms demonstrate compelling evidence for the effectiveness of SGAE against other ST clustering methods. Specifically, SGAE exhibits potential for extension and application on multislice 3D reconstruction and tissue structure investigation. The source code and a collection of spatial clustering results can be accessed at https://github.com/STOmics/SGAE/.
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Affiliation(s)
- Lei Cao
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Chao Yang
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Luni Hu
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Wenjian Jiang
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Yating Ren
- School of Software, Beihang University, Beijing 100191, China
| | - Tianyi Xia
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
| | - Mengyang Xu
- BGI Research, Shenzhen 518083, China
- BGI Research, Qingdao 266555, China
| | | | - Mei Li
- BGI Research, Shenzhen 518083, China
| | - Xun Xu
- BGI Research, Wuhan 430074, China
| | - Yuxiang Li
- BGI Research, Shenzhen 518083, China
- BGI Research, Wuhan 430074, China
- Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen 518083, China
| | - Yong Zhang
- BGI Research, Shenzhen 518083, China
- BGI Research, Wuhan 430074, China
- Guangdong Bigdata Engineering Technology Research Center for Life Sciences, BGI Research, Shenzhen 518083, China
| | - Shuangsang Fang
- BGI Research, Beijing 102601, China
- BGI Research, Shenzhen 518083, China
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15
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Zhou R, Yang G, Zhang Y, Wang Y. Spatial transcriptomics in development and disease. MOLECULAR BIOMEDICINE 2023; 4:32. [PMID: 37806992 PMCID: PMC10560656 DOI: 10.1186/s43556-023-00144-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/29/2023] [Indexed: 10/10/2023] Open
Abstract
The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.
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Affiliation(s)
- Ran Zhou
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Gaoxia Yang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yan Zhang
- National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Yuan Wang
- Department of Neurosurgery, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China.
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16
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Chen A, Sun Y, Lei Y, Li C, Liao S, Meng J, Bai Y, Liu Z, Liang Z, Zhu Z, Yuan N, Yang H, Wu Z, Lin F, Wang K, Li M, Zhang S, Yang M, Fei T, Zhuang Z, Huang Y, Zhang Y, Xu Y, Cui L, Zhang R, Han L, Sun X, Chen B, Li W, Huangfu B, Ma K, Ma J, Li Z, Lin Y, Wang H, Zhong Y, Zhang H, Yu Q, Wang Y, Liu X, Peng J, Liu C, Chen W, Pan W, An Y, Xia S, Lu Y, Wang M, Song X, Liu S, Wang Z, Gong C, Huang X, Yuan Y, Zhao Y, Chai Q, Tan X, Liu J, Zheng M, Li S, Huang Y, Hong Y, Huang Z, Li M, Jin M, Li Y, Zhang H, Sun S, Gao L, Bai Y, Cheng M, Hu G, Liu S, Wang B, Xiang B, Li S, Li H, Chen M, Wang S, Li M, Liu W, Liu X, Zhao Q, Lisby M, Wang J, Fang J, Lin Y, Xie Q, Liu Z, He J, Xu H, Huang W, Mulder J, Yang H, Sun Y, Uhlen M, Poo M, Wang J, Yao J, Wei W, Li Y, Shen Z, Liu L, Liu Z, Xu X, Li C. Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex. Cell 2023; 186:3726-3743.e24. [PMID: 37442136 DOI: 10.1016/j.cell.2023.06.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 02/24/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023]
Abstract
Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.
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Affiliation(s)
- Ao Chen
- BGI-Shenzhen, Shenzhen 518103, China; Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark; BGI Research-Southwest, BGI, Chongqing 401329, China; JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing 401329, China
| | - Yidi Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Ying Lei
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China
| | - Chao Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Sha Liao
- BGI-Shenzhen, Shenzhen 518103, China; BGI Research-Southwest, BGI, Chongqing 401329, China; JFL-BGI STOmics Center, Jinfeng Laboratory, Chongqing 401329, China
| | - Juan Meng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yiqin Bai
- Lingang Laboratory, Shanghai 200031, China
| | - Zhen Liu
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhifeng Liang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Nini Yuan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hao Yang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zihan Wu
- Tencent AI Lab, Shenzhen 518057, China
| | - Feng Lin
- BGI-Shenzhen, Shenzhen 518103, China
| | - Kexin Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mei Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Shuzhen Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Tianyi Fei
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhenkun Zhuang
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yiming Huang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yong Zhang
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Yuanfang Xu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Luman Cui
- BGI-Shenzhen, Shenzhen 518103, China
| | - Ruiyi Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Lei Han
- BGI-Shenzhen, Shenzhen 518103, China
| | - Xing Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | | | - Baoqian Huangfu
- BGI-Shenzhen, Shenzhen 518103, China; School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | | | - Jianyun Ma
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhao Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yikun Lin
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - He Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanqing Zhong
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huifang Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qian Yu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yaqian Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xing Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | - Jian Peng
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Wei Chen
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Yingjie An
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shihui Xia
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yanbing Lu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingli Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xinxiang Song
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuai Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Chun Gong
- BGI-Shenzhen, Shenzhen 518103, China; China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Xin Huang
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yue Yuan
- BGI-Shenzhen, Shenzhen 518103, China
| | - Yun Zhao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Qinwen Chai
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xing Tan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jianfeng Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mingyuan Zheng
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shengkang Li
- BGI-Shenzhen, Shenzhen 518103, China; Guangdong Bigdata Engineering Technology Research Center for Life Sciences, Shenzhen 518083, China
| | | | - Yan Hong
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Min Li
- BGI-Shenzhen, Shenzhen 518103, China
| | - Mengmeng Jin
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yan Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Hui Zhang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Suhong Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Gao
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yinqi Bai
- BGI-Shenzhen, Shenzhen 518103, China
| | | | - Guohai Hu
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Shiping Liu
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China
| | - Bo Wang
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Bin Xiang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shuting Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huanhuan Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mengni Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shiwen Wang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Minglong Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Xin Liu
- BGI-Shenzhen, Shenzhen 518103, China
| | - Qian Zhao
- BGI-Shenzhen, Shenzhen 518103, China
| | - Michael Lisby
- Department of Biology, University of Copenhagen, Copenhagen 2200, Denmark
| | - Jing Wang
- BGI-Shenzhen, Shenzhen 518103, China
| | - Jiao Fang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yun Lin
- BGI-Shenzhen, Shenzhen 518103, China
| | - Qing Xie
- BGI-Shenzhen, Shenzhen 518103, China
| | - Zhen Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Jie He
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huatai Xu
- Lingang Laboratory, Shanghai 200031, China
| | - Wei Huang
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jan Mulder
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17121, Sweden; Department of Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
| | | | - Yangang Sun
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Mathias Uhlen
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, Stockholm 17121, Sweden; Department of Neuroscience, Karolinska Institute, Stockholm 17177, Sweden
| | - Muming Poo
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen 518103, China; China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | | | - Wu Wei
- Lingang Laboratory, Shanghai 200031, China; CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
| | - Yuxiang Li
- BGI-Shenzhen, Shenzhen 518103, China; BGI Research-Wuhan, BGI, Wuhan 430074, China.
| | - Zhiming Shen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China.
| | - Longqi Liu
- BGI-Shenzhen, Shenzhen 518103, China; BGI-Hangzhou, Hangzhou 310012, China.
| | - Zhiyong Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China.
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518103, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen 518120, China.
| | - Chengyu Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China; School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China.
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Xie B, Gao D, Zhou B, Chen S, Wang L. New discoveries in the field of metabolism by applying single-cell and spatial omics. J Pharm Anal 2023; 13:711-725. [PMID: 37577385 PMCID: PMC10422156 DOI: 10.1016/j.jpha.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 08/15/2023] Open
Abstract
Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.
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Affiliation(s)
- Baocai Xie
- Department of Critical Care Medicine, Shenzhen Institute of Translational Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, Guangdong, 518060, China
- Department of Respiratory Diseases, The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450014, China
| | - Dengfeng Gao
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Biqiang Zhou
- Department of Geriatric & Spinal Pain Multi-Department Treatment, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen, Guangdong, 518035, China
| | - Shi Chen
- Department of Critical Care Medicine, Shenzhen Institute of Translational Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, Guangdong, 518060, China
- Department of Gastroenterology, Ministry of Education Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China
| | - Lianrong Wang
- Department of Respiratory Diseases, The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450014, China
- Department of Gastroenterology, Ministry of Education Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China
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Castillo SP, Galvez-Cancino F, Liu J, Pollard SM, Quezada SA, Yuan Y. The tumour ecology of quiescence: Niches across scales of complexity. Semin Cancer Biol 2023; 92:139-149. [PMID: 37037400 DOI: 10.1016/j.semcancer.2023.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 03/06/2023] [Accepted: 04/08/2023] [Indexed: 04/12/2023]
Abstract
Quiescence is a state of cell cycle arrest, allowing cancer cells to evade anti-proliferative cancer therapies. Quiescent cancer stem cells are thought to be responsible for treatment resistance in glioblastoma, an aggressive brain cancer with poor patient outcomes. However, the regulation of quiescence in glioblastoma cells involves a myriad of intrinsic and extrinsic mechanisms that are not fully understood. In this review, we synthesise the literature on quiescence regulatory mechanisms in the context of glioblastoma and propose an ecological perspective to stemness-like phenotypes anchored to the contemporary concepts of niche theory. From this perspective, the cell cycle regulation is multiscale and multidimensional, where the niche dimensions extend to extrinsic variables in the tumour microenvironment that shape cell fate. Within this conceptual framework and powered by ecological niche modelling, the discovery of microenvironmental variables related to hypoxia and mechanosignalling that modulate proliferative plasticity and intratumor immune activity may open new avenues for therapeutic targeting of emerging biological vulnerabilities in glioblastoma.
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Affiliation(s)
- Simon P Castillo
- Centre for Evolution and Cancer & Division of Molecular Pathology, The Institute of Cancer Research, London SM2 5NG, UK
| | - Felipe Galvez-Cancino
- Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London WC1E 6DD, UK
| | - Jiali Liu
- Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London WC1E 6DD, UK
| | - Steven M Pollard
- Centre for Regenerative Medicine and Cancer Research UK Scotland Centre, Institute for Regeneration and Repair, University of Edinburgh, Edinburgh EH16 4UU, UK
| | - Sergio A Quezada
- Immune Regulation and Tumor Immunotherapy Group, Cancer Immunology Unit, Research Department of Haematology, UCL Cancer Institute, London WC1E 6DD, UK
| | - Yinyin Yuan
- Centre for Evolution and Cancer & Division of Molecular Pathology, The Institute of Cancer Research, London SM2 5NG, UK.
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Mori T, Takase T, Lan KC, Yamane J, Alev C, Kimura A, Osafune K, Yamashita JK, Akutsu T, Kitano H, Fujibuchi W. eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio-temporal architectures of cells. BMC Bioinformatics 2023; 24:252. [PMID: 37322439 PMCID: PMC10268514 DOI: 10.1186/s12859-023-05355-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/25/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND Bioinformatics capability to analyze spatio-temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. RESULTS This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio-temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio-temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate 'temporal' discriminator genes responsible for various cell type differentiations. CONCLUSIONS eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio-temporal formation of cellular organizations.
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Affiliation(s)
- Tomoya Mori
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan
| | - Toshiro Takase
- Life Sciences, IBM Consulting, IBM Japan Ltd., 19-21 Nihonbashi Hakozaki-cho , Chuo-ku, Tokyo, 103-8510, Japan
| | - Kuan-Chun Lan
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Junko Yamane
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Cantas Alev
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Azuma Kimura
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Kenji Osafune
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Jun K Yamashita
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan
| | - Hiroaki Kitano
- The Systems Biology Institute, Tokyo, Japan
- Okinawa Institute of Science and Technology Graduate School, Okinawa, Japan
- Sony Computer Science Laboratories, Inc., Tokyo, Japan
- Sony AI, Inc., Tokyo, Japan
- The Alan Turing Institute, London, UK
| | - Wataru Fujibuchi
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan.
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20
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Lee AJ, Cahill R, Abbasi-Asl R. Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data. ARXIV 2023:arXiv:2303.16725v1. [PMID: 37033464 PMCID: PMC10081350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed immunofluorescence and spatial transcriptomics (ST) provide exciting new opportunities to augment our fundamental understanding of these processes in health and disease. The large and complex datasets resulting from these techniques, particularly ST, have led to rapid development of innovative machine learning (ML) tools primarily based on deep learning techniques. These ML tools are now increasingly featured in integrated experimental and computational workflows to disentangle signals from noise in complex biological systems. However, it can be difficult to understand and balance the different implicit assumptions and methodologies of a rapidly expanding toolbox of analytical tools in ST. To address this, we summarize major ST analysis goals that ML can help address and current analysis trends. We also describe four major data science concepts and related heuristics that can help guide practitioners in their choices of the right tools for the right biological questions.
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