Conference Background Spatial transcriptomics is the annual method selected by Nature methods in 2020. It is a new direction in the current field of single cell omics research and has broad application prospects in many clinical fields such as oncology, pathology, immunology, and

Conference Background

Spatial transcriptomics is the annual method selected by Nature Methods in 2020. It is a new direction in the current field of single cell omics research and has been used in many clinical applications such as oncology, pathology, immunology, developmental biology and so on. The field has broad application prospects.

In recent years, researchers have developed a variety of spatial transcriptome technologies, which can obtain omics information such as gene expression characteristics and epigenetic regulation while retaining the spatial location of tissues. This adds space to traditional sequencing technologies. The information dimension allows researchers to analyze biological information from a more refined perspective.

However, there are two shortcomings in current spatial transcriptome technology. First, sequencing-based spatial transcriptome technology cannot achieve true single-cell resolution; second, the gene throughput that can be detected by imaging spatial transcriptome technology is limited.

In order to break through the limitations of technology, bioinformaticians have designed a variety of algorithms to integrate spatial transcriptome and single-cell transcriptome data to predict the spatial distribution of cell types and/or the complete transcriptome information of a single cell. These algorithms have greatly enhanced our understanding of spatial transcriptomic data and related biological and pathological processes.

However, due to significant differences in the working principles and applicable scope of different algorithms , it is difficult for researchers to choose the best algorithm to predict the spatial distribution of cell types and gene expression. To this end, the Hanyin Life Team collected 45 pairs of spatial transcriptome and single-cell transcriptome data sets from the same tissue source, and 32 simulated data sets, and designed a variety of indicators, ranging from accuracy, robustness, calculation The performance of 16 integration algorithms was systematically evaluated from multiple dimensions such as resource consumption and time consumption to solve the problem of difficult idling analysis. Relevant results were recently published in Nature Methods.

In order to better understand spatial transcriptomics technology, analysis algorithms and related scientific research applications, we are honored to invite 10x Genomics China Senior Marketing Manager Zhao Linlin , Researcher at the Institute of Zoology, Chinese Academy of Sciences Professor Liu Feng , Professor Zhang Shihua, a researcher at the Institute of Mathematics and Systems Science, Chinese Academy of Sciences, Professor Wang Chenfei, a doctoral supervisor at the School of Life Sciences and Technology, Tongji University, and Dr. Guo Chuang, VP of Hanyin Life Research and Development, shared spatial multi-omics technology products with everyone. and application progress, spatiotemporal analysis of vertebrate hematopoietic stem cell expansion, transcriptomics of smart space, computational reconstruction of single cells and spatial multi-omics, and opportunities for spatial omics research.

Expert profile

Ms. Zhao Linlin

has been engaged in life sciences and translational medicine related work for many years, and has rich experience in single cell multi-omics, spatial multi-omics, in situ technology and next-generation sequencing industries. experience of. Joined 10x Genomics in September 2018 and currently serves as Senior Marketing Manager in China.

Professor Liu Feng

Institute of Zoology, Chinese Academy of Sciences

Researcher and doctoral supervisor at the Institute of Zoology, Chinese Academy of Sciences

Winner of "National Science Fund for Distinguished Young Scholars"

Ministry of Science and Technology of the People's Republic of China"Young and Middle-aged Leading Talents in Scientific and Technological Innovation"

Winner of the " Special Government Allowance " of the State Council of the People's Republic of China

Winner of the "Tan Jiazhen Life Science Innovation Award"

Chinese Academy of Sciences Excellent Mentor Award

On August 2, 2021, Liu Feng's team at the Institute of Zoology, Chinese Academy of Sciences and Peking University School of Life Sciences Li Cheng's team collaborated and published a research paper titled Identification of HSC/MPP expansion units in fetal liver by single- Cell spatiotemporal transcriptomics in Cell Research magazine. This paper uses the method of joint analysis of single-cell transcriptome and spatial transcriptome to draw a single-cell spatiotemporal transcriptome map of mouse fetal liver development and analyze the transcriptome and functional heterogeneity of hematopoietic stem/progenitor cells. The functional units of hematopoietic stem cell expansion (HSC'pocket-like' units, HSC PLUS) were identified and their molecular mechanisms were revealed.

Zhang Shihua Professor

Institute of Mathematics and Systems Science, Chinese Academy of Sciences

Researcher at the Institute of Mathematics and Systems Science, Chinese Academy of Sciences, Deputy Director of the Key Laboratory of Stochastic Complex Structures and Data Science, Chinese Academy of Sciences, Position professor of at the University of Chinese Academy of Sciences. He is mainly engaged in cross-research on bioinformatics computing, machine intelligence and optimization. His main results have been published in Cell, Nature Communications, Advanced Science, Cell Reports, National Science Review, Science Bulletin, Nucleic Acids Research, IEEE TPAMI, IEEE TKDE, IEEE TNNLS and other magazines. He has won the National 100 Outstanding Doctoral Dissertations Award (2010), China Youth Science and Technology Award (2013), Chinese Academy of Sciences Lu Jiaxi Young Talent Award (2013), National Natural Science Foundation Outstanding Youth Fund (2014), etc. The results were selected into the Top Ten Progress of Bioinformatics and in China in 2021 and the Top Ten Algorithms and Tools in China's Bioinformatics in 2019.

On April 1, 2022, Zhang Shihua’s research group at the Institute of Mathematics and Systems Science, Chinese Academy of Sciences published a research paper titled “Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder” in Nature Communications, targeting different spatial transcriptions. Group technology and different biological tissues establish a new tool to decipher the spatial substructure of biological tissues - STAGATE.

Wang Chenfei Professor

School of Life Science and Technology, Tongji University

Professor and doctoral supervisor at the School of Life Science and Technology, Tongji University, winner of Wu Rui Scholarship, Tongji University Pursuit of Excellence Award, Postdoctoral Innovation Talent Support Program , Shanghai Science and Technology Morning Star and other honors. Hosted a number of national-level scientific research projects, including the National Natural Science Youth Fund, the general project , etc.

On March 7, 2022, Wang Chenfei’s research group at Tongji University published an article STRIDE: accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing in the journal Nucleic Acids Research. They developed the spatial transcriptome and single-cell integrated analysis tool STRIDE, which uses machines Learning methods and data integration improve spatial transcriptomic data to single-cell accuracy.

Guo Chuang PhD

Hanyin Life Research and Development VP

University of Science and Technology of China PhD in immunology, associate researcher at University of Science and Technology of China. On May 16, 2022, a research team led by Hanin Life's founder and chief scientist, Professor Qu Kun and Dr. Guo Chuang of the Department of Life Sciences and Medicine, University of Science and Technology of China, designed a complete set of analysis processes to systematically evaluate 16 The performance of this spatial transcriptome and single-cell transcriptome data integration algorithm in predicting the spatial distribution of genes or cell types provides a guide for researchers to select the best data analysis tools and a theoretical basis for researchers to develop new algorithms. . The research results, titled "Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution", were published in the internationally renowned magazine Nature Methods. Hanyin Life simultaneously released spatial transcriptome sequencing and analysis services.