1
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Zhang Y, Wang M, Zhu Q, Guo Y, Liu B, Li J, Yao X, Kong C, Zhang Y, Huang Y, Qi H, Wu J, Guo ZV, Dai Q. Long-term mesoscale imaging of 3D intercellular dynamics across a mammalian organ. Cell 2024:S0092-8674(24)00917-6. [PMID: 39276776 DOI: 10.1016/j.cell.2024.08.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 06/06/2024] [Accepted: 08/13/2024] [Indexed: 09/17/2024]
Abstract
A comprehensive understanding of physio-pathological processes necessitates non-invasive intravital three-dimensional (3D) imaging over varying spatial and temporal scales. However, huge data throughput, optical heterogeneity, surface irregularity, and phototoxicity pose great challenges, leading to an inevitable trade-off between volume size, resolution, speed, sample health, and system complexity. Here, we introduce a compact real-time, ultra-large-scale, high-resolution 3D mesoscope (RUSH3D), achieving uniform resolutions of 2.6 × 2.6 × 6 μm3 across a volume of 8,000 × 6,000 × 400 μm3 at 20 Hz with low phototoxicity. Through the integration of multiple computational imaging techniques, RUSH3D facilitates a 13-fold improvement in data throughput and an orders-of-magnitude reduction in system size and cost. With these advantages, we observed premovement neural activity and cross-day visual representational drift across the mouse cortex, the formation and progression of multiple germinal centers in mouse inguinal lymph nodes, and heterogeneous immune responses following traumatic brain injury-all at single-cell resolution, opening up a horizon for intravital mesoscale study of large-scale intercellular interactions at the organ level.
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Affiliation(s)
- Yuanlong Zhang
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Mingrui Wang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China
| | - Qiyu Zhu
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Yuduo Guo
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China
| | - Bo Liu
- School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China; Laboratory of Dynamic Immunobiology, Institute for Immunology, Tsinghua University, Beijing 100084, China
| | - Jiamin Li
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Xiao Yao
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Chui Kong
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Yi Zhang
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Yuchao Huang
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Hai Qi
- School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China; Laboratory of Dynamic Immunobiology, Institute for Immunology, Tsinghua University, Beijing 100084, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
| | - Zengcai V Guo
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
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2
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Nasser R, Schaffer LV, Ideker T, Sharan R. Multi-modal contrastive learning of subcellular organization using DICE. Bioinformatics 2024; 40:ii105-ii110. [PMID: 39230695 DOI: 10.1093/bioinformatics/btae387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024] Open
Abstract
The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information. Here, we introduce DICE (Data Integration through Contrastive Embedding), a contrastive learning model for multi-modal data integration. We apply this model to study the subcellular organization of proteins by integrating protein-protein interaction data and protein image data measured in HEK293 cells. We demonstrate the advantage of data integration over any single modality and show that our framework outperforms previous integration approaches. Availability: https://github.com/raminass/protein-contrastive Contact: raminass@gmail.com.
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Affiliation(s)
- Rami Nasser
- School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
| | - Leah V Schaffer
- Department of Medicine, University of California, San Diego, San Diego, CA 92037, United States
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, San Diego, CA 92037, United States
- Department of Computer Science and Engineering, University of California, San Diego, San Diego, CA 92037, United States
- Moores Cancer Center, University of California, San Diego, San Diego, CA 92037, United States
- Department of Bioengineering, University of California, San Diego, San Diego, CA 92037, United States
| | - Roded Sharan
- School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
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3
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Sun X, Wu LF, Altschuler SJ, Hata AN. Targeting therapy-persistent residual disease. NATURE CANCER 2024; 5:1298-1304. [PMID: 39289594 DOI: 10.1038/s43018-024-00819-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 07/26/2024] [Indexed: 09/19/2024]
Abstract
Disease relapse driven by acquired drug resistance limits the effectiveness of most systemic anti-cancer agents. Targeting persistent cancer cells in residual disease before relapse has emerged as a potential strategy for enhancing the efficacy and the durability of current therapies. However, barriers remain to implementing persister-directed approaches in the clinic. This Perspective discusses current preclinical and clinical complexities and outlines key steps toward the development of clinical strategies that target therapy-persistent residual disease.
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Affiliation(s)
- Xiaoxiao Sun
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Lani F Wu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
| | - Steven J Altschuler
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
| | - Aaron N Hata
- Massachusetts General Hospital Cancer Center, Boston, MA, USA.
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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4
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Zhu S, Kubota N, Wang S, Wang T, Xiao G, Hoshida Y. STIE: Single-cell level deconvolution, convolution, and clustering in in situ capturing-based spatial transcriptomics. Nat Commun 2024; 15:7559. [PMID: 39214995 PMCID: PMC11364663 DOI: 10.1038/s41467-024-51728-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 08/14/2024] [Indexed: 09/04/2024] Open
Abstract
In in situ capturing-based spatial transcriptomics, spots of the same size and printed at fixed locations cannot precisely capture the randomly-located single cells, therefore inherently failing to profile transcriptome at the single-cell level. To this end, we present STIE, an Expectation Maximization algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from ~70% gap area, thereby achieving the real single-cell level and whole-slide scale deconvolution, convolution, and clustering for both low- and high-resolution spots. STIE characterizes cell-type-specific gene expression and demonstrates outperforming concordance with true cell-type-specific transcriptomic signatures than the other spot- and subspot-level methods. Furthermore, STIE reveals the single-cell level insights, for instance, lower actual spot resolution than its reported spot size, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatial cell-cell interactions at the single-cell level other than spot level.
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Affiliation(s)
- Shijia Zhu
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA.
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Naoto Kubota
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yujin Hoshida
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA.
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5
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Chang Y, Liu J, Jiang Y, Ma A, Yeo YY, Guo Q, McNutt M, Krull JE, Rodig SJ, Barouch DH, Nolan GP, Xu D, Jiang S, Li Z, Liu B, Ma Q. Graph Fourier transform for spatial omics representation and analyses of complex organs. Nat Commun 2024; 15:7467. [PMID: 39209833 PMCID: PMC11362340 DOI: 10.1038/s41467-024-51590-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024] Open
Abstract
Spatial omics technologies decipher functional components of complex organs at cellular and subcellular resolutions. We introduce Spatial Graph Fourier Transform (SpaGFT) and apply graph signal processing to a wide range of spatial omics profiling platforms to generate their interpretable representations. This representation supports spatially variable gene identification and improves gene expression imputation, outperforming existing tools in analyzing human and mouse spatial transcriptomics data. SpaGFT can identify immunological regions for B cell maturation in human lymph nodes Visium data and characterize variations in secondary follicles using in-house human tonsil CODEX data. Furthermore, it can be integrated seamlessly into other machine learning frameworks, enhancing accuracy in spatial domain identification, cell type annotation, and subcellular feature inference by up to 40%. Notably, SpaGFT detects rare subcellular organelles, such as Cajal bodies and Set1/COMPASS complexes, in high-resolution spatial proteomics data. This approach provides an explainable graph representation method for exploring tissue biology and function.
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Affiliation(s)
- Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Jixin Liu
- School of Mathematics, Shandong University, 250100, Jinan, China
| | - Yi Jiang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
- Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, 20115, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Megan McNutt
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
| | - Jordan E Krull
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Scott J Rodig
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, 02115, USA
- Department of Pathology, Brigham & Women's Hospital, Boston, MA, 02115, USA
| | - Dan H Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, 02139, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA, 02115, USA
- Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, 20115, USA
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, 02115, USA
| | - Zihai Li
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, 250100, Jinan, China.
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH, 43210, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA.
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6
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Jiang J, Liu Y, Qin J, Chen J, Wu J, Pizzi MP, Lazcano R, Yamashita K, Xu Z, Pei G, Cho KS, Chu Y, Sinjab A, Peng F, Yan X, Han G, Wang R, Dai E, Dai Y, Czerniak BA, Futreal A, Maitra A, Lazar A, Kadara H, Jazaeri AA, Cheng X, Ajani J, Gao J, Hu J, Wang L. METI: deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics. Nat Commun 2024; 15:7312. [PMID: 39181865 PMCID: PMC11344794 DOI: 10.1038/s41467-024-51708-9] [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: 10/06/2023] [Accepted: 08/14/2024] [Indexed: 08/27/2024] Open
Abstract
Recent advances in spatial transcriptomics (ST) techniques provide valuable insights into cellular interactions within the tumor microenvironment (TME). However, most analytical tools lack consideration of histological features and rely on matched single-cell RNA sequencing data, limiting their effectiveness in TME studies. To address this, we introduce the Morphology-Enhanced Spatial Transcriptome Analysis Integrator (METI), an end-to-end framework that maps cancer cells and TME components, stratifies cell types and states, and analyzes cell co-localization. By integrating spatial transcriptomics, cell morphology, and curated gene signatures, METI enhances our understanding of the molecular landscape and cellular interactions within the tissue. We evaluate the performance of METI on ST data generated from various tumor tissues, including gastric, lung, and bladder cancers, as well as premalignant tissues. We also conduct a quantitative comparison of METI with existing clustering and cell deconvolution tools, demonstrating METI's robust and consistent performance.
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Affiliation(s)
- Jiahui Jiang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jiangjiang Qin
- Department of Gastric Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Jianfeng Chen
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jingjing Wu
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Melissa P Pizzi
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rossana Lazcano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kohei Yamashita
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zhiyuan Xu
- Department of Gastric Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
| | - Guangsheng Pei
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kyung Serk Cho
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Yanshuo Chu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ansam Sinjab
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fuduan Peng
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinmiao Yan
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Guangchun Han
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ruiping Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Enyu Dai
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yibo Dai
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences (GSBS), Houston, TX, USA
| | - Bogdan A Czerniak
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anirban Maitra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexander Lazar
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amir A Jazaeri
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiangdong Cheng
- Department of Gastric Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
| | - Jaffer Ajani
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jianjun Gao
- Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jian Hu
- Department of Human Genetics, Emory School of Medicine, Atlanta, GA, USA.
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences (GSBS), Houston, TX, USA.
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7
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Li B, Bao F, Hou Y, Li F, Li H, Deng Y, Dai Q. Tissue characterization at an enhanced resolution across spatial omics platforms with deep generative model. Nat Commun 2024; 15:6541. [PMID: 39095360 PMCID: PMC11297205 DOI: 10.1038/s41467-024-50837-5] [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: 12/09/2023] [Accepted: 07/22/2024] [Indexed: 08/04/2024] Open
Abstract
Recent advances in spatial omics have expanded the spectrum of profiled molecular categories beyond transcriptomics. However, many of these technologies are constrained by limited spatial resolution, hindering our ability to deeply characterize intricate tissue architectures. Existing computational methods primarily focus on the resolution enhancement of transcriptomics data, lacking the adaptability to address the emerging spatial omics technologies that profile various omics types. Here, we introduce soScope, a unified generative framework designed to enhance data quality and spatial resolution for molecular profiles obtained from diverse spatial technologies. soScope aggregates multimodal tissue information from omics, spatial relations and images, and jointly infers omics profiles at enhanced resolutions with omics-specific modeling through distribution priors. With comprehensive evaluations on diverse spatial omics platforms, including Visium, Xenium, spatial-CUT&Tag, and slide-DNA/RNA-seq, soScope improves performances in identifying biologically meaningful intestine and kidney architectures, revealing embryonic heart structure that cannot be resolved at the original resolution and correcting sample and technical biases arising from sequencing and sample processing. Furthermore, soScope extends to spatial multiomics technology spatial-CITE-seq and spatial ATAC-RNA-seq, leveraging cross-omics reference for simultaneous multiomics enhancement. soScope provides a versatile tool to improve the utilization of continually expanding spatial omics technologies and resources.
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Affiliation(s)
- Bohan Li
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Feng Bao
- Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China
- Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS), Beijing, China
- Department of Automation, Tsinghua University, Beijing, China
| | - Yimin Hou
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Fengji Li
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Hongjue Li
- School of Artificial Intelligence, Beihang University, Beijing, China
| | - Yue Deng
- School of Artificial Intelligence, Beihang University, Beijing, China.
| | - Qionghai Dai
- Beijing National Research Center for Information Science and Technology (BNRist), Beijing, China.
- Institute for Brain and Cognitive Science, Tsinghua University (THUIBCS), Beijing, China.
- Department of Automation, Tsinghua University, Beijing, China.
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8
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Cesnik A, Schaffer LV, Gaur I, Jain M, Ideker T, Lundberg E. Mapping the Multiscale Proteomic Organization of Cellular and Disease Phenotypes. Annu Rev Biomed Data Sci 2024; 7:369-389. [PMID: 38748859 PMCID: PMC11343683 DOI: 10.1146/annurev-biodatasci-102423-113534] [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] [Indexed: 06/23/2024]
Abstract
While the primary sequences of human proteins have been cataloged for over a decade, determining how these are organized into a dynamic collection of multiprotein assemblies, with structures and functions spanning biological scales, is an ongoing venture. Systematic and data-driven analyses of these higher-order structures are emerging, facilitating the discovery and understanding of cellular phenotypes. At present, knowledge of protein localization and function has been primarily derived from manual annotation and curation in resources such as the Gene Ontology, which are biased toward richly annotated genes in the literature. Here, we envision a future powered by data-driven mapping of protein assemblies. These maps can capture and decode cellular functions through the integration of protein expression, localization, and interaction data across length scales and timescales. In this review, we focus on progress toward constructing integrated cell maps that accelerate the life sciences and translational research.
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Affiliation(s)
- Anthony Cesnik
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Leah V Schaffer
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Ishan Gaur
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Mayank Jain
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Trey Ideker
- Departments of Computer Science and Engineering and Bioengineering, University of California San Diego, La Jolla, California, USA
- Department of Medicine, University of California San Diego, La Jolla, California, USA;
| | - Emma Lundberg
- Chan Zuckerberg Biohub, San Francisco, California, USA
- Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Pathology, Stanford University, Palo Alto, California, USA
- Department of Bioengineering, Stanford University, Stanford, California, USA;
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9
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Lv T, Zhang Y, Liu J, Kang Q, Liu L. Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder. Brief Bioinform 2024; 25:bbae450. [PMID: 39293805 PMCID: PMC11410375 DOI: 10.1093/bib/bbae450] [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: 06/07/2024] [Revised: 08/05/2024] [Accepted: 08/30/2024] [Indexed: 09/20/2024] Open
Abstract
Single-cell multi-omics integration enables joint analysis at the single-cell level of resolution to provide more accurate understanding of complex biological systems, while spatial multi-omics integration is benefit to the exploration of cell spatial heterogeneity to facilitate more comprehensive downstream analyses. Existing methods are mainly designed for single-cell multi-omics data with little consideration of spatial information and still have room for performance improvement. A reliable multi-omics integration method designed for both single-cell and spatially resolved data is necessary and significant. We propose a multi-omics integration method based on dual-path graph attention auto-encoder (SSGATE). It can construct the neighborhood graphs based on single-cell expression profiles or spatial coordinates, enabling it to process single-cell data and utilize spatial information from spatially resolved data. It can also perform self-supervised learning for integration through the graph attention auto-encoders from two paths. SSGATE is applied to integration of transcriptomics and proteomics, including single-cell and spatially resolved data of various tissues from different sequencing technologies. SSGATE shows better performance and stronger robustness than competitive methods and facilitates downstream analysis.
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Affiliation(s)
- Tongxuan Lv
- BGI Research, No. 9, Yunhua Road, Yantian District, Shenzhen 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, No. 19, Yuquan Road, Shijingshan District, Beijing 100049, China
| | - Yong Zhang
- BGI Research, No. 9, Yunhua Road, Yantian District, Shenzhen 518083, China
| | - Junlin Liu
- BGI Research, No. 9, Yunhua Road, Yantian District, Shenzhen 518083, China
| | - Qiang Kang
- BGI Research, No. 9, Yunhua Road, Yantian District, Shenzhen 518083, China
| | - Lin Liu
- BGI Research, No. 9, Yunhua Road, Yantian District, Shenzhen 518083, China
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10
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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11
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Zhang Z, Melzer ME, Arun KM, Sun H, Eriksson CJ, Fabian I, Shaashua S, Kiani K, Oren Y, Goyal Y. Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms. CELL GENOMICS 2024; 4:100592. [PMID: 38925122 PMCID: PMC11293576 DOI: 10.1016/j.xgen.2024.100592] [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: 08/08/2023] [Revised: 03/26/2024] [Accepted: 06/05/2024] [Indexed: 06/28/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.
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Affiliation(s)
- Ziyang Zhang
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Madeline E Melzer
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
| | - Keerthana M Arun
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Hanxiao Sun
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Carl-Johan Eriksson
- Department of Cell and Molecular Biology, Karolinska Institute, Stockholm, Sweden
| | - Itai Fabian
- Department of Human Molecular Genetics & Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Sagi Shaashua
- Department of Human Molecular Genetics & Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Karun Kiani
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yaara Oren
- Department of Human Molecular Genetics & Biochemistry, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; Center for Synthetic Biology, Northwestern University, Chicago, IL, USA; Robert H. Lurie Comprehensive Cancer Center, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA; CZ Biohub Chicago, Chicago, IL, USA.
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12
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Ren P, Bao H, Wang S, Wang Y, Bai Y, Lai J, Yi L, Liu Q, Li W, Zhang X, Sun L, Liu Q, Cui X, Zhang X, Liang P, Liang X. Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes. Int J Cancer 2024. [PMID: 38949756 DOI: 10.1002/ijc.35068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Revised: 04/11/2024] [Accepted: 06/06/2024] [Indexed: 07/02/2024]
Abstract
Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.
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Affiliation(s)
- Peng Ren
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hongbo Bao
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shuai Wang
- Medical Imaging Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yan Bai
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jiacheng Lai
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Liye Yi
- Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qing Liu
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Wenting Li
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xinyu Zhang
- Department of Neurology, The First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lili Sun
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qiuyi Liu
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xuehua Cui
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiushi Zhang
- Medical Imaging Department, Harbin Medical University Cancer Hospital, Harbin, China
| | - Peng Liang
- Department of Neurosurgery, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xia Liang
- Laboratory for Space Environment and Physical Science, Harbin Institute of Technology, Harbin, China
- Frontiers Science Center for Matter Behave in Space Environment, Harbin Institute of Technology, Harbin, China
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13
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Jiang X, Wang S, Guo L, Zhu B, Wen Z, Jia L, Xu L, Xiao G, Li Q. iIMPACT: integrating image and molecular profiles for spatial transcriptomics analysis. Genome Biol 2024; 25:147. [PMID: 38844966 DOI: 10.1186/s13059-024-03289-5] [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: 06/28/2023] [Accepted: 05/23/2024] [Indexed: 07/04/2024] Open
Abstract
Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.
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Affiliation(s)
- Xi Jiang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, USA
| | - Shidan Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Lei Guo
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bencong Zhu
- Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, USA
| | - Zhuoyu Wen
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Liwei Jia
- Department of Pathology, The University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Lin Xu
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, The University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, USA.
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14
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Clark T, Mohan J, Schaffer L, Obernier K, Al Manir S, Churas CP, Dailamy A, Doctor Y, Forget A, Hansen JN, Hu M, Lenkiewicz J, Levinson MA, Marquez C, Nourreddine S, Niestroy J, Pratt D, Qian G, Thaker S, Bélisle-Pipon JC, Brandt C, Chen J, Ding Y, Fodeh S, Krogan N, Lundberg E, Mali P, Payne-Foster P, Ratcliffe S, Ravitsky V, Sali A, Schulz W, Ideker T. Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.21.589311. [PMID: 38826258 PMCID: PMC11142054 DOI: 10.1101/2024.05.21.589311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools , status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Health's (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.
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15
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Zhu B, Gao S, Chen S, Yeung J, Bai Y, Huang AY, Yeo YY, Liao G, Mao S, Jiang ZG, Rodig SJ, Shalek AK, Nolan GP, Jiang S, Ma Z. Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.12.593710. [PMID: 38798592 PMCID: PMC11118457 DOI: 10.1101/2024.05.12.593710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sheng Gao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Shuxiao Chen
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, PA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amy Y Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Guanrui Liao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, People's Republic of China
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
| | - Zhenghui G Jiang
- Division of Gastroenterology/Liver Center, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Zongming Ma
- Department of Statistics and Data Science, Yale University, New Haven, CT, United States
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16
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Camunas-Soler J. Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca 2+ imaging and electrophysiology. Biophys Rev 2024; 16:89-107. [PMID: 38495444 PMCID: PMC10937895 DOI: 10.1007/s12551-023-01174-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/29/2023] [Indexed: 03/19/2024] Open
Abstract
I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.
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Affiliation(s)
- Joan Camunas-Soler
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, 405 30 Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
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17
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Tao Y, Sun X, Wang F. BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices. Brief Bioinform 2024; 25:bbae045. [PMID: 38385877 PMCID: PMC10883416 DOI: 10.1093/bib/bbae045] [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: 11/12/2023] [Revised: 01/10/2024] [Accepted: 01/23/2024] [Indexed: 02/23/2024] Open
Abstract
Recent advancements in spatial transcriptomics technology have revolutionized our ability to comprehensively characterize gene expression patterns within the tissue microenvironment, enabling us to grasp their functional significance in a spatial context. One key field of research in spatial transcriptomics is the identification of spatial domains, which refers to distinct regions within the tissue where specific gene expression patterns are observed. Diverse methodologies have been proposed, each with its unique characteristics. As the availability of spatial transcriptomics data continues to expand, there is a growing need for methods that can integrate information from multiple slices to discover spatial domains. To extend the applicability of existing single-slice analysis methods to multi-slice clustering, we introduce BiGATAE (Bipartite Graph Attention Auto Encoder) that leverages gene expression information from adjacent tissue slices to enhance spatial transcriptomics data. BiGATAE comprises two steps: aligning slices to generate an adjacency matrix for different spots in consecutive slices and constructing a bipartite graph. Subsequently, it utilizes a graph attention network to integrate information across different slices. Then it can seamlessly integrate with pre-existing techniques. To evaluate the performance of BiGATAE, we conducted benchmarking analyses on three different datasets. The experimental results demonstrate that for existing single-slice clustering methods, the integration of BiGATAE significantly enhances their performance. Moreover, single-slice clustering methods integrated with BiGATAE outperform methods specifically designed for multi-slice integration. These results underscore the proficiency of BiGATAE in facilitating information transfer across multiple slices and its capacity to broaden the applicability and sustainability of pre-existing methods.
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Affiliation(s)
- Yuhao Tao
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan University Handan Street, 200433 Shanghai, China
| | - Xiaoang Sun
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan University Handan Street, 200433 Shanghai, China
| | - Fei Wang
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan University Handan Street, 200433 Shanghai, China
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18
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Xun D, Wang R, Zhang X, Wang Y. Microsnoop: A generalist tool for microscopy image representation. Innovation (N Y) 2024; 5:100541. [PMID: 38235187 PMCID: PMC10794109 DOI: 10.1016/j.xinn.2023.100541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 11/17/2023] [Indexed: 01/19/2024] Open
Abstract
Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research. Here, we present Microsnoop, a novel deep learning-based representation tool trained on large-scale microscopy images using masked self-supervised learning. Microsnoop can process various complex and heterogeneous images, and we classified images into three categories: single-cell, full-field, and batch-experiment images. Our benchmark study on 10 high-quality evaluation datasets, containing over 2,230,000 images, demonstrated Microsnoop's robust and state-of-the-art microscopy image representation ability, surpassing existing generalist and even several custom algorithms. Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal analysis. Furthermore, Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms. We will regularly retrain and reevaluate the model using community-contributed data to consistently improve Microsnoop.
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Affiliation(s)
- Dejin Xun
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Rui Wang
- State Key Lab of Computer-Aided Design & Computer Graphics, Zhejiang University, Hangzhou 310058, China
| | - Xingcai Zhang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Hangzhou 310018, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China
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19
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Zhu S, Kubota N, Wang S, Wang T, Xiao G, Hoshida Y. Single-cell level deconvolution, convolution, and clustering in spatial transcriptomics by aligning spot level transcriptome to nuclear morphology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.17.572084. [PMID: 38187541 PMCID: PMC10769305 DOI: 10.1101/2023.12.17.572084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
In spot-based spatial transcriptomics, spots that are of the same size and printed at the fixed location cannot precisely capture the actual randomly located single cells, therefore failing to profile the transcriptome at the single-cell level. The current studies primarily focused on enhancing the spot resolution in size via computational imputation or technical improvement, however, they largely overlooked that single-cell resolution, i.e., resolution in cellular or even smaller size, does not equal single-cell level. Using both real and simulated spatial transcriptomics data, we demonstrated that even the high-resolution spatial transcriptomics still has a large number of spots partially covering multiple cells simultaneously, revealing the intrinsic non-single-cell level of spot-based spatial transcriptomics regardless of spot size. To this end, we present STIE, an EM algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from up to ~70% gap area between spots via the nuclear morphological similarity and neighborhood information, thereby achieving the real single-cell level and whole-slide scale deconvolution/convolution and clustering for both low- and high-resolution spots. On both real and simulation spatial transcriptomics data, STIE characterizes the cell-type specific gene expression variation and demonstrates the outperforming concordance with the single-cell RNAseq-derived cell type transcriptomic signatures compared to the other spot- and subspot-level methods. Furthermore, STIE enabled us to gain novel insights that failed to be revealed by the existing methods due to the lack of single-cell level, for instance, lower actual spot resolution than its reported spot size, the additional contribution of cellular morphology to cell typing beyond transcriptome, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatially resolved cell-cell interactions at the single-cell level other than spot level. The STIE code is publicly available as an R package at https://github.com/zhushijia/STIE.
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Affiliation(s)
- Shijia Zhu
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, Minnesota, USA
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Naoto Kubota
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Shidan Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Tao Wang
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Yujin Hoshida
- Division of Digestive and Liver Diseases, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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20
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Tippani M, Divecha HR, Catallini JL, Kwon SH, Weber LM, Spangler A, Jaffe AE, Hyde TM, Kleinman JE, Hicks SC, Martinowich K, Collado-Torres L, Page SC, Maynard KR. VistoSeg: Processing utilities for high-resolution images for spatially resolved transcriptomics data. BIOLOGICAL IMAGING 2023; 3:e23. [PMID: 38510173 PMCID: PMC10951916 DOI: 10.1017/s2633903x23000235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/14/2023] [Accepted: 09/19/2023] [Indexed: 03/22/2024]
Abstract
Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.
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Affiliation(s)
- Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Heena R. Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Joseph L. Catallini
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang H. Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Lukas M. Weber
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Andrew E. Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Stephanie C. Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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21
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Zhao S, Chen DP, Fu T, Yang JC, Ma D, Zhu XZ, Wang XX, Jiao YP, Jin X, Xiao Y, Xiao WX, Zhang HY, Lv H, Madabhushi A, Yang WT, Jiang YZ, Xu J, Shao ZM. Single-cell morphological and topological atlas reveals the ecosystem diversity of human breast cancer. Nat Commun 2023; 14:6796. [PMID: 37880211 PMCID: PMC10600153 DOI: 10.1038/s41467-023-42504-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 10/12/2023] [Indexed: 10/27/2023] Open
Abstract
Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.
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Affiliation(s)
- Shen Zhao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - De-Pin Chen
- Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Tong Fu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Jing-Cheng Yang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- Greater Bay Area Institute of Precision Medicine, Guangzhou, China
| | - Ding Ma
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiu-Zhi Zhu
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Xiang-Xue Wang
- Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Yi-Ping Jiao
- Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
| | - Xi Jin
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yi Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wen-Xuan Xiao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Hu-Yunlong Zhang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Hong Lv
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Anant Madabhushi
- Wallace H Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Atlanta Veterans Affairs Medical Center, Atlanta, GA, USA
| | - Wen-Tao Yang
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Yi-Zhou Jiang
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Jun Xu
- Institute for Artificial Intelligence in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China.
| | - Zhi-Ming Shao
- Key Laboratory of Breast Cancer in Shanghai, Department of Breast Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
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22
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Guo T, Yuan Z, Pan Y, Wang J, Chen F, Zhang MQ, Li X. SPIRAL: integrating and aligning spatially resolved transcriptomics data across different experiments, conditions, and technologies. Genome Biol 2023; 24:241. [PMID: 37864231 PMCID: PMC10590036 DOI: 10.1186/s13059-023-03078-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 09/29/2023] [Indexed: 10/22/2023] Open
Abstract
Properly integrating spatially resolved transcriptomics (SRT) generated from different batches into a unified gene-spatial coordinate system could enable the construction of a comprehensive spatial transcriptome atlas. Here, we propose SPIRAL, consisting of two consecutive modules: SPIRAL-integration, with graph domain adaptation-based data integration, and SPIRAL-alignment, with cluster-aware optimal transport-based coordination alignment. We verify SPIRAL with both synthetic and real SRT datasets. By encoding spatial correlations to gene expressions, SPIRAL-integration surpasses state-of-the-art methods in both batch effect removal and joint spatial domain identification. By aligning spots cluster-wise, SPIRAL-alignment achieves more accurate coordinate alignments than existing methods.
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Affiliation(s)
- Tiantian Guo
- School of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China
- MOE Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist, Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China
| | - Yan Pan
- School of Biomedical Sciences, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Jiakang Wang
- School of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China
| | - Fengling Chen
- Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Michael Q Zhang
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, 75080-3021, USA.
| | - Xiangyu Li
- School of Software Engineering, Beijing Jiaotong University, Beijing, 100044, China.
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23
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Shi H, He Y, Zhou Y, Huang J, Maher K, Wang B, Tang Z, Luo S, Tan P, Wu M, Lin Z, Ren J, Thapa Y, Tang X, Chan KY, Deverman BE, Shen H, Liu A, Liu J, Wang X. Spatial atlas of the mouse central nervous system at molecular resolution. Nature 2023; 622:552-561. [PMID: 37758947 PMCID: PMC10709140 DOI: 10.1038/s41586-023-06569-5] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 08/22/2023] [Indexed: 09/29/2023]
Abstract
Spatially charting molecular cell types at single-cell resolution across the 3D volume is critical for illustrating the molecular basis of brain anatomy and functions. Single-cell RNA sequencing has profiled molecular cell types in the mouse brain1,2, but cannot capture their spatial organization. Here we used an in situ sequencing method, STARmap PLUS3,4, to profile 1,022 genes in 3D at a voxel size of 194 × 194 × 345 nm3, mapping 1.09 million high-quality cells across the adult mouse brain and spinal cord. We developed computational pipelines to segment, cluster and annotate 230 molecular cell types by single-cell gene expression and 106 molecular tissue regions by spatial niche gene expression. Joint analysis of molecular cell types and molecular tissue regions enabled a systematic molecular spatial cell-type nomenclature and identification of tissue architectures that were undefined in established brain anatomy. To create a transcriptome-wide spatial atlas, we integrated STARmap PLUS measurements with a published single-cell RNA-sequencing atlas1, imputing single-cell expression profiles of 11,844 genes. Finally, we delineated viral tropisms of a brain-wide transgene delivery tool, AAV-PHP.eB5,6. Together, this annotated dataset provides a single-cell resource that integrates the molecular spatial atlas, brain anatomy and the accessibility to genetic manipulation of the mammalian central nervous system.
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Affiliation(s)
- Hailing Shi
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yichun He
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Yiming Zhou
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jiahao Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kamal Maher
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Computational and Systems Biology PhD Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brandon Wang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zefang Tang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shuchen Luo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Peng Tan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Morgan Wu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Zuwan Lin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Jingyi Ren
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Yaman Thapa
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xin Tang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Ken Y Chan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin E Deverman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hao Shen
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Albert Liu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
| | - Xiao Wang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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24
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Tang Z, Li Z, Hou T, Zhang T, Yang B, Su J, Song Q. SiGra: single-cell spatial elucidation through an image-augmented graph transformer. Nat Commun 2023; 14:5618. [PMID: 37699885 PMCID: PMC10497630 DOI: 10.1038/s41467-023-41437-w] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 09/01/2023] [Indexed: 09/14/2023] Open
Abstract
Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27-50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems.
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Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
- Department of Computer Graphics Technology, Purdue University, Indiana, USA
| | - Tieying Hou
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA.
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA.
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest University School of Medicine, North Carolina, USA.
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Florida, USA.
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25
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Huo Y, Guo Y, Wang J, Xue H, Feng Y, Chen W, Li X. Integrating multi-modal information to detect spatial domains of spatial transcriptomics by graph attention network. J Genet Genomics 2023; 50:720-733. [PMID: 37356752 DOI: 10.1016/j.jgg.2023.06.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 06/27/2023]
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).
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Affiliation(s)
- Yuying Huo
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Yilang Guo
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Jiakang Wang
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Huijie Xue
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China
| | - Yujuan Feng
- School of Software Engineering, Beijing University of Technology, Beijing 100124, China
| | | | - Xiangyu Li
- School of Software Engineering, Beijing Jiaotong University, Beijing 100044, China.
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26
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Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet 2023; 24:494-515. [PMID: 36864178 PMCID: PMC9979144 DOI: 10.1038/s41576-023-00580-2] [Citation(s) in RCA: 266] [Impact Index Per Article: 266.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 03/04/2023]
Abstract
The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.
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Affiliation(s)
- Katy Vandereyken
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Alejandro Sifrim
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Bernard Thienpont
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Thierry Voet
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium.
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
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27
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O'Connor LM, O'Connor BA, Lim SB, Zeng J, Lo CH. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective. J Pharm Anal 2023; 13:836-850. [PMID: 37719197 PMCID: PMC10499660 DOI: 10.1016/j.jpha.2023.06.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/19/2023] Open
Abstract
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
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Affiliation(s)
- Lance M. O'Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Blake A. O'Connor
- School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
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28
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Grandi E, Navedo MF, Saucerman JJ, Bers DM, Chiamvimonvat N, Dixon RE, Dobrev D, Gomez AM, Harraz OF, Hegyi B, Jones DK, Krogh-Madsen T, Murfee WL, Nystoriak MA, Posnack NG, Ripplinger CM, Veeraraghavan R, Weinberg S. Diversity of cells and signals in the cardiovascular system. J Physiol 2023; 601:2547-2592. [PMID: 36744541 PMCID: PMC10313794 DOI: 10.1113/jp284011] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 01/19/2023] [Indexed: 02/07/2023] Open
Abstract
This white paper is the outcome of the seventh UC Davis Cardiovascular Research Symposium on Systems Approach to Understanding Cardiovascular Disease and Arrhythmia. This biannual meeting aims to bring together leading experts in subfields of cardiovascular biomedicine to focus on topics of importance to the field. The theme of the 2022 Symposium was 'Cell Diversity in the Cardiovascular System, cell-autonomous and cell-cell signalling'. Experts in the field contributed their experimental and mathematical modelling perspectives and discussed emerging questions, controversies, and challenges in examining cell and signal diversity, co-ordination and interrelationships involved in cardiovascular function. This paper originates from the topics of formal presentations and informal discussions from the Symposium, which aimed to develop a holistic view of how the multiple cell types in the cardiovascular system integrate to influence cardiovascular function, disease progression and therapeutic strategies. The first section describes the major cell types (e.g. cardiomyocytes, vascular smooth muscle and endothelial cells, fibroblasts, neurons, immune cells, etc.) and the signals involved in cardiovascular function. The second section emphasizes the complexity at the subcellular, cellular and system levels in the context of cardiovascular development, ageing and disease. Finally, the third section surveys the technological innovations that allow the interrogation of this diversity and advancing our understanding of the integrated cardiovascular function and dysfunction.
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Affiliation(s)
- Eleonora Grandi
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - Manuel F. Navedo
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - Jeffrey J. Saucerman
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Donald M. Bers
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - Nipavan Chiamvimonvat
- Department of Pharmacology, University of California Davis, Davis, CA, USA
- Department of Internal Medicine, University of California Davis, Davis, CA, USA
| | - Rose E. Dixon
- Department of Physiology and Membrane Biology, University of California Davis, Davis, CA, USA
| | - Dobromir Dobrev
- Institute of Pharmacology, West German Heart and Vascular Center, University Duisburg-Essen, Essen, Germany
- Department of Medicine, Montreal Heart Institute and Université de Montréal, Montréal, Canada
- Department of Molecular Physiology & Biophysics, Baylor College of Medicine, Houston, TX, USA
| | - Ana M. Gomez
- Signaling and Cardiovascular Pathophysiology-UMR-S 1180, INSERM, Université Paris-Saclay, Orsay, France
| | - Osama F. Harraz
- Department of Pharmacology, Larner College of Medicine, and Vermont Center for Cardiovascular and Brain Health, University of Vermont, Burlington, VT, USA
| | - Bence Hegyi
- Department of Pharmacology, University of California Davis, Davis, CA, USA
| | - David K. Jones
- Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Trine Krogh-Madsen
- Department of Physiology & Biophysics, Weill Cornell Medicine, New York, New York, USA
| | - Walter Lee Murfee
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
| | - Matthew A. Nystoriak
- Department of Medicine, Division of Environmental Medicine, Center for Cardiometabolic Science, University of Louisville, Louisville, KY, 40202, USA
| | - Nikki G. Posnack
- Department of Pediatrics, Department of Pharmacology and Physiology, The George Washington University, Washington, DC, USA
- Sheikh Zayed Institute for Pediatric and Surgical Innovation, Children’s National Heart Institute, Children’s National Hospital, Washington, DC, USA
| | | | - Rengasayee Veeraraghavan
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, USA
- Dorothy M. Davis Heart & Lung Research Institute, The Ohio State University – Wexner Medical Center, Columbus, OH, USA
| | - Seth Weinberg
- Department of Biomedical Engineering, The Ohio State University, Columbus, OH, USA
- Dorothy M. Davis Heart & Lung Research Institute, The Ohio State University – Wexner Medical Center, Columbus, OH, USA
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29
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Mathy CJP, Kortemme T. Emerging maps of allosteric regulation in cellular networks. Curr Opin Struct Biol 2023; 80:102602. [PMID: 37150039 PMCID: PMC10960510 DOI: 10.1016/j.sbi.2023.102602] [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: 12/29/2022] [Revised: 03/24/2023] [Accepted: 04/04/2023] [Indexed: 05/09/2023]
Abstract
Allosteric regulation is classically defined as action at a distance, where a perturbation outside of a protein active site affects function. While this definition has motivated many studies of allosteric mechanisms at the level of protein structure, translating these insights to the allosteric regulation of entire cellular processes - and their crosstalk - has received less attention, despite the broad importance of allostery for cellular regulation foreseen by Jacob and Monod. Here, we revisit an evolutionary model for the widespread emergence of allosteric regulation in colocalized proteins, describe supporting evidence, and discuss emerging advances in mapping allostery in cellular networks that link precise and often allosteric perturbations at the molecular level to functional changes at the pathway and systems levels.
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Affiliation(s)
- Christopher J P Mathy
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, 94158, USA; Quantitative Biosciences Institute, University of California, San Francisco, CA, 94158, USA; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, 94158, USA.
| | - Tanja Kortemme
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, 94158, USA; Quantitative Biosciences Institute, University of California, San Francisco, CA, 94158, USA; The UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, CA, 94158, USA; Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
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30
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Zhao Z, Zhou Y, Liu B, He J, Zhao J, Cai Y, Fan J, Li X, Wang Z, Lu Z, Wu J, Qi H, Dai Q. Two-photon synthetic aperture microscopy for minimally invasive fast 3D imaging of native subcellular behaviors in deep tissue. Cell 2023; 186:2475-2491.e22. [PMID: 37178688 DOI: 10.1016/j.cell.2023.04.016] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 02/21/2023] [Accepted: 04/10/2023] [Indexed: 05/15/2023]
Abstract
Holistic understanding of physio-pathological processes requires noninvasive 3D imaging in deep tissue across multiple spatial and temporal scales to link diverse transient subcellular behaviors with long-term physiogenesis. Despite broad applications of two-photon microscopy (TPM), there remains an inevitable tradeoff among spatiotemporal resolution, imaging volumes, and durations due to the point-scanning scheme, accumulated phototoxicity, and optical aberrations. Here, we harnessed the concept of synthetic aperture radar in TPM to achieve aberration-corrected 3D imaging of subcellular dynamics at a millisecond scale for over 100,000 large volumes in deep tissue, with three orders of magnitude reduction in photobleaching. With its advantages, we identified direct intercellular communications through migrasome generation following traumatic brain injury, visualized the formation process of germinal center in the mouse lymph node, and characterized heterogeneous cellular states in the mouse visual cortex, opening up a horizon for intravital imaging to understand the organizations and functions of biological systems at a holistic level.
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Affiliation(s)
- Zhifeng Zhao
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; Hangzhou Zhuoxi Institute of Brain and Intelligence, Hangzhou 311100, China
| | - Yiliang Zhou
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; Hangzhou Zhuoxi Institute of Brain and Intelligence, Hangzhou 311100, China
| | - Bo Liu
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China; Laboratory of Dynamic Immunobiology, Institute for Immunology, Tsinghua University, Beijing 100084, China; Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China
| | - Jing He
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Jiayin Zhao
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China
| | - Yeyi Cai
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Jingtao Fan
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
| | - Xinyang Li
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; Hangzhou Zhuoxi Institute of Brain and Intelligence, Hangzhou 311100, China; Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China
| | - Zilin Wang
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Department of Anesthesiology, the First Medical Center, Chinese PLA General Hospital, Beijing 100853, China
| | - Zhi Lu
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; Hangzhou Zhuoxi Institute of Brain and Intelligence, Hangzhou 311100, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
| | - Hai Qi
- Tsinghua-Peking Center for Life Sciences, Beijing 100084, China; Laboratory of Dynamic Immunobiology, Institute for Immunology, Tsinghua University, Beijing 100084, China; Department of Basic Medical Sciences, School of Medicine, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory for Immunological Research on Chronic Diseases, Tsinghua University, Beijing 100084, China; Beijing Frontier Research Center for Biological Structure, Tsinghua University, Beijing 100084, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
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31
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Tang X, Zhang J, He Y, Zhang X, Lin Z, Partarrieu S, Hanna EB, Ren Z, Shen H, Yang Y, Wang X, Li N, Ding J, Liu J. Explainable multi-task learning for multi-modality biological data analysis. Nat Commun 2023; 14:2546. [PMID: 37137905 PMCID: PMC10156823 DOI: 10.1038/s41467-023-37477-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 03/17/2023] [Indexed: 05/05/2023] Open
Abstract
Current biotechnologies can simultaneously measure multiple high-dimensional modalities (e.g., RNA, DNA accessibility, and protein) from the same cells. A combination of different analytical tasks (e.g., multi-modal integration and cross-modal analysis) is required to comprehensively understand such data, inferring how gene regulation drives biological diversity and functions. However, current analytical methods are designed to perform a single task, only providing a partial picture of the multi-modal data. Here, we present UnitedNet, an explainable multi-task deep neural network capable of integrating different tasks to analyze single-cell multi-modality data. Applied to various multi-modality datasets (e.g., Patch-seq, multiome ATAC + gene expression, and spatial transcriptomics), UnitedNet demonstrates similar or better accuracy in multi-modal integration and cross-modal prediction compared with state-of-the-art methods. Moreover, by dissecting the trained UnitedNet with the explainable machine learning algorithm, we can directly quantify the relationship between gene expression and other modalities with cell-type specificity. UnitedNet is a comprehensive end-to-end framework that could be broadly applicable to single-cell multi-modality biology. This framework has the potential to facilitate the discovery of cell-type-specific regulation kinetics across transcriptomics and other modalities.
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Affiliation(s)
- Xin Tang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Jiawei Zhang
- School of Statistics, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Yichun He
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Xinhe Zhang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Zuwan Lin
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Sebastian Partarrieu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Emma Bou Hanna
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Zhaolin Ren
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Hao Shen
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Yuhong Yang
- School of Statistics, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA
| | - Xiao Wang
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Chemistry, MIT, Cambridge, MA, 02139, USA
| | - Na Li
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA
| | - Jie Ding
- School of Statistics, University of Minnesota Twin Cities, Minneapolis, MN, 55455, USA.
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, 02134, USA.
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32
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Pillai M, Hojel E, Jolly MK, Goyal Y. Unraveling non-genetic heterogeneity in cancer with dynamical models and computational tools. NATURE COMPUTATIONAL SCIENCE 2023; 3:301-313. [PMID: 38177938 DOI: 10.1038/s43588-023-00427-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/03/2023] [Indexed: 01/06/2024]
Abstract
Individual cells within an otherwise genetically homogenous population constantly undergo fluctuations in their molecular state, giving rise to non-genetic heterogeneity. Such diversity is being increasingly implicated in cancer therapy resistance and metastasis. Identifying the origins of non-genetic heterogeneity is therefore crucial for making clinical breakthroughs. We discuss with examples how dynamical models and computational tools have provided critical multiscale insights into the nature and consequences of non-genetic heterogeneity in cancer. We demonstrate how mechanistic modeling has been pivotal in establishing key concepts underlying non-genetic diversity at various biological scales, from population dynamics to gene regulatory networks. We discuss advances in single-cell longitudinal profiling techniques to reveal patterns of non-genetic heterogeneity, highlighting the ongoing efforts and challenges in statistical frameworks to robustly interpret such multimodal datasets. Moving forward, we stress the need for data-driven statistical and mechanistically motivated dynamical frameworks to come together to develop predictive cancer models and inform therapeutic strategies.
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Affiliation(s)
- Maalavika Pillai
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India
| | - Emilia Hojel
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bangalore, India.
| | - Yogesh Goyal
- Department of Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
- Center for Synthetic Biology, Northwestern University, Chicago, IL, USA.
- Robert H. Lurie Comprehensive Cancer Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Department of Biomedical Engineering, Northwestern University McCormick School of Engineering, Evanston, IL, USA.
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33
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Ya D, Zhang Y, Cui Q, Jiang Y, Yang J, Tian N, Xiang W, Lin X, Li Q, Liao R. Application of spatial transcriptome technologies to neurological diseases. Front Cell Dev Biol 2023; 11:1142923. [PMID: 36936681 PMCID: PMC10020196 DOI: 10.3389/fcell.2023.1142923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Spatial transcriptome technology acquires gene expression profiles while retaining spatial location information, it displays the gene expression properties of cells in situ. Through the investigation of cell heterogeneity, microenvironment, function, and cellular interactions, spatial transcriptome technology can deeply explore the pathogenic mechanisms of cell-type-specific responses and spatial localization in neurological diseases. The present article overviews spatial transcriptome technologies based on microdissection, in situ hybridization, in situ sequencing, in situ capture, and live cell labeling. Each technology is described along with its methods, detection throughput, spatial resolution, benefits, and drawbacks. Furthermore, their applications in neurodegenerative disease, neuropsychiatric illness, stroke and epilepsy are outlined. This information can be used to understand disease mechanisms, pick therapeutic targets, and establish biomarkers.
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Affiliation(s)
- Dongshan Ya
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yingmei Zhang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qi Cui
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yanlin Jiang
- Department of Pharmacology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Jiaxin Yang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Ning Tian
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Wenjing Xiang
- Department of Neurology ward 2, Guilin People’s Hospital, Guilin, China
| | - Xiaohui Lin
- Department of Geriatrics, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qinghua Li
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Rujia Liao
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- *Correspondence: Rujia Liao,
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34
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Park J, Kim J, Lewy T, Rice CM, Elemento O, Rendeiro AF, Mason CE. Spatial omics technologies at multimodal and single cell/subcellular level. Genome Biol 2022; 23:256. [PMID: 36514162 PMCID: PMC9746133 DOI: 10.1186/s13059-022-02824-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 11/29/2022] [Indexed: 12/15/2022] Open
Abstract
Spatial omics technologies enable a deeper understanding of cellular organizations and interactions within a tissue of interest. These assays can identify specific compartments or regions in a tissue with differential transcript or protein abundance, delineate their interactions, and complement other methods in defining cellular phenotypes. A variety of spatial methodologies are being developed and commercialized; however, these techniques differ in spatial resolution, multiplexing capability, scale/throughput, and coverage. Here, we review the current and prospective landscape of single cell to subcellular resolution spatial omics technologies and analysis tools to provide a comprehensive picture for both research and clinical applications.
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Affiliation(s)
- Jiwoon Park
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, 10065, USA
| | - Junbum Kim
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA
| | - Tyler Lewy
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, 10065, USA
| | - Charles M Rice
- Laboratory of Virology and Infectious Disease, The Rockefeller University, New York, NY, 10065, USA
| | - Olivier Elemento
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - André F Rendeiro
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
| | - Christopher E Mason
- Department of Physiology, Biophysics and Systems Biology, Weill Cornell Medicine, New York, NY, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA.
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA.
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, USA.
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35
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Zhang X, Wang X, Shivashankar GV, Uhler C. Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer's disease. Nat Commun 2022; 13:7480. [PMID: 36463283 PMCID: PMC9719477 DOI: 10.1038/s41467-022-35233-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 11/23/2022] [Indexed: 12/07/2022] Open
Abstract
Tissue development and disease lead to changes in cellular organization, nuclear morphology, and gene expression, which can be jointly measured by spatial transcriptomic technologies. However, methods for jointly analyzing the different spatial data modalities in 3D are still lacking. We present a computational framework to integrate Spatial Transcriptomic data using over-parameterized graph-based Autoencoders with Chromatin Imaging data (STACI) to identify molecular and functional alterations in tissues. STACI incorporates multiple modalities in a single representation for downstream tasks, enables the prediction of spatial transcriptomic data from nuclear images in unseen tissue sections, and provides built-in batch correction of gene expression and tissue morphology through over-parameterization. We apply STACI to analyze the spatio-temporal progression of Alzheimer's disease and identify the associated nuclear morphometric and coupled gene expression features. Collectively, we demonstrate the importance of characterizing disease progression by integrating multiple data modalities and its potential for the discovery of disease biomarkers.
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Affiliation(s)
- Xinyi Zhang
- Massachusetts Institute of Technology, Cambridge, USA
- Broad Institute of MIT and Harvard, Cambridge, USA
| | - Xiao Wang
- Massachusetts Institute of Technology, Cambridge, USA
- Broad Institute of MIT and Harvard, Cambridge, USA
| | - G V Shivashankar
- ETH Zurich, Zurich, Switzerland
- Paul Scherrer Institute, Villigen, Switzerland
| | - Caroline Uhler
- Massachusetts Institute of Technology, Cambridge, USA.
- Broad Institute of MIT and Harvard, Cambridge, USA.
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36
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Wang L, Zhang M, Pan X, Zhao M, Huang L, Hu X, Wang X, Qiao L, Guo Q, Xu W, Qian W, Xue T, Ye X, Li M, Su H, Kuang Y, Lu X, Ye X, Qian K, Lou J. Integrative Serum Metabolic Fingerprints Based Multi-Modal Platforms for Lung Adenocarcinoma Early Detection and Pulmonary Nodule Classification. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2203786. [PMID: 36257825 PMCID: PMC9731719 DOI: 10.1002/advs.202203786] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/21/2022] [Indexed: 05/16/2023]
Abstract
Identification of novel non-invasive biomarkers is critical for the early diagnosis of lung adenocarcinoma (LUAD), especially for the accurate classification of pulmonary nodule. Here, a multiplexed assay is developed on an optimized nanoparticle-based laser desorption/ionization mass spectrometry platform for the sensitive and selective detection of serum metabolic fingerprints (SMFs). Integrative SMFs based multi-modal platforms are constructed for the early detection of LUAD and the classification of pulmonary nodule. The dual modal model, metabolic fingerprints with protein tumor marker neural network (MP-NN), integrating SMFs with protein tumor marker carcinoembryonic antigen (CEA) via deep learning, shows superior performance compared with the single modal model Met-NN (p < 0.001). Based on MP-NN, the tri modal model MPI-RF integrating SMFs, tumor marker CEA, and image features via random forest demonstrates significantly higher performance than the clinical models (Mayo Clinic and Veterans Affairs) and the image artificial intelligence in pulmonary nodule classification (p < 0.001). The developed platforms would be promising tools for LUAD screening and pulmonary nodule management, paving the conceptual and practical foundation for the clinical application of omics tools.
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Affiliation(s)
- Lin Wang
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
- Department of Laboratory MedicineShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Mengji Zhang
- State Key Laboratory for Oncogenes and Related GenesSchool of Biomedical EngineeringInstitute of Medical Robotics and Med‐X Research InstituteShanghai Jiao Tong UniversityShanghai200030P. R. China
- State Key Laboratory for Oncogenes and Related GenesDivision of CardiologyRenji HospitalShanghai Jiao Tong University School of MedicineShanghai200127P. R. China
| | - Xufeng Pan
- Department of Thoracic SurgeryShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Mingna Zhao
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
- Department of Laboratory MedicineShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Lin Huang
- Department of Laboratory MedicineShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Xiaomeng Hu
- Department of Laboratory MedicineThe Third Hospital of Hebei Medical UniversityShijiazhuang050051P. R. China
| | - Xueqing Wang
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
| | - Lihua Qiao
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
| | - Qiaomei Guo
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
| | - Wanxing Xu
- School of MedicineJiangsu UniversityZhenjiang212013P. R. China
| | - Wenli Qian
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
| | - Tingjia Xue
- Department of RadiologyShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
| | - Xiaodan Ye
- Department of RadiologyShanghai Institute of Medical ImagingZhongshan HospitalFudan UniversityShanghai200032P. R. China
| | - Ming Li
- Department of Laboratory DiagnosticsThe First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhui230001P. R. China
| | - Haixiang Su
- Gansu Academic Institute for Medical ResearchGansu Cancer HospitalLanzhouGansu730050P. R. China
| | - Yinglan Kuang
- Department of A. I. ResearchJoint Research Center of Liquid Biopsy in Guangdong, Hong Kong, and MacaoZhuhaiGuangdong519000P. R. China
| | - Xing Lu
- Department of A. I. ResearchJoint Research Center of Liquid Biopsy in Guangdong, Hong Kong, and MacaoZhuhaiGuangdong519000P. R. China
| | - Xin Ye
- Department of Product DevelopmentJoint Research Center of Liquid Biopsy in Guangdong, Hong Kong, and MacaoZhuhaiGuangdong519000P. R. China
| | - Kun Qian
- State Key Laboratory for Oncogenes and Related GenesSchool of Biomedical EngineeringInstitute of Medical Robotics and Med‐X Research InstituteShanghai Jiao Tong UniversityShanghai200030P. R. China
- State Key Laboratory for Oncogenes and Related GenesDivision of CardiologyRenji HospitalShanghai Jiao Tong University School of MedicineShanghai200127P. R. China
| | - Jiatao Lou
- Department of Laboratory MedicineShanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080P. R. China
- Department of Laboratory MedicineShanghai Chest HospitalShanghai Jiao Tong University School of MedicineShanghai200030P. R. China
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Sun H, Fu X, Abraham S, Jin S, Murphy RF. Improving and evaluating deep learning models of cellular organization. Bioinformatics 2022; 38:5299-5306. [PMID: 36264139 PMCID: PMC9710556 DOI: 10.1093/bioinformatics/btac688] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/10/2022] [Accepted: 10/18/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Cells contain dozens of major organelles and thousands of other structures, many of which vary extensively in their number, size, shape and spatial distribution. This complexity and variation dramatically complicates the use of both traditional and deep learning methods to build accurate models of cell organization. Most cellular organelles are distinct objects with defined boundaries that do not overlap, while the pixel resolution of most imaging methods is n sufficient to resolve these boundaries. Thus while cell organization is conceptually object-based, most current methods are pixel-based. Using extensive image collections in which particular organelles were fluorescently labeled, deep learning methods can be used to build conditional autoencoder models for particular organelles. A major advance occurred with the use of a U-net approach to make multiple models all conditional upon a common reference, unlabeled image, allowing the relationships between different organelles to be at least partially inferred. RESULTS We have developed improved Generative Adversarial Networks-based approaches for learning these models and have also developed novel criteria for evaluating how well synthetic cell images reflect the properties of real images. The first set of criteria measure how well models preserve the expected property that organelles do not overlap. We also developed a modified loss function that allows retraining of the models to minimize that overlap. The second set of criteria uses object-based modeling to compare object shape and spatial distribution between synthetic and real images. Our work provides the first demonstration that, at least for some organelles, deep learning models can capture object-level properties of cell images. AVAILABILITY AND IMPLEMENTATION http://murphylab.cbd.cmu.edu/Software/2022_insilico. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Fang S, Chen B, Zhang Y, Sun H, Liu L, Liu S, Li Y, Xu X. Computational Approaches and Challenges in Spatial Transcriptomics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00129-2. [PMID: 36252814 PMCID: PMC10372921 DOI: 10.1016/j.gpb.2022.10.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 09/08/2022] [Accepted: 10/09/2022] [Indexed: 01/19/2023]
Abstract
The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.
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