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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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2
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Marghi Y, Gala R, Baftizadeh F, Sümbül U. Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.02.560574. [PMID: 37873271 PMCID: PMC10592946 DOI: 10.1101/2023.10.02.560574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution. Current approaches model variability in data as continuous latent factors, followed by clustering as a separate step, or immediately apply clustering on the data. We show that such approaches can suffer from qualitative mistakes in identifying cell types robustly, particularly when the number of such cell types is in the hundreds or even thousands. Here, we propose an unsupervised method, MMIDAS, which combines a generalized mixture model with a multi-armed deep neural network, to jointly infer the discrete type and continuous type-specific variability. Using four recent datasets of brain cells spanning different technologies, species, and conditions, we demonstrate that MMIDAS can identify reproducible cell types and infer cell type-dependent continuous variability in both uni-modal and multi-modal datasets.
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Affiliation(s)
| | - Rohan Gala
- Allen Institute, 615 Westlake Ave N, Seattle, WA, USA
| | | | - Uygar Sümbül
- Allen Institute, 615 Westlake Ave N, Seattle, WA, USA
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
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3
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Xenaki A, Pailhas Y, Monti A. Platform motion estimation in multi-band synthetic aperture sonar with coupled variational autoencoders. JASA EXPRESS LETTERS 2024; 4:024802. [PMID: 38376374 DOI: 10.1121/10.0024998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 02/05/2024] [Indexed: 02/21/2024]
Abstract
Coherent processing in synthetic aperture sonar (SAS) requires platform motion estimation and compensation with sub-wavelength accuracy for high-resolution imaging. Micronavigation, i.e., through-the-sensor platform motion estimation, is essential when positioning information from navigational instruments is absent or inadequately accurate. A machine learning method based on variational Bayesian inference has been proposed for unsupervised data-driven micronavigation. Herein, the multiple-input multiple-output arrangement of a multi-band SAS system is exploited and combined with a hierarchical variational inference scheme, which self-supervises the learning of platform motion and results in improved micronavigation accuracy.
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Affiliation(s)
- Angeliki Xenaki
- Centre for Maritime Research and Experimentation, STO-NATO, 19126 La Spezia, , ,
| | - Yan Pailhas
- Centre for Maritime Research and Experimentation, STO-NATO, 19126 La Spezia, , ,
| | - Alessandro Monti
- Centre for Maritime Research and Experimentation, STO-NATO, 19126 La Spezia, , ,
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4
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Sorensen SA, Gouwens NW, Wang Y, Mallory M, Budzillo A, Dalley R, Lee B, Gliko O, Kuo HC, Kuang X, Mann R, Ahmadinia L, Alfiler L, Baftizadeh F, Baker K, Bannick S, Bertagnolli D, Bickley K, Bohn P, Brown D, Bomben J, Brouner K, Chen C, Chen K, Chvilicek M, Collman F, Daigle T, Dawes T, de Frates R, Dee N, DePartee M, Egdorf T, El-Hifnawi L, Enstrom R, Esposito L, Farrell C, Gala R, Glomb A, Gamlin C, Gary A, Goldy J, Gu H, Hadley K, Hawrylycz M, Henry A, Hill D, Hirokawa KE, Huang Z, Johnson K, Juneau Z, Kebede S, Kim L, Lee C, Lesnar P, Li A, Glomb A, Li Y, Liang E, Link K, Maxwell M, McGraw M, McMillen DA, Mukora A, Ng L, Ochoa T, Oldre A, Park D, Pom CA, Popovich Z, Potekhina L, Rajanbabu R, Ransford S, Reding M, Ruiz A, Sandman D, Siverts L, Smith KA, Stoecklin M, Sulc J, Tieu M, Ting J, Trinh J, Vargas S, Vumbaco D, Walker M, Wang M, Wanner A, Waters J, Williams G, Wilson J, Xiong W, Lein E, Berg J, Kalmbach B, Yao S, Gong H, Luo Q, Ng L, Sümbül U, Jarsky T, Yao Z, Tasic B, Zeng H. Connecting single-cell transcriptomes to projectomes in mouse visual cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.25.568393. [PMID: 38168270 PMCID: PMC10760188 DOI: 10.1101/2023.11.25.568393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neuron's role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morpho-electric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.
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Affiliation(s)
| | | | - Yun Wang
- Allen Institute for Brain Science
| | | | | | | | | | | | | | - Xiuli Kuang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | | | | | | | | | | | | | | | - Chao Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Kai Chen
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | - Nick Dee
- Allen Institute for Brain Science
| | | | | | | | | | | | | | | | | | | | | | | | - Hong Gu
- Allen Institute for Brain Science
| | | | | | | | | | | | - Zili Huang
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | - Lisa Kim
- Allen Institute for Brain Science
| | | | | | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | | | - Yaoyao Li
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | | | | | | | | | | | | | | | | | | | | | | | - Zoran Popovich
- University of Washington, Dept. of Computer Science and Engineering
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Wei Xiong
- School of Optometry and Ophthalmology, Wenzhou Medical University, Wenzhou, China
| | - Ed Lein
- Allen Institute for Brain Science
| | - Jim Berg
- Allen Institute for Brain Science
| | | | | | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Qingming Luo
- State Key Laboratory of Digital Medical Engineering, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Lydia Ng
- Allen Institute for Brain Science
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5
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Olson RH, Cohen Kalafut N, Wang D. MANGEM: A web app for multimodal analysis of neuronal gene expression, electrophysiology, and morphology. PATTERNS (NEW YORK, N.Y.) 2023; 4:100847. [PMID: 38035195 PMCID: PMC10682747 DOI: 10.1016/j.patter.2023.100847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 08/07/2023] [Accepted: 09/01/2023] [Indexed: 12/02/2023]
Abstract
Single-cell techniques like Patch-seq have enabled the acquisition of multimodal data from individual neuronal cells, offering systematic insights into neuronal functions. However, these data can be heterogeneous and noisy. To address this, machine learning methods have been used to align cells from different modalities onto a low-dimensional latent space, revealing multimodal cell clusters. The use of those methods can be challenging without computational expertise or suitable computing infrastructure for computationally expensive methods. To address this, we developed a cloud-based web application, MANGEM (multimodal analysis of neuronal gene expression, electrophysiology, and morphology). MANGEM provides a step-by-step accessible and user-friendly interface to machine learning alignment methods of neuronal multimodal data. It can run asynchronously for large-scale data alignment, provide users with various downstream analyses of aligned cells, and visualize the analytic results. We demonstrated the usage of MANGEM by aligning multimodal data of neuronal cells in the mouse visual cortex.
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Affiliation(s)
| | - Noah Cohen Kalafut
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
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6
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Alatkar SA, Wang D. CMOT: Cross-Modality Optimal Transport for multimodal inference. Genome Biol 2023; 24:163. [PMID: 37434182 PMCID: PMC10334579 DOI: 10.1186/s13059-023-02989-8] [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: 11/04/2022] [Accepted: 06/14/2023] [Indexed: 07/13/2023] Open
Abstract
Multimodal measurements of single-cell sequencing technologies facilitate a comprehensive understanding of specific cellular and molecular mechanisms. However, simultaneous profiling of multiple modalities of single cells is challenging, and data integration remains elusive due to missing modalities and cell-cell correspondences. To address this, we developed a computational approach, Cross-Modality Optimal Transport (CMOT), which aligns cells within available multi-modal data (source) onto a common latent space and infers missing modalities for cells from another modality (target) of mapped source cells. CMOT outperforms existing methods in various applications from developing brain, cancers to immunology, and provides biological interpretations improving cell-type or cancer classifications.
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Affiliation(s)
- Sayali Anil Alatkar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53076, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53076, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53076, USA.
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7
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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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8
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Li Q, Lin Z, Liu R, Tang X, Huang J, He Y, Sui X, Tian W, Shen H, Zhou H, Sheng H, Shi H, Xiao L, Wang X, Liu J. Multimodal charting of molecular and functional cell states via in situ electro-sequencing. Cell 2023; 186:2002-2017.e21. [PMID: 37080201 DOI: 10.1016/j.cell.2023.03.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/21/2022] [Accepted: 03/16/2023] [Indexed: 04/22/2023]
Abstract
Paired mapping of single-cell gene expression and electrophysiology is essential to understand gene-to-function relationships in electrogenic tissues. Here, we developed in situ electro-sequencing (electro-seq) that combines flexible bioelectronics with in situ RNA sequencing to stably map millisecond-timescale electrical activity and profile single-cell gene expression from the same cells across intact biological networks, including cardiac and neural patches. When applied to human-induced pluripotent stem-cell-derived cardiomyocyte patches, in situ electro-seq enabled multimodal in situ analysis of cardiomyocyte electrophysiology and gene expression at the cellular level, jointly defining cell states and developmental trajectories. Using machine-learning-based cross-modal analysis, in situ electro-seq identified gene-to-electrophysiology relationships throughout cardiomyocyte development and accurately reconstructed the evolution of gene expression profiles based on long-term stable electrical measurements. In situ electro-seq could be applicable to create spatiotemporal multimodal maps in electrogenic tissues, potentiating the discovery of cell types and gene programs responsible for electrophysiological function and dysfunction.
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Affiliation(s)
- Qiang Li
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Zuwan Lin
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Ren Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - 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
| | - Jiahao Huang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, 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
| | - Xin Sui
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Weiwen Tian
- 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
| | - Haowen Zhou
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hao Sheng
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Hailing Shi
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ling Xiao
- Cardiovascular Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Cardiovascular Disease Initiative and Precision Cardiology Laboratory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Xiao Wang
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA.
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9
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Covert I, Gala R, Wang T, Svoboda K, Sümbül U, Lee SI. Predictive and robust gene selection for spatial transcriptomics. Nat Commun 2023; 14:2091. [PMID: 37045821 PMCID: PMC10097645 DOI: 10.1038/s41467-023-37392-1] [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: 06/08/2022] [Accepted: 03/16/2023] [Indexed: 04/14/2023] Open
Abstract
A prominent trend in single-cell transcriptomics is providing spatial context alongside a characterization of each cell's molecular state. This typically requires targeting an a priori selection of genes, often covering less than 1% of the genome, and a key question is how to optimally determine the small gene panel. We address this challenge by introducing a flexible deep learning framework, PERSIST, to identify informative gene targets for spatial transcriptomics studies by leveraging reference scRNA-seq data. Using datasets spanning different brain regions, species, and scRNA-seq technologies, we show that PERSIST reliably identifies panels that provide more accurate prediction of the genome-wide expression profile, thereby capturing more information with fewer genes. PERSIST can be adapted to specific biological goals, and we demonstrate that PERSIST's binarization of gene expression levels enables models trained on scRNA-seq data to generalize with to spatial transcriptomics data, despite the complex shift between these technologies.
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Affiliation(s)
- Ian Covert
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| | - Rohan Gala
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tim Wang
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Karel Svoboda
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Allen Institute for Neural Dynamics, Seattle, WA, USA
| | - Uygar Sümbül
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Su-In Lee
- Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
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10
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Smith SJ, von Zastrow M. A Molecular Landscape of Mouse Hippocampal Neuromodulation. Front Neural Circuits 2022; 16:836930. [PMID: 35601530 PMCID: PMC9120848 DOI: 10.3389/fncir.2022.836930] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/30/2022] [Indexed: 12/23/2022] Open
Abstract
Adaptive neuronal circuit function requires a continual adjustment of synaptic network parameters known as “neuromodulation.” This process is now understood to be based primarily on the binding of myriad secreted “modulatory” ligands such as dopamine, serotonin and the neuropeptides to G protein-coupled receptors (GPCRs) that, in turn, regulate the function of the ion channels that establish synaptic weights and membrane excitability. Many of the basic molecular mechanisms of neuromodulation are now known, but the organization of neuromodulation at a network level is still an enigma. New single-cell RNA sequencing data and transcriptomic neurotaxonomies now offer bright new lights to shine on this critical “dark matter” of neuroscience. Here we leverage these advances to explore the cell-type-specific expression of genes encoding GPCRs, modulatory ligands, ion channels and intervening signal transduction molecules in mouse hippocampus area CA1, with the goal of revealing broad outlines of this well-studied brain structure’s neuromodulatory network architecture.
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Affiliation(s)
- Stephen J Smith
- Allen Institute for Brain Science, Seattle, WA, United States
- *Correspondence: Stephen J Smith,
| | - Mark von Zastrow
- Departments of Psychiatry and Pharmacology, University of California, San Francisco, San Francisco, CA, United States
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11
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A multi-encoder variational autoencoder controls multiple transformational features in single-cell image analysis. Commun Biol 2022; 5:255. [PMID: 35322205 PMCID: PMC8943013 DOI: 10.1038/s42003-022-03218-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 03/07/2022] [Indexed: 01/02/2023] Open
Abstract
Image-based cell phenotyping relies on quantitative measurements as encoded representations of cells; however, defining suitable representations that capture complex imaging features is challenged by the lack of robust methods to segment cells, identify subcellular compartments, and extract relevant features. Variational autoencoder (VAE) approaches produce encouraging results by mapping an image to a representative descriptor, and outperform classical hand-crafted features for morphology, intensity, and texture at differentiating data. Although VAEs show promising results for capturing morphological and organizational features in tissue, single cell image analyses based on VAEs often fail to identify biologically informative features due to uninformative technical variation. Here we propose a multi-encoder VAE (ME-VAE) in single cell image analysis using transformed images as a self-supervised signal to extract transform-invariant biologically meaningful features, including emergent features not obvious from prior knowledge. We show that the proposed architecture improves analysis by making distinct cell populations more separable compared to traditional and recent extensions of VAE architectures and intensity measurements by enhancing phenotypic differences between cells and by improving correlations to other analytic modalities. Better feature extraction and image analysis methods enabled by the ME-VAE will advance our understanding of complex cell biology and enable discoveries previously hidden behind image complexity ultimately improving medical outcomes and drug discovery. The Multi-Encoder Variational AutoEncoder (ME-VAE) is a computational model that can control for multiple transformational features in single-cell imaging data, enabling researchers to extract meaningful single-cell information and better separate heterogeneous cell types.
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12
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Eschenburg KM, Grabowski TJ, Haynor DR. Learning Cortical Parcellations Using Graph Neural Networks. Front Neurosci 2021; 15:797500. [PMID: 35002611 PMCID: PMC8739886 DOI: 10.3389/fnins.2021.797500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Deep learning has been applied to magnetic resonance imaging (MRI) for a variety of purposes, ranging from the acceleration of image acquisition and image denoising to tissue segmentation and disease diagnosis. Convolutional neural networks have been particularly useful for analyzing MRI data due to the regularly sampled spatial and temporal nature of the data. However, advances in the field of brain imaging have led to network- and surface-based analyses that are often better represented in the graph domain. In this analysis, we propose a general purpose cortical segmentation method that, given resting-state connectivity features readily computed during conventional MRI pre-processing and a set of corresponding training labels, can generate cortical parcellations for new MRI data. We applied recent advances in the field of graph neural networks to the problem of cortical surface segmentation, using resting-state connectivity to learn discrete maps of the human neocortex. We found that graph neural networks accurately learn low-dimensional representations of functional brain connectivity that can be naturally extended to map the cortices of new datasets. After optimizing over algorithm type, network architecture, and training features, our approach yielded mean classification accuracies of 79.91% relative to a previously published parcellation. We describe how some hyperparameter choices including training and testing data duration, network architecture, and algorithm choice affect model performance.
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Affiliation(s)
- Kristian M. Eschenburg
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA, United States
| | - Thomas J. Grabowski
- Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA, United States
- Department of Radiology, University of Washington Medical Center, Seattle, WA, United States
- Department of Neurology, University of Washington Medical Center, Seattle, WA, United States
| | - David R. Haynor
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- Integrated Brain Imaging Center, University of Washington Medical Center, Seattle, WA, United States
- Department of Radiology, University of Washington Medical Center, Seattle, WA, United States
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13
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Huang J, Sheng J, Wang D. Manifold learning analysis suggests strategies to align single-cell multimodal data of neuronal electrophysiology and transcriptomics. Commun Biol 2021; 4:1308. [PMID: 34799674 PMCID: PMC8604989 DOI: 10.1038/s42003-021-02807-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 10/26/2021] [Indexed: 12/30/2022] Open
Abstract
Recent single-cell multimodal data reveal multi-scale characteristics of single cells, such as transcriptomics, morphology, and electrophysiology. However, integrating and analyzing such multimodal data to deeper understand functional genomics and gene regulation in various cellular characteristics remains elusive. To address this, we applied and benchmarked multiple machine learning methods to align gene expression and electrophysiological data of single neuronal cells in the mouse brain from the Brain Initiative. We found that nonlinear manifold learning outperforms other methods. After manifold alignment, the cells form clusters highly corresponding to transcriptomic and morphological cell types, suggesting a strong nonlinear relationship between gene expression and electrophysiology at the cell-type level. Also, the electrophysiological features are highly predictable by gene expression on the latent space from manifold alignment. The aligned cells further show continuous changes of electrophysiological features, implying cross-cluster gene expression transitions. Functional enrichment and gene regulatory network analyses for those cell clusters revealed potential genome functions and molecular mechanisms from gene expression to neuronal electrophysiology.
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Affiliation(s)
- Jiawei Huang
- Department of Statistics, University of Wisconsin - Madison, Madison, WI, 53706, USA
- Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH, 45223, USA
| | - Jie Sheng
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin - Madison, Madison, WI, 53705, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison, Madison, WI, 53706, USA.
- Department of Computer Sciences, University of Wisconsin - Madison, Madison, WI, 53706, USA.
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14
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Smith SJ. Transcriptomic evidence for dense peptidergic networks within forebrains of four widely divergent tetrapods. Curr Opin Neurobiol 2021; 71:100-109. [PMID: 34775262 DOI: 10.1016/j.conb.2021.09.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 09/21/2021] [Accepted: 09/27/2021] [Indexed: 12/15/2022]
Abstract
The primary function common to every neuron is communication with other neurons. Such cell-cell signaling can take numerous forms, including fast synaptic transmission and slower neuromodulation via secreted messengers, such as neuropeptides, dopamine, and many other diffusible small molecules. Individual neurons are quite diverse, however, in all particulars of both synaptic and neuromodulatory communication. Neuron classification schemes have therefore proven very useful in exploring the emergence of network function, behavior, and cognition from the communication functions of individual neurons. Recently published single-cell mRNA sequencing data and corresponding transcriptomic neuron classifications from turtle, songbird, mouse, and human provide evidence for a long evolutionary history and adaptive significance of localized peptidergic signaling. Across all four species, sets of at least twenty orthologous cognate pairs of neuropeptide precursor protein and receptor genes are expressed in individually sparse but heavily overlapping patterns suggesting that all forebrain neuron types are densely interconnected by local peptidergic signals.
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15
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Kobak D, Bernaerts Y, Weis MA, Scala F, Tolias AS, Berens P. Sparse reduced‐rank regression for exploratory visualisation of paired multivariate data. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12494] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Dmitry Kobak
- Institute for Ophthalmic Research University of Tübingen Tübingen Germany
| | - Yves Bernaerts
- Institute for Ophthalmic Research University of Tübingen Tübingen Germany
- International Max Planck Research School for Intelligent Systems Germany
| | - Marissa A. Weis
- Institute for Ophthalmic Research University of Tübingen Tübingen Germany
| | - Federico Scala
- Department of Neuroscience Baylor College of Medicine Houston Texas USA
| | - Andreas S. Tolias
- Department of Neuroscience Baylor College of Medicine Houston Texas USA
| | - Philipp Berens
- Institute for Ophthalmic Research University of Tübingen Tübingen Germany
- Department of Computer Science University of Tübingen Tübingen Germany
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16
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Bugeon S. Towards a consistent neuron classification. NATURE COMPUTATIONAL SCIENCE 2021; 1:100-101. [PMID: 38217231 DOI: 10.1038/s43588-021-00027-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Affiliation(s)
- Stephane Bugeon
- UCL Queen Square Institute of Neurology, University College London, London, UK.
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