1
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Lin Y, Liang Y, Wang D, Chang Y, Ma Q, Wang Y, He F, Xu D. A contrastive learning approach to integrate spatial transcriptomics and histological images. Comput Struct Biotechnol J 2024; 23:1786-1795. [PMID: 38707535 PMCID: PMC11068546 DOI: 10.1016/j.csbj.2024.04.039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 04/14/2024] [Accepted: 04/15/2024] [Indexed: 05/07/2024] Open
Abstract
The rapid growth of spatially resolved transcriptomics technology provides new perspectives on spatial tissue architecture. Deep learning has been widely applied to derive useful representations for spatial transcriptome analysis. However, effectively integrating spatial multi-modal data remains challenging. Here, we present ConGcR, a contrastive learning-based model for integrating gene expression, spatial location, and tissue morphology for data representation and spatial tissue architecture identification. Graph convolution and ResNet were used as encoders for gene expression with spatial location and histological image inputs, respectively. We further enhanced ConGcR with a graph auto-encoder as ConGaR to better model spatially embedded representations. We validated our models using 16 human brains, four chicken hearts, eight breast tumors, and 30 human lung spatial transcriptomics samples. The results showed that our models generated more effective embeddings for obtaining tissue architectures closer to the ground truth than other methods. Overall, our models not only can improve tissue architecture identification's accuracy but also may provide valuable insights and effective data representation for other tasks in spatial transcriptome analyses.
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Affiliation(s)
- Yu Lin
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Yanchun Liang
- School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Duolin Wang
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Yuzhou Chang
- Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, United States
| | - Qin Ma
- Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, United States
| | - Yan Wang
- School of Artificial Intelligence, Jilin University, Changchun 130012, China
- Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Fei He
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
- School of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Dong Xu
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
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2
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Zacharjasz J, Sztachera M, Smuszkiewicz M, Piwecka M. Micromanaging the neuroendocrine system - A review on miR-7 and the other physiologically relevant miRNAs in the hypothalamic-pituitary axis. FEBS Lett 2024. [PMID: 38858179 DOI: 10.1002/1873-3468.14948] [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: 03/27/2024] [Revised: 05/16/2024] [Accepted: 05/20/2024] [Indexed: 06/12/2024]
Abstract
The hypothalamic-pituitary axis is central to the functioning of the neuroendocrine system and essential for regulating physiological and behavioral homeostasis and coordinating fundamental body functions. The expanding line of evidence shows the indispensable role of the microRNA pathway in regulating the gene expression profile in the developing and adult hypothalamus and pituitary gland. Experiments provoking a depletion of miRNA maturation in the context of the hypothalamic-pituitary axis brought into focus a prominent involvement of miRNAs in neuroendocrine functions. There are also a few individual miRNAs and miRNA families that have been studied in depth revealing their crucial role in mediating the regulation of fundamental processes such as temporal precision of puberty timing, hormone production, fertility and reproduction capacity, and energy balance. Among these miRNAs, miR-7 was shown to be hypothalamus-enriched and the top one highly expressed in the pituitary gland, where it has a profound impact on gene expression regulation. Here, we review miRNA profiles, knockout phenotypes, and miRNA interaction (targets) in the hypothalamic-pituitary axis that advance our understanding of the roles of miRNAs in mammalian neurosecretion and related physiology.
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Affiliation(s)
- Julian Zacharjasz
- Department of Non-coding RNAs, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznań, Poland
| | - Marta Sztachera
- Department of Non-coding RNAs, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznań, Poland
| | - Michał Smuszkiewicz
- Department of Non-coding RNAs, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznań, Poland
| | - Monika Piwecka
- Department of Non-coding RNAs, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznań, Poland
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3
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Rogers JF, Vandendoren M, Prather JF, Landen JG, Bedford NL, Nelson AC. Neural cell-types and circuits linking thermoregulation and social behavior. Neurosci Biobehav Rev 2024; 161:105667. [PMID: 38599356 PMCID: PMC11163828 DOI: 10.1016/j.neubiorev.2024.105667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 04/05/2024] [Accepted: 04/07/2024] [Indexed: 04/12/2024]
Abstract
Understanding how social and affective behavioral states are controlled by neural circuits is a fundamental challenge in neurobiology. Despite increasing understanding of central circuits governing prosocial and agonistic interactions, how bodily autonomic processes regulate these behaviors is less resolved. Thermoregulation is vital for maintaining homeostasis, but also associated with cognitive, physical, affective, and behavioral states. Here, we posit that adjusting body temperature may be integral to the appropriate expression of social behavior and argue that understanding neural links between behavior and thermoregulation is timely. First, changes in behavioral states-including social interaction-often accompany changes in body temperature. Second, recent work has uncovered neural populations controlling both thermoregulatory and social behavioral pathways. We identify additional neural populations that, in separate studies, control social behavior and thermoregulation, and highlight their relevance to human and animal studies. Third, dysregulation of body temperature is linked to human neuropsychiatric disorders. Although body temperature is a "hidden state" in many neurobiological studies, it likely plays an underappreciated role in regulating social and affective states.
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Affiliation(s)
- Joseph F Rogers
- Department of Zoology & Physiology, University of Wyoming, Laramie, WY, USA; University of Wyoming Sensory Biology Center, USA
| | - Morgane Vandendoren
- Department of Zoology & Physiology, University of Wyoming, Laramie, WY, USA; University of Wyoming Sensory Biology Center, USA
| | - Jonathan F Prather
- Department of Zoology & Physiology, University of Wyoming, Laramie, WY, USA
| | - Jason G Landen
- Department of Zoology & Physiology, University of Wyoming, Laramie, WY, USA; University of Wyoming Sensory Biology Center, USA
| | - Nicole L Bedford
- Department of Zoology & Physiology, University of Wyoming, Laramie, WY, USA
| | - Adam C Nelson
- Department of Zoology & Physiology, University of Wyoming, Laramie, WY, USA; University of Wyoming Sensory Biology Center, USA.
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4
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Hiraki-Kajiyama T, Miyasaka N, Ando R, Wakisaka N, Itoga H, Onami S, Yoshihara Y. An atlas and database of neuropeptide gene expression in the adult zebrafish forebrain. J Comp Neurol 2024; 532:e25619. [PMID: 38831653 DOI: 10.1002/cne.25619] [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: 03/29/2023] [Revised: 03/21/2024] [Accepted: 04/16/2024] [Indexed: 06/05/2024]
Abstract
Zebrafish is a useful model organism in neuroscience; however, its gene expression atlas in the adult brain is not well developed. In the present study, we examined the expression of 38 neuropeptides, comparing with GABAergic and glutamatergic neuron marker genes in the adult zebrafish brain by comprehensive in situ hybridization. The results are summarized as an expression atlas in 19 coronal planes of the forebrain. Furthermore, the scanned data of all brain sections were made publicly available in the Adult Zebrafish Brain Gene Expression Database (https://ssbd.riken.jp/azebex/). Based on these data, we performed detailed comparative neuroanatomical analyses of the hypothalamus and found that several regions previously described as one nucleus in the reference zebrafish brain atlas contain two or more subregions with significantly different neuropeptide/neurotransmitter expression profiles. Subsequently, we compared the expression data in zebrafish telencephalon and hypothalamus obtained in this study with those in mice, by performing a cluster analysis. As a result, several nuclei in zebrafish and mice were clustered in close vicinity. The present expression atlas, database, and anatomical findings will contribute to future neuroscience research using zebrafish.
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Affiliation(s)
- Towako Hiraki-Kajiyama
- Laboratory for Systems Molecular Ethology, RIKEN Center for Brain Science, Wako, Saitama, Japan
- Laboratory of Molecular Ethology, Graduate School of Life Science, Tohoku University, Sendai, Miyagi, Japan
| | - Nobuhiko Miyasaka
- Laboratory for Systems Molecular Ethology, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Reiko Ando
- Support Unit for Bio-Material Analysis, Research Resources Division, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Noriko Wakisaka
- Laboratory for Systems Molecular Ethology, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Hiroya Itoga
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, Japan
| | - Shuichi Onami
- Laboratory for Developmental Dynamics, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, Japan
- Life Science Data Sharing Unit, RIKEN Information R&D and Strategy Headquarters, Kobe, Hyogo, Japan
| | - Yoshihiro Yoshihara
- Laboratory for Systems Molecular Ethology, RIKEN Center for Brain Science, Wako, Saitama, Japan
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5
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Aspesi D, Cornil CA. Role of neuroestrogens in the regulation of social behaviors - From social recognition to mating. Neurosci Biobehav Rev 2024; 161:105679. [PMID: 38642866 DOI: 10.1016/j.neubiorev.2024.105679] [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: 12/07/2023] [Revised: 03/12/2024] [Accepted: 04/15/2024] [Indexed: 04/22/2024]
Abstract
In this mini-review, we summarize the brain distribution of aromatase, the enzyme catalyzing the synthesis of estrogens from androgens, and the mechanisms responsible for regulating estrogen production within the brain. Understanding this local synthesis of estrogens by neurons is pivotal as it profoundly influences various facets of social behavior. Neuroestrogen action spans from the initial processing of socially pertinent sensory cues to integrating this information with an individual's internal state, ultimately resulting in the manifestation of either pro-affiliative or - aggressive behaviors. We focus here in particular on aggressive and sexual behavior as the result of correct individual recognition of intruders and potential mates. The data summarized in this review clearly point out the crucial role of locally synthesized estrogens in facilitating rapid adaptation to the social environment in rodents and birds of both sexes. These observations not only shed light on the evolutionary significance but also indicate the potential implications of these findings in the realm of human health, suggesting a compelling avenue for further investigation.
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Affiliation(s)
- Dario Aspesi
- Center for Behavioral Neuroscience, Georgia State University, Atlanta, GA 30303, USA
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6
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Li Y, Zhang J, Gao X, Zhang QC. Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding. Cell Syst 2024:S2405-4712(24)00124-8. [PMID: 38823396 DOI: 10.1016/j.cels.2024.05.001] [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: 05/25/2023] [Revised: 01/08/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
Computational methods are desired for single-cell-resolution spatial transcriptomics (ST) data analysis to uncover spatial organization principles for how individual cells exert tissue-specific functions. Here, we present ST data analysis via interaction-aware cell embedding (SPACE), a deep-learning method for cell-type identification and tissue module discovery from single-cell-resolution ST data by learning a cell representation that captures its gene expression profile and interactions with its spatial neighbors. SPACE identified spatially informed cell subtypes defined by their special spatial distribution patterns and distinct proximal-interacting cell types. SPACE also automatically discovered "cell communities"-tissue modules with discernible boundaries and a uniform spatial distribution of constituent cell types. For each cell community, SPACE outputs a characteristic proximal cell-cell interaction network associated with physiological processes, which can be used to refine ligand-receptor-based intercellular signaling analyses. We envision that SPACE can be used in large-scale ST projects to understand how proximal cell-cell interactions contribute to emergent biological functions within cell communities. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Yuzhe Li
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Jinsong Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China; Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia; KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia; BioMap, Beijing 100086, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
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7
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Wang H, Zhao J, Nie Q, Zheng C, Sun X. Dissecting Spatiotemporal Structures in Spatial Transcriptomics via Diffusion-Based Adversarial Learning. RESEARCH (WASHINGTON, D.C.) 2024; 7:0390. [PMID: 38812530 PMCID: PMC11134684 DOI: 10.34133/research.0390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 04/23/2024] [Indexed: 05/31/2024]
Abstract
Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.
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Affiliation(s)
- Haiyun Wang
- College of Mathematics and System Sciences,
Xinjiang University, Urumqi, China
| | - Jianping Zhao
- College of Mathematics and System Sciences,
Xinjiang University, Urumqi, China
| | - Qing Nie
- Department of Mathematics and Department of Developmental and Cell Biology, NSF-Simons Center for Multiscale Cell Fate Research,
University of California Irvine, Irvine, CA, USA
| | - Chunhou Zheng
- School of Artificial Intelligence,
Anhui University, Hefei, China
| | - Xiaoqiang Sun
- School of Mathematics,
Sun Yat-sen University, Guangzhou, China
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8
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Guo X, Keenan BT, Reiner BC, Lian J, Pack AI. Single-nucleus RNA-seq identifies one galanin neuronal subtype in mouse preoptic hypothalamus activated during recovery from sleep deprivation. Cell Rep 2024; 43:114192. [PMID: 38703367 DOI: 10.1016/j.celrep.2024.114192] [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: 12/01/2023] [Revised: 03/13/2024] [Accepted: 04/18/2024] [Indexed: 05/06/2024] Open
Abstract
The preoptic area of the hypothalamus (POA) is essential for sleep regulation. However, the cellular makeup of the POA is heterogeneous, and the molecular identities of the sleep-promoting cells remain elusive. To address this question, this study compares mice during recovery sleep following sleep deprivation to mice allowed extended sleep. Single-nucleus RNA sequencing (single-nucleus RNA-seq) identifies one galanin inhibitory neuronal subtype that shows upregulation of rapid and delayed activity-regulated genes during recovery sleep. This cell type expresses higher levels of growth hormone receptor and lower levels of estrogen receptor compared to other galanin subtypes. single-nucleus RNA-seq also reveals cell-type-specific upregulation of purinergic receptor (P2ry14) and serotonin receptor (Htr2a) during recovery sleep in this neuronal subtype, suggesting possible mechanisms for sleep regulation. Studies with RNAscope validate the single-nucleus RNA-seq findings. Thus, the combined use of single-nucleus RNA-seq and activity-regulated genes identifies a neuronal subtype functionally involved in sleep regulation.
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Affiliation(s)
- Xiaofeng Guo
- Circadian Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brendan T Keenan
- Circadian Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin C Reiner
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jie Lian
- Circadian Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Allan I Pack
- Circadian Sleep Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Sleep Medicine, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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9
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Wang Y, Liu Z, Ma X. MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data. Genome Biol 2024; 25:133. [PMID: 38783355 PMCID: PMC11112797 DOI: 10.1186/s13059-024-03272-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for identifying spatial domains in spatial transcriptomics data with joint learning. We demonstrate that spatial domains can be precisely characterized and discriminated by the topological structure of cell networks, facilitating identification and interpretability of spatial domains, which outperforms state-of-the-art baselines. Furthermore, we prove that network model offers an effective and efficient strategy for integrative analysis of spatial transcriptomics data from various platforms.
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Affiliation(s)
- Yu Wang
- School of Computer Science and Technology, Xidian University, No.2 South Taibai Road, Xi'an, 710071, Shaanxi, China
- Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xidian University, No.2 South Taibai Road, Xi'an, 710071, Shaanxi, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 106 Zhongshan Er Road, Guangzhou, 510080, Guangdong, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, Guangdong, China
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, No.2 South Taibai Road, Xi'an, 710071, Shaanxi, China.
- Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xidian University, No.2 South Taibai Road, Xi'an, 710071, Shaanxi, China.
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10
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Pham T, Chen Y, Labaer J, Guo J. Ultrasensitive and Multiplexed Protein Imaging with Clickable and Cleavable Fluorophores. Anal Chem 2024; 96:7281-7288. [PMID: 38663032 DOI: 10.1021/acs.analchem.4c01273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Single-cell spatial proteomic analysis holds great promise to advance our understanding of the composition, organization, interaction, and function of the various cell types in complex biological systems. However, the current multiplexed protein imaging technologies suffer from low detection sensitivity, limited multiplexing capacity, or are technically demanding. To tackle these issues, here, we report the development of a highly sensitive and multiplexed in situ protein profiling method using off-the-shelf antibodies. In this approach, the protein targets are stained with horseradish peroxidase (HRP) conjugated antibodies and cleavable fluorophores via click chemistry. Through repeated cycles of target staining, fluorescence imaging, and fluorophore cleavage, many proteins can be profiled in single cells in situ. Applying this approach, we successfully quantified 28 different proteins in human formalin-fixed paraffin-embedded (FFPE) tonsil tissue, which represents the highest multiplexing capacity among the tyramide signal amplification (TSA) methods. Based on their unique protein expression patterns and their microenvironment, ∼820,000 cells in the tissue are classified into distinct cell clusters. We also explored the cell-cell interactions between these varied cell clusters and observed that different subregions of the tissue are composed of cells from specific clusters.
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Affiliation(s)
- Thai Pham
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Yi Chen
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Joshua Labaer
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
| | - Jia Guo
- Biodesign Institute & School of Molecular Sciences, Arizona State University, Tempe, Arizona 85287, United States
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11
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Niepoth N, Merritt JR, Uminski M, Lei E, Esquibies VS, Bando IB, Hernandez K, Gebhardt C, Wacker SA, Lutzu S, Poudel A, Soma KK, Rudolph S, Bendesky A. Evolution of a novel adrenal cell type that promotes parental care. Nature 2024; 629:1082-1090. [PMID: 38750354 DOI: 10.1038/s41586-024-07423-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 04/15/2024] [Indexed: 05/25/2024]
Abstract
Cell types with specialized functions fundamentally regulate animal behaviour, and yet the genetic mechanisms that underlie the emergence of novel cell types and their consequences for behaviour are not well understood1. Here we show that the monogamous oldfield mouse (Peromyscus polionotus) has recently evolved a novel cell type in the adrenal gland that expresses the enzyme AKR1C18, which converts progesterone into 20α-hydroxyprogesterone. We then demonstrate that 20α-hydroxyprogesterone is more abundant in oldfield mice, where it induces monogamous-typical parental behaviours, than in the closely related promiscuous deer mice (Peromyscus maniculatus). Using quantitative trait locus mapping in a cross between these species, we ultimately find interspecific genetic variation that drives expression of the nuclear protein GADD45A and the glycoprotein tenascin N, which contribute to the emergence and function of this cell type in oldfield mice. Our results provide an example by which the recent evolution of a new cell type in a gland outside the brain contributes to the evolution of social behaviour.
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Affiliation(s)
- Natalie Niepoth
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Jennifer R Merritt
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Michelle Uminski
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Emily Lei
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Victoria S Esquibies
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Ina B Bando
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Kimberly Hernandez
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Christoph Gebhardt
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA
| | - Sarah A Wacker
- Department of Chemistry and Biochemistry, Manhattan College, New York, NY, USA
| | - Stefano Lutzu
- Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, New York, NY, USA
| | - Asmita Poudel
- Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Kiran K Soma
- Department of Psychology, University of British Columbia, Vancouver, British Columbia, Canada
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Stephanie Rudolph
- Department of Neuroscience, Albert Einstein College of Medicine, New York, NY, USA
- Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, New York, NY, USA
| | - Andres Bendesky
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
- Department of Ecology, Evolution and Environmental Biology, Columbia University, New York, NY, USA.
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12
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Inoue S. Hormonal and circuit mechanisms controlling female sexual behavior. Front Neural Circuits 2024; 18:1409349. [PMID: 38752168 PMCID: PMC11094328 DOI: 10.3389/fncir.2024.1409349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 04/15/2024] [Indexed: 05/18/2024] Open
Abstract
Sexual behavior is crucial for reproduction in many animals. In many vertebrates, females exhibit sexual behavior only during a brief period surrounding ovulation. Over the decades, studies have identified the roles of ovarian sex hormones, which peak in levels around the time of ovulation, and the critical brain regions involved in the regulation of female sexual behavior. Modern technical innovations have enabled a deeper understanding of the neural circuit mechanisms controlling this behavior. In this review, I summarize our current knowledge and discuss the neural circuit mechanisms by which female sexual behavior occurs in association with the ovulatory phase of their cycle.
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Affiliation(s)
- Sayaka Inoue
- Department of Psychiatry, School of Medicine, Washington University in St. Louis, St. Louis, MO, United States
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13
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Daly AC, Cambuli F, Äijö T, Lötstedt B, Marjanovic N, Kuksenko O, Smith-Erb M, Fernandez S, Domovic D, Van Wittenberghe N, Drokhlyansky E, Griffin GK, Phatnani H, Bonneau R, Regev A, Vickovic S. Tissue and cellular spatiotemporal dynamics in colon aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590125. [PMID: 38712088 PMCID: PMC11071407 DOI: 10.1101/2024.04.22.590125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.
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Affiliation(s)
- Aidan C. Daly
- New York Genome Center, New York, NY, USA
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | | | - Tarmo Äijö
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Britta Lötstedt
- New York Genome Center, New York, NY, USA
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Nemanja Marjanovic
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Olena Kuksenko
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | | | | | | | | | - Eugene Drokhlyansky
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabriel K Griffin
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Hemali Phatnani
- New York Genome Center, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Center for Data Science, New York University, New York, NY, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Aviv Regev
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Sanja Vickovic
- New York Genome Center, New York, NY, USA
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Engineering and Herbert Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Beijer Laboratory for Gene and Neuro Research, Uppsala University, Uppsala, Sweden
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14
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Yang J, Jiang X, Jin KW, Shin S, Li Q. Bayesian hidden mark interaction model for detecting spatially variable genes in imaging-based spatially resolved transcriptomics data. Front Genet 2024; 15:1356709. [PMID: 38725485 PMCID: PMC11079231 DOI: 10.3389/fgene.2024.1356709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 04/08/2024] [Indexed: 05/12/2024] Open
Abstract
Recent technology breakthroughs in spatially resolved transcriptomics (SRT) have enabled the comprehensive molecular characterization of cells whilst preserving their spatial and gene expression contexts. One of the fundamental questions in analyzing SRT data is the identification of spatially variable genes whose expressions display spatially correlated patterns. Existing approaches are built upon either the Gaussian process-based model, which relies on ad hoc kernels, or the energy-based Ising model, which requires gene expression to be measured on a lattice grid. To overcome these potential limitations, we developed a generalized energy-based framework to model gene expression measured from imaging-based SRT platforms, accommodating the irregular spatial distribution of measured cells. Our Bayesian model applies a zero-inflated negative binomial mixture model to dichotomize the raw count data, reducing noise. Additionally, we incorporate a geostatistical mark interaction model with a generalized energy function, where the interaction parameter is used to identify the spatial pattern. Auxiliary variable MCMC algorithms were employed to sample from the posterior distribution with an intractable normalizing constant. We demonstrated the strength of our method on both simulated and real data. Our simulation study showed that our method captured various spatial patterns with high accuracy; moreover, analysis of a seqFISH dataset and a STARmap dataset established that our proposed method is able to identify genes with novel and strong spatial patterns.
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Affiliation(s)
- Jie Yang
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, United States
| | - Xi Jiang
- Department of Statistics and Data Science, Southern Methodist University, Dallas, TX, United States
| | - Kevin Wang Jin
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, United States
| | - Sunyoung Shin
- Department of Mathematics, Pohang University of Science and Technology, Pohang, Republic of Korea
| | - Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX, United States
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15
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Inada K. Neurobiological mechanisms underlying oxytocin-mediated parental behavior in rodents. Neurosci Res 2024:S0168-0102(24)00052-X. [PMID: 38642676 DOI: 10.1016/j.neures.2024.04.001] [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: 02/20/2024] [Revised: 03/29/2024] [Accepted: 04/07/2024] [Indexed: 04/22/2024]
Abstract
Parental behavior is essential for mammalian offspring to survive. Because of this significance, elucidating the neurobiological mechanisms that facilitate parental behavior has received strong interest. Decades of studies utilizing pharmacology and molecular biology have revealed that in addition to its facilitatory effects on parturition and lactation, oxytocin (OT) promotes the expression of parental behavior in rodents. Recent studies have also described the modulation of sensory processing by OT and the interaction of the OT system with other brain regions associated with parental behavior. However, the precise neurobiological mechanisms underlying the facilitation of caregiving behaviors by OT remain unclear. In this Review, I summarize the findings from rats and mice with a view toward integrating past and recent progress. I then review recent advances in the understanding of the molecular, cellular, and circuit mechanisms of OT-mediated parental behavior. Based on these observations, I propose a hypothetical model that would explain the mechanisms underlying OT-mediated parental behavior. Finally, I conclude by discussing some major remaining questions and propose potential future research directions.
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Affiliation(s)
- Kengo Inada
- RIKEN Center for Biosystems Dynamics Research, 2-2-3 Minatojima minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan.
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16
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Tabarean IV. Opposing actions of co-released GABA and neurotensin on the activity of preoptic neurons and on body temperature. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.15.589556. [PMID: 38659782 PMCID: PMC11042348 DOI: 10.1101/2024.04.15.589556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Neurotensin (Nts) is a neuropeptide acting as a neuromodulator in the brain. Pharmacological studies have identified Nts as a potent hypothermic agent. The medial preoptic area, a region that plays an important role in the control of thermoregulation, contains a high density of neurotensinergic neurons and Nts receptors. The conditions in which neurotensinergic neurons play a role in thermoregulation are not known. In this study optogenetic stimulation of preoptic Nts neurons induced a small hyperthermia. In vitro, optogenetic stimulation of preoptic Nts neurons resulted in synaptic release of GABA and net inhibition of the preoptic pituitary adenylate cyclase-activating polypeptide (PACAP) neurons firing activity. GABA-A receptor antagonist or genetic deletion of VGAT in Nts neurons unmasked also an excitatory effect that was blocked by a Nts receptor 1 antagonist. Stimulation of preoptic Nts neurons lacking VGAT resulted in excitation of PACAP neurons and hypothermia. Mice lacking VGAT expression in Nts neurons presented changes in the fever response and in the responses to heat or cold exposure as well as an altered circadian rhythm of body temperature. Chemogenetic activation of all Nts neurons in the brain induced a 4-5 °C hypothermia, which could be blocked by Nts receptor antagonists in the preoptic area. Chemogenetic activation of preoptic neurotensinergic projections resulted in robust excitation of preoptic PACAP neurons. Taken together our data demonstrate that endogenously released Nts can induce potent hypothermia and that excitation of preoptic PACAP neurons is the cellular mechanism that triggers this response.
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17
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Samadi Z, Askary A. Spatial motifs reveal patterns in cellular architecture of complex tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588586. [PMID: 38645046 PMCID: PMC11030378 DOI: 10.1101/2024.04.08.588586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Spatial organization of cells is crucial to both proper physiological function of tissues and pathological conditions like cancer. Recent advances in spatial transcriptomics have enabled joint profiling of gene expression and spatial context of the cells. The outcome is an information rich map of the tissue where individual cells, or small regions, can be labeled based on their gene expression state. While spatial transcriptomics excels in its capacity to profile numerous genes within the same sample, most existing methods for analysis of spatial data only examine distribution of one or two labels at a time. These approaches overlook the potential for identifying higher-order associations between cell types - associations that can play a pivotal role in understanding development and function of complex tissues. In this context, we introduce a novel method for detecting motifs in spatial neighborhood graphs. Each motif represents a spatial arrangement of cell types that occurs in the tissue more frequently than expected by chance. To identify spatial motifs, we developed an algorithm for uniform sampling of paths from neighborhood graphs and combined it with a motif finding algorithm on graphs inspired by previous methods for finding motifs in DNA sequences. Using synthetic data with known ground truth, we show that our method can identify spatial motifs with high accuracy and sensitivity. Applied to spatial maps of mouse retinal bipolar cells and hypothalamic preoptic region, our method reveals previously unrecognized patterns in cell type arrangements. In some cases, cells within these spatial patterns differ in their gene expression from other cells of the same type, providing insights into the functional significance of the spatial motifs. These results suggest that our method can illuminate the substantial complexity of neural tissues, provide novel insight even in well studied models, and generate experimentally testable hypotheses.
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Affiliation(s)
- Zainalabedin Samadi
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, 90095, CA, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, 90095, CA, USA
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18
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Cadinu P, Sivanathan KN, Misra A, Xu RJ, Mangani D, Yang E, Rone JM, Tooley K, Kye YC, Bod L, Geistlinger L, Lee T, Mertens RT, Ono N, Wang G, Sanmarco L, Quintana FJ, Anderson AC, Kuchroo VK, Moffitt JR, Nowarski R. Charting the cellular biogeography in colitis reveals fibroblast trajectories and coordinated spatial remodeling. Cell 2024; 187:2010-2028.e30. [PMID: 38569542 PMCID: PMC11017707 DOI: 10.1016/j.cell.2024.03.013] [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: 04/20/2023] [Revised: 11/20/2023] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
Gut inflammation involves contributions from immune and non-immune cells, whose interactions are shaped by the spatial organization of the healthy gut and its remodeling during inflammation. The crosstalk between fibroblasts and immune cells is an important axis in this process, but our understanding has been challenged by incomplete cell-type definition and biogeography. To address this challenge, we used multiplexed error-robust fluorescence in situ hybridization (MERFISH) to profile the expression of 940 genes in 1.35 million cells imaged across the onset and recovery from a mouse colitis model. We identified diverse cell populations, charted their spatial organization, and revealed their polarization or recruitment in inflammation. We found a staged progression of inflammation-associated tissue neighborhoods defined, in part, by multiple inflammation-associated fibroblasts, with unique expression profiles, spatial localization, cell-cell interactions, and healthy fibroblast origins. Similar signatures in ulcerative colitis suggest conserved human processes. Broadly, we provide a framework for understanding inflammation-induced remodeling in the gut and other tissues.
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Affiliation(s)
- Paolo Cadinu
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Kisha N Sivanathan
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Aditya Misra
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Rosalind J Xu
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Davide Mangani
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Evan Yang
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Joseph M Rone
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Katherine Tooley
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Yoon-Chul Kye
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Lloyd Bod
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ludwig Geistlinger
- Center for Computational Biomedicine, Harvard Medical School, Boston, MA 02115, USA
| | - Tyrone Lee
- Center for Computational Biomedicine, Harvard Medical School, Boston, MA 02115, USA
| | - Randall T Mertens
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Noriaki Ono
- University of Texas Health Science Center at Houston School of Dentistry, Houston, TX 77030, USA
| | - Gang Wang
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Liliana Sanmarco
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Francisco J Quintana
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Ana C Anderson
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Vijay K Kuchroo
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
| | - Roni Nowarski
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
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19
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Tagawa N, Mori K, Koebis M, Aiba A, Iino Y, Tsuneoka Y, Funato H. Activation of lateral preoptic neurons is associated with nest-building in male mice. Sci Rep 2024; 14:8346. [PMID: 38594484 PMCID: PMC11004109 DOI: 10.1038/s41598-024-59061-z] [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: 12/26/2023] [Accepted: 04/06/2024] [Indexed: 04/11/2024] Open
Abstract
Nest-building behavior is a widely observed innate behavior. A nest provides animals with a secure environment for parenting, sleep, feeding, reproduction, and temperature maintenance. Since animal infants spend their time in a nest, nest-building behavior has been generally studied as parental behaviors, and the medial preoptic area (MPOA) neurons are known to be involved in parental nest-building. However, nest-building of singly housed male mice has been less examined. Here we show that male mice spent longer time in nest-building at the early to middle dark phase and at the end of the dark phase. These two periods are followed by sleep-rich periods. When a nest was removed and fresh nest material was introduced, both male and female mice built nests at Zeitgeber time (ZT) 6, but not at ZT12. Using Fos-immunostaining combined with double in situ hybridization of Vgat and Vglut2, we found that Vgat- and Vglut2-positive cells of the lateral preoptic area (LPOA) were the only hypothalamic neuron population that exhibited a greater number of activated cells in response to fresh nest material at ZT6, compared to being naturally awake at ZT12. Fos-positive LPOA neurons were negative for estrogen receptor 1 (Esr1). Both Vgat-positive and Vglut2-positive neurons in both the LPOA and MPOA were activated at pup retrieval by male mice. Our findings suggest the possibility that GABAergic and glutamatergic neurons in the LPOA are associated with nest-building behavior in male mice.
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Affiliation(s)
- Natsuki Tagawa
- Department of Anatomy, Graduate School of Medicine, Toho University, Tokyo, 143-8540, Japan
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Keita Mori
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Michinori Koebis
- Laboratory of Animal Resources, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan
| | - Atsu Aiba
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-Ku, Tokyo, 113-0033, Japan
- Laboratory of Animal Resources, Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, 113-0033, Japan
| | - Yuichi Iino
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-Ku, Tokyo, 113-0033, Japan
| | - Yousuke Tsuneoka
- Department of Anatomy, Graduate School of Medicine, Toho University, Tokyo, 143-8540, Japan.
| | - Hiromasa Funato
- Department of Anatomy, Graduate School of Medicine, Toho University, Tokyo, 143-8540, Japan.
- International Institute for Integrative Sleep Medicine (IIIS), University of Tsukuba, Tsukuba, Japan.
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20
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Haviv D, Remšík J, Gatie M, Snopkowski C, Takizawa M, Pereira N, Bashkin J, Jovanovich S, Nawy T, Chaligne R, Boire A, Hadjantonakis AK, Pe'er D. The covariance environment defines cellular niches for spatial inference. Nat Biotechnol 2024:10.1038/s41587-024-02193-4. [PMID: 38565973 DOI: 10.1038/s41587-024-02193-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024]
Abstract
A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.
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Affiliation(s)
- Doron Haviv
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ján Remšík
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mohamed Gatie
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Catherine Snopkowski
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meril Takizawa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronan Chaligne
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Adrienne Boire
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anna-Katerina Hadjantonakis
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, New York, NY, USA.
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21
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Kuroda KO, Fukumitsu K, Kurachi T, Ohmura N, Shiraishi Y, Yoshihara C. Parental brain through time: The origin and development of the neural circuit of mammalian parenting. Ann N Y Acad Sci 2024; 1534:24-44. [PMID: 38426943 DOI: 10.1111/nyas.15111] [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: 03/02/2024]
Abstract
This review consolidates current knowledge on mammalian parental care, focusing on its neural mechanisms, evolutionary origins, and derivatives. Neurobiological studies have identified specific neurons in the medial preoptic area as crucial for parental care. Unexpectedly, these neurons are characterized by the expression of molecules signaling satiety, such as calcitonin receptor and BRS3, and overlap with neurons involved in the reproductive behaviors of males but not females. A synthesis of comparative ecology and paleontology suggests an evolutionary scenario for mammalian parental care, possibly stemming from male-biased guarding of offspring in basal vertebrates. The terrestrial transition of tetrapods led to prolonged egg retention in females and the emergence of amniotes, skewing care toward females. The nocturnal adaptation of Mesozoic mammalian ancestors reinforced maternal care for lactation and thermal regulation via endothermy, potentially introducing metabolic gate control in parenting neurons. The established maternal care may have served as the precursor for paternal and cooperative care in mammals and also fostered the development of group living, which may have further contributed to the emergence of empathy and altruism. These evolution-informed working hypotheses require empirical validation, yet they offer promising avenues to investigate the neural underpinnings of mammalian social behaviors.
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Affiliation(s)
- Kumi O Kuroda
- RIKEN Center for Brain Science, Saitama, Japan
- School of Life Sciences and Technologies, Tokyo Institute of Technology, Kanagawa, Japan
| | - Kansai Fukumitsu
- RIKEN Center for Brain Science, Saitama, Japan
- Department of Physiology, Fujita Health University School of Medicine, Toyoake, Japan
| | - Takuma Kurachi
- RIKEN Center for Brain Science, Saitama, Japan
- Department of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Nami Ohmura
- RIKEN Center for Brain Science, Saitama, Japan
- Center for Brain, Mind and Kansei Sciences Research, Hiroshima University, Hiroshima, Japan
| | - Yuko Shiraishi
- RIKEN Center for Brain Science, Saitama, Japan
- Kawamura Gakuen Woman's University, Chiba, Japan
| | - Chihiro Yoshihara
- RIKEN Center for Brain Science, Saitama, Japan
- School of Life Sciences and Technologies, Tokyo Institute of Technology, Kanagawa, Japan
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22
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Minakuchi T, Guthman EM, Acharya P, Hinson J, Fleming W, Witten IB, Oline SN, Falkner AL. Independent inhibitory control mechanisms for aggressive motivation and action. Nat Neurosci 2024; 27:702-715. [PMID: 38347201 DOI: 10.1038/s41593-023-01563-6] [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: 11/07/2022] [Accepted: 12/19/2023] [Indexed: 04/10/2024]
Abstract
Social behaviors often consist of a motivational phase followed by action. Here we show that neurons in the ventromedial hypothalamus ventrolateral area (VMHvl) of mice encode the temporal sequence of aggressive motivation to action. The VMHvl receives local inhibitory input (VMHvl shell) and long-range input from the medial preoptic area (MPO) with functional coupling to neurons with specific temporal profiles. Encoding models reveal that during aggression, VMHvl shellvgat+ activity peaks at the start of an attack, whereas activity from the MPO-VMHvlvgat+ input peaks at specific interaction endpoints. Activation of the MPO-VMHvlvgat+ input promotes and prolongs a low motivation state, whereas activation of VMHvl shellvgat+ results in action-related deficits, acutely terminating attack. Moreover, stimulation of MPO-VMHvlvgat+ input is positively valenced and anxiolytic. Together, these data demonstrate how distinct inhibitory inputs to the hypothalamus can independently gate the motivational and action phases of aggression through a single locus of control.
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Affiliation(s)
| | | | | | - Justin Hinson
- Princeton Neuroscience Institute, Princeton, NJ, USA
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23
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Miller MI, Trouvé A, Younes L. Space-feature measures on meshes for mapping spatial transcriptomics. Med Image Anal 2024; 93:103068. [PMID: 38176357 DOI: 10.1016/j.media.2023.103068] [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: 01/20/2023] [Revised: 09/18/2023] [Accepted: 12/19/2023] [Indexed: 01/06/2024]
Abstract
Advances in the development of largely automated microscopy methods such as MERFISH for imaging cellular structures in mouse brains are providing spatial detection of micron resolution gene expression. While there has been tremendous progress made in the field of Computational Anatomy (CA) to perform diffeomorphic mapping technologies at the tissue scales for advanced neuroinformatic studies in common coordinates, integration of molecular- and cellular-scale populations through statistical averaging via common coordinates remains yet unattained. This paper describes the first set of algorithms for calculating geodesics in the space of diffeomorphisms, what we term space-feature-measure LDDMM, extending the family of large deformation diffeomorphic metric mapping (LDDMM) algorithms to accommodate a space-feature action on marked particles which extends consistently to the tissue scales. It leads to the derivation of a cross-modality alignment algorithm of transcriptomic data to common coordinate systems attached to standard atlases. We represent the brain data as geometric measures, termed as space-feature measures supported by a large number of unstructured points, each point representing a small volume in space and carrying a list of densities of features elements of a high-dimensional feature space. The shape of space-feature measure brain spaces is measured by transforming them by diffeomorphisms. The metric between these measures is obtained after embedding these objects in a linear space equipped with the norm, yielding a so-called "chordal metric".
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Affiliation(s)
- Michael I Miller
- Center of Imaging Science and Department of Biomedical Engineering, Johns Hopkins University, United States of America.
| | - Alain Trouvé
- Centre Giovanni Borelli (UMR 9010), Ecole Normale Supérieure Paris-Saclay, Université Paris-Saclay, France.
| | - Laurent Younes
- Center of imaging Science and Department of Applied Mathematics and Statistics, Johns Hopkins University, United States of America.
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24
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Yuan Z, Zhao F, Lin S, Zhao Y, Yao J, Cui Y, Zhang XY, Zhao Y. Benchmarking spatial clustering methods with spatially resolved transcriptomics data. Nat Methods 2024; 21:712-722. [PMID: 38491270 DOI: 10.1038/s41592-024-02215-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 02/16/2024] [Indexed: 03/18/2024]
Abstract
Spatial clustering, which shares an analogy with single-cell clustering, has expanded the scope of tissue physiology studies from cell-centroid to structure-centroid with spatially resolved transcriptomics (SRT) data. Computational methods have undergone remarkable development in recent years, but a comprehensive benchmark study is still lacking. Here we present a benchmark study of 13 computational methods on 34 SRT data (7 datasets). The performance was evaluated on the basis of accuracy, spatial continuity, marker genes detection, scalability, and robustness. We found existing methods were complementary in terms of their performance and functionality, and we provide guidance for selecting appropriate methods for given scenarios. On testing additional 22 challenging datasets, we identified challenges in identifying noncontinuous spatial domains and limitations of existing methods, highlighting their inadequacies in handling recent large-scale tasks. Furthermore, with 145 simulated data, we examined the robustness of these methods against four different factors, and assessed the impact of pre- and postprocessing approaches. Our study offers a comprehensive evaluation of existing spatial clustering methods with SRT data, paving the way for future advancements in this rapidly evolving field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
| | - Fangyuan Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Senlin Lin
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yu Zhao
- Tencent AI Lab, Shenzhen, China
| | | | - Yan Cui
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Kyoto, Japan
| | - Xiao-Yong Zhang
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China
| | - Yi Zhao
- Research Center for Ubiquitous Computing Systems, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
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25
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Yin W, Wan Y, Zhou Y. SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding. Brief Bioinform 2024; 25:bbae130. [PMID: 38557675 PMCID: PMC10982953 DOI: 10.1093/bib/bbae130] [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: 05/18/2023] [Revised: 02/22/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Spatial transcriptomics (ST) data have emerged as a pivotal approach to comprehending the function and interplay of cells within intricate tissues. Nonetheless, analyses of ST data are restricted by the low spatial resolution and limited number of ribonucleic acid transcripts that can be detected with several popular ST techniques. In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network-based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition. Moreover, with competitive accuracy, SpatialcoGCN could also recover the spatial distribution of transcripts that are not detected by raw ST data. With a similar co-embedding framework, we further established a spatial information-aware ST data simulation method, SpatialcoGCN-Sim. SpatialcoGCN-Sim could generate simulated ST data with high similarity to real datasets. Together, our approaches provide efficient tools for studying the spatial organization of heterogeneous cells within complex tissues.
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Affiliation(s)
- Wang Yin
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - You Wan
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
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26
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Li K, Li J, Tao Y, Wang F. stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics. Brief Bioinform 2024; 25:bbae171. [PMID: 38628114 PMCID: PMC11021815 DOI: 10.1093/bib/bbae171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/11/2024] [Accepted: 04/01/2024] [Indexed: 04/19/2024] Open
Abstract
Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.
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Affiliation(s)
- Kongming Li
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
| | - Jiahao Li
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
| | - Yuhao Tao
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan 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 UniversityHandan Street, 200433 Shanghai, China
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27
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Zhai Y, Chen L, Deng M. scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data. Brief Bioinform 2024; 25:bbae188. [PMID: 38678389 PMCID: PMC11056022 DOI: 10.1093/bib/bbae188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/11/2024] [Accepted: 04/14/2024] [Indexed: 04/30/2024] Open
Abstract
MOTIVATION Over the past decade, single-cell transcriptomic technologies have experienced remarkable advancements, enabling the simultaneous profiling of gene expressions across thousands of individual cells. Cell type identification plays an essential role in exploring tissue heterogeneity and characterizing cell state differences. With more and more well-annotated reference data becoming available, massive automatic identification methods have sprung up to simplify the annotation process on unlabeled target data by transferring the cell type knowledge. However, in practice, the target data often include some novel cell types that are not in the reference data. Most existing works usually classify these private cells as one generic 'unassigned' group and learn the features of known and novel cell types in a coupled way. They are susceptible to the potential batch effects and fail to explore the fine-grained semantic knowledge of novel cell types, thus hurting the model's discrimination ability. Additionally, emerging spatial transcriptomic technologies, such as in situ hybridization, sequencing and multiplexed imaging, present a novel challenge to current cell type identification strategies that predominantly neglect spatial organization. Consequently, it is imperative to develop a versatile method that can proficiently annotate single-cell transcriptomics data, encompassing both spatial and non-spatial dimensions. RESULTS To address these issues, we propose a new, challenging yet realistic task called universal cell type identification for single-cell and spatial transcriptomics data. In this task, we aim to give semantic labels to target cells from known cell types and cluster labels to those from novel ones. To tackle this problem, instead of designing a suboptimal two-stage approach, we propose an end-to-end algorithm called scBOL from the perspective of Bipartite prototype alignment. Firstly, we identify the mutual nearest clusters in reference and target data as their potential common cell types. On this basis, we mine the cycle-consistent semantic anchor cells to build the intrinsic structure association between two data. Secondly, we design a neighbor-aware prototypical learning paradigm to strengthen the inter-cluster separability and intra-cluster compactness within each data, thereby inspiring the discriminative feature representations. Thirdly, driven by the semantic-aware prototypical learning framework, we can align the known cell types and separate the private cell types from them among reference and target data. Such an algorithm can be seamlessly applied to various data types modeled by different foundation models that can generate the embedding features for cells. Specifically, for non-spatial single-cell transcriptomics data, we use the autoencoder neural network to learn latent low-dimensional cell representations, and for spatial single-cell transcriptomics data, we apply the graph convolution network to capture molecular and spatial similarities of cells jointly. Extensive results on our carefully designed evaluation benchmarks demonstrate the superiority of scBOL over various state-of-the-art cell type identification methods. To our knowledge, we are the pioneers in presenting this pragmatic annotation task, as well as in devising a comprehensive algorithmic framework aimed at resolving this challenge across varied types of single-cell data. Finally, scBOL is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/aimeeyaoyao/scBOL.
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Affiliation(s)
- Yuyao Zhai
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Liang Chen
- Huawei Technologies Co., Ltd., Beijing, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, China
- Center for Statistical Science, Peking University, Beijing, China
- Center for Quantitative Biology, Peking University, Beijing, China
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28
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Lee S, Cho Y, Li Y, Li R, Brown D, McAuliffe P, Lee AV, Oesterreich S, Zervantonakis IK, Osmanbeyoglu HU. Cancer-cell derived S100A11 promotes macrophage recruitment in ER+ breast cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.586041. [PMID: 38585952 PMCID: PMC10996512 DOI: 10.1101/2024.03.21.586041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Macrophages are pivotal in driving breast tumor development, progression, and resistance to treatment, particularly in estrogen receptor-positive (ER+) tumors, where they infiltrate the tumor microenvironment (TME) influenced by cancer cell-secreted factors. By analyzing single-cell RNA-sequencing data from 25 ER+ tumors, we elucidated interactions between cancer cells and macrophages, correlating macrophage density with epithelial cancer cell density. We identified that S100A11, a previously unexplored factor in macrophage-cancer crosstalk, predicts high macrophage density and poor outcomes in ER+ tumors. We found that recombinant S100A11 enhances macrophage infiltration and migration in a dose-dependent manner. Additionally, in 3D models, we showed that S100A11 expression levels in ER+ cancer cells predict macrophage infiltration patterns. Neutralizing S100A11 decreased macrophage recruitment, both in cancer cell lines and in a clinically relevant patient-derived organoid model, underscoring its role as a paracrine regulator of cancer-macrophage interactions in the protumorigenic TME. This study offers novel insights into the interplay between macrophages and cancer cells in ER+ breast tumors, highlighting S100A11 as a potential therapeutic target to modulate the macrophage-rich tumor microenvironment.
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Affiliation(s)
- Sanghoon Lee
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, 15206, U.S.A
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
| | - Youngbin Cho
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
- Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Yiting Li
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Ruxuan Li
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
- Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Daniel Brown
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Priscilla McAuliffe
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Adrian V Lee
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Steffi Oesterreich
- Women’s Cancer Research Center, University of Pittsburgh Medical Center (UPMC) Hillman Cancer Center (HCC), Magee-Womens Research Institute, Pittsburgh, PA, 15213, U.S.A
- Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Ioannis K. Zervantonakis
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
- Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
| | - Hatice Ulku Osmanbeyoglu
- Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, 15206, U.S.A
- UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, 15213 U.S.A
- Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15213, U.S.A
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29
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Kalhor K, Chen CJ, Lee HS, Cai M, Nafisi M, Que R, Palmer CR, Yuan Y, Zhang Y, Li X, Song J, Knoten A, Lake BB, Gaut JP, Keene CD, Lein E, Kharchenko PV, Chun J, Jain S, Fan JB, Zhang K. Mapping human tissues with highly multiplexed RNA in situ hybridization. Nat Commun 2024; 15:2511. [PMID: 38509069 PMCID: PMC10954689 DOI: 10.1038/s41467-024-46437-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 02/23/2024] [Indexed: 03/22/2024] Open
Abstract
In situ transcriptomic techniques promise a holistic view of tissue organization and cell-cell interactions. There has been a surge of multiplexed RNA in situ mapping techniques but their application to human tissues has been limited due to their large size, general lower tissue quality and high autofluorescence. Here we report DART-FISH, a padlock probe-based technology capable of profiling hundreds to thousands of genes in centimeter-sized human tissue sections. We introduce an omni-cell type cytoplasmic stain that substantially improves the segmentation of cell bodies. Our enzyme-free isothermal decoding procedure allows us to image 121 genes in large sections from the human neocortex in <10 h. We successfully recapitulated the cytoarchitecture of 20 neuronal and non-neuronal subclasses. We further performed in situ mapping of 300 genes on a diseased human kidney, profiled >20 healthy and pathological cell states, and identified diseased niches enriched in transcriptionally altered epithelial cells and myofibroblasts.
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Affiliation(s)
- Kian Kalhor
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Chien-Ju Chen
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Program in Bioinformatics and Systems Biology, University of California San Diego, La Jolla, CA, USA
| | - Ho Suk Lee
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Electrical Engineering, University of California San Diego, La Jolla, CA, USA
| | - Matthew Cai
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Mahsa Nafisi
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Richard Que
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Carter R Palmer
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
- Program in Biomedical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Yixu Yuan
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Yida Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Jinghui Song
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Amanda Knoten
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Blue B Lake
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Altos Labs, San Diego, CA, USA
| | - Joseph P Gaut
- Department of Pathology and Immunology, Washington University School of Medicine, St.Louis, MO, USA
| | - C Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Ed Lein
- Allen Institute for Brain Science, Seattle, WA, 98103, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Altos Labs, San Diego, CA, USA
| | - Jerold Chun
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Sanjay Jain
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Department of Pathology and Immunology, Washington University School of Medicine, St.Louis, MO, USA
| | | | - Kun Zhang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
- Altos Labs, San Diego, CA, USA.
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30
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Duan B, Chen S, Cheng X, Liu Q. Multi-slice spatial transcriptome domain analysis with SpaDo. Genome Biol 2024; 25:73. [PMID: 38504325 PMCID: PMC10949687 DOI: 10.1186/s13059-024-03213-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 03/08/2024] [Indexed: 03/21/2024] Open
Abstract
With the rapid advancements in spatial transcriptome sequencing, multiple tissue slices are now available, enabling the integration and interpretation of spatial cellular landscapes. Herein, we introduce SpaDo, a tool for multi-slice spatial domain analysis, including modules for multi-slice spatial domain detection, reference-based annotation, and multiple slice clustering at both single-cell and spot resolutions. We demonstrate SpaDo's effectiveness with over 40 multi-slice spatial transcriptome datasets from 7 sequencing platforms. Our findings highlight SpaDo's potential to reveal novel biological insights in multi-slice spatial transcriptomes.
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Affiliation(s)
- Bin Duan
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
| | - Shaoqi Chen
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Xiaojie Cheng
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Qi Liu
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
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31
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Reis FMCV, Maesta-Pereira S, Ollivier M, Schuette PJ, Sethi E, Miranda BA, Iniguez E, Chakerian M, Vaughn E, Sehgal M, Nguyen DCT, Yuan FTH, Torossian A, Ikebara JM, Kihara AH, Silva AJ, Kao JC, Khakh BS, Adhikari A. Control of feeding by a bottom-up midbrain-subthalamic pathway. Nat Commun 2024; 15:2111. [PMID: 38454000 PMCID: PMC10920831 DOI: 10.1038/s41467-024-46430-5] [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: 06/06/2022] [Accepted: 02/26/2024] [Indexed: 03/09/2024] Open
Abstract
Investigative exploration and foraging leading to food consumption have vital importance, but are not well-understood. Since GABAergic inputs to the lateral and ventrolateral periaqueductal gray (l/vlPAG) control such behaviors, we dissected the role of vgat-expressing GABAergic l/vlPAG cells in exploration, foraging and hunting. Here, we show that in mice vgat l/vlPAG cells encode approach to food and consumption of both live prey and non-prey foods. The activity of these cells is necessary and sufficient for inducing food-seeking leading to subsequent consumption. Activation of vgat l/vlPAG cells produces exploratory foraging and compulsive eating without altering defensive behaviors. Moreover, l/vlPAG vgat cells are bidirectionally interconnected to several feeding, exploration and investigation nodes, including the zona incerta. Remarkably, the vgat l/vlPAG projection to the zona incerta bidirectionally controls approach towards food leading to consumption. These data indicate the PAG is not only a final downstream target of top-down exploration and foraging-related inputs, but that it also influences these behaviors through a bottom-up pathway.
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Affiliation(s)
- Fernando M C V Reis
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
| | - Sandra Maesta-Pereira
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Matthias Ollivier
- Department of Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Peter J Schuette
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Ekayana Sethi
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Blake A Miranda
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Emily Iniguez
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Meghmik Chakerian
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Eric Vaughn
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Megha Sehgal
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, USA
| | - Darren C T Nguyen
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Faith T H Yuan
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Anita Torossian
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Juliane M Ikebara
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, 09606-070, Brazil
| | - Alexandre H Kihara
- Centro de Matemática, Computação e Cognição, Universidade Federal do ABC, São Bernardo do Campo, SP, 09606-070, Brazil
| | - Alcino J Silva
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Neurobiology, University of California, Los Angeles, Los Angeles, USA
- Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, USA
| | - Jonathan C Kao
- Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Baljit S Khakh
- Department of Physiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Avishek Adhikari
- Department of Psychology, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
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Atta L, Clifton K, Anant M, Aihara G, Fan J. Gene count normalization in single-cell imaging-based spatially resolved transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.30.555624. [PMID: 37693542 PMCID: PMC10491191 DOI: 10.1101/2023.08.30.555624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.
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Affiliation(s)
- Lyla Atta
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Kalen Clifton
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Manjari Anant
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Gohta Aihara
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
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Paniccia JE, Vollmer KM, Green LM, Grant RI, Winston KT, Buchmaier S, Westphal AM, Clarke RE, Doncheck EM, Bordieanu B, Manusky LM, Martino MR, Ward AL, Rinker JA, McGinty JF, Scofield MD, Otis JM. Restoration of a paraventricular thalamo-accumbal behavioral suppression circuit prevents reinstatement of heroin seeking. Neuron 2024; 112:772-785.e9. [PMID: 38141605 PMCID: PMC10939883 DOI: 10.1016/j.neuron.2023.11.024] [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: 04/10/2023] [Revised: 10/17/2023] [Accepted: 11/29/2023] [Indexed: 12/25/2023]
Abstract
Lack of behavioral suppression typifies substance use disorders, yet the neural circuit underpinnings of drug-induced behavioral disinhibition remain unclear. Here, we employ deep-brain two-photon calcium imaging in heroin self-administering mice, longitudinally tracking adaptations within a paraventricular thalamus to nucleus accumbens behavioral inhibition circuit from the onset of heroin use to reinstatement. We find that select thalamo-accumbal neuronal ensembles become profoundly hypoactive across the development of heroin seeking and use. Electrophysiological experiments further reveal persistent adaptations at thalamo-accumbal parvalbumin interneuronal synapses, whereas functional rescue of these synapses prevents multiple triggers from initiating reinstatement of heroin seeking. Finally, we find an enrichment of μ-opioid receptors in output- and cell-type-specific paraventricular thalamic neurons, which provide a mechanism for heroin-induced synaptic plasticity and behavioral disinhibition. These findings reveal key circuit adaptations that underlie behavioral disinhibition in opioid dependence and further suggest that recovery of this system would reduce relapse susceptibility.
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Affiliation(s)
- Jacqueline E Paniccia
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA; Department of Anesthesia & Perioperative Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Kelsey M Vollmer
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Lisa M Green
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Roger I Grant
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Kion T Winston
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Sophie Buchmaier
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Annaka M Westphal
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA; Department of Anesthesia & Perioperative Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Rachel E Clarke
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA; Department of Anesthesia & Perioperative Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Elizabeth M Doncheck
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Bogdan Bordieanu
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Logan M Manusky
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Michael R Martino
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Amy L Ward
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Jennifer A Rinker
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Jacqueline F McGinty
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Michael D Scofield
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA; Department of Anesthesia & Perioperative Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
| | - James M Otis
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC 29425, USA; Hollings Cancer Center, Medical University of South Carolina, Charleston, SC 29425, USA; Ralph Johnson Veterans Administration, Charleston, SC 29425, USA.
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Nardone S, De Luca R, Zito A, Klymko N, Nicoloutsopoulos D, Amsalem O, Brannigan C, Resch JM, Jacobs CL, Pant D, Veregge M, Srinivasan H, Grippo RM, Yang Z, Zeidel ML, Andermann ML, Harris KD, Tsai LT, Arrigoni E, Verstegen AMJ, Saper CB, Lowell BB. A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution. Nat Commun 2024; 15:1966. [PMID: 38438345 PMCID: PMC10912765 DOI: 10.1038/s41467-024-45907-7] [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: 05/05/2023] [Accepted: 02/07/2024] [Indexed: 03/06/2024] Open
Abstract
The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed the unique marker genes of many neuronal subtypes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard ( http://harvard.heavy.ai:6273/ ) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.
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Affiliation(s)
- Stefano Nardone
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Roberto De Luca
- Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Antonino Zito
- Department of Twin Research & Genetic Epidemiology, King's College London, London, UK
- Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA
- Department of Genetics, The Blavatnik Institute, Harvard Medical School, Boston, MA, USA
| | - Nataliya Klymko
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA
| | | | - Oren Amsalem
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Cory Brannigan
- HEAVY.AI, 100 Montgomery St Fl 5, San Francisco, California, 94104, USA
| | - Jon M Resch
- Department of Neuroscience and Pharmacology, University of Iowa, Iowa City, IA, USA
- Fraternal Order of Eagles Diabetes Research Center. University of Iowa Carver College of Medicine, Iowa City, IA, 52242, USA
| | - Christopher L Jacobs
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Deepti Pant
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Molly Veregge
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Harini Srinivasan
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ryan M Grippo
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Zongfang Yang
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Mark L Zeidel
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA
| | - Mark L Andermann
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Kenneth D Harris
- UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Linus T Tsai
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Elda Arrigoni
- Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA
| | - Anne M J Verstegen
- Division of Nephrology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA.
| | - Clifford B Saper
- Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02215, USA.
| | - Bradford B Lowell
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA.
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Ren J, Lyu X, Guo J, Shi X, Zhou Y, Li Q. CDSKNN XMBD: a novel clustering framework for large-scale single-cell data based on a stable graph structure. J Transl Med 2024; 22:233. [PMID: 38433205 PMCID: PMC10910752 DOI: 10.1186/s12967-024-05009-w] [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: 11/09/2023] [Accepted: 02/19/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Accurate and efficient cell grouping is essential for analyzing single-cell transcriptome sequencing (scRNA-seq) data. However, the existing clustering techniques often struggle to provide timely and accurate cell type groupings when dealing with datasets with large-scale or imbalanced cell types. Therefore, there is a need for improved methods that can handle the increasing size of scRNA-seq datasets while maintaining high accuracy and efficiency. METHODS We propose CDSKNNXMBD (Community Detection based on a Stable K-Nearest Neighbor Graph Structure), a novel single-cell clustering framework integrating partition clustering algorithm and community detection algorithm, which achieves accurate and fast cell type grouping by finding a stable graph structure. RESULTS We evaluated the effectiveness of our approach by analyzing 15 tissues from the human fetal atlas. Compared to existing methods, CDSKNN effectively counteracts the high imbalance in single-cell data, enabling effective clustering. Furthermore, we conducted comparisons across multiple single-cell datasets from different studies and sequencing techniques. CDSKNN is of high applicability and robustness, and capable of balancing the complexities of across diverse types of data. Most importantly, CDSKNN exhibits higher operational efficiency on datasets at the million-cell scale, requiring an average of only 6.33 min for clustering 1.46 million single cells, saving 33.3% to 99% of running time compared to those of existing methods. CONCLUSIONS The CDSKNN is a flexible, resilient, and promising clustering tool that is particularly suitable for clustering imbalanced data and demonstrates high efficiency on large-scale scRNA-seq datasets.
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Affiliation(s)
- Jun Ren
- School of Informatics, Xiamen University, Xiamen, 361105, China
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361102, China
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China
| | - Xuejing Lyu
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China
| | - Jintao Guo
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China
| | - Xiaodong Shi
- School of Informatics, Xiamen University, Xiamen, 361105, China
| | - Ying Zhou
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361102, China.
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Qiyuan Li
- Department of Hematology, The First Affiliated Hospital of Xiamen University and Institute of Hematology, School of Medicine, Xiamen University, Xiamen, 361102, China.
- National Institute for Data Science in Health and Medicine, School of Medicine, Xiamen University, Xiamen, 361102, China.
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36
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Khodosevich K, Dragicevic K, Howes O. Drug targeting in psychiatric disorders - how to overcome the loss in translation? Nat Rev Drug Discov 2024; 23:218-231. [PMID: 38114612 DOI: 10.1038/s41573-023-00847-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/03/2023] [Indexed: 12/21/2023]
Abstract
In spite of major efforts and investment in development of psychiatric drugs, many clinical trials have failed in recent decades, and clinicians still prescribe drugs that were discovered many years ago. Although multiple reasons have been discussed for the drug development deadlock, we focus here on one of the major possible biological reasons: differences between the characteristics of drug targets in preclinical models and the corresponding targets in patients. Importantly, based on technological advances in single-cell analysis, we propose here a framework for the use of available and newly emerging knowledge from single-cell and spatial omics studies to evaluate and potentially improve the translational predictivity of preclinical models before commencing preclinical and, in particular, clinical studies. We believe that these recommendations will improve preclinical models and the ability to assess drugs in clinical trials, reducing failure rates in expensive late-stage trials and ultimately benefitting psychiatric drug discovery and development.
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Affiliation(s)
- Konstantin Khodosevich
- Biotech Research and Innovation Centre, Faculty of Health, University of Copenhagen, Copenhagen, Denmark.
| | - Katarina Dragicevic
- Biotech Research and Innovation Centre, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | - Oliver Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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37
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Roggenbuck EC, Hall EA, Hanson IB, Roby AA, Zhang KK, Alkatib KA, Carter JA, Clewner JE, Gelfius AL, Gong S, Gordon FR, Iseler JN, Kotapati S, Li M, Maysun A, McCormick EO, Rastogi G, Sengupta S, Uzoma CU, Wolkov MA, Clowney EJ. Let's talk about sex: Mechanisms of neural sexual differentiation in Bilateria. WIREs Mech Dis 2024; 16:e1636. [PMID: 38185860 DOI: 10.1002/wsbm.1636] [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: 05/09/2023] [Revised: 11/20/2023] [Accepted: 11/21/2023] [Indexed: 01/09/2024]
Abstract
In multicellular organisms, sexed gonads have evolved that facilitate release of sperm versus eggs, and bilaterian animals purposefully combine their gametes via mating behaviors. Distinct neural circuits have evolved that control these physically different mating events for animals producing eggs from ovaries versus sperm from testis. In this review, we will describe the developmental mechanisms that sexually differentiate neural circuits across three major clades of bilaterian animals-Ecdysozoa, Deuterosomia, and Lophotrochozoa. While many of the mechanisms inducing somatic and neuronal sex differentiation across these diverse organisms are clade-specific rather than evolutionarily conserved, we develop a common framework for considering the developmental logic of these events and the types of neuronal differences that produce sex-differentiated behaviors. This article is categorized under: Congenital Diseases > Stem Cells and Development Neurological Diseases > Stem Cells and Development.
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Affiliation(s)
- Emma C Roggenbuck
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Elijah A Hall
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Isabel B Hanson
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Alyssa A Roby
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Katherine K Zhang
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Kyle A Alkatib
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Joseph A Carter
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, USA
| | - Jarred E Clewner
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Anna L Gelfius
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Shiyuan Gong
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Finley R Gordon
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, USA
| | - Jolene N Iseler
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Samhita Kotapati
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Marilyn Li
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Areeba Maysun
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Elise O McCormick
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Geetanjali Rastogi
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Srijani Sengupta
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Chantal U Uzoma
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - Madison A Wolkov
- MCDB 464 - Cellular Diversity: Sex Differentiation of the Brain, University of Michigan, Ann Arbor, Michigan, USA
| | - E Josephine Clowney
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, USA
- Michigan Neuroscience Institute Affiliate, University of Michigan, Ann Arbor, Michigan, USA
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Jung R, Trivedi CM. Unveiling the Spatiotemporal Diversity of the Endothelium in Development: A Multi-Omics Approach. Circ Res 2024; 134:547-549. [PMID: 38422180 PMCID: PMC10906738 DOI: 10.1161/circresaha.124.324328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2024]
Affiliation(s)
- Roy Jung
- Division of Cardiovascular Medicine
- Department of Medicine, UMass Chan Medical School, Worcester, MA 01605 USA
- Translational Science Program, Morningside Graduate School of Biomedical Sciences, UMass Chan Medical School, Worcester, MA 01605 USA
| | - Chinmay M. Trivedi
- Division of Cardiovascular Medicine
- Department of Medicine, UMass Chan Medical School, Worcester, MA 01605 USA
- Translational Science Program, Morningside Graduate School of Biomedical Sciences, UMass Chan Medical School, Worcester, MA 01605 USA
- Department of Molecular, Cell, and Cancer Biology, UMass Chan Medical School; Worcester, MA 01605 USA
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39
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You Y, Chen Z, Hu WW. The role of microglia heterogeneity in synaptic plasticity and brain disorders: Will sequencing shed light on the discovery of new therapeutic targets? Pharmacol Ther 2024; 255:108606. [PMID: 38346477 DOI: 10.1016/j.pharmthera.2024.108606] [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: 10/31/2023] [Revised: 01/05/2024] [Accepted: 02/02/2024] [Indexed: 02/18/2024]
Abstract
Microglia play a crucial role in interacting with neuronal synapses and modulating synaptic plasticity. This function is particularly significant during postnatal development, as microglia are responsible for removing excessive synapses to prevent neurodevelopmental deficits. Dysregulation of microglial synaptic function has been well-documented in various pathological conditions, notably Alzheimer's disease and multiple sclerosis. The recent application of RNA sequencing has provided a powerful and unbiased means to decipher spatial and temporal microglial heterogeneity. By identifying microglia with varying gene expression profiles, researchers have defined multiple subgroups of microglia associated with specific pathological states, including disease-associated microglia, interferon-responsive microglia, proliferating microglia, and inflamed microglia in multiple sclerosis, among others. However, the functional roles of these distinct subgroups remain inadequately characterized. This review aims to refine our current understanding of the potential roles of heterogeneous microglia in regulating synaptic plasticity and their implications for various brain disorders, drawing from recent sequencing research and functional studies. This knowledge may aid in the identification of pathogenetic biomarkers and potential factors contributing to pathogenesis, shedding new light on the discovery of novel drug targets. The field of sequencing-based data mining is evolving toward a multi-omics approach. With advances in viral tools for precise microglial regulation and the development of brain organoid models, we are poised to elucidate the functional roles of microglial subgroups detected through sequencing analysis, ultimately identifying valuable therapeutic targets.
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Affiliation(s)
- Yi You
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Zhong Chen
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China; Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Wei-Wei Hu
- Department of Pharmacology and Department of Pharmacy of the Second Affiliated Hospital, Key Laboratory of Medical Neurobiology of the Ministry of Health of China, School of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou 310058, China.
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40
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Farah EN, Hu RK, Kern C, Zhang Q, Lu TY, Ma Q, Tran S, Zhang B, Carlin D, Monell A, Blair AP, Wang Z, Eschbach J, Li B, Destici E, Ren B, Evans SM, Chen S, Zhu Q, Chi NC. Spatially organized cellular communities form the developing human heart. Nature 2024; 627:854-864. [PMID: 38480880 PMCID: PMC10972757 DOI: 10.1038/s41586-024-07171-z] [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: 11/21/2022] [Accepted: 02/07/2024] [Indexed: 03/18/2024]
Abstract
The heart, which is the first organ to develop, is highly dependent on its form to function1,2. However, how diverse cardiac cell types spatially coordinate to create the complex morphological structures that are crucial for heart function remains unclear. Here we integrated single-cell RNA-sequencing with high-resolution multiplexed error-robust fluorescence in situ hybridization to resolve the identity of the cardiac cell types that develop the human heart. This approach also provided a spatial mapping of individual cells that enables illumination of their organization into cellular communities that form distinct cardiac structures. We discovered that many of these cardiac cell types further specified into subpopulations exclusive to specific communities, which support their specialization according to the cellular ecosystem and anatomical region. In particular, ventricular cardiomyocyte subpopulations displayed an unexpected complex laminar organization across the ventricular wall and formed, with other cell subpopulations, several cellular communities. Interrogating cell-cell interactions within these communities using in vivo conditional genetic mouse models and in vitro human pluripotent stem cell systems revealed multicellular signalling pathways that orchestrate the spatial organization of cardiac cell subpopulations during ventricular wall morphogenesis. These detailed findings into the cellular social interactions and specialization of cardiac cell types constructing and remodelling the human heart offer new insights into structural heart diseases and the engineering of complex multicellular tissues for human heart repair.
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Affiliation(s)
- Elie N Farah
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Robert K Hu
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Colin Kern
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Qingquan Zhang
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Ting-Yu Lu
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
| | - Qixuan Ma
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Shaina Tran
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Bo Zhang
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Daniel Carlin
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Alexander Monell
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Andrew P Blair
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Zilu Wang
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Jacqueline Eschbach
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
| | - Bin Li
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Eugin Destici
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
| | - Bing Ren
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, School of Medicine, University of California San Diego, La Jolla, CA, USA
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Sylvia M Evans
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA
- Department of Pharmacology, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
| | - Shaochen Chen
- Materials Science and Engineering Program, University of California San Diego, La Jolla, CA, USA
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of NanoEngineering, University of California San Diego, La Jolla, CA, USA
- Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA
| | - Quan Zhu
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
| | - Neil C Chi
- Department of Medicine, Division of Cardiology, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
- Institute of Engineering in Medicine, University of California San Diego, La Jolla, CA, USA.
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41
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Singhal V, Chou N, Lee J, Yue Y, Liu J, Chock WK, Lin L, Chang YC, Teo EML, Aow J, Lee HK, Chen KH, Prabhakar S. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat Genet 2024; 56:431-441. [PMID: 38413725 PMCID: PMC10937399 DOI: 10.1038/s41588-024-01664-3] [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: 04/03/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
Spatial omics data are clustered to define both cell types and tissue domains. We present Building Aggregates with a Neighborhood Kernel and Spatial Yardstick (BANKSY), an algorithm that unifies these two spatial clustering problems by embedding cells in a product space of their own and the local neighborhood transcriptome, representing cell state and microenvironment, respectively. BANKSY's spatial feature augmentation strategy improved performance on both tasks when tested on diverse RNA (imaging, sequencing) and protein (imaging) datasets. BANKSY revealed unexpected niche-dependent cell states in the mouse brain and outperformed competing methods on domain segmentation and cell typing benchmarks. BANKSY can also be used for quality control of spatial transcriptomics data and for spatially aware batch effect correction. Importantly, it is substantially faster and more scalable than existing methods, enabling the processing of millions of cell datasets. In summary, BANKSY provides an accurate, biologically motivated, scalable and versatile framework for analyzing spatially resolved omics data.
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Affiliation(s)
- Vipul Singhal
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Nigel Chou
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Joseph Lee
- Faculty of Science, National University of Singapore, Singapore, Republic of Singapore
| | - Yifei Yue
- Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Republic of Singapore
| | - Jinyue Liu
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Wan Kee Chock
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Li Lin
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | | | | | - Jonathan Aow
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Hwee Kuan Lee
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- School of Computing, National University of Singapore, Singapore, Republic of Singapore
- Singapore Eye Research Institute, Singapore, Republic of Singapore
- International Research Laboratory on Artificial Intelligence, Singapore, Republic of Singapore
- School of Biological Sciences, Nanyang Technological University, Singapore, Republic of Singapore
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Republic of Singapore
| | - Kok Hao Chen
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
| | - Shyam Prabhakar
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore.
- Population and Global Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Republic of Singapore.
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42
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Liu Y, Zhao ZD, Xie G, Chen R, Zhang Y. A molecularly defined NAcSh D1 subtype controls feeding and energy homeostasis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.02.27.530275. [PMID: 36909586 PMCID: PMC10002697 DOI: 10.1101/2023.02.27.530275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
Orchestrating complex behavioral states, such as approach and consumption of food, is critical for survival. In addition to hypothalamus neuronal circuits, the nucleus accumbens (NAc) also plays an important role in controlling appetite and satiety in responses to changing external stimuli. However, the specific neuronal subtypes of NAc involved as well as how the humoral and neuronal signals coordinate to regulate feeding remain incompletely understood. Here, we deciphered the spatial diversity of neuron subtypes of the NAc shell (NAcSh) and defined a dopamine receptor D1(Drd1)- and Serpinb2-expressing subtype located in NAcSh encoding food consumption. Chemogenetics- and optogenetics-mediated regulation of Serpinb2 + neurons bidirectionally regulates food seeking and consumption specifically. Circuitry stimulation revealed the NAcSh Serpinb2 →LH LepR projection controls refeeding and can overcome leptin-mediated feeding suppression. Furthermore, NAcSh Serpinb2 + neuron ablation reduces food intake and upregulates energy expenditure resulting in body weight loss. Together, our study reveals a neural circuit consisted of molecularly distinct neuronal subtype that bidirectionally regulates energy homeostasis, which can serve as a potential therapeutic target for eating disorders.
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Affiliation(s)
- Yiqiong Liu
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
| | - Zheng-dong Zhao
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
| | - Guoguang Xie
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
| | - Renchao Chen
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
| | - Yi Zhang
- Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Division of Hematology/Oncology, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts 02115, USA
- Department of Genetics, Harvard Medical School, Boston, Massachusetts 02115, USA
- Harvard Stem Cell Institute, WAB-149G, 200 Longwood Avenue, Boston, Massachusetts 02115, USA
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43
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Yafi MA, Hisham MHH, Grisanti F, Martin JF, Rahman A, Samee MAH. scGIST: gene panel design for spatial transcriptomics with prioritized gene sets. Genome Biol 2024; 25:57. [PMID: 38408997 PMCID: PMC10895727 DOI: 10.1186/s13059-024-03185-y] [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: 10/26/2022] [Accepted: 02/14/2024] [Indexed: 02/28/2024] Open
Abstract
A critical challenge of single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, they are typically limited to panels of about a thousand genes. This constrains researchers to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or those in specific pathways. We propose scGIST, a constrained feature selection tool that designs sc-ST panels prioritizing user-specified genes without compromising cell type detection accuracy. We demonstrate scGIST's efficacy in diverse use cases, highlighting it as a valuable addition to sc-ST's algorithmic toolbox.
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Affiliation(s)
- Mashrur Ahmed Yafi
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Md Hasibul Husain Hisham
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh
| | - Francisco Grisanti
- Department of Integrative Physiology, Baylor College of Medicine, Houston, 77030, TX, USA
| | - James F Martin
- Department of Integrative Physiology, Baylor College of Medicine, Houston, 77030, TX, USA
- Cardiomyocyte Renewal Laboratory, Texas Heart Institute, Houston, 77030, TX, USA
| | - Atif Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, 1205, Bangladesh.
| | - Md Abul Hassan Samee
- Department of Integrative Physiology, Baylor College of Medicine, Houston, 77030, TX, USA.
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44
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Wu M, Zheng W, Song X, Bao B, Wang Y, Ramanan D, Yang D, Liu R, Macbeth JC, Do EA, Andrade WA, Yang T, Cho HS, Gazzaniga FS, Ilves M, Coronado D, Thompson C, Hang S, Chiu IM, Moffitt JR, Hsiao A, Mekalanos JJ, Benoist C, Kasper DL. Gut complement induced by the microbiota combats pathogens and spares commensals. Cell 2024; 187:897-913.e18. [PMID: 38280374 PMCID: PMC10922926 DOI: 10.1016/j.cell.2023.12.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 09/25/2023] [Accepted: 12/30/2023] [Indexed: 01/29/2024]
Abstract
Canonically, the complement system is known for its rapid response to remove microbes in the bloodstream. However, relatively little is known about a functioning complement system on intestinal mucosal surfaces. Herein, we report the local synthesis of complement component 3 (C3) in the gut, primarily by stromal cells. C3 is expressed upon commensal colonization and is regulated by the composition of the microbiota in healthy humans and mice, leading to an individual host's specific luminal C3 levels. The absence of membrane attack complex (MAC) components in the gut ensures that C3 deposition does not result in the lysis of commensals. Pathogen infection triggers the immune system to recruit neutrophils to the infection site for pathogen clearance. Basal C3 levels directly correlate with protection against enteric infection. Our study reveals the gut complement system as an innate immune mechanism acting as a vigilant sentinel that combats pathogens and spares commensals.
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Affiliation(s)
- Meng Wu
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Wen Zheng
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Xinyang Song
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Bin Bao
- Division of Gastroenterology, Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115, USA
| | - Yuanyou Wang
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA
| | - Deepshika Ramanan
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Daping Yang
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Rui Liu
- Department of Microbiology & Plant Pathology, University of California, Riverside, CA 92521, USA
| | - John C Macbeth
- Department of Microbiology & Plant Pathology, University of California, Riverside, CA 92521, USA
| | - Elyza A Do
- Department of Microbiology & Plant Pathology, University of California, Riverside, CA 92521, USA
| | | | - Tiandi Yang
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Hyoung-Soo Cho
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Marit Ilves
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Daniela Coronado
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Saiyu Hang
- Genentech LLC, South San Francisco, CA 94080, USA
| | - Isaac M Chiu
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA
| | - Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA
| | - Ansel Hsiao
- Department of Microbiology & Plant Pathology, University of California, Riverside, CA 92521, USA
| | - John J Mekalanos
- Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA
| | | | - Dennis L Kasper
- Department of Immunology, Harvard Medical School, Boston, MA 02115, USA.
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45
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Harkany T, Tretiakov E, Varela L, Jarc J, Rebernik P, Newbold S, Keimpema E, Verkhratsky A, Horvath T, Romanov R. Molecularly stratified hypothalamic astrocytes are cellular foci for obesity. RESEARCH SQUARE 2024:rs.3.rs-3748581. [PMID: 38405925 PMCID: PMC10889077 DOI: 10.21203/rs.3.rs-3748581/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
Astrocytes safeguard the homeostasis of the central nervous system1,2. Despite their prominent morphological plasticity under conditions that challenge the brain's adaptive capacity3-5, the classification of astrocytes, and relating their molecular make-up to spatially devolved neuronal operations that specify behavior or metabolism, remained mostly futile6,7. Although it seems unexpected in the era of single-cell biology, the lack of a major advance in stratifying astrocytes under physiological conditions rests on the incompatibility of 'neurocentric' algorithms that rely on stable developmental endpoints, lifelong transcriptional, neurotransmitter, and neuropeptide signatures for classification6-8 with the dynamic functional states, anatomic allocation, and allostatic plasticity of astrocytes1. Simplistically, therefore, astrocytes are still grouped as 'resting' vs. 'reactive', the latter referring to pathological states marked by various inducible genes3,9,10. Here, we introduced a machine learning-based feature recognition algorithm that benefits from the cumulative power of published single-cell RNA-seq data on astrocytes as a reference map to stepwise eliminate pleiotropic and inducible cellular features. For the healthy hypothalamus, this walk-back approach revealed gene regulatory networks (GRNs) that specified subsets of astrocytes, and could be used as landmarking tools for their anatomical assignment. The core molecular censuses retained by astrocyte subsets were sufficient to stratify them by allostatic competence, chiefly their signaling and metabolic interplay with neurons. Particularly, we found differentially expressed mitochondrial genes in insulin-sensing astrocytes and demonstrated their reciprocal signaling with neurons that work antagonistically within the food intake circuitry. As a proof-of-concept, we showed that disrupting Mfn2 expression in astrocytes reduced their ability to support dynamic circuit reorganization, a time-locked feature of satiety in the hypothalamus, thus leading to obesity in mice. Overall, our results suggest that astrocytes in the healthy brain are fundamentally more heterogeneous than previously thought and topologically mirror the specificity of local neurocircuits.
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Affiliation(s)
- Tibor Harkany
- Center for Brain Research, Medical University of Vienna
| | | | | | - Jasna Jarc
- Center for Brain Research, Medical University of Vienna
| | | | | | - Erik Keimpema
- Medical University of Vienna, Center for Brain Research
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46
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Bialy N, Alber F, Andrews B, Angelo M, Beliveau B, Bintu L, Boettiger A, Boehm U, Brown CM, Maina MB, Chambers JJ, Cimini BA, Eliceiri K, Errington R, Faklaris O, Gaudreault N, Germain RN, Goscinski W, Grunwald D, Halter M, Hanein D, Hickey JW, Lacoste J, Laude A, Lundberg E, Ma J, Malacrida L, Moore J, Nelson G, Neumann EK, Nitschke R, Onami S, Pimentel JA, Plant AL, Radtke AJ, Sabata B, Schapiro D, Schöneberg J, Spraggins JM, Sudar D, Adrien Maria Vierdag WM, Volkmann N, Wählby C, Wang SS, Yaniv Z, Strambio-De-Castillia C. Harmonizing the Generation and Pre-publication Stewardship of FAIR Image data. ARXIV 2024:arXiv:2401.13022v4. [PMID: 38351940 PMCID: PMC10862930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
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Affiliation(s)
- Nikki Bialy
- Morgridge Institute for Research, Madison, USA
| | | | | | | | | | | | | | | | | | | | | | - Beth A Cimini
- Broad Institute of MIT and Harvard, Imaging Platform, Cambridge, USA
| | - Kevin Eliceiri
- Morgridge Institute for Research, Madison, USA
- University of Wisconsin-Madison, Madison, USA
| | | | | | | | - Ronald N Germain
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | - Michael Halter
- National Institute of Standards and Technology, Gaithersburg, USA
| | | | | | | | - Alex Laude
- Newcastle University, Newcastle upon Tyne, UK
| | - Emma Lundberg
- Stanford University, Palo Alto, USA
- SciLifeLab, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Jian Ma
- Carnegie Mellon University, Pittsburgh, USA
| | - Leonel Malacrida
- Institut Pasteur de Montevideo, & Universidad de la República, Montevideo, Uruguay
| | - Josh Moore
- German BioImaging-Gesellschaft für Mikroskopie und Bildanalyse e.V., Constance, Germany
| | - Glyn Nelson
- Newcastle University, Newcastle upon Tyne, UK
| | | | | | - Shuichi Onami
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Anne L Plant
- National Institute of Standards and Technology, Gaithersburg, USA
| | - Andrea J Radtke
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
| | | | | | | | | | - Damir Sudar
- Quantitative Imaging Systems LLC, Portland, USA
| | | | | | | | | | - Ziv Yaniv
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, USA
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47
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Lei Y, Liang X, Sun Y, Yao T, Gong H, Chen Z, Gao Y, Wang H, Wang R, Huang Y, Yang T, Yu M, Liu L, Yi CX, Wu QF, Kong X, Xu X, Liu S, Zhang Z, Liu T. Region-specific transcriptomic responses to obesity and diabetes in macaque hypothalamus. Cell Metab 2024; 36:438-453.e6. [PMID: 38325338 DOI: 10.1016/j.cmet.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/27/2023] [Accepted: 01/05/2024] [Indexed: 02/09/2024]
Abstract
The hypothalamus plays a crucial role in the progression of obesity and diabetes; however, its structural complexity and cellular heterogeneity impede targeted treatments. Here, we profiled the single-cell and spatial transcriptome of the hypothalamus in obese and sporadic type 2 diabetic macaques, revealing primate-specific distributions of clusters and genes as well as spatial region, cell-type-, and gene-feature-specific changes. The infundibular (INF) and paraventricular nuclei (PVN) are most susceptible to metabolic disruption, with the PVN being more sensitive to diabetes. In the INF, obesity results in reduced synaptic plasticity and energy sensing capability, whereas diabetes involves molecular reprogramming associated with impaired tanycytic barriers, activated microglia, and neuronal inflammatory response. In the PVN, cellular metabolism and neural activity are suppressed in diabetic macaques. Spatial transcriptomic data reveal microglia's preference for the parenchyma over the third ventricle in diabetes. Our findings provide a comprehensive view of molecular changes associated with obesity and diabetes.
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Affiliation(s)
- Ying Lei
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Xian Liang
- State Key Laboratory of Genetic Engineering, Department of Endocrinology and Metabolism, Human Phenome Institute, Institute of Metabolism and Integrative Biology, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200438, China; School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Yunong Sun
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Ting Yao
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi'an Jiaotong University School of Medicine, Xi'an, Shanxi 710063, China
| | - Hongyu Gong
- School of Life Sciences, Institues of Biomedical Sciences, Inner Mongolia University, Hohhot 010000, China
| | - Zhenhua Chen
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yuanqing Gao
- Jiangsu Provincial Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing 211166, China
| | - Hui Wang
- School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Ru Wang
- School of Kinesiology, Shanghai University of Sport, Shanghai 200438, China
| | - Yunqi Huang
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Tao Yang
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Miao Yu
- School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Longqi Liu
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Chun-Xia Yi
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105AZ Amsterdam, the Netherlands
| | - Qing-Feng Wu
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xingxing Kong
- School of Life Sciences, Fudan University, Shanghai 200438, China.
| | - Xun Xu
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China.
| | - Shiping Liu
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China.
| | - Zhi Zhang
- State Key Laboratory of Genetic Engineering, Department of Endocrinology and Metabolism, Human Phenome Institute, Institute of Metabolism and Integrative Biology, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200438, China; School of Life Sciences, Fudan University, Shanghai 200438, China.
| | - Tiemin Liu
- State Key Laboratory of Genetic Engineering, Department of Endocrinology and Metabolism, Human Phenome Institute, Institute of Metabolism and Integrative Biology, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200438, China; School of Life Sciences, Fudan University, Shanghai 200438, China; School of Life Sciences, Institues of Biomedical Sciences, Inner Mongolia University, Hohhot 010000, China.
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48
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Liu X, Yang H, Liu J, Liu K, Jin L, Zhang Y, Khan MR, Zhong K, Cao J, He Q, Xia X, Deng R. In Situ Cas12a-Based Allele-Specific PCR for Imaging Single-Nucleotide Variations in Foodborne Pathogenic Bacteria. Anal Chem 2024; 96:2032-2040. [PMID: 38277772 DOI: 10.1021/acs.analchem.3c04532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2024]
Abstract
In situ profiling of single-nucleotide variations (SNVs) can elucidate drug-resistant genotypes with single-cell resolution. The capacity to directly "see" genetic information is crucial for investigating the relationship between mutated genes and phenotypes. Fluorescence in situ hybridization serves as a canonical tool for genetic imaging; however, it cannot detect subtle sequence alteration including SNVs. Herein, we develop an in situ Cas12a-based amplification refractory mutation system-PCR (ARMS-PCR) method that allows the visualization of SNVs related to quinolone resistance inside cells. The capacity of discriminating SNVs is enhanced by incorporating optimized mismatched bases in the allele-specific primers, thus allowing to specifically amplify quinolone-resistant related genes. After in situ ARMS-PCR, we employed a modified Cas12a/CRISPR RNA to tag the amplicon, thereby enabling specific binding of fluorophore-labeled DNA probes. The method allows to precisely quantify quinolone-resistant Salmonella enterica in the bacterial mixture. Utilizing this method, we investigated the survival competition capacity of quinolone-resistant and quinolone-sensitive bacteria toward antimicrobial peptides and indicated the enrichment of quinolone-resistant bacteria under colistin sulfate stress. The in situ Cas12a-based ARMS-PCR method holds the potential for profiling cellular phenotypes and gene regulation with single-nucleotide resolution at the single-cell level.
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Affiliation(s)
- Xinmiao Liu
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Hao Yang
- School of Chemical Engineering, Sichuan University, Chengdu 610065, China
| | - Jun Liu
- Chengdu Customs Technology Center, Chengdu 610041, China
| | - Kerui Liu
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Lulu Jin
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Yong Zhang
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Mohammad Rizwan Khan
- Department of Chemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
| | - Kai Zhong
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Jijuan Cao
- Key Laboratory of Biotechnology and Bioresources Utilization of Ministry of Education, Dalian Minzu University, Dalian, Liaoning 116600, China
| | - Qiang He
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Xuhan Xia
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
| | - Ruijie Deng
- College of Biomass Science and Engineering, Healthy Food Evaluation Research Center, Sichuan University, Chengdu 610065, China
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49
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Hu Y, Rong J, Xu Y, Xie R, Peng J, Gao L, Tan K. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nat Methods 2024; 21:267-278. [PMID: 38191930 PMCID: PMC10864185 DOI: 10.1038/s41592-023-02124-2] [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: 03/07/2023] [Accepted: 11/08/2023] [Indexed: 01/10/2024]
Abstract
It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal and breast cancer data, CytoCommunity revealed new granulocyte-enriched and cancer-associated fibroblast-enriched TCNs specific to high-risk tumors and altered interactions between neoplastic and immune or stromal cells within and between TCNs. CytoCommunity can perform unsupervised and supervised analyses of spatial omics maps and enable the discovery of condition-specific cell-cell communication patterns across spatial scales.
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Affiliation(s)
- Yuxuan Hu
- School of Computer Science and Technology, Xidian University, Xi'an, China.
| | - Jiazhen Rong
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yafei Xu
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Runzhi Xie
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Jacqueline Peng
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, China
| | - Kai Tan
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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50
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Mañanes D, Rivero-García I, Relaño C, Torres M, Sancho D, Jimenez-Carretero D, Torroja C, Sánchez-Cabo F. SpatialDDLS: an R package to deconvolute spatial transcriptomics data using neural networks. Bioinformatics 2024; 40:btae072. [PMID: 38366652 PMCID: PMC10881086 DOI: 10.1093/bioinformatics/btae072] [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: 07/25/2023] [Revised: 01/10/2024] [Accepted: 02/13/2024] [Indexed: 02/18/2024] Open
Abstract
SUMMARY Spatial transcriptomics has changed our way to study tissue structure and cellular organization. However, there are still limitations in its resolution, and most available platforms do not reach a single cell resolution. To address this issue, we introduce SpatialDDLS, a fast neural network-based algorithm for cell type deconvolution of spatial transcriptomics data. SpatialDDLS leverages single-cell RNA sequencing data to simulate mixed transcriptional profiles with predefined cellular composition, which are subsequently used to train a fully connected neural network to uncover cell type diversity within each spot. By comparing it with two state-of-the-art spatial deconvolution methods, we demonstrate that SpatialDDLS is an accurate and fast alternative to the available state-of-the art tools. AVAILABILITY AND IMPLEMENTATION The R package SpatialDDLS is available via CRAN-The Comprehensive R Archive Network: https://CRAN.R-project.org/package=SpatialDDLS. A detailed manual of the main functionalities implemented in the package can be found at https://diegommcc.github.io/SpatialDDLS.
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Affiliation(s)
- Diego Mañanes
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - Inés Rivero-García
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
- Departamento de Ingeniería Biomédica, ETSI de Telecomunicaciones, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Carlos Relaño
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - Miguel Torres
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - David Sancho
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | | | - Carlos Torroja
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
| | - Fátima Sánchez-Cabo
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), 28029 Madrid, Spain
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