1
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Lewis CM, Hoffmann A, Helmchen F. Linking brain activity across scales with simultaneous opto- and electrophysiology. NEUROPHOTONICS 2024; 11:033403. [PMID: 37662552 PMCID: PMC10472193 DOI: 10.1117/1.nph.11.3.033403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/19/2023] [Accepted: 07/24/2023] [Indexed: 09/05/2023]
Abstract
The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.
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Affiliation(s)
| | - Adrian Hoffmann
- University of Zurich, Brain Research Institute, Zurich, Switzerland
- University of Zurich, Neuroscience Center Zurich, Zurich, Switzerland
| | - Fritjof Helmchen
- University of Zurich, Brain Research Institute, Zurich, Switzerland
- University of Zurich, Neuroscience Center Zurich, Zurich, Switzerland
- University of Zurich, University Research Priority Program, Adaptive Brain Circuits in Development and Learning, Zurich, Switzerland
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2
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Lee AJ, Yao S, Lusk N, Ng L, Kunst M, Zeng H, Tasic B, Abbasi-Asl R. Data-driven fine-grained region discovery in the mouse brain with transformers. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.05.592608. [PMID: 38766132 PMCID: PMC11100623 DOI: 10.1101/2024.05.05.592608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Technologies such as spatial transcriptomics offer unique opportunities to define the spatial organization of the mouse brain. We developed an unsupervised training scheme and novel transformer-based deep learning architecture to detect spatial domains in mouse whole-brain spatial transcriptomics data. Our model learns local representations of molecular and cellular statistical patterns. These local representations can be clustered to identify spatial domains within the brain from coarse to fine-grained. Discovered domains are spatially regular, even with several hundreds of spatial clusters. They are also consistent with existing anatomical ontologies such as the Allen Mouse Brain Common Coordinate Framework version 3 (CCFv31) and can be visually interpreted at the cell type or transcript level. We demonstrate our method can be used to identify previously uncatalogued subregions, such as in the midbrain, where we uncover gradients of inhibitory neuron complexity and abundance. We apply our method to a separate multi-animal whole-brain spatial transcriptomic dataset and observe that inclusion of both sagittal and coronal tissue slices in region identification improves correspondence of spatial domains to CCF.
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3
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Qiao M. Deciphering the genetic code of neuronal type connectivity through bilinear modeling. eLife 2024; 12:RP91532. [PMID: 38857169 PMCID: PMC11164534 DOI: 10.7554/elife.91532] [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] [Indexed: 06/12/2024] Open
Abstract
Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.
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Affiliation(s)
- Mu Qiao
- LinkedInMountain ViewUnited States
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4
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Valihrach L, Zucha D, Abaffy P, Kubista M. A practical guide to spatial transcriptomics. Mol Aspects Med 2024; 97:101276. [PMID: 38776574 DOI: 10.1016/j.mam.2024.101276] [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: 10/30/2023] [Revised: 04/30/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.
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Affiliation(s)
- Lukas Valihrach
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Cellular Neurophysiology, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague, Czech Republic.
| | - Daniel Zucha
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic; Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology, Prague, Czech Republic
| | - Pavel Abaffy
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic
| | - Mikael Kubista
- Laboratory of Gene Expression, Institute of Biotechnology of the Czech Academy of Sciences, Vestec, Czech Republic.
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5
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Cao J, Li C, Cui Z, Deng S, Lei T, Liu W, Yang H, Chen P. Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery. Theranostics 2024; 14:2946-2968. [PMID: 38773973 PMCID: PMC11103497 DOI: 10.7150/thno.95908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 04/25/2024] [Indexed: 05/24/2024] Open
Abstract
Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.
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Affiliation(s)
- Junxian Cao
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Caifeng Li
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhao Cui
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Shiwen Deng
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Tong Lei
- Institute of Basic Theory for Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Wei Liu
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Hongjun Yang
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
| | - Peng Chen
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Analysis of Complex Effects of Proprietary Chinese Medicine, Hunan Provincial Key Laboratory, Yongzhou City, Hunan Province, China
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6
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Yang L, Liu F, Hahm H, Okuda T, Li X, Zhang Y, Kalyanaraman V, Heitmeier MR, Samineni VK. Projection-TAGs enable multiplex projection tracing and multi-modal profiling of projection neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.590975. [PMID: 38712231 PMCID: PMC11071495 DOI: 10.1101/2024.04.24.590975] [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
Single-cell multiomic techniques have sparked immense interest in developing a comprehensive multi-modal map of diverse neuronal cell types and their brain wide projections. However, investigating the spatial organization, transcriptional and epigenetic landscapes of brain wide projection neurons is hampered by the lack of efficient and easily adoptable tools. Here we introduce Projection-TAGs, a retrograde AAV platform that allows multiplex tagging of projection neurons using RNA barcodes. By using Projection-TAGs, we performed multiplex projection tracing of the mouse cortex and high-throughput single-cell profiling of the transcriptional and epigenetic landscapes of the cortical projection neurons. Projection-TAGs can be leveraged to obtain a snapshot of activity-dependent recruitment of distinct projection neurons and their molecular features in the context of a specific stimulus. Given its flexibility, usability, and compatibility, we envision that Projection-TAGs can be readily applied to build a comprehensive multi-modal map of brain neuronal cell types and their projections.
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Affiliation(s)
- Lite Yang
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
- Neuroscience Graduate Program, Division of Biology & Biomedical Sciences, Washington University School of Medicine, St. Louis, MO, United States
| | - Fang Liu
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Hannah Hahm
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Takao Okuda
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Xiaoyue Li
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Yufen Zhang
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Vani Kalyanaraman
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Monique R. Heitmeier
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
| | - Vijay K. Samineni
- Department of Anesthesiology, Washington University Pain Center, Washington University School of Medicine, St. Louis, MO, United States
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7
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Stouffer KM, Trouvé A, Younes L, Kunst M, Ng L, Zeng H, Anant M, Fan J, Kim Y, Chen X, Rue M, Miller MI. Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections. Nat Commun 2024; 15:3530. [PMID: 38664422 PMCID: PMC11045777 DOI: 10.1038/s41467-024-47883-4] [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: 04/13/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.
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Affiliation(s)
- Kaitlin M Stouffer
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France.
| | - Alain Trouvé
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Manjari Anant
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, Penn State University, College of Medicine, State College, PA, USA
| | - Xiaoyin Chen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Mara Rue
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
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8
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Chen X, Fischer S, Rue MCP, Zhang A, Mukherjee D, Kanold PO, Gillis J, Zador AM. Whole-cortex in situ sequencing reveals input-dependent area identity. Nature 2024:10.1038/s41586-024-07221-6. [PMID: 38658747 DOI: 10.1038/s41586-024-07221-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 02/22/2024] [Indexed: 04/26/2024]
Abstract
The cerebral cortex is composed of neuronal types with diverse gene expression that are organized into specialized cortical areas. These areas, each with characteristic cytoarchitecture1,2, connectivity3,4 and neuronal activity5,6, are wired into modular networks3,4,7. However, it remains unclear whether these spatial organizations are reflected in neuronal transcriptomic signatures and how such signatures are established in development. Here we used BARseq, a high-throughput in situ sequencing technique, to interrogate the expression of 104 cell-type marker genes in 10.3 million cells, including 4,194,658 cortical neurons over nine mouse forebrain hemispheres, at cellular resolution. De novo clustering of gene expression in single neurons revealed transcriptomic types consistent with previous single-cell RNA sequencing studies8,9. The composition of transcriptomic types is highly predictive of cortical area identity. Moreover, areas with similar compositions of transcriptomic types, which we defined as cortical modules, overlap with areas that are highly connected, suggesting that the same modular organization is reflected in both transcriptomic signatures and connectivity. To explore how the transcriptomic profiles of cortical neurons depend on development, we assessed cell-type distributions after neonatal binocular enucleation. Notably, binocular enucleation caused the shifting of the cell-type compositional profiles of visual areas towards neighbouring cortical areas within the same module, suggesting that peripheral inputs sharpen the distinct transcriptomic identities of areas within cortical modules. Enabled by the high throughput, low cost and reproducibility of BARseq, our study provides a proof of principle for the use of large-scale in situ sequencing to both reveal brain-wide molecular architecture and understand its development.
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Affiliation(s)
- Xiaoyin Chen
- Allen Institute for Brain Science, Seattle, WA, USA.
| | - Stephan Fischer
- Institut Pasteur, Université Paris Cité, Bioinformatics and Biostatistics Hub, Paris, France
| | - Mara C P Rue
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Aixin Zhang
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Didhiti Mukherjee
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Patrick O Kanold
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Jesse Gillis
- Department of Physiology, University of Toronto, Toronto, Ontario, Canada.
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9
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Heindorf M, Keller GB. Antipsychotic drugs selectively decorrelate long-range interactions in deep cortical layers. eLife 2024; 12:RP86805. [PMID: 38578678 PMCID: PMC10997332 DOI: 10.7554/elife.86805] [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: 04/06/2024] Open
Abstract
Psychosis is characterized by a diminished ability of the brain to distinguish externally driven activity patterns from self-generated activity patterns. Antipsychotic drugs are a class of small molecules with relatively broad binding affinity for a variety of neuromodulator receptors that, in humans, can prevent or ameliorate psychosis. How these drugs influence the function of cortical circuits, and in particular their ability to distinguish between externally and self-generated activity patterns, is still largely unclear. To have experimental control over self-generated sensory feedback, we used a virtual reality environment in which the coupling between movement and visual feedback can be altered. We then used widefield calcium imaging to determine the cell type-specific functional effects of antipsychotic drugs in mouse dorsal cortex under different conditions of visuomotor coupling. By comparing cell type-specific activation patterns between locomotion onsets that were experimentally coupled to self-generated visual feedback and locomotion onsets that were not coupled, we show that deep cortical layers were differentially activated in these two conditions. We then show that the antipsychotic drug clozapine disrupted visuomotor integration at locomotion onsets also primarily in deep cortical layers. Given that one of the key components of visuomotor integration in cortex is long-range cortico-cortical connections, we tested whether the effect of clozapine was detectable in the correlation structure of activity patterns across dorsal cortex. We found that clozapine as well as two other antipsychotic drugs, aripiprazole and haloperidol, resulted in a strong reduction in correlations of layer 5 activity between cortical areas and impaired the spread of visuomotor prediction errors generated in visual cortex. Our results are consistent with the interpretation that a major functional effect of antipsychotic drugs is a selective alteration of long-range layer 5-mediated communication.
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Affiliation(s)
- Matthias Heindorf
- Friedrich Miescher Institute for Biomedical ResearchBaselSwitzerland
| | - Georg B Keller
- Friedrich Miescher Institute for Biomedical ResearchBaselSwitzerland
- Faculty of Science, University of BaselBaselSwitzerland
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10
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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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11
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Li R, Chen X, Yang X. Navigating the landscapes of spatial transcriptomics: How computational methods guide the way. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1839. [PMID: 38527900 DOI: 10.1002/wrna.1839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024]
Abstract
Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.
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Affiliation(s)
- Runze Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xu Chen
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
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12
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Liao M, Gao X, Chen C, Li Q, Guo Q, Huang H, Zhang E, Ju D. Integrated neural tracing and in-situ barcoded sequencing reveals the logic of SCN efferent circuits in regulating circadian behaviors. SCIENCE CHINA. LIFE SCIENCES 2024; 67:518-528. [PMID: 38057622 DOI: 10.1007/s11427-023-2420-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 06/30/2023] [Indexed: 12/08/2023]
Abstract
The circadian clock coordinates rhythms in numerous physiological processes to maintain organismal homeostasis. Since the suprachiasmatic nucleus (SCN) is widely accepted as the circadian pacemaker, it is critical to understand the neural mechanisms by which rhythmic information is transferred from the SCN to peripheral clocks. Here, we present the first comprehensive map of SCN efferent connections and suggest a molecular logic underlying these projections. The SCN projects broadly to most major regions of the brain, rather than solely to the hypothalamus and thalamus. The efferent projections from different subtypes of SCN neurons vary in distance and intensity, and blocking synaptic transmission of these circuits affects circadian rhythms in locomotion and feeding to different extents. We also developed a barcoding system to integrate retrograde tracing with in-situ sequencing, allowing us to link circuit anatomy and spatial patterns of gene expression. Analyses using this system revealed that brain regions functioning downstream of the SCN receive input from multiple neuropeptidergic cell types within the SCN, and that individual SCN neurons generally project to a single downstream brain region. This map of SCN efferent connections provides a critical foundation for future investigations into the neural circuits underlying SCN-mediated rhythms in physiology. Further, our new barcoded tracing method provides a tool for revealing the molecular logic of neuronal circuits within heterogeneous brain regions.
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Affiliation(s)
- Meimei Liao
- College of Biological Sciences, China Agriculture University, Beijing, 100193, China
- National Institute of Biological Sciences (NIBS), Beijing, 102206, China
| | - Xinwei Gao
- Chinese Institute for Brain Research, Beijing, 102206, China
| | - Chen Chen
- National Institute of Biological Sciences (NIBS), Beijing, 102206, China
| | - Qi Li
- National Institute of Biological Sciences (NIBS), Beijing, 102206, China
- Tsinghua Institute of Multidisciplinar^ Biomedical Research, Tsinghua University, Beijing, 102206, China
| | - Qingchun Guo
- Chinese Institute for Brain Research, Beijing, 102206, China
- School of Biomedical Engineering, Capital Medical University, Beijing, 100069, China
| | - He Huang
- Department of Anesthesiology, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, 401336, China
| | - Erquan Zhang
- National Institute of Biological Sciences (NIBS), Beijing, 102206, China
- Tsinghua Institute of Multidisciplinar^ Biomedical Research, Tsinghua University, Beijing, 102206, China
| | - Dapeng Ju
- Department of Anesthesiology, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, 401336, China.
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13
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Xu P, Peng J, Yuan T, Chen Z, He H, Wu Z, Li T, Li X, Wang L, Gao L, Yan J, Wei W, Li CT, Luo ZG, Chen Y. High-throughput mapping of single-neuron projection and molecular features by retrograde barcoded labeling. eLife 2024; 13:e85419. [PMID: 38390967 PMCID: PMC10914349 DOI: 10.7554/elife.85419] [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: 12/07/2022] [Accepted: 02/22/2024] [Indexed: 02/24/2024] Open
Abstract
Deciphering patterns of connectivity between neurons in the brain is a critical step toward understanding brain function. Imaging-based neuroanatomical tracing identifies area-to-area or sparse neuron-to-neuron connectivity patterns, but with limited throughput. Barcode-based connectomics maps large numbers of single-neuron projections, but remains a challenge for jointly analyzing single-cell transcriptomics. Here, we established a rAAV2-retro barcode-based multiplexed tracing method that simultaneously characterizes the projectome and transcriptome at the single neuron level. We uncovered dedicated and collateral projection patterns of ventromedial prefrontal cortex (vmPFC) neurons to five downstream targets and found that projection-defined vmPFC neurons are molecularly heterogeneous. We identified transcriptional signatures of projection-specific vmPFC neurons, and verified Pou3f1 as a marker gene enriched in neurons projecting to the lateral hypothalamus, denoting a distinct subset with collateral projections to both dorsomedial striatum and lateral hypothalamus. In summary, we have developed a new multiplexed technique whose paired connectome and gene expression data can help reveal organizational principles that form neural circuits and process information.
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Affiliation(s)
- Peibo Xu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Jian Peng
- School of Life Science and Technology & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech UniversityShanghaiChina
| | - Tingli Yuan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
| | - Zhaoqin Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
| | - Hui He
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Ziyan Wu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
| | - Ting Li
- School of Life Science and Technology & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech UniversityShanghaiChina
| | - Xiaodong Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- University of Chinese Academy of SciencesBeijingChina
| | - Luyue Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of ScienceShanghaiChina
| | - Le Gao
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
| | - Jun Yan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- Shanghai Center for Brain Science and Brain-Inspired Intelligence TechnologyShanghaiChina
- School of Future Technology, University of Chinese Academy of SciencesBeijingChina
| | - Wu Wei
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of ScienceShanghaiChina
- Lingang LaboratoryShanghaiChina
| | - Chengyu T Li
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- Shanghai Center for Brain Science and Brain-Inspired Intelligence TechnologyShanghaiChina
- School of Future Technology, University of Chinese Academy of SciencesBeijingChina
- Lingang LaboratoryShanghaiChina
| | - Zhen-Ge Luo
- School of Life Science and Technology & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech UniversityShanghaiChina
| | - Yuejun Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Center for Brain Science and Brain-Inspired TechnologyShanghaiChina
- Shanghai Center for Brain Science and Brain-Inspired Intelligence TechnologyShanghaiChina
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14
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Ford AN, Czarny JE, Rogalla MM, Quass GL, Apostolides PF. Auditory Corticofugal Neurons Transmit Auditory and Non-auditory Information During Behavior. J Neurosci 2024; 44:e1190232023. [PMID: 38123993 PMCID: PMC10869159 DOI: 10.1523/jneurosci.1190-23.2023] [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: 06/27/2023] [Revised: 11/08/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Layer 5 pyramidal neurons of sensory cortices project "corticofugal" axons to myriad sub-cortical targets, thereby broadcasting high-level signals important for perception and learning. Recent studies suggest dendritic Ca2+ spikes as key biophysical mechanisms supporting corticofugal neuron function: these long-lasting events drive burst firing, thereby initiating uniquely powerful signals to modulate sub-cortical representations and trigger learning-related plasticity. However, the behavioral relevance of corticofugal dendritic spikes is poorly understood. We shed light on this issue using 2-photon Ca2+ imaging of auditory corticofugal dendrites as mice of either sex engage in a GO/NO-GO sound-discrimination task. Unexpectedly, only a minority of dendritic spikes were triggered by behaviorally relevant sounds under our conditions. Task related dendritic activity instead mostly followed sound cue termination and co-occurred with mice's instrumental licking during the answer period of behavioral trials, irrespective of reward consumption. Temporally selective, optogenetic silencing of corticofugal neurons during the trial answer period impaired auditory discrimination learning. Thus, auditory corticofugal systems' contribution to learning and plasticity may be partially nonsensory in nature.
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Affiliation(s)
- Alexander N Ford
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
| | - Jordyn E Czarny
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
| | - Meike M Rogalla
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
| | - Gunnar L Quass
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
| | - Pierre F Apostolides
- Department of Otolaryngology/Head and Neck Surgery, Kresge Hearing Research Institute, Ann Arbor, Michigan 48109
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan 48109
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15
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Zhang A, Jin L, Yao S, Matsuyama M, van Velthoven CTJ, Sullivan HA, Sun N, Kellis M, Tasic B, Wickersham I, Chen X. Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing. eLife 2024; 12:RP87866. [PMID: 38319699 PMCID: PMC10942611 DOI: 10.7554/elife.87866] [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: 02/07/2024] Open
Abstract
Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to map circuits at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNA-seq, and 4130 retrogradely labeled cells and 2914 transsynaptically labeled cells in situ. We found that the transcriptomic identities of rabies virus-infected cells can be robustly identified using both single-cell RNA-seq and in situ sequencing. By associating gene expression with connectivity inferred from barcode sequencing, we distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.
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Affiliation(s)
- Aixin Zhang
- Allen Institute for Brain ScienceSeattleUnited States
| | - Lei Jin
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Shenqin Yao
- Allen Institute for Brain ScienceSeattleUnited States
| | - Makoto Matsuyama
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | | | - Heather Anne Sullivan
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Na Sun
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Broad Institute of MIT and HarvardCambridgeUnited States
- Broad Institute of MIT and HarvardCambridgeUnited States
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Broad Institute of MIT and HarvardCambridgeUnited States
- Broad Institute of MIT and HarvardCambridgeUnited States
| | | | - Ian Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Xiaoyin Chen
- Allen Institute for Brain ScienceSeattleUnited States
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16
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Krall RF, Chambers CN, Arnold MP, Brougher LI, Chen J, Deshmukh R, King HB, Morford HJ, Wiemann JM, Williamson RS. Primary auditory cortex is necessary for the acquisition and expression of categorical behavior. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.02.578700. [PMID: 38352355 PMCID: PMC10862902 DOI: 10.1101/2024.02.02.578700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/07/2024]
Abstract
The primary auditory cortex (ACtx) is critically involved in the association of sensory information with specific behavioral outcomes. Such sensory-guided behaviors are necessarily brain-wide endeavors, requiring a plethora of distinct brain areas, including those that are involved in aspects of decision making, motor planning, motor initiation, and reward prediction. ACtx comprises a number of distinct excitatory cell-types that allow for the brain-wide propagation of behaviorally-relevant sensory information. Exactly how ACtx involvement changes as a function of learning, as well as the functional role of distinct excitatory cell-types is unclear. Here, we addressed these questions by designing a two-choice auditory task in which water-restricted, head-fixed mice were trained to categorize the temporal rate of a sinusoidal amplitude modulated (sAM) noise burst and used transient cell-type specific optogenetics to probe ACtx necessity across the duration of learning. Our data demonstrate that ACtx is necessary for the ability to categorize the rate of sAM noise, and this necessity grows across learning. ACtx silencing substantially altered the behavioral strategies used to solve the task by introducing a fluctuating choice bias and increasing dependence on prior decisions. Furthermore, ACtx silencing did not impact the animal's motor report, suggesting that ACtx is necessary for the conversion of sensation to action. Targeted inhibition of extratelencephalic projections on just 20% of trials had a minimal effect on task performance, but significantly degraded learning. Taken together, our data suggest that distinct cortical cell-types synergistically control auditory-guided behavior and that extratelencephalic neurons play a critical role in learning and plasticity.
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17
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Zhang Y, Petukhov V, Biederstedt E, Que R, Zhang K, Kharchenko PV. Gene panel selection for targeted spatial transcriptomics. Genome Biol 2024; 25:35. [PMID: 38273415 PMCID: PMC10811939 DOI: 10.1186/s13059-024-03174-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 01/12/2024] [Indexed: 01/27/2024] Open
Abstract
Targeted spatial transcriptomics hold particular promise in analyzing complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is their reliance on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method performing gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.
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Affiliation(s)
- Yida Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - Viktor Petukhov
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Evan Biederstedt
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Richard Que
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA.
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18
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Smith SS, Jahn KN, Sugai JA, Hancock KE, Polley DB. The human pupil and face encode sound affect and provide objective signatures of tinnitus and auditory hypersensitivity disorders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.22.571929. [PMID: 38187580 PMCID: PMC10769427 DOI: 10.1101/2023.12.22.571929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Sound is jointly processed along acoustic and emotional dimensions. These dimensions can become distorted and entangled in persons with sensory disorders, producing a spectrum of loudness hypersensitivity, phantom percepts, and - in some cases - debilitating sound aversion. Here, we looked for objective signatures of disordered hearing (DH) in the human face. Pupil dilations and micro facial movement amplitudes scaled with sound valence in neurotypical listeners but not DH participants with chronic tinnitus (phantom ringing) and sound sensitivity. In DH participants, emotionally evocative sounds elicited abnormally large pupil dilations but blunted and invariant facial reactions that jointly provided an accurate prediction of individual tinnitus and hyperacusis questionnaire handicap scores. By contrast, EEG measures of central auditory gain identified steeper neural response growth functions but no association with symptom severity. These findings highlight dysregulated affective sound processing in persons with bothersome tinnitus and sound sensitivity disorders and introduce approaches for their objective measurement.
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Affiliation(s)
- Samuel S Smith
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear, Boston MA, 02114 USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston MA 02114 USA
- Lead contact
| | - Kelly N Jahn
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear, Boston MA, 02114 USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston MA 02114 USA
| | - Jenna A Sugai
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear, Boston MA, 02114 USA
| | - Ken E Hancock
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear, Boston MA, 02114 USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston MA 02114 USA
| | - Daniel B Polley
- Eaton-Peabody Laboratories, Massachusetts Eye and Ear, Boston MA, 02114 USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston MA 02114 USA
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19
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Zhou J, Zhang Z, Wu M, Liu H, Pang Y, Bartlett A, Peng Z, Ding W, Rivkin A, Lagos WN, Williams E, Lee CT, Miyazaki PA, Aldridge A, Zeng Q, Salinda JLA, Claffey N, Liem M, Fitzpatrick C, Boggeman L, Yao Z, Smith KA, Tasic B, Altshul J, Kenworthy MA, Valadon C, Nery JR, Castanon RG, Patne NS, Vu M, Rashid M, Jacobs M, Ito T, Osteen J, Emerson N, Lee J, Cho S, Rink J, Huang HH, Pinto-Duartec A, Dominguez B, Smith JB, O'Connor C, Zeng H, Chen S, Lee KF, Mukamel EA, Jin X, Margarita Behrens M, Ecker JR, Callaway EM. Brain-wide correspondence of neuronal epigenomics and distant projections. Nature 2023; 624:355-365. [PMID: 38092919 PMCID: PMC10719087 DOI: 10.1038/s41586-023-06823-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 11/01/2023] [Indexed: 12/17/2023]
Abstract
Single-cell analyses parse the brain's billions of neurons into thousands of 'cell-type' clusters residing in different brain structures1. Many cell types mediate their functions through targeted long-distance projections allowing interactions between specific cell types. Here we used epi-retro-seq2 to link single-cell epigenomes and cell types to long-distance projections for 33,034 neurons dissected from 32 different regions projecting to 24 different targets (225 source-to-target combinations) across the whole mouse brain. We highlight uses of these data for interrogating principles relating projection types to transcriptomics and epigenomics, and for addressing hypotheses about cell types and connections related to genetics. We provide an overall synthesis with 926 statistical comparisons of discriminability of neurons projecting to each target for every source. We integrate this dataset into the larger BRAIN Initiative Cell Census Network atlas, composed of millions of neurons, to link projection cell types to consensus clusters. Integration with spatial transcriptomics further assigns projection-enriched clusters to smaller source regions than the original dissections. We exemplify this by presenting in-depth analyses of projection neurons from the hypothalamus, thalamus, hindbrain, amygdala and midbrain to provide insights into properties of those cell types, including differentially expressed genes, their associated cis-regulatory elements and transcription-factor-binding motifs, and neurotransmitter use.
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Affiliation(s)
- Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Zhuzhu Zhang
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Department of Human Genetics, The University of Chicago, Chicago, IL, USA
| | - May Wu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Yan Pang
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Anna Bartlett
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Zihao Peng
- School of Mathematics and Computer Science, Nanchang University, Nanchang, China
- Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, China
| | - Wubin Ding
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Angeline Rivkin
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Will N Lagos
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Elora Williams
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Cheng-Ta Lee
- Peptide Biology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Paula Assakura Miyazaki
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Andrew Aldridge
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Qiurui Zeng
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA
| | - J L Angelo Salinda
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Naomi Claffey
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Michelle Liem
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Conor Fitzpatrick
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Lara Boggeman
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Jordan Altshul
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Mia A Kenworthy
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Cynthia Valadon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Nery
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Rosa G Castanon
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Neelakshi S Patne
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Minh Vu
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Mohammad Rashid
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Matthew Jacobs
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Tony Ito
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Julia Osteen
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Nora Emerson
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jasper Lee
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Silvia Cho
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jon Rink
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Hsiang-Hsuan Huang
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - António Pinto-Duartec
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bertha Dominguez
- Peptide Biology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jared B Smith
- Molecular Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Carolyn O'Connor
- Flow Cytometry Core Facility, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Shengbo Chen
- Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, China
- School of Computer and Information Engineering, Henan University, Kaifeng, China
| | - Kuo-Fen Lee
- Peptide Biology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Eran A Mukamel
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
| | - Xin Jin
- Center for Motor Control and Disease, Key Laboratory of Brain Functional Genomics, East China Normal University, Shanghai, China
- NYU-ECNU Institute of Brain and Cognitive Science, New York University Shanghai, Shanghai, China
| | - M Margarita Behrens
- Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Joseph R Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA.
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA.
| | - Edward M Callaway
- Division of Biological Sciences, University of California San Diego, La Jolla, CA, USA.
- Systems Neurobiology Laboratories, The Salk Institute for Biological Studies, La Jolla, CA, USA.
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20
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Chang T, Han W, Jiang M, Li J, Liao Z, Tang M, Zhang J, Shen J, Chen Z, Fei P, Ren X, Pang Y, Wang G, Wang J, Huang Y. Rapid and signal crowdedness-robust in situ sequencing through hybrid block coding. Proc Natl Acad Sci U S A 2023; 120:e2309227120. [PMID: 37963245 PMCID: PMC10666108 DOI: 10.1073/pnas.2309227120] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 09/24/2023] [Indexed: 11/16/2023] Open
Abstract
Spatial transcriptomics technology has revolutionized our understanding of cell types and tissue organization, opening possibilities for researchers to explore transcript distributions at subcellular levels. However, existing methods have limitations in resolution, sensitivity, or speed. To overcome these challenges, we introduce SPRINTseq (Spatially Resolved and signal-diluted Next-generation Targeted sequencing), an innovative in situ sequencing strategy that combines hybrid block coding and molecular dilution strategies. Our method enables fast and sensitive high-resolution data acquisition, as demonstrated by recovering over 142 million transcripts using a 108-gene panel from 453,843 cells from four mouse brain coronal slices in less than 2 d. Using this advanced technology, we uncover the cellular and subcellular molecular architecture of Alzheimer's disease, providing additional information into abnormal cellular behaviors and their subcellular mRNA distribution. This improved spatial transcriptomics technology holds great promise for exploring complex biological processes and disease mechanisms.
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Affiliation(s)
- Tianyi Chang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing100871, China
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China
| | - Wuji Han
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Yuanpei College, Peking University, Beijing100871, China
| | - Mengcheng Jiang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Materials Science and Engineering, College of Engineering, Peking University, Beijing100871, China
| | - Jizhou Li
- School of Life Sciences, Tsinghua University, Beijing100084, China
| | - Zhizhao Liao
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing100871, China
- Changping Laboratory, Changping District, Beijing102206, China
| | - Mingchuan Tang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Yuanpei College, Peking University, Beijing100871, China
| | - Jianyun Zhang
- Department of Oral Pathology, Peking University School and Hospital of Stomatology, National Center for Stomatology, National Clinical Research Center for Oral Diseases, National Engineering Research Center of Oral Biomaterials and Digital Medical Devices, Beijing100081, China
| | - Jie Shen
- School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing100069, China
| | - Zitian Chen
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
| | - Peng Fei
- School of Optical and Electronic Information – Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan430074, China
| | - Xianwen Ren
- Changping Laboratory, Changping District, Beijing102206, China
| | - Yuhong Pang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
| | - Guanbo Wang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Institute for Cell Analysis, Shenzhen Bay Laboratory, Guangdong528107, China
| | - Jianbin Wang
- School of Life Sciences, Tsinghua University, Beijing100084, China
| | - Yanyi Huang
- Biomedical Pioneering Innovation Center, Peking University, Beijing100871, China
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing100871, China
- Institute for Cell Analysis, Shenzhen Bay Laboratory, Guangdong528107, China
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking University, Beijing100871, China
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21
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Zhang A, Jin L, Yao S, Matsuyama M, van Velthoven C, Sullivan H, Sun N, Kellis M, Tasic B, Wickersham IR, Chen X. Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.16.532873. [PMID: 36993334 PMCID: PMC10055146 DOI: 10.1101/2023.03.16.532873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to map circuits at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNA-seq, and 4,130 retrogradely labeled cells and 2,914 transsynaptically labeled cells in situ. We found that the transcriptomic identities of rabies virus-infected cells can be robustly identified using both single-cell RNA-seq and in situ sequencing. By associating gene expression with connectivity inferred from barcode sequencing, we distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.
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Affiliation(s)
- Aixin Zhang
- Allen Institute for Brain Science, Seattle, WA
| | - Lei Jin
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Current address: Lingang Laboratory, Shanghai, China
| | - Shenqin Yao
- Allen Institute for Brain Science, Seattle, WA
| | - Makoto Matsuyama
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
- Current address: Metcela Inc., Kawasaki, Kanagawa, Japan
| | | | - Heather Sullivan
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
| | - Na Sun
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Ian R. Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA
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22
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Zeisler ZR, London L, Janssen WG, Fredericks JM, Elorette C, Fujimoto A, Zhan H, Russ BE, Clem RL, Hof PR, Stoll FM, Rudebeck PH. Single basolateral amygdala neurons in macaques exhibit distinct connectional motifs with frontal cortex. Neuron 2023; 111:3307-3320.e5. [PMID: 37857091 PMCID: PMC10593429 DOI: 10.1016/j.neuron.2023.09.024] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 08/14/2023] [Accepted: 09/20/2023] [Indexed: 10/21/2023]
Abstract
Basolateral amygdala (BLA) projects widely across the macaque frontal cortex, and amygdalo-frontal projections are critical for appropriate emotional responding and decision making. While it is appreciated that single BLA neurons branch and project to multiple areas in frontal cortex, the organization and frequency of this branching has yet to be fully characterized. Here, we determined the projection patterns of more than 3,000 macaque BLA neurons. We found that one-third of BLA neurons had two or more distinct projection targets in frontal cortex and subcortical structures. The patterns of single BLA neuron projections to multiple areas were organized into repeating motifs that targeted distinct sets of areas in medial and ventral frontal cortex, indicative of separable BLA networks. Our findings begin to reveal the rich structure of single-neuron connections in the non-human primate brain, providing a neuroanatomical basis for the role of BLA in coordinating brain-wide responses to valent stimuli.
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Affiliation(s)
- Zachary R Zeisler
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Liza London
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - William G Janssen
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Microscopy and Advanced Bioimaging CoRE, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - J Megan Fredericks
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Catherine Elorette
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Atsushi Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, 1 Bungtown Road, Syosset, NY 11791, USA
| | - Brian E Russ
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA; Department of Psychiatry, New York University at Langone, One, 8 Park Avenue, New York, NY 10016, USA
| | - Roger L Clem
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA
| | - Frederic M Stoll
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Peter H Rudebeck
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029, USA; Lipschultz Center for Cognitive Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
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23
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Jung N, Kim TK. Spatial transcriptomics in neuroscience. Exp Mol Med 2023; 55:2105-2115. [PMID: 37779145 PMCID: PMC10618223 DOI: 10.1038/s12276-023-01093-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: 02/28/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 10/03/2023] Open
Abstract
The brain is one of the most complex living tissue types and is composed of an exceptional diversity of cell types displaying unique functional connectivity. Single-cell RNA sequencing (scRNA-seq) can be used to efficiently map the molecular identities of the various cell types in the brain by providing the transcriptomic profiles of individual cells isolated from the tissue. However, the lack of spatial context in scRNA-seq prevents a comprehensive understanding of how different configurations of cell types give rise to specific functions in individual brain regions and how each distinct cell is connected to form a functional unit. To understand how the various cell types contribute to specific brain functions, it is crucial to correlate the identities of individual cells obtained through scRNA-seq with their spatial information in intact tissue. Spatial transcriptomics (ST) can resolve the complex spatial organization of cell types in the brain and their connectivity. Various ST tools developed during the past decade based on imaging and sequencing technology have permitted the creation of functional atlases of the brain and have pulled the properties of neural circuits into ever-sharper focus. In this review, we present a summary of several ST tools and their applications in neuroscience and discuss the unprecedented insights these tools have made possible.
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Affiliation(s)
- Namyoung Jung
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea
| | - Tae-Kyung Kim
- Department of Life Sciences, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea.
- Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Seoul, 03722, Republic of Korea.
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24
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Bender PTR, McCollum M, Boyd-Pratt H, Mendelson BZ, Anderson CT. Synaptic zinc potentiates AMPA receptor function in mouse auditory cortex. Cell Rep 2023; 42:112932. [PMID: 37585291 PMCID: PMC10514716 DOI: 10.1016/j.celrep.2023.112932] [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: 04/05/2023] [Revised: 06/23/2023] [Accepted: 07/18/2023] [Indexed: 08/18/2023] Open
Abstract
Synaptic zinc signaling modulates synaptic activity and is present in specific populations of cortical neurons, suggesting that synaptic zinc contributes to the diversity of intracortical synaptic microcircuits and their functional specificity. To understand the role of zinc signaling in the cortex, we performed whole-cell patch-clamp recordings from intratelencephalic (IT)-type neurons and pyramidal tract (PT)-type neurons in layer 5 of the mouse auditory cortex during optogenetic stimulation of specific classes of presynaptic neurons. Our results show that synaptic zinc potentiates AMPA receptor (AMPAR) function in a synapse-specific manner. We performed in vivo 2-photon calcium imaging of the same classes of neurons in awake mice and found that changes in synaptic zinc can widen or sharpen the sound-frequency tuning bandwidth of IT-type neurons but only widen the tuning bandwidth of PT-type neurons. These results provide evidence for synapse- and cell-type-specific actions of synaptic zinc in the cortex.
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Affiliation(s)
- Philip T R Bender
- Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University School of Medicine, Morgantown, WV 26506, USA
| | - Mason McCollum
- Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University School of Medicine, Morgantown, WV 26506, USA
| | - Helen Boyd-Pratt
- Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University School of Medicine, Morgantown, WV 26506, USA
| | - Benjamin Z Mendelson
- Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University School of Medicine, Morgantown, WV 26506, USA
| | - Charles T Anderson
- Department of Neuroscience, Rockefeller Neuroscience Institute, West Virginia University School of Medicine, Morgantown, WV 26506, USA.
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25
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Magoulopoulou A, Salas SM, Tiklová K, Samuelsson ER, Hilscher MM, Nilsson M. Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications. Annu Rev Genomics Hum Genet 2023; 24:133-150. [PMID: 37018847 DOI: 10.1146/annurev-genom-102722-092013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.
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Affiliation(s)
- Anastasia Magoulopoulou
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Sergio Marco Salas
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Katarína Tiklová
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Erik Reinhold Samuelsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Markus M Hilscher
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Mats Nilsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
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26
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Barabási DL, Bianconi G, Bullmore E, Burgess M, Chung S, Eliassi-Rad T, George D, Kovács IA, Makse H, Nichols TE, Papadimitriou C, Sporns O, Stachenfeld K, Toroczkai Z, Towlson EK, Zador AM, Zeng H, Barabási AL, Bernard A, Buzsáki G. Neuroscience Needs Network Science. J Neurosci 2023; 43:5989-5995. [PMID: 37612141 PMCID: PMC10451115 DOI: 10.1523/jneurosci.1014-23.2023] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/10/2023] [Accepted: 07/14/2023] [Indexed: 08/25/2023] Open
Abstract
The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.
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Affiliation(s)
- Dániel L Barabási
- Biophysics Program, Harvard University, Cambridge, 02138, Massachusetts
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, 02138, Massachusetts
| | - Ginestra Bianconi
- School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, United Kingdom
- Alan Turing Institute, The British Library, London, NW1 2DB, United Kingdom
| | - Ed Bullmore
- Department of Psychiatry and Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, CB2 0QQ, United Kingdom
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, New York 10003
- Center for Computational Neuroscience, Flatiron Institute, Simons Foundation, New York, New York 10010
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, 02115, Massachusetts
- Khoury College of Computer Sciences, Northeastern University, Boston, 02115, Massachusetts
- Santa Fe Institute, Santa Fe, New Mexico 87501
| | | | - István A Kovács
- Department of Physics and Astronomy, Northwestern University, Evanston, Illinois 60208
- Northwestern Institute on Complex Systems, Northwestern University, Evanston, Illinois 60208
| | - Hernán Makse
- Levich Institute and Physics Department, City College of New York, New York, New York 10031
| | - Thomas E Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | | | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana 47405
| | - Kim Stachenfeld
- DeepMind, London, EC4A 3TW, United Kingdom
- Columbia University, New York, New York 10027
| | - Zoltán Toroczkai
- Department of Physics, University of Notre Dame, Notre Dame, Indiana 46556
| | - Emma K Towlson
- Department of Computer Science, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
- Department of Physics and Astronomy, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, AB T2N 1N4, Canada
| | - Anthony M Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, 98109, Washington
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, 02115, Massachusetts
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts 02115
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary
| | - Amy Bernard
- The Kavli Foundation, Los Angeles, 90230, California
| | - György Buzsáki
- Center for Neural Science, New York University, New York, New York 10003
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, New York 10016
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27
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Huilgol D, Russ JB, Srivas S, Huang ZJ. The progenitor basis of cortical projection neuron diversity. Curr Opin Neurobiol 2023; 81:102726. [PMID: 37148649 PMCID: PMC10557529 DOI: 10.1016/j.conb.2023.102726] [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: 12/26/2022] [Revised: 04/04/2023] [Accepted: 04/09/2023] [Indexed: 05/08/2023]
Abstract
Diverse glutamatergic projection neurons (PNs) mediate myriad processing streams and output channels of the cerebral cortex. Yet, how different types of neural progenitors, such as radial glia (RGs) and intermediate progenitors (IPs), produce PN diversity, and hierarchical organization remains unclear. A fundamental issue is whether RGs constitute a homogeneous, multipotent lineage capable of generating all major PN types through a temporally regulated developmental program, or whether RGs comprise multiple transcriptionally heterogenous pools, each fated to generate a subset of PNs. Beyond RGs, the role of IPs in PN diversification remains underexplored. Addressing these questions requires tracking PN developmental trajectories with cell-type resolution - from transcription factor-defined RGs and IPs to their PN progeny, which are defined not only by laminar location but also by projection patterns and gene expression. Advances in cell-type resolution genetic fate mapping, axon tracing, and spatial transcriptomics may provide the technical capability for answering these fundamental questions.
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Affiliation(s)
- Dhananjay Huilgol
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Jeffrey B Russ
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA; Department of Pediatrics, Division of Neurology, Duke University Medical Center, Durham, NC 27710, USA
| | - Sweta Srivas
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Z Josh Huang
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA; Department of Biomedical Engineering, Duke University Pratt School of Engineering, Durham, NC 27708, USA.
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28
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Fangma Y, Liu M, Liao J, Chen Z, Zheng Y. Dissecting the brain with spatially resolved multi-omics. J Pharm Anal 2023; 13:694-710. [PMID: 37577383 PMCID: PMC10422112 DOI: 10.1016/j.jpha.2023.04.003] [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: 10/31/2022] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 08/15/2023] Open
Abstract
Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.
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Affiliation(s)
- Yijia Fangma
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Mengting Liu
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Jie Liao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhong Chen
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
| | - Yanrong Zheng
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, 310053, China
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29
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Stoufflet J, Tielens S, Nguyen L. Shaping the cerebral cortex by cellular crosstalk. Cell 2023; 186:2733-2747. [PMID: 37352835 DOI: 10.1016/j.cell.2023.05.040] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/30/2023] [Accepted: 05/26/2023] [Indexed: 06/25/2023]
Abstract
The cerebral cortex is the brain's outermost layer. It is responsible for processing motor and sensory information that support high-level cognitive abilities and shape personality. Its development and functional organization strongly rely on cell communication that is established via an intricate system of diffusible signals and physical contacts during development. Interfering with this cellular crosstalk can cause neurodevelopmental disorders. Here, we review how crosstalk between migrating cells and their environment influences cerebral cortex development, ranging from neurogenesis to synaptogenesis and assembly of cortical circuits.
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Affiliation(s)
- Julie Stoufflet
- Laboratory of Molecular Regulation of Neurogenesis, GIGA-Stem Cells and GIGA-Neurosciences, Interdisciplinary Cluster for Applied Genoproteomics (GIGA-R), University of Liège, CHU Sart Tilman, Liège 4000, Belgium
| | - Sylvia Tielens
- Laboratory of Molecular Regulation of Neurogenesis, GIGA-Stem Cells and GIGA-Neurosciences, Interdisciplinary Cluster for Applied Genoproteomics (GIGA-R), University of Liège, CHU Sart Tilman, Liège 4000, Belgium
| | - Laurent Nguyen
- Laboratory of Molecular Regulation of Neurogenesis, GIGA-Stem Cells and GIGA-Neurosciences, Interdisciplinary Cluster for Applied Genoproteomics (GIGA-R), University of Liège, CHU Sart Tilman, Liège 4000, Belgium; Walloon Excellence in Life Sciences and Biotechnology (WELBIO), Wavres, Belgium.
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30
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Zhang Y, Miller JA, Park J, Lelieveldt BP, Long B, Abdelaal T, Aevermann BD, Biancalani T, Comiter C, Dzyubachyk O, Eggermont J, Langseth CM, Petukhov V, Scalia G, Vaishnav ED, Zhao Y, Lein ES, Scheuermann RH. Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain. Sci Rep 2023; 13:9567. [PMID: 37311768 DOI: 10.1038/s41598-023-36638-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 06/07/2023] [Indexed: 06/15/2023] Open
Abstract
With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer ( https://viewer.cytosplore.org ) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.
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Affiliation(s)
- Yun Zhang
- J. Craig Venter Institute, La Jolla, CA, USA
| | | | - Jeongbin Park
- School of Biomedical Convergence Engineering, Pusan National University, Busan, Korea
| | - Boudewijn P Lelieveldt
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics Group, Delft University of Technology, Delft, The Netherlands
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tamim Abdelaal
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
- Pattern Recognition and Bioinformatics Group, Delft University of Technology, Delft, The Netherlands
| | - Brian D Aevermann
- J. Craig Venter Institute, La Jolla, CA, USA
- Chan Zuckerberg Initiative, Redwood City, CA, USA
| | - Tommaso Biancalani
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | | | - Oleh Dzyubachyk
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Jeroen Eggermont
- LKEB, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Viktor Petukhov
- Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gabriele Scalia
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | | | - Yilin Zhao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Richard H Scheuermann
- J. Craig Venter Institute, La Jolla, CA, USA.
- Department of Pathology, University of California, San Diego, CA, USA.
- Division of Vaccine Discovery, La Jolla Institute for Immunology, La Jolla, CA, USA.
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31
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Kouvaros S, Bizup B, Solis O, Kumar M, Ventriglia E, Curry FP, Michaelides M, Tzounopoulos T. A CRE/DRE dual recombinase transgenic mouse reveals synaptic zinc-mediated thalamocortical neuromodulation. SCIENCE ADVANCES 2023; 9:eadf3525. [PMID: 37294760 PMCID: PMC10256168 DOI: 10.1126/sciadv.adf3525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 05/05/2023] [Indexed: 06/11/2023]
Abstract
Synaptic zinc is a neuromodulator that shapes synaptic transmission and sensory processing. The maintenance of synaptic zinc is dependent on the vesicular zinc transporter, ZnT3. Hence, the ZnT3 knockout mouse has been a key tool for studying the mechanisms and functions of synaptic zinc. However, the use of this constitutive knockout mouse has notable limitations, including developmental, compensatory, and brain and cell type specificity issues. To overcome these limitations, we developed and characterized a dual recombinase transgenic mouse, which combines the Cre and Dre recombinase systems. This mouse allows for tamoxifen-inducible Cre-dependent expression of exogenous genes or knockout of floxed genes in ZnT3-expressing neurons and DreO-dependent region and cell type-specific conditional ZnT3 knockout in adult mice. Using this system, we reveal a neuromodulatory mechanism whereby zinc release from thalamic neurons modulates N-methyl-d-aspartate receptor activity in layer 5 pyramidal tract neurons, unmasking previously unknown features of cortical neuromodulation.
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Affiliation(s)
- Stylianos Kouvaros
- Pittsburgh Hearing Research Center, Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Brandon Bizup
- Pittsburgh Hearing Research Center, Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Oscar Solis
- Biobehavioral Imaging and Molecular Neuropsychopharmacology Unit, National Institute on Drug Abuse Intramural Research Program, Baltimore, MD 21224, USA
| | - Manoj Kumar
- Pittsburgh Hearing Research Center, Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Emilya Ventriglia
- Biobehavioral Imaging and Molecular Neuropsychopharmacology Unit, National Institute on Drug Abuse Intramural Research Program, Baltimore, MD 21224, USA
| | - Fallon P. Curry
- Biobehavioral Imaging and Molecular Neuropsychopharmacology Unit, National Institute on Drug Abuse Intramural Research Program, Baltimore, MD 21224, USA
| | - Michael Michaelides
- Biobehavioral Imaging and Molecular Neuropsychopharmacology Unit, National Institute on Drug Abuse Intramural Research Program, Baltimore, MD 21224, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Thanos Tzounopoulos
- Pittsburgh Hearing Research Center, Department of Otolaryngology, University of Pittsburgh, Pittsburgh, PA 15261, USA
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Luan L, Yin R, Zhu H, Xie C. Emerging Penetrating Neural Electrodes: In Pursuit of Large Scale and Longevity. Annu Rev Biomed Eng 2023; 25:185-205. [PMID: 37289556 PMCID: PMC11078330 DOI: 10.1146/annurev-bioeng-090622-050507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Penetrating neural electrodes provide a powerful approach to decipher brain circuitry by allowing for time-resolved electrical detections of individual action potentials. This unique capability has contributed tremendously to basic and translational neuroscience, enabling both fundamental understandings of brain functions and applications of human prosthetic devices that restore crucial sensations and movements. However, conventional approaches are limited by the scarce number of available sensing channels and compromised efficacy over long-term implantations. Recording longevity and scalability have become the most sought-after improvements in emerging technologies. In this review, we discuss the technological advances in the past 5-10 years that have enabled larger-scale, more detailed, and longer-lasting recordings of neural circuits at work than ever before. We present snapshots of the latest advances in penetration electrode technology, showcase their applications in animal models and humans, and outline the underlying design principles and considerations to fuel future technological development.
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Affiliation(s)
- Lan Luan
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA;
- Rice Neuroengineering Initiative, Rice University, Houston, Texas, USA
- Department of Bioengineering, Rice University, Houston, Texas, USA
| | - Rongkang Yin
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA;
- Rice Neuroengineering Initiative, Rice University, Houston, Texas, USA
| | - Hanlin Zhu
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA;
- Rice Neuroengineering Initiative, Rice University, Houston, Texas, USA
| | - Chong Xie
- Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA;
- Rice Neuroengineering Initiative, Rice University, Houston, Texas, USA
- Department of Bioengineering, Rice University, Houston, Texas, USA
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33
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Tian L, Chen F, Macosko EZ. The expanding vistas of spatial transcriptomics. Nat Biotechnol 2023; 41:773-782. [PMID: 36192637 PMCID: PMC10091579 DOI: 10.1038/s41587-022-01448-2] [Citation(s) in RCA: 81] [Impact Index Per Article: 81.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 07/26/2022] [Indexed: 11/09/2022]
Abstract
The formation and maintenance of tissue integrity requires complex, coordinated activities by thousands of genes and their encoded products. Until recently, transcript levels could only be quantified for a few genes in tissues, but advances in DNA sequencing, oligonucleotide synthesis and fluorescence microscopy have enabled the invention of a suite of spatial transcriptomics technologies capable of measuring the expression of many, or all, genes in situ. These technologies have evolved rapidly in sensitivity, multiplexing and throughput. As such, they have enabled the determination of the cell-type architecture of tissues, the querying of cell-cell interactions and the monitoring of molecular interactions between tissue components. The rapidly evolving spatial genomics landscape will enable generalized high-throughput genomic measurements and perturbations to be performed in the context of tissues. These advances will empower hypothesis generation and biological discovery and bridge the worlds of tissue biology and genomics.
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Affiliation(s)
- Luyi Tian
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Harvard Stem Cell and Regenerative Biology, Cambridge, MA, USA.
| | - Evan Z Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
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34
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Park H, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206939. [PMID: 37026425 PMCID: PMC10238226 DOI: 10.1002/advs.202206939] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/10/2023] [Indexed: 06/04/2023]
Abstract
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.
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Affiliation(s)
- Han‐Eol Park
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
- School of Biological SciencesSeoul National UniversitySeoul08826Republic of Korea
| | - Song Hyun Jo
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Rosalind H. Lee
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Christian P. Macks
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
| | - Taeyun Ku
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Jihwan Park
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Chung Whan Lee
- Department of ChemistryGachon UniversitySeongnamGyeonggi‐do13120Republic of Korea
| | - Junho K. Hur
- Department of GeneticsCollege of MedicineHanyang UniversitySeoul04763Republic of Korea
| | - Chang Ho Sohn
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
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Barabási DL, Bianconi G, Bullmore E, Burgess M, Chung S, Eliassi-Rad T, George D, Kovács IA, Makse H, Papadimitriou C, Nichols TE, Sporns O, Stachenfeld K, Toroczkai Z, Towlson EK, Zador AM, Zeng H, Barabási AL, Bernard A, Buzsáki G. Neuroscience needs Network Science. ARXIV 2023:arXiv:2305.06160v2. [PMID: 37214134 PMCID: PMC10197734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
The brain is a complex system comprising a myriad of interacting elements, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such intricate systems, offering a framework for integrating multiscale data and complexity. Here, we discuss the application of network science in the study of the brain, addressing topics such as network models and metrics, the connectome, and the role of dynamics in neural networks. We explore the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease, and discuss the potential for collaboration between network science and neuroscience communities. We underscore the importance of fostering interdisciplinary opportunities through funding initiatives, workshops, and conferences, as well as supporting students and postdoctoral fellows with interests in both disciplines. By uniting the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way towards a deeper understanding of the brain and its functions.
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Affiliation(s)
- Dániel L Barabási
- Biophysics Program, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, USA
| | - Ginestra Bianconi
- School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK
- The Alan Turing Institute, The British Library, London, NW1 2DB, UK
| | - Ed Bullmore
- Department of Psychiatry and Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | | | - SueYeon Chung
- Center for Neural Science, New York University, New York, NY, USA
- Center for Computational Neuroscience, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Tina Eliassi-Rad
- Network Science Institute, Northeastern University, Boston, MA, USA
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | | | - István A. Kovács
- Department of Physics and Astronomy, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
- Northwestern Institute on Complex Systems, Chambers Hall, 600 Foster St, Northwestern University, Evanston, IL 60208
| | - Hernán Makse
- Levich Institute and Physics Department, City College of New York, New York, NY 10031 US
| | | | - Thomas E. Nichols
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington IN 47405
| | | | - Zoltán Toroczkai
- Department of Physics, University of Notre Dame, 225 Nieuwland Science Hall, Notre Dame IN 46556, USA
| | - Emma K. Towlson
- Department of Computer Science, Department of Physics and Astronomy, Hotchkiss Brain Institute, Children’s Research Hospital, University of Calgary, Calgary, Alberta, Canada 22
| | - Anthony M Zador
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, 02115, USA
- Department of Network and Data Science, Central European University, Budapest, H-1051, Hungary
| | | | - György Buzsáki
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, NY, USA
- Center for Neural Science, New York University, New York, NY, USA
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36
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Ma HT, Zhang HC, Zuo ZF, Liu YX. Heterogeneous organization of Locus coeruleus: An intrinsic mechanism for functional complexity. Physiol Behav 2023; 268:114231. [PMID: 37172640 DOI: 10.1016/j.physbeh.2023.114231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 04/06/2023] [Accepted: 05/09/2023] [Indexed: 05/15/2023]
Abstract
Locus coeruleus (LC) is a small nucleus located deep in the brainstem that contains the majority of central noradrenergic neurons, which provide the primary source of noradrenaline (NA) throughout the entire central nervous system (CNS).The release of neurotransmitter NA is considered to modulate arousal, sensory processing, attention, aversive and adaptive stress responses as well as high-order cognitive function and memory, with the highly ramified axonal arborizations of LC-NA neurons sending wide projections to the targeted brain areas. For over 30 years, LC was thought to be a homogeneous nucleus in structure and function due to the widespread uniform release of NA by LC-NA neurons and simultaneous action in several CNS regions, such as the prefrontal cortex, hippocampus, cerebellum, and spinal cord. However, recent advances in neuroscience tools have revealed that LC is probably not so homogeneous as we previous thought and exhibits heterogeneity in various aspects. Accumulating studies have shown that the functional complexity of LC may be attributed to its heterogeneity in developmental origin, projection patterns, topography distribution, morphology and molecular organization, electrophysiological properties and sex differences. This review will highlight the heterogeneity of LC and its critical role in modulating diverse behavioral outcomes.
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Affiliation(s)
- Hai-Tao Ma
- Department of Neurobiology, School of Basic Medicine, Jinzhou Medical University, Jinzhou, Liaoning, 121000, China; Department of Neurobiology, School of Basic Medicine, Capital Medical University, Beijing, 100069, China.
| | - Hao-Chen Zhang
- Department of Neurobiology, School of Basic Medicine, Capital Medical University, Beijing, 100069, China
| | - Zhong-Fu Zuo
- Department of Human Anatomy, Histology and Embryology, Jinzhou Medical University, Jinzhou, 121000, China
| | - Ying-Xue Liu
- Department of Human Anatomy, Histology and Embryology, Jinzhou Medical University, Jinzhou, 121000, China.
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37
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Johnson GA, Tian Y, Ashbrook DG, Cofer GP, Cook JJ, Gee JC, Hall A, Hornburg K, Qi Y, Yeh FC, Wang N, White LE, Williams RW. Merged magnetic resonance and light sheet microscopy of the whole mouse brain. Proc Natl Acad Sci U S A 2023; 120:e2218617120. [PMID: 37068254 PMCID: PMC10151475 DOI: 10.1073/pnas.2218617120] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 03/10/2023] [Indexed: 04/19/2023] Open
Abstract
We have developed workflows to align 3D magnetic resonance histology (MRH) of the mouse brain with light sheet microscopy (LSM) and 3D delineations of the same specimen. We start with MRH of the brain in the skull with gradient echo and diffusion tensor imaging (DTI) at 15 μm isotropic resolution which is ~ 1,000 times higher than that of most preclinical MRI. Connectomes are generated with superresolution tract density images of ~5 μm. Brains are cleared, stained for selected proteins, and imaged by LSM at 1.8 μm/pixel. LSM data are registered into the reference MRH space with labels derived from the ABA common coordinate framework. The result is a high-dimensional integrated volume with registration (HiDiver) with alignment precision better than 50 µm. Throughput is sufficiently high that HiDiver is being used in quantitative studies of the impact of gene variants and aging on mouse brain cytoarchitecture and connectomics.
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Affiliation(s)
| | - Yuqi Tian
- Center for In Vivo Microscopy, Duke University, Durham, NC27710
| | - David G. Ashbrook
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN38162
| | - Gary P. Cofer
- Center for In Vivo Microscopy, Duke University, Durham, NC27710
| | - James J. Cook
- Center for In Vivo Microscopy, Duke University, Durham, NC27710
| | - James C. Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA19104
| | - Adam Hall
- LifeCanvas Technology, Cambridge, MA02141
| | | | - Yi Qi
- Center for In Vivo Microscopy, Duke University, Durham, NC27710
| | - Fang-Cheng Yeh
- Department of Neurologic Surgery, University of Pittsburgh, Pittsburgh, PA15260
| | - Nian Wang
- Department of Radiology, Indiana University, Bloomington, IN47401
| | | | - Robert W. Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN38162
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38
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Diaz C, Franks KM, Blazing RM. Neuroscience: Seq-ing maps in the olfactory cortex. Curr Biol 2023; 33:R266-R269. [PMID: 37040708 PMCID: PMC10644302 DOI: 10.1016/j.cub.2023.02.055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Many cortical brain regions are spatially organized to optimize sensory representation. Such topographic maps have so far been elusive in the olfactory cortex. A high-throughput tracing study reveals that the neural circuits connecting olfactory regions are indeed topographically organized.
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Affiliation(s)
- Carolyn Diaz
- Department of Neurobiology, Duke University Medical School, Durham, NC 27705, USA
| | - Kevin M Franks
- Department of Neurobiology, Duke University Medical School, Durham, NC 27705, USA.
| | - Robin M Blazing
- Department of Neurobiology, Duke University Medical School, Durham, NC 27705, USA
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39
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Grosu GF, Hopp AV, Moca VV, Bârzan H, Ciuparu A, Ercsey-Ravasz M, Winkel M, Linde H, Mureșan RC. The fractal brain: scale-invariance in structure and dynamics. Cereb Cortex 2023; 33:4574-4605. [PMID: 36156074 PMCID: PMC10110456 DOI: 10.1093/cercor/bhac363] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 08/09/2022] [Accepted: 08/10/2022] [Indexed: 11/12/2022] Open
Abstract
The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture has yet to emerge, and needs further linking to a mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels of organization, from both a structural and functional perspective. We argue that, paradoxically, the level of cortical circuits is the least understood from a structural point of view and perhaps the best studied from a dynamical one. We further link observations about scale-freeness and fractality with evidence that the environment provides constraints that may explain the usefulness of fractal structure and scale-free dynamics in the brain. Moreover, we discuss evidence that behavior exhibits scale-free properties, likely emerging from similarly organized brain dynamics, enabling an organism to thrive in an environment that shares the same organizational principles. Finally, we review the sparse evidence for and try to speculate on the functional consequences of fractality and scale-freeness for brain computation. These properties may endow the brain with computational capabilities that transcend current models of neural computation and could hold the key to unraveling how the brain constructs percepts and generates behavior.
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Affiliation(s)
- George F Grosu
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | | | - Vasile V Moca
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
| | - Harald Bârzan
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | - Andrei Ciuparu
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Electronics, Telecommunications and Information Technology, Technical University of Cluj-Napoca, Str. Memorandumului 28, 400114 Cluj-Napoca, Romania
| | - Maria Ercsey-Ravasz
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Faculty of Physics, Babes-Bolyai University, Str. Mihail Kogalniceanu 1, 400084 Cluj-Napoca, Romania
| | - Mathias Winkel
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | - Helmut Linde
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
- Merck KGaA, Frankfurter Straße 250, 64293 Darmstadt, Germany
| | - Raul C Mureșan
- Department of Experimental and Theoretical Neuroscience, Transylvanian Institute of Neuroscience, Str. Ploiesti 33, 400157 Cluj-Napoca, Romania
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40
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Stouffer KM, Trouv A, Younes L, Kunst M, Ng L, Zeng H, Anant M, Fan J, Kim Y, Miller MI. A Universal Method for Crossing Molecular and Atlas Modalities using Simplex-Based Image Varifolds and Quadratic Programming. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.28.534622. [PMID: 37034802 PMCID: PMC10081224 DOI: 10.1101/2023.03.28.534622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
This paper explicates a solution to the problem of building correspondences between molecular-scale transcriptomics and tissue-scale atlases. The central model represents spatial transcriptomics as generalized functions encoding molecular position and high-dimensional transcriptomic-based (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling each atlas compartment as a homogeneous random field with unknown transcriptomic feature distribution. The algorithm presented solves simultaneously for the minimizing geodesic diffeomorphism of coordinates and latent atlas transcriptomic feature fractions by alternating LDDMM optimization for coordinate transformations and quadratic programming for the latent transcriptomic variables. We demonstrate the universality of the algorithm in mapping tissue atlases to gene-based and cell-based MERFISH datasets as well as to other tissue scale atlases. The joint estimation of diffeomorphisms and latent feature distributions allows integration of diverse molecular and cellular datasets into a single coordinate system and creates an avenue of comparison amongst atlas ontologies for continued future development.
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41
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Charvet CJ. Mapping Human Brain Pathways: Challenges and Opportunities in the Integration of Scales. BRAIN, BEHAVIOR AND EVOLUTION 2023; 98:194-209. [PMID: 36972574 DOI: 10.1159/000530317] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/16/2023] [Indexed: 03/29/2023]
Abstract
The human brain is composed of a complex web of pathways. Diffusion magnetic resonance (MR) tractography is a neuroimaging technique that relies on the principle of diffusion to reconstruct brain pathways. Its tractography is broadly applicable to a range of problems as it is amenable for study in individuals of any age and from any species. However, it is well known that this technique can generate biologically implausible pathways, especially in regions of the brain where multiple fibers cross. This review highlights potential misconnections in two cortico-cortical association pathways with a focus on the aslant tract and inferior frontal occipital fasciculus. The lack of alternative methods to validate observations from diffusion MR tractography means there is a need to develop new integrative approaches to trace human brain pathways. This review discusses integrative approaches in neuroimaging, anatomical, and transcriptional variation as having much potential to trace the evolution of human brain pathways.
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Affiliation(s)
- Christine J Charvet
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama, USA
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42
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Zhang Y, Petukhov V, Biederstedt E, Que R, Zhang K, Kharchenko PV. Gene panel selection for targeted spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.03.527053. [PMID: 36993340 PMCID: PMC10054990 DOI: 10.1101/2023.02.03.527053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Targeted spatial transcriptomics hold particular promise in analysis of complex tissues. Most such methods, however, measure only a limited panel of transcripts, which need to be selected in advance to inform on the cell types or processes being studied. A limitation of existing gene selection methods is that they rely on scRNA-seq data, ignoring platform effects between technologies. Here we describe gpsFISH, a computational method to perform gene selection through optimizing detection of known cell types. By modeling and adjusting for platform effects, gpsFISH outperforms other methods. Furthermore, gpsFISH can incorporate cell type hierarchies and custom gene preferences to accommodate diverse design requirements.
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Affiliation(s)
- Yida Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - Viktor Petukhov
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Evan Biederstedt
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Richard Que
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Peter V. Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
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43
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Tasaka GI, Maggi C, Taha E, Mizrahi A. The local and long-range input landscape of inhibitory neurons in mouse auditory cortex. J Comp Neurol 2023; 531:502-514. [PMID: 36453284 PMCID: PMC10107844 DOI: 10.1002/cne.25437] [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: 04/09/2022] [Revised: 09/27/2022] [Accepted: 11/04/2022] [Indexed: 12/05/2022]
Abstract
Roughly 20% of the neurons in the mouse cortex are inhibitory interneurons (INs). Of these, the three major subtypes are parvalbumin (PV), somatostatin (SST), and vasoactive intestinal polypeptide (VIP) expressing neurons. We used monosynaptic rabies tracing to compare the presynaptic input landscape onto these three IN subtypes in the mouse primary auditory cortex (A1). We compared both local patterns of monosynaptic inputs as well as long-range input patterns. The local monosynaptic input landscape to SST neurons was more widespread as compared to PV and VIP neurons. The brain-wide input landscape was rich and heterogeneous with >40 brain regions connecting to all the three INs subtypes from both hemispheres. The general pattern of the long-range input landscape was similar among the groups of INs. Nevertheless, a few differences could be identified. At low resolution, the proportion of local versus long-range inputs was smaller for PV neurons. At mesoscale resolution, we found fewer inputs from temporal association area to VIP INs, and more inputs to SST neurons from basal forebrain and lateral amygdala. Our work can be used as a resource for a quantitative comparison of the location and level of inputs impinging onto discrete populations of neurons in mouse A1.
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Affiliation(s)
- Gen-Ichi Tasaka
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Claudia Maggi
- The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Elham Taha
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Adi Mizrahi
- The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.,The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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44
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Projection-Specific Heterogeneity of the Axon Initial Segment of Pyramidal Neurons in the Prelimbic Cortex. Neurosci Bull 2023:10.1007/s12264-023-01038-5. [PMID: 36849716 DOI: 10.1007/s12264-023-01038-5] [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: 06/08/2022] [Accepted: 11/22/2022] [Indexed: 03/01/2023] Open
Abstract
The axon initial segment (AIS) is a highly specialized axonal compartment where the action potential is initiated. The heterogeneity of AISs has been suggested to occur between interneurons and pyramidal neurons (PyNs), which likely contributes to their unique spiking properties. However, whether the various characteristics of AISs can be linked to specific PyN subtypes remains unknown. Here, we report that in the prelimbic cortex (PL) of the mouse, two types of PyNs with axon projections either to the contralateral PL or to the ipsilateral basal lateral amygdala, possess distinct AIS properties reflected by morphology, ion channel expression, action potential initiation, and axo-axonic synaptic inputs from chandelier cells. Furthermore, projection-specific AIS diversity is more prominent in the superficial layer than in the deep layer. Thus, our study reveals the cortical layer- and axon projection-specific heterogeneity of PyN AISs, which may endow the spiking of various PyN types with exquisite modulation.
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45
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Zeisler ZR, London L, Janssen WG, Fredericks JM, Elorette C, Fujimoto A, Zhan H, Russ BE, Clem RL, Hof PR, Stoll FM, Rudebeck PH. High-throughput sequencing of macaque basolateral amygdala projections reveals dissociable connectional motifs with frontal cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.18.524407. [PMID: 36711708 PMCID: PMC9882200 DOI: 10.1101/2023.01.18.524407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
The basolateral amygdala (BLA) projects widely across the macaque frontal cortex1-4, and amygdalo-frontal projections are critical for optimal emotional responding5 and decision-making6. Yet, little is known about the single-neuron architecture of these projections: namely, whether single BLA neurons project to multiple parts of the frontal cortex. Here, we use MAPseq7 to determine the projection patterns of over 3000 macaque BLA neurons. We found that one-third of BLA neurons have two or more distinct targets in parts of frontal cortex and of subcortical structures. Further, we reveal non-random structure within these branching patterns such that neurons with four targets are more frequently observed than those with two or three, indicative of widespread networks. Consequently, these multi-target single neurons form distinct networks within medial and ventral frontal cortex consistent with their known functions in regulating mood and decision-making. Additionally, we show that branching patterns of single neurons shape functional networks in the brain as assessed by fMRI-based functional connectivity. These results provide a neuroanatomical basis for the role of the BLA in coordinating brain-wide responses to valent stimuli8 and highlight the importance of high-resolution neuroanatomical data for understanding functional networks in the brain.
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Affiliation(s)
- Zachary R Zeisler
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Liza London
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - William G Janssen
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
- Microscopy and Advanced Bioimaging CoRE, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - J Megan Fredericks
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Catherine Elorette
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Atsushi Fujimoto
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Syosset, NY 11791
| | - Brian E Russ
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140 Old Orangeburg Road, 10 Orangeburg, NY 10962
- Department of Psychiatry, New York University at Langone, One, 8 Park Ave, New York, NY 10016
| | - Roger L Clem
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Frederic M Stoll
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
| | - Peter H Rudebeck
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029
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46
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Yuan L, Chen X, Zhan H, Gilbert HL, Zador AM. Massive Multiplexing of Spatially Resolved Single Neuron Projections with Axonal BARseq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.18.528865. [PMID: 36824753 PMCID: PMC9949159 DOI: 10.1101/2023.02.18.528865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
Neurons in the cortex are heterogenous, sending diverse axonal projections to multiple brain regions. Unraveling the logic of these projections requires single-neuron resolution. Although a growing number of techniques have enabled high-throughput reconstruction, these techniques are typically limited to dozens or at most hundreds of neurons per brain, requiring that statistical analyses combine data from different specimens. Here we present axonal BARseq, a high-throughput approach based on reading out nucleic acid barcodes using in situ RNA sequencing, which enables analysis of even densely labeled neurons. As a proof of principle, we have mapped the long-range projections of >8000 mouse primary auditory cortex neurons from a single brain. We identified major cell types based on projection targets and axonal trajectory. The large sample size enabled us to systematically quantify the projections of intratelencephalic (IT) neurons, and revealed that individual IT neurons project to different layers in an area-dependent fashion. Axonal BARseq is a powerful technique for studying the heterogeneity of single neuronal projections at high throughput within individual brains.
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Affiliation(s)
- Li Yuan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Xiaoyin Chen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Huiqing Zhan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
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47
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Wang Y, Krabbe S, Eddison M, Henry FE, Fleishman G, Lemire AL, Wang L, Korff W, Tillberg PW, Lüthi A, Sternson SM. Multimodal mapping of cell types and projections in the central nucleus of the amygdala. eLife 2023; 12:84262. [PMID: 36661218 PMCID: PMC9977318 DOI: 10.7554/elife.84262] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 01/18/2023] [Indexed: 01/21/2023] Open
Abstract
The central nucleus of the amygdala (CEA) is a brain region that integrates external and internal sensory information and executes innate and adaptive behaviors through distinct output pathways. Despite its complex functions, the diversity of molecularly defined neuronal types in the CEA and their contributions to major axonal projection targets have not been examined systematically. Here, we performed single-cell RNA-sequencing (scRNA-seq) to classify molecularly defined cell types in the CEA and identified marker genes to map the location of these neuronal types using expansion-assisted iterative fluorescence in situ hybridization (EASI-FISH). We developed new methods to integrate EASI-FISH with 5-plex retrograde axonal labeling to determine the spatial, morphological, and connectivity properties of ~30,000 molecularly defined CEA neurons. Our study revealed spatiomolecular organization of the CEA, with medial and lateral CEA associated with distinct molecularly defined cell families. We also found a long-range axon projection network from the CEA, where target regions receive inputs from multiple molecularly defined cell types. Axon collateralization was found primarily among projections to hindbrain targets, which are distinct from forebrain projections. This resource reports marker gene combinations for molecularly defined cell types and axon-projection types, which will be useful for selective interrogation of these neuronal populations to study their contributions to the diverse functions of the CEA.
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Affiliation(s)
- Yuhan Wang
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Sabine Krabbe
- Friedrich Miescher Institute for Biomedical ResearchBaselSwitzerland
- German Center for Neurodegenerative Diseases (DZNE)BonnGermany
| | - Mark Eddison
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Fredrick E Henry
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Greg Fleishman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Andrew L Lemire
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Lihua Wang
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Wyatt Korff
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Paul W Tillberg
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Andreas Lüthi
- Friedrich Miescher Institute for Biomedical ResearchBaselSwitzerland
| | - Scott M Sternson
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
- Howard Hughes Medical Institute & Department of Neurosciences, University of California, San DiegoSan DiegoUnited States
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48
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Boato F, Guan X, Zhu Y, Ryu Y, Voutounou M, Rynne C, Freschlin CR, Zumbo P, Betel D, Matho K, Makarov SN, Wu Z, Son YJ, Nummenmaa A, Huang JZ, Edwards DJ, Zhong J. Activation of MAP2K signaling by genetic engineering or HF-rTMS promotes corticospinal axon sprouting and functional regeneration. Sci Transl Med 2023; 15:eabq6885. [PMID: 36599003 DOI: 10.1126/scitranslmed.abq6885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Facilitating axon regeneration in the injured central nervous system remains a challenging task. RAF-MAP2K signaling plays a key role in axon elongation during nervous system development. Here, we show that conditional expression of a constitutively kinase-activated BRAF in mature corticospinal neurons elicited the expression of a set of transcription factors previously implicated in the regeneration of zebrafish retinal ganglion cell axons and promoted regeneration and sprouting of corticospinal tract (CST) axons after spinal cord injury in mice. Newly sprouting axon collaterals formed synaptic connections with spinal interneurons, resulting in improved recovery of motor function. Noninvasive suprathreshold high-frequency repetitive transcranial magnetic stimulation (HF-rTMS) activated the BRAF canonical downstream effectors MAP2K1/2 and modulated the expression of a set of regeneration-related transcription factors in a pattern consistent with that induced by BRAF activation. HF-rTMS enabled CST axon regeneration and sprouting, which was abolished in MAP2K1/2 conditional null mice. These data collectively demonstrate a central role of MAP2K signaling in augmenting the growth capacity of mature corticospinal neurons and suggest that HF-rTMS might have potential for treating spinal cord injury by modulating MAP2K signaling.
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Affiliation(s)
- Francesco Boato
- Molecular Regeneration and Neuroimaging Laboratory, Burke Neurological Institute, White Plains, NY 10605, USA.,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Xiaofei Guan
- Molecular Regeneration and Neuroimaging Laboratory, Burke Neurological Institute, White Plains, NY 10605, USA.,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Yanjie Zhu
- Molecular Regeneration and Neuroimaging Laboratory, Burke Neurological Institute, White Plains, NY 10605, USA.,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Youngjae Ryu
- Molecular Regeneration and Neuroimaging Laboratory, Burke Neurological Institute, White Plains, NY 10605, USA.,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Mariel Voutounou
- Molecular Regeneration and Neuroimaging Laboratory, Burke Neurological Institute, White Plains, NY 10605, USA.,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Christopher Rynne
- Molecular Regeneration and Neuroimaging Laboratory, Burke Neurological Institute, White Plains, NY 10605, USA.,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Chase R Freschlin
- Molecular Regeneration and Neuroimaging Laboratory, Burke Neurological Institute, White Plains, NY 10605, USA.,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
| | - Paul Zumbo
- Applied Bioinformatics Core, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Doron Betel
- Applied Bioinformatics Core, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
| | - Katie Matho
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | - Sergey N Makarov
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.,Electrical and Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
| | - Zhuhao Wu
- Icahn School of Medicine at Mount Sinai, New York, NY 10065, USA
| | - Young-Jin Son
- Shriners Hospitals Pediatric Research Center, Temple University, Philadelphia, PA 19140, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Josh Z Huang
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA.,Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Dylan J Edwards
- Molecular Regeneration and Neuroimaging Laboratory, Burke Neurological Institute, White Plains, NY 10605, USA.,Moss Rehabilitation Research Institute, Elkins Park, PA 19027, USA.,Thomas Jefferson University, Philadelphia, PA 19108, USA.,Exercise Medicine Research Institute, School of Biomedical and Health Sciences, Edith Cowan University, Joondalup 6027, Australia
| | - Jian Zhong
- Molecular Regeneration and Neuroimaging Laboratory, Burke Neurological Institute, White Plains, NY 10605, USA.,Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10065, USA
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49
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Ya D, Zhang Y, Cui Q, Jiang Y, Yang J, Tian N, Xiang W, Lin X, Li Q, Liao R. Application of spatial transcriptome technologies to neurological diseases. Front Cell Dev Biol 2023; 11:1142923. [PMID: 36936681 PMCID: PMC10020196 DOI: 10.3389/fcell.2023.1142923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Spatial transcriptome technology acquires gene expression profiles while retaining spatial location information, it displays the gene expression properties of cells in situ. Through the investigation of cell heterogeneity, microenvironment, function, and cellular interactions, spatial transcriptome technology can deeply explore the pathogenic mechanisms of cell-type-specific responses and spatial localization in neurological diseases. The present article overviews spatial transcriptome technologies based on microdissection, in situ hybridization, in situ sequencing, in situ capture, and live cell labeling. Each technology is described along with its methods, detection throughput, spatial resolution, benefits, and drawbacks. Furthermore, their applications in neurodegenerative disease, neuropsychiatric illness, stroke and epilepsy are outlined. This information can be used to understand disease mechanisms, pick therapeutic targets, and establish biomarkers.
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Affiliation(s)
- Dongshan Ya
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yingmei Zhang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qi Cui
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Yanlin Jiang
- Department of Pharmacology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Jiaxin Yang
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Ning Tian
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Wenjing Xiang
- Department of Neurology ward 2, Guilin People’s Hospital, Guilin, China
| | - Xiaohui Lin
- Department of Geriatrics, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Qinghua Li
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
| | - Rujia Liao
- Laboratory of Neuroscience, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Department of Neurology, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- Guangxi Clinical Research Center for Neurological Diseases, Affiliated Hospital of Guilin Medical University, Guilin Medical University, Guilin, China
- *Correspondence: Rujia Liao,
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50
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Zhang Y, Lin X, Yao Z, Sun D, Lin X, Wang X, Yang C, Song J. Deconvolution algorithms for inference of the cell-type composition of the spatial transcriptome. Comput Struct Biotechnol J 2022; 21:176-184. [PMID: 36544473 PMCID: PMC9755226 DOI: 10.1016/j.csbj.2022.12.001] [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: 08/14/2022] [Revised: 12/01/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
The spatial transcriptome has enabled researchers to resolve transcriptome expression profiles while preserving information about cell location to better understand the complex biological processes that occur in organisms. Due to technical limitations, the current high-throughput spatial transcriptome sequencing methods (known as next-generation sequencing with spatial barcoding methods or spot-based methods) cannot achieve single-cell resolution. A single measurement site, called a spot, in these technologies frequently contains multiple cells of various types. Computational tools for determining the cellular composition of a spot have emerged as a way to break through these limitations. These tools are known as deconvolution tools. Recently, a couple of deconvolution tools based on different strategies have been developed and have shown promise in different aspects. The resulting single-cell resolution expression profiles and/or single-cell composition of spots will significantly affect downstream data mining; thus, it is crucial to choose a suitable deconvolution tool. In this review, we present a list of currently available tools for spatial transcriptome deconvolution, categorize them based on the strategies they employ, and explain their advantages and limitations in detail in order to guide the selection of these tools in future studies.
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Affiliation(s)
- Yingkun Zhang
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China,State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Xinrui Lin
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Zhixian Yao
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Di Sun
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
| | - Xin Lin
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China,Chemistry and Materials Science College, Shanghai Normal University, Shanghai 200234, China
| | - Xiaoyu Wang
- State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Chaoyong Yang
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China,State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China,Corresponding author.
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