1
|
Gurdon B, Yates SC, Csucs G, Groeneboom NE, Hadad N, Telpoukhovskaia M, Ouellette A, Ouellette T, O'Connell KMS, Singh S, Murdy TJ, Merchant E, Bjerke I, Kleven H, Schlegel U, Leergaard TB, Puchades MA, Bjaalie JG, Kaczorowski CC. Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model. Commun Biol 2024; 7:605. [PMID: 38769398 PMCID: PMC11106287 DOI: 10.1038/s42003-024-06242-1] [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: 08/16/2023] [Accepted: 04/24/2024] [Indexed: 05/22/2024] Open
Abstract
Alzheimer's disease (AD) is broadly characterized by neurodegeneration, pathology accumulation, and cognitive decline. There is considerable variation in the progression of clinical symptoms and pathology in humans, highlighting the importance of genetic diversity in the study of AD. To address this, we analyze cell composition and amyloid-beta deposition of 6- and 14-month-old AD-BXD mouse brains. We utilize the analytical QUINT workflow- a suite of software designed to support atlas-based quantification, which we expand to deliver a highly effective method for registering and quantifying cell and pathology changes in diverse disease models. In applying the expanded QUINT workflow, we quantify near-global age-related increases in microglia, astrocytes, and amyloid-beta, and we identify strain-specific regional variation in neuron load. To understand how individual differences in cell composition affect the interpretation of bulk gene expression in AD, we combine hippocampal immunohistochemistry analyses with bulk RNA-sequencing data. This approach allows us to categorize genes whose expression changes in response to AD in a cell and/or pathology load-dependent manner. Ultimately, our study demonstrates the use of the QUINT workflow to standardize the quantification of immunohistochemistry data in diverse mice, - providing valuable insights into regional variation in cellular load and amyloid deposition in the AD-BXD model.
Collapse
Affiliation(s)
- Brianna Gurdon
- The Jackson Laboratory, Bar Harbor, ME, USA
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME, USA
| | - Sharon C Yates
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Gergely Csucs
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Nicolaas E Groeneboom
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Niran Hadad
- The Jackson Laboratory, Bar Harbor, ME, USA
- Translational Genomics Research Institute, Phoenix, AZ, USA
| | | | - Andrew Ouellette
- The Jackson Laboratory, Bar Harbor, ME, USA
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME, USA
| | - Tionna Ouellette
- The Jackson Laboratory, Bar Harbor, ME, USA
- Tufts University Graduate School of Biomedical Sciences, Medford, MA, USA
| | - Kristen M S O'Connell
- The Jackson Laboratory, Bar Harbor, ME, USA
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME, USA
- Tufts University Graduate School of Biomedical Sciences, Medford, MA, USA
| | - Surjeet Singh
- The Jackson Laboratory, Bar Harbor, ME, USA
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Ingvild Bjerke
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Heidi Kleven
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Ulrike Schlegel
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Trygve B Leergaard
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Maja A Puchades
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jan G Bjaalie
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
| | - Catherine C Kaczorowski
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME, USA.
- Tufts University Graduate School of Biomedical Sciences, Medford, MA, USA.
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA.
| |
Collapse
|
2
|
Gurdon B, Yates SC, Csucs G, Groeneboom NE, Hadad N, Telpoukhovskaia M, Ouellette A, Ouellette T, O'Connell K, Singh S, Murdy T, Merchant E, Bjerke I, Kleven H, Schlegel U, Leergaard TB, Puchades MA, Bjaalie JG, Kaczorowski CC. Detecting the effect of genetic diversity on brain composition in an Alzheimer's disease mouse model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.27.530226. [PMID: 36909528 PMCID: PMC10002670 DOI: 10.1101/2023.02.27.530226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
Alzheimer's disease (AD) is characterized by neurodegeneration, pathology accumulation, and progressive cognitive decline. There is significant variation in age at onset and severity of symptoms highlighting the importance of genetic diversity in the study of AD. To address this, we analyzed cell and pathology composition of 6- and 14-month-old AD-BXD mouse brains using the semi-automated workflow (QUINT); which we expanded to allow for nonlinear refinement of brain atlas-registration, and quality control assessment of atlas-registration and brain section integrity. Near global age-related increases in microglia, astrocyte, and amyloid-beta accumulation were measured, while regional variation in neuron load existed among strains. Furthermore, hippocampal immunohistochemistry analyses were combined with bulk RNA-sequencing results to demonstrate the relationship between cell composition and gene expression. Overall, the additional functionality of the QUINT workflow delivers a highly effective method for registering and quantifying cell and pathology changes in diverse disease models.
Collapse
Affiliation(s)
- Brianna Gurdon
- The Jackson Laboratory, Bar Harbor, ME
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME
| | - Sharon C Yates
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Gergely Csucs
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Nicolaas E Groeneboom
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | | | | | - Andrew Ouellette
- The Jackson Laboratory, Bar Harbor, ME
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME
| | - Tionna Ouellette
- The Jackson Laboratory, Bar Harbor, ME
- Tufts University Graduate School of Biomedical Sciences, Medford, MA
| | - Kristen O'Connell
- The Jackson Laboratory, Bar Harbor, ME
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME
- Tufts University Graduate School of Biomedical Sciences, Medford, MA
| | | | - Tom Murdy
- The Jackson Laboratory, Bar Harbor, ME
| | | | - Ingvild Bjerke
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Heidi Kleven
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Ulrike Schlegel
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Trygve B Leergaard
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Maja A Puchades
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jan G Bjaalie
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Catherine C Kaczorowski
- The Jackson Laboratory, Bar Harbor, ME
- The University of Maine Graduate School of Biomedical Sciences and Engineering, Orono, ME
- Tufts University Graduate School of Biomedical Sciences, Medford, MA
| |
Collapse
|
3
|
Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas. Neuroinformatics 2021; 20:405-426. [PMID: 34825350 PMCID: PMC9546954 DOI: 10.1007/s12021-021-09555-2] [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] [Accepted: 11/09/2021] [Indexed: 11/29/2022]
Abstract
Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools.
Collapse
|
4
|
Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models. Neurobiol Dis 2021; 161:105558. [PMID: 34767943 PMCID: PMC10112395 DOI: 10.1016/j.nbd.2021.105558] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 11/05/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.
Collapse
|
5
|
Ni H, Feng Z, Guan Y, Jia X, Chen W, Jiang T, Zhong Q, Yuan J, Ren M, Li X, Gong H, Luo Q, Li A. DeepMapi: a Fully Automatic Registration Method for Mesoscopic Optical Brain Images Using Convolutional Neural Networks. Neuroinformatics 2021; 19:267-284. [PMID: 32754778 PMCID: PMC8004526 DOI: 10.1007/s12021-020-09483-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
The extreme complexity of mammalian brains requires a comprehensive deconstruction of neuroanatomical structures. Scientists normally use a brain stereotactic atlas to determine the locations of neurons and neuronal circuits. However, different brain images are normally not naturally aligned even when they are imaged with the same setup, let alone under the differing resolutions and dataset sizes used in mesoscopic imaging. As a result, it is difficult to achieve high-throughput automatic registration without manual intervention. Here, we propose a deep learning-based registration method called DeepMapi to predict a deformation field used to register mesoscopic optical images to an atlas. We use a self-feedback strategy to address the problem of imbalanced training sets (sampling at a fixed step size in nonuniform brains of structures and deformations) and use a dual-hierarchical network to capture the large and small deformations. By comparing DeepMapi with other registration methods, we demonstrate its superiority over a set of ground truth images, including both optical and MRI images. DeepMapi achieves fully automatic registration of mesoscopic micro-optical images, even macroscopic MRI datasets, in minutes, with an accuracy comparable to those of manual annotations by anatomists.
Collapse
Affiliation(s)
- Hong Ni
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Zhao Feng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yue Guan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xueyan Jia
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Wu Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Jiang
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Qiuyuan Zhong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Miao Ren
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China.
| |
Collapse
|
6
|
A Robust Image Registration Interface for Large Volume Brain Atlas. Sci Rep 2020; 10:2139. [PMID: 32034219 PMCID: PMC7005806 DOI: 10.1038/s41598-020-59042-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 01/23/2020] [Indexed: 11/12/2022] Open
Abstract
Accurately mapping brain structures in three-dimensions is critical for an in-depth understanding of brain functions. Using the brain atlas as a hub, mapping detected datasets into a standard brain space enables efficient use of various datasets. However, because of the heterogeneous and nonuniform brain structure characteristics at the cellular level introduced by recently developed high-resolution whole-brain microscopy techniques, it is difficult to apply a single standard to robust registration of various large-volume datasets. In this study, we propose a robust Brain Spatial Mapping Interface (BrainsMapi) to address the registration of large-volume datasets by introducing extracted anatomically invariant regional features and a large-volume data transformation method. By performing validation on model data and biological images, BrainsMapi achieves accurate registration on intramodal, individual, and multimodality datasets and can also complete the registration of large-volume datasets (approximately 20 TB) within 1 day. In addition, it can register and integrate unregistered vectorized datasets into a common brain space. BrainsMapi will facilitate the comparison, reuse and integration of a variety of brain datasets.
Collapse
|
7
|
Yates SC, Groeneboom NE, Coello C, Lichtenthaler SF, Kuhn PH, Demuth HU, Hartlage-Rübsamen M, Roßner S, Leergaard T, Kreshuk A, Puchades MA, Bjaalie JG. QUINT: Workflow for Quantification and Spatial Analysis of Features in Histological Images From Rodent Brain. Front Neuroinform 2019; 13:75. [PMID: 31849633 PMCID: PMC6901597 DOI: 10.3389/fninf.2019.00075] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 11/15/2019] [Indexed: 01/22/2023] Open
Abstract
Transgenic animal models are invaluable research tools for elucidating the pathways and mechanisms involved in the development of neurodegenerative diseases. Mechanistic clues can be revealed by applying labelling techniques such as immunohistochemistry or in situ hybridisation to brain tissue sections. Precision in both assigning anatomical location to the sections and quantifying labelled features is crucial for output validity, with a stereological approach or image-based feature extraction typically used. However, both approaches are restricted by the need to manually delineate anatomical regions. To circumvent this limitation, we present the QUINT workflow for quantification and spatial analysis of labelling in series of rodent brain section images based on available 3D reference atlases. The workflow is semi-automated, combining three open source software that can be operated without scripting knowledge, making it accessible to most researchers. As an example, a brain region-specific quantification of amyloid plaques across whole transgenic Tg2576 mouse brain series, immunohistochemically labelled for three amyloid-related antigens is demonstrated. First, the whole brain image series were registered to the Allen Mouse Brain Atlas to produce customised atlas maps adapted to match the cutting plan and proportions of the sections (QuickNII software). Second, the labelling was segmented from the original images by the Random Forest Algorithm for supervised classification (ilastik software). Finally, the segmented images and atlas maps were used to generate plaque quantifications for each region in the reference atlas (Nutil software). The method yielded comparable results to manual delineations and to the output of a stereological method. While the use case demonstrates the QUINT workflow for quantification of amyloid plaques only, the workflow is suited to all mouse or rat brain series with labelling that is visually distinct from the background, for example for the quantification of cells or labelled proteins.
Collapse
Affiliation(s)
- Sharon C Yates
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Nicolaas E Groeneboom
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Christopher Coello
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Stefan F Lichtenthaler
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.,Neuroproteomics, School of Medicine, Klinikum rechts der Isar, and Institute for Advanced Study, Technical University of Munich, Munich, Germany.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Peer-Hendrik Kuhn
- Institute of Pathology, Technical University of Munich, Munich, Germany
| | - Hans-Ulrich Demuth
- Department of Molecular Drug Design and Target Validation Fraunhofer Institute for Cell Therapy and Immunology, Halle (Saale), Leipzig, Germany
| | | | - Steffen Roßner
- Paul Flechsig Institute for Brain Research, University of Leipzig, Leipzig, Germany
| | - Trygve Leergaard
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Anna Kreshuk
- European Molecular Biology Laboratory, Heidelberg, Germany
| | - Maja A Puchades
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jan G Bjaalie
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| |
Collapse
|
8
|
Chon U, Vanselow DJ, Cheng KC, Kim Y. Enhanced and unified anatomical labeling for a common mouse brain atlas. Nat Commun 2019; 10:5067. [PMID: 31699990 PMCID: PMC6838086 DOI: 10.1038/s41467-019-13057-w] [Citation(s) in RCA: 74] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 10/17/2019] [Indexed: 01/13/2023] Open
Abstract
Anatomical atlases in standard coordinates are necessary for the interpretation and integration of research findings in a common spatial context. However, the two most-used mouse brain atlases, the Franklin-Paxinos (FP) and the common coordinate framework (CCF) from the Allen Institute for Brain Science, have accumulated inconsistencies in anatomical delineations and nomenclature, creating confusion among neuroscientists. To overcome these issues, we adopt here the FP labels into the CCF to merge the labels in the single atlas framework. We use cell type-specific transgenic mice and an MRI atlas to adjust and further segment our labels. Moreover, detailed segmentations are added to the dorsal striatum using cortico-striatal connectivity data. Lastly, we digitize our anatomical labels based on the Allen ontology, create a web-interface for visualization, and provide tools for comprehensive comparisons between the CCF and FP labels. Our open-source labels signify a key step towards a unified mouse brain atlas.
Collapse
Affiliation(s)
- Uree Chon
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA
| | - Daniel J Vanselow
- Department of Pathology, College of Medicine, Penn State University, Hershey, PA, USA
| | - Keith C Cheng
- Department of Pathology, College of Medicine, Penn State University, Hershey, PA, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, College of Medicine, Penn State University, Hershey, PA, USA.
| |
Collapse
|
9
|
Li A, Guan Y, Gong H, Luo Q. Challenges of Processing and Analyzing Big Data in Mesoscopic Whole-brain Imaging. GENOMICS, PROTEOMICS & BIOINFORMATICS 2019; 17:337-343. [PMID: 31805368 PMCID: PMC6943785 DOI: 10.1016/j.gpb.2019.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2019] [Revised: 09/15/2019] [Accepted: 10/12/2019] [Indexed: 01/09/2023]
Affiliation(s)
- Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China; HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215125, China
| | - Yue Guan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China; HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215125, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China; MOE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China.
| |
Collapse
|
10
|
Bjerke IE, Øvsthus M, Andersson KA, Blixhavn CH, Kleven H, Yates SC, Puchades MA, Bjaalie JG, Leergaard TB. Navigating the Murine Brain: Toward Best Practices for Determining and Documenting Neuroanatomical Locations in Experimental Studies. Front Neuroanat 2018; 12:82. [PMID: 30450039 PMCID: PMC6224483 DOI: 10.3389/fnana.2018.00082] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 09/19/2018] [Indexed: 12/24/2022] Open
Abstract
In experimental neuroscientific research, anatomical location is a key attribute of experimental observations and critical for interpretation of results, replication of findings, and comparison of data across studies. With steadily rising numbers of publications reporting basic experimental results, there is an increasing need for integration and synthesis of data. Since comparison of data relies on consistently defined anatomical locations, it is a major concern that practices and precision in the reporting of location of observations from different types of experimental studies seem to vary considerably. To elucidate and possibly meet this challenge, we have evaluated and compared current practices for interpreting and documenting the anatomical location of measurements acquired from murine brains with different experimental methods. Our observations show substantial differences in approach, interpretation and reproducibility of anatomical locations among reports of different categories of experimental research, and strongly indicate that ambiguous reports of anatomical location can be attributed to missing descriptions. Based on these findings, we suggest a set of minimum requirements for documentation of anatomical location in experimental murine brain research. We furthermore demonstrate how these requirements have been applied in the EU Human Brain Project to optimize workflows for integration of heterogeneous data in common reference atlases. We propose broad adoption of some straightforward steps for improving the precision of location metadata and thereby facilitating interpretation, reuse and integration of data.
Collapse
Affiliation(s)
- Ingvild E Bjerke
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Martin Øvsthus
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Krister A Andersson
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Camilla H Blixhavn
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Heidi Kleven
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Sharon C Yates
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Maja A Puchades
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jan G Bjaalie
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Trygve B Leergaard
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| |
Collapse
|
11
|
Bjerke IE, Øvsthus M, Papp EA, Yates SC, Silvestri L, Fiorilli J, Pennartz CMA, Pavone FS, Puchades MA, Leergaard TB, Bjaalie JG. Data integration through brain atlasing: Human Brain Project tools and strategies. Eur Psychiatry 2018. [PMID: 29519589 DOI: 10.1016/j.eurpsy.2018.02.004] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
The Human Brain Project (HBP), an EU Flagship Initiative, is currently building an infrastructure that will allow integration of large amounts of heterogeneous neuroscience data. The ultimate goal of the project is to develop a unified multi-level understanding of the brain and its diseases, and beyond this to emulate the computational capabilities of the brain. Reference atlases of the brain are one of the key components in this infrastructure. Based on a new generation of three-dimensional (3D) reference atlases, new solutions for analyzing and integrating brain data are being developed. HBP will build services for spatial query and analysis of brain data comparable to current online services for geospatial data. The services will provide interactive access to a wide range of data types that have information about anatomical location tied to them. The 3D volumetric nature of the brain, however, introduces a new level of complexity that requires a range of tools for making use of and interacting with the atlases. With such new tools, neuroscience research groups will be able to connect their data to atlas space, share their data through online data systems, and search and find other relevant data through the same systems. This new approach partly replaces earlier attempts to organize research data based only on a set of semantic terminologies describing the brain and its subdivisions.
Collapse
Affiliation(s)
- Ingvild E Bjerke
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Martin Øvsthus
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Eszter A Papp
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Sharon C Yates
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Ludovico Silvestri
- European Laboratory for Non-linear Spectroscopy, Sesto Fiorentino, Italy
| | - Julien Fiorilli
- Cognitive and Systems Neuroscience Group, SILS Center for Neuroscience, University of Amsterdam, The Netherlands
| | - Cyriel M A Pennartz
- Cognitive and Systems Neuroscience Group, SILS Center for Neuroscience, University of Amsterdam, The Netherlands
| | - Francesco S Pavone
- European Laboratory for Non-linear Spectroscopy, Sesto Fiorentino, Italy
| | - Maja A Puchades
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Trygve B Leergaard
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Norway
| | - Jan G Bjaalie
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Norway.
| |
Collapse
|
12
|
Majka P, Chlodzinska N, Turlejski K, Banasik T, Djavadian RL, Węglarz WP, Wójcik DK. A three-dimensional stereotaxic atlas of the gray short-tailed opossum (Monodelphis domestica) brain. Brain Struct Funct 2017; 223:1779-1795. [PMID: 29214509 PMCID: PMC5884921 DOI: 10.1007/s00429-017-1540-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Accepted: 10/15/2017] [Indexed: 12/22/2022]
Abstract
The gray short-tailed opossum (Monodelphis domestica) is a small marsupial gaining recognition as a laboratory animal in biomedical research. Despite numerous studies on opossum neuroanatomy, a consistent and comprehensive neuroanatomical reference for this species is still missing. Here we present the first three-dimensional, multimodal atlas of the Monodelphis opossum brain. It is based on four complementary imaging modalities: high resolution ex vivo magnetic resonance images, micro-computed tomography scans of the cranium, images of the face of the cutting block, and series of sections stained with the Nissl method and for myelinated fibers. Individual imaging modalities were reconstructed into a three-dimensional form and then registered to the MR image by means of affine and deformable registration routines. Based on a superimposition of the 3D images, 113 anatomical structures were demarcated and the volumes of individual regions were measured. The stereotaxic coordinate system was defined using a set of cranial landmarks: interaural line, bregma, and lambda, which allows for easy expression of any location within the brain with respect to the skull. The atlas is released under the Creative Commons license and available through various digital atlasing web services.
Collapse
Affiliation(s)
- Piotr Majka
- Laboratory of Neuroinformatics, Department of Neurophysiology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland.
| | - Natalia Chlodzinska
- Laboratory of Neurobiology of Development and Evolution, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
| | - Krzysztof Turlejski
- Department of Biology and Environmental Science, Cardinal Stefan Wyszynski University, 1/3 Woycicki Street, 01-938, Warsaw, Poland
| | - Tomasz Banasik
- H. Niewodniczański Institute of Nuclear Physics of Polish Academy of Sciences, Radzikowskiego 152, 31-342, Kraków, Poland
| | - Ruzanna L Djavadian
- Department of Molecular and Cellular Neurobiology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
| | - Władysław P Węglarz
- H. Niewodniczański Institute of Nuclear Physics of Polish Academy of Sciences, Radzikowskiego 152, 31-342, Kraków, Poland
| | - Daniel K Wójcik
- Laboratory of Neuroinformatics, Department of Neurophysiology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
| |
Collapse
|
13
|
Clarkson MD. Representation of anatomy in online atlases and databases: a survey and collection of patterns for interface design. BMC DEVELOPMENTAL BIOLOGY 2016; 16:18. [PMID: 27206491 PMCID: PMC4875762 DOI: 10.1186/s12861-016-0116-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Accepted: 05/09/2016] [Indexed: 12/17/2022]
Abstract
BACKGROUND A large number of online atlases and databases have been developed to mange the rapidly growing amount of data describing embryogenesis. As these community resources continue to evolve, it is important to understand how representations of anatomy can facilitate the sharing and integration of data. In addition, attention to the design of the interfaces is critical to make online resources useful and usable. RESULTS I first present a survey of online atlases and gene expression resources for model organisms, with a focus on methods of semantic and spatial representation of anatomy. A total of 14 anatomical atlases and 21 gene expression resources are included. This survey demonstrates how choices in semantic representation, in the form of ontologies, can enhance interface search functions and provide links between relevant information. This survey also reviews methods for spatially representing anatomy in online resources. I then provide a collection of patterns for interface design based on the atlases and databases surveyed. These patterns include methods for displaying graphics, integrating semantic and spatial representations, organizing information, and querying databases to find genes expressed in anatomical structures. CONCLUSIONS This collection of patterns for interface design will assist biologists and software developers in planning the interfaces of new atlases and databases or enhancing existing ones. They also show the benefits of standardizing semantic and spatial representations of anatomy by demonstrating how interfaces can use standardization to provide enhanced functionality.
Collapse
Affiliation(s)
- Melissa D Clarkson
- Department of Biological Structure, School of Medicine, University of Washington, Seattle, WA, USA.
| |
Collapse
|
14
|
Ferguson AR, Nielson JL, Cragin MH, Bandrowski AE, Martone ME. Big data from small data: data-sharing in the 'long tail' of neuroscience. Nat Neurosci 2014; 17:1442-7. [PMID: 25349910 PMCID: PMC4728080 DOI: 10.1038/nn.3838] [Citation(s) in RCA: 117] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2014] [Accepted: 09/17/2014] [Indexed: 11/08/2022]
Abstract
The launch of the US BRAIN and European Human Brain Projects coincides with growing international efforts toward transparency and increased access to publicly funded research in the neurosciences. The need for data-sharing standards and neuroinformatics infrastructure is more pressing than ever. However, 'big science' efforts are not the only drivers of data-sharing needs, as neuroscientists across the full spectrum of research grapple with the overwhelming volume of data being generated daily and a scientific environment that is increasingly focused on collaboration. In this commentary, we consider the issue of sharing of the richly diverse and heterogeneous small data sets produced by individual neuroscientists, so-called long-tail data. We consider the utility of these data, the diversity of repositories and options available for sharing such data, and emerging best practices. We provide use cases in which aggregating and mining diverse long-tail data convert numerous small data sources into big data for improved knowledge about neuroscience-related disorders.
Collapse
Affiliation(s)
- Adam R Ferguson
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California at San Francisco, San Francisco, California, USA
| | - Jessica L Nielson
- Brain and Spinal Injury Center, Department of Neurological Surgery, University of California at San Francisco, San Francisco, California, USA
| | - Melissa H Cragin
- Directorate for Biological Sciences, National Science Foundation, Arlington, Virginia, USA
| | - Anita E Bandrowski
- Center for Research in Biological Structure, University of California at San Diego, San Diego, California, USA
| | - Maryann E Martone
- 1] Center for Research in Biological Structure, University of California at San Diego, San Diego, California, USA. [2] Department of Neuroscience, University of California at San Diego, San Diego, California, USA
| |
Collapse
|
15
|
Zaslavsky I, Baldock RA, Boline J. Cyberinfrastructure for the digital brain: spatial standards for integrating rodent brain atlases. Front Neuroinform 2014; 8:74. [PMID: 25309417 PMCID: PMC4162418 DOI: 10.3389/fninf.2014.00074] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2014] [Accepted: 08/08/2014] [Indexed: 11/21/2022] Open
Abstract
Biomedical research entails capture and analysis of massive data volumes and new discoveries arise from data-integration and mining. This is only possible if data can be mapped onto a common framework such as the genome for genomic data. In neuroscience, the framework is intrinsically spatial and based on a number of paper atlases. This cannot meet today's data-intensive analysis and integration challenges. A scalable and extensible software infrastructure that is standards based but open for novel data and resources, is required for integrating information such as signal distributions, gene-expression, neuronal connectivity, electrophysiology, anatomy, and developmental processes. Therefore, the International Neuroinformatics Coordinating Facility (INCF) initiated the development of a spatial framework for neuroscience data integration with an associated Digital Atlasing Infrastructure (DAI). A prototype implementation of this infrastructure for the rodent brain is reported here. The infrastructure is based on a collection of reference spaces to which data is mapped at the required resolution, such as the Waxholm Space (WHS), a 3D reconstruction of the brain generated using high-resolution, multi-channel microMRI. The core standards of the digital atlasing service-oriented infrastructure include Waxholm Markup Language (WaxML): XML schema expressing a uniform information model for key elements such as coordinate systems, transformations, points of interest (POI)s, labels, and annotations; and Atlas Web Services: interfaces for querying and updating atlas data. The services return WaxML-encoded documents with information about capabilities, spatial reference systems (SRSs) and structures, and execute coordinate transformations and POI-based requests. Key elements of INCF-DAI cyberinfrastructure have been prototyped for both mouse and rat brain atlas sources, including the Allen Mouse Brain Atlas, UCSD Cell-Centered Database, and Edinburgh Mouse Atlas Project.
Collapse
Affiliation(s)
- Ilya Zaslavsky
- San Diego Supercomputer Center, University of California San Diego La Jolla, CA, USA
| | - Richard A Baldock
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh Edinburgh, UK
| | | |
Collapse
|
16
|
Majka P, Kowalski JM, Chlodzinska N, Wójcik DK. 3D brain atlas reconstructor service--online repository of three-dimensional models of brain structures. Neuroinformatics 2013; 11:507-18. [PMID: 23943281 PMCID: PMC3824210 DOI: 10.1007/s12021-013-9199-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Brain atlases are important tools of neuroscience. Traditionally prepared in paper book format, more and more commonly they take digital form which extends their utility. To simplify work with different atlases, to lay the ground for developing universal tools which could abstract from the origin of the atlas, efforts are being made to provide common interfaces to these atlases. 3D Brain Atlas Reconstructor service (3dBARs) described here is a repository of digital representations of different brain atlases in CAF format which we recently proposed and a repository of 3D models of brain structures. A graphical front-end is provided for creating and viewing the reconstructed models as well as the underlying 2D atlas data. An application programming interface (API) facilitates programmatic access to the service contents from other websites. From a typical user's point of view, 3dBARs offers an accessible way to mine publicly available atlasing data with a convenient browser based interface, without the need to install extra software. For a developer of services related to brain atlases, 3dBARs supplies mechanisms for enhancing functionality of other software. The policy of the service is to accept new datasets as delivered by interested parties and we work with the researchers who obtain original data to make them available to the neuroscience community at large. The functionality offered by the 3dBARs situates it at the core of present and future general atlasing services tying it strongly to the global atlasing neuroinformatics infrastructure.
Collapse
Affiliation(s)
- Piotr Majka
- Nencki Institute of Experimental Biology, 3 Pasteur Street, 02-093, Warsaw, Poland,
| | | | | | | |
Collapse
|
17
|
Mohd Zaizi NJ, Awang Iskandar DN. Using image mapping towards biomedical and biological data sharing. Gigascience 2013; 2:12. [PMID: 24059352 PMCID: PMC3852063 DOI: 10.1186/2047-217x-2-12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2013] [Accepted: 09/12/2013] [Indexed: 11/30/2022] Open
Abstract
Image-based data integration in eHealth and life sciences is typically concerned with the method used for anatomical space mapping, needed to retrieve, compare and analyse large volumes of biomedical data. In mapping one image onto another image, a mechanism is used to match and find the corresponding spatial regions which have the same meaning between the source and the matching image. Image-based data integration is useful for integrating data of various information structures. Here we discuss a broad range of issues related to data integration of various information structures, review exemplary work on image representation and mapping, and discuss the challenges that these techniques may bring.
Collapse
Affiliation(s)
- Nurzi Juana Mohd Zaizi
- Department of Computer Science, Heriot-Watt University, Edinburgh, Scotland, EH14 4AS, UK.
| | | |
Collapse
|
18
|
|
19
|
Castro-González C, Ledesma-Carbayo MJ, Peyriéras N, Santos A. Assembling models of embryo development: Image analysis and the construction of digital atlases. ACTA ACUST UNITED AC 2012; 96:109-20. [DOI: 10.1002/bdrc.21012] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
20
|
Zakiewicz IM, van Dongen YC, Leergaard TB, Bjaalie JG. Workflow and atlas system for brain-wide mapping of axonal connectivity in rat. PLoS One 2011; 6:e22669. [PMID: 21829640 PMCID: PMC3148247 DOI: 10.1371/journal.pone.0022669] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2010] [Accepted: 07/03/2011] [Indexed: 11/22/2022] Open
Abstract
Detailed knowledge about the anatomical organization of axonal connections is important for understanding normal functions of brain systems and disease-related dysfunctions. Such connectivity data are typically generated in neuroanatomical tract-tracing experiments in which specific axonal connections are visualized in histological sections. Since journal publications typically only accommodate restricted data descriptions and example images, literature search is a cumbersome way to retrieve overviews of brain connectivity. To explore more efficient ways of mapping, analyzing, and sharing detailed axonal connectivity data from the rodent brain, we have implemented a workflow for data production and developed an atlas system tailored for online presentation of axonal tracing data. The system is available online through the Rodent Brain WorkBench (www.rbwb.org; Whole Brain Connectivity Atlas) and holds experimental metadata and high-resolution images of histological sections from experiments in which axonal tracers were injected in the primary somatosensory cortex. We here present the workflow and the data system, and exemplify how the online image repository can be used to map different aspects of the brain-wide connectivity of the rat primary somatosensory cortex, including not only presence of connections but also morphology, densities, and spatial organization. The accuracy of the approach is validated by comparing results generated with our system with findings reported in previous publications. The present study is a contribution to a systematic mapping of rodent brain connections and represents a starting point for further large-scale mapping efforts.
Collapse
Affiliation(s)
- Izabela M. Zakiewicz
- Centre for Molecular Biology and Neuroscience, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Yvette C. van Dongen
- Centre for Molecular Biology and Neuroscience, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Trygve B. Leergaard
- Centre for Molecular Biology and Neuroscience, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jan G. Bjaalie
- Centre for Molecular Biology and Neuroscience, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
- * E-mail:
| |
Collapse
|
21
|
Johnson GA, Badea A, Brandenburg J, Cofer G, Fubara B, Liu S, Nissanov J. Waxholm space: an image-based reference for coordinating mouse brain research. Neuroimage 2010; 53:365-72. [PMID: 20600960 DOI: 10.1016/j.neuroimage.2010.06.067] [Citation(s) in RCA: 190] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2010] [Revised: 06/07/2010] [Accepted: 06/25/2010] [Indexed: 11/25/2022] Open
Abstract
We describe an atlas of the C57BL/6 mouse brain based on MRI and conventional Nissl histology. Magnetic resonance microscopy was performed on a total of 14 specimens that were actively stained to enhance tissue contrast. Images were acquired with three different MR protocols yielding contrast dependent on spin lattice relaxation (T1), spin spin relaxation (T2), and magnetic susceptibility (T2*). Spatial resolution was 21.5 mum (isotropic). Conventional histology (Nissl) was performed on a limited set of these same specimens and the Nissl images were registered (3D-to-3D) to the MR data. Probabilistic atlases for 37 structures are provided, along with average atlases. The availability of three different MR protocols, the Nissl data, and the labels provides a rich set of options for registration of other atlases to the same coordinate system, thus facilitating data-sharing. All the data is available for download via the web.
Collapse
Affiliation(s)
- G Allan Johnson
- Duke Center for In Vivo Microscopy, Radiology, Duke University Medical Center, Durham, NC 27710, USA.
| | | | | | | | | | | | | |
Collapse
|
22
|
Validation of MRI-based 3D digital atlas registration with histological and autoradiographic volumes: an anatomofunctional transgenic mouse brain imaging study. Neuroimage 2010; 51:1037-46. [PMID: 20226256 DOI: 10.1016/j.neuroimage.2010.03.014] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2009] [Revised: 02/09/2010] [Accepted: 03/03/2010] [Indexed: 11/23/2022] Open
Abstract
Murine models are commonly used in neuroscience to improve our knowledge of disease processes and to test drug effects. To accurately study neuroanatomy and brain function in small animals, histological staining and ex vivo autoradiography remain the gold standards to date. These analyses are classically performed by manually tracing regions of interest, which is time-consuming. For this reason, only a few 2D tissue sections are usually processed, resulting in a loss of information. We therefore proposed to match a 3D digital atlas with previously 3D-reconstructed post mortem data to automatically evaluate morphology and function in mouse brain structures. We used a freely available MRI-based 3D digital atlas derived from C57Bl/6J mouse brain scans (9.4T). The histological and autoradiographic volumes used were obtained from a preliminary study in APP(SL)/PS1(M146L) transgenic mice, models of Alzheimer's disease, and their control littermates (PS1(M146L)). We first deformed the original 3D MR images to match our experimental volumes. We then applied deformation parameters to warp the 3D digital atlas to match the data to be studied. The reliability of our method was qualitatively and quantitatively assessed by comparing atlas-based and manual segmentations in 3D. Our approach yields faster and more robust results than standard methods in the investigation of post mortem mouse data sets at the level of brain structures. It also constitutes an original method for the validation of an MRI-based atlas using histology and autoradiography as anatomical and functional references, respectively.
Collapse
|
23
|
|
24
|
Ng L, Lau C, Sunkin SM, Bernard A, Chakravarty MM, Lein ES, Jones AR, Hawrylycz M. Surface-based mapping of gene expression and probabilistic expression maps in the mouse cortex. Methods 2009; 50:55-62. [PMID: 19818854 DOI: 10.1016/j.ymeth.2009.10.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2009] [Accepted: 10/01/2009] [Indexed: 10/20/2022] Open
Abstract
The Allen Brain Atlas (ABA, www.brain-map.org) is a genome wide, spatially registered collection of cellular resolution in situ hybridization gene expression image data of the C57Bl/6J mouse brain. Derived from the ABA, the Anatomic Gene Expression Atlas (AGEA, http://mouse.brain-map.org/agea) has demonstrated both laminar and areal spatial gene expression correlations in the mouse cortex. While the mouse cortex is lissencephalic, its curvature and substantial bending in boundary areas renders it difficult to visualize and analyze laminar versus areal effects in a rectilinear coordinate framework. In context of human and non-human primate cortex, surface-based representation has proven useful for understanding relative locations of laminar, columnar, and areal features. In this paper, we describe a methodology for constructing surface-based flatmaps of the mouse cortex that enables mapping of gene expression data from individual genes in the ABA, or probabilistic expression maps from the AGEA, to identify and visualize genetic relationships between layers and areas.
Collapse
Affiliation(s)
- Lydia Ng
- Allen Institute for Brain Science, Seattle, WA 98103, USA
| | | | | | | | | | | | | | | |
Collapse
|