Journal articles: 'Biochemistry – atlases'

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Peng, Hanchuan, Phuong Chung, Fuhui Long, Lei Qu, Arnim Jenett, Andrew M. Seeds, Eugene W. Myers, and Julie H. Simpson. "BrainAligner: 3D registration atlases of Drosophila brains." Nature Methods 8, no. 6 (May 1, 2011): 493–98. http://dx.doi.org/10.1038/nmeth.1602.

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Krag, Sharon S. "Special Issue: Human and murine redox protein atlases." Biochimica et Biophysica Acta (BBA) - General Subjects 1810, no. 1 (January 2011): 1. http://dx.doi.org/10.1016/j.bbagen.2010.11.003.

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Ciric, Rastko, William H. Thompson, Romy Lorenz, Mathias Goncalves, Eilidh E. MacNicol, Christopher J. Markiewicz, Yaroslav O. Halchenko, et al. "TemplateFlow: FAIR-sharing of multi-scale, multi-species brain models." Nature Methods 19, no. 12 (December 2022): 1568–71. http://dx.doi.org/10.1038/s41592-022-01681-2.

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AbstractReference anatomies of the brain (‘templates’) and corresponding atlases are the foundation for reporting standardized neuroimaging results. Currently, there is no registry of templates and atlases; therefore, the redistribution of these resources occurs either bundled within existing software or in ad hoc ways such as downloads from institutional sites and general-purpose data repositories. We introduce TemplateFlow as a publicly available framework for human and non-human brain models. The framework combines an open database with software for access, management, and vetting, allowing scientists to share their resources under FAIR—findable, accessible, interoperable, and reusable—principles. TemplateFlow enables multifaceted insights into brains across species, and supports multiverse analyses testing whether results generalize across standard references, scales, and in the long term, species.

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Pliner, Hannah A., Jay Shendure, and Cole Trapnell. "Supervised classification enables rapid annotation of cell atlases." Nature Methods 16, no. 10 (September 9, 2019): 983–86. http://dx.doi.org/10.1038/s41592-019-0535-3.

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Haniffa, Muzlifah, Aidan Maartens, and Sarah A. Teichmann. "How developmental cell atlases inform stem cell embryo models." Nature Methods 20, no. 12 (December 2023): 1849–51. http://dx.doi.org/10.1038/s41592-023-02072-x.

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Ali, Mohamed T., Yaser ElNakieb, Ahmed Elnakib, Ahmed Shalaby, Ali Mahmoud, Mohammed Ghazal, Jawad Yousaf, et al. "The Role of Structure MRI in Diagnosing Autism." Diagnostics 12, no. 1 (January 11, 2022): 165. http://dx.doi.org/10.3390/diagnostics12010165.

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This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

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Bryant, Katherine L., Longchuan Li, Nicole Eichert, and Rogier B. Mars. "A comprehensive atlas of white matter tracts in the chimpanzee." PLOS Biology 18, no. 12 (December 31, 2020): e3000971. http://dx.doi.org/10.1371/journal.pbio.3000971.

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Chimpanzees (Pan troglodytes) are, along with bonobos, humans’ closest living relatives. The advent of diffusion MRI tractography in recent years has allowed a resurgence of comparative neuroanatomical studies in humans and other primate species. Here we offer, in comparative perspective, the first chimpanzee white matter atlas, constructed from in vivo chimpanzee diffusion-weighted scans. Comparative white matter atlases provide a useful tool for identifying neuroanatomical differences and similarities between humans and other primate species. Until now, comprehensive fascicular atlases have been created for humans (Homo sapiens), rhesus macaques (Macaca mulatta), and several other nonhuman primate species, but never in a nonhuman ape. Information on chimpanzee neuroanatomy is essential for understanding the anatomical specializations of white matter organization that are unique to the human lineage.

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Ruffins, Seth W., and Russell E. Jacobs. "MRI in Developmental Biology and the Construction of Developmental Atlases." Cold Spring Harbor Protocols 2011, no. 3 (March 2011): top100. http://dx.doi.org/10.1101/pdb.top100.

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Younes, Subhi Talal, and Betty Herrington. "In silico analysis identifies a putative cell-of-origin for BRAF fusion-positive cerebellar pilocytic astrocytoma." PLOS ONE 15, no. 11 (November 18, 2020): e0242521. http://dx.doi.org/10.1371/journal.pone.0242521.

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Childhood cancers are increasingly recognized as disorders of cellular development. This study sought to identify the cellular and developmental origins of cerebellar pilocytic astrocytoma, the most common brain tumor of childhood. Using publicly available gene expression data from pilocytic astrocytoma tumors and controlling for driver mutation, a set of developmental-related genes which were overexpressed in cerebellar pilocytic astrocytoma was identified. These genes were then mapped onto several developmental atlases in order to identify normal cells with similar gene expression patterns and the developmental trajectory of those cells was interrogated. Eight known neuro-developmental genes were identified as being expressed in cerebellar pilocytic astrocytoma. Mapping those genes or their orthologs onto mouse neuro-developmental atlases identified overlap in their expression within the ventricular zone of the cerebellar anlage. Further analysis with a single cell RNA-sequencing atlas of the developing mouse cerebellum defined this overlap as occurring in ventricular zone progenitor cells at the division point between GABA-ergic neuronal and glial lineages, a developmental trajectory which closely mirrors that previously described to occur within pilocytic astrocytoma cells. Furthermore, ventricular zone progenitor cells and their progeny exhibited evidence of MAPK pathway activation, the paradigmatic oncogenic cascade known to be active in cerebellar pilocytic astrocytoma. Gene expression from developing human brain atlases recapitulated the same anatomic localizations and developmental trajectories as those found in mice. Taken together, these data suggest this population of ventricular zone progenitor cells as the cell-of-origin for BRAF fusion-positive cerebellar pilocytic astrocytoma.

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Ruffins, Seth W., Melanie Martin, Lindsey Keough, Salina Truong, Scott E. Fraser, Russell E. Jacobs, and Rusty Lansford. "Digital Three-Dimensional Atlas of Quail Development Using High-Resolution MRI." Scientific World JOURNAL 7 (2007): 592–604. http://dx.doi.org/10.1100/tsw.2007.125.

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We present an archetypal set of three-dimensional digital atlases of the quail embryo based on microscopic magnetic resonance imaging (μMRI). The atlases are composed of three modules: (1) images of fixedex ovoquail, ranging in age from embryonic day 5 to 10 (e05 to e10); (2) a coarsely delineated anatomical atlas of the μMRI data; and (3) an organ system-based hierarchical graph linked to the anatomical delineations. The atlas is designed to be accessed using SHIVA, a free Java application. The atlas is extensible and can contain other types of information including anatomical, physiological, and functional descriptors. It can also be linked to online resources and references. This digital atlas provides a framework to place various data types, such as gene expression and cell migration data, within the normal three-dimensional anatomy of the developing quail embryo. This provides a method for the analysis and examination of the spatial relationships among the different types of information within the context of the entire embryo.

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Wang, Jingjing, Huiyu Sun, Mengmeng Jiang, Jiaqi Li, Peijing Zhang, Haide Chen, Yuqing Mei, et al. "Tracing cell-type evolution by cross-species comparison of cell atlases." Cell Reports 34, no. 9 (March 2021): 108803. http://dx.doi.org/10.1016/j.celrep.2021.108803.

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Ichbiah, Sacha, Fabrice Delbary, Alex McDougall, Rémi Dumollard, and Hervé Turlier. "Embryo mechanics cartography: inference of 3D force atlases from fluorescence microscopy." Nature Methods 20, no. 12 (December 2023): 1989–99. http://dx.doi.org/10.1038/s41592-023-02084-7.

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AbstractTissue morphogenesis results from a tight interplay between gene expression, biochemical signaling and mechanics. Although sequencing methods allow the generation of cell-resolved spatiotemporal maps of gene expression, creating similar maps of cell mechanics in three-dimensional (3D) developing tissues has remained a real challenge. Exploiting the foam-like arrangement of cells, we propose a robust end-to-end computational method called ‘foambryo’ to infer spatiotemporal atlases of cellular forces from fluorescence microscopy images of cell membranes. Our method generates precise 3D meshes of cells’ geometry and successively predicts relative cell surface tensions and pressures. We validate it with 3D foam simulations, study its noise sensitivity and prove its biological relevance in mouse, ascidian and worm embryos. 3D force inference allows us to recover mechanical features identified previously, but also predicts new ones, unveiling potential new insights on the spatiotemporal regulation of cell mechanics in developing embryos. Our code is freely available and paves the way for unraveling the unknown mechanochemical feedbacks that control embryo and tissue morphogenesis.

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Reznik, Ed, Alex Watson, and Osman Chaudhary. "The stubborn roots of metabolic cycles." Journal of The Royal Society Interface 10, no. 83 (June 6, 2013): 20130087. http://dx.doi.org/10.1098/rsif.2013.0087.

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Efforts to catalogue the structure of metabolic networks have generated highly detailed, genome-scale atlases of biochemical reactions in the cell. Unfortunately, these atlases fall short of capturing the kinetic details of metabolic reactions, instead offering only topological information from which to make predictions. As a result, studies frequently consider the extent to which the topological structure of a metabolic network determines its dynamic behaviour, irrespective of kinetic details. Here, we study a class of metabolic networks known as non-autocatalytic metabolic cycles, and analytically prove an open conjecture regarding the stability of their steady states. Importantly, our results are invariant to the choice of kinetic parameters, rate laws, equilibrium fluxes and metabolite concentrations. Unexpectedly, our proof exposes an elementary but apparently open problem of locating the roots of a sum of two polynomials S = P + Q , when the roots of the summand polynomials P and Q are known. We derive two new results named the Stubborn Roots Theorems, which provide sufficient conditions under which the roots of S remain qualitatively identical to the roots of P . Our study illustrates how complementary feedback, from classical fields such as dynamical systems to biology and vice versa, can expose fundamental and potentially overlooked questions.

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Xing, Wang, Chen Nan, Zuo ZhenTao, Xue Rong, Jing Luo, Yan Zhuo, Shen DingGang, and Li KunCheng. "Probabilistic MRI Brain Anatomical Atlases Based on 1,000 Chinese Subjects." PLoS ONE 8, no. 1 (January 2, 2013): e50939. http://dx.doi.org/10.1371/journal.pone.0050939.

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Jones, Edward G., James M. Stone, and Harvey J. Karten. "High-resolution digital brain atlases: a Hubble telescope for the brain." Annals of the New York Academy of Sciences 1225, S1 (May 2011): E147—E159. http://dx.doi.org/10.1111/j.1749-6632.2011.06009.x.

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Wang, Lin, Francisca Catalan, Karin Shamardani, Husam Babikir, and Aaron Diaz. "Ensemble learning for classifying single-cell data and projection across reference atlases." Bioinformatics 36, no. 11 (February 27, 2020): 3585–87. http://dx.doi.org/10.1093/bioinformatics/btaa137.

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Abstract Summary Single-cell data are being generated at an accelerating pace. How best to project data across single-cell atlases is an open problem. We developed a boosted learner that overcomes the greatest challenge with status quo classifiers: low sensitivity, especially when dealing with rare cell types. By comparing novel and published data from distinct scRNA-seq modalities that were acquired from the same tissues, we show that this approach preserves cell-type labels when mapping across diverse platforms. Availability and implementation https://github.com/diazlab/ELSA Contact aaron.diaz@ucsf.edu Supplementary information Supplementary data are available at Bioinformatics online.

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Busarello, Emma, Zuhairia Ibnat, Giulia Biancon, Jennifer VanOudenhove, Fabio Lauria, Gabriella Viero, Stephanie Halene, and Toma Tebaldi. "Harmonizing the Annotation of Hematopoietic Populations in Single-Cell Atlases with the Cell Marker Accordion." Blood 140, Supplement 1 (November 15, 2022): 4968–69. http://dx.doi.org/10.1182/blood-2022-162180.

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Vaneechoutte, Dries, and Klaas Vandepoele. "Curse: building expression atlases and co-expression networks from public RNA-Seq data." Bioinformatics 35, no. 16 (December 24, 2018): 2880–81. http://dx.doi.org/10.1093/bioinformatics/bty1052.

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Abstract Summary Public RNA-Sequencing (RNA-Seq) datasets are a valuable resource for transcriptome analyses, but their accessibility is hindered by the imperfect quality and presentation of their metadata and by the complexity of processing raw sequencing data. The Curse suite was created to alleviate these problems. It consists of an online curation tool named Curse to efficiently build compendia of experiments hosted on the Sequence Read Archive, and a lightweight pipeline named Prose to download and process the RNA-Seq data into expression atlases and co-expression networks. Curse networks showed improved linking of functionally related genes compared to the state-of-the-art. Availability and implementation Curse, Prose and their manuals are available at http://bioinformatics.psb.ugent.be/webtools/Curse/. Prose was implemented in Java. Supplementary information Supplementary data are available at Bioinformatics online.

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Rezvani, Yasaman, Caroline D. Keroack, Brendan Elsworth, Argenis Arriojas, Marc-Jan Gubbels, Manoj T. Duraisingh, and Kourosh Zarringhalam. "Comparative single-cell transcriptional atlases of Babesia species reveal conserved and species-specific expression profiles." PLOS Biology 20, no. 9 (September 22, 2022): e3001816. http://dx.doi.org/10.1371/journal.pbio.3001816.

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Babesia is a genus of apicomplexan parasites that infect red blood cells in vertebrate hosts. Pathology occurs during rapid replication cycles in the asexual blood stage of infection. Current knowledge of Babesia replication cycle progression and regulation is limited and relies mostly on comparative studies with related parasites. Due to limitations in synchronizing Babesia parasites, fine-scale time-course transcriptomic resources are not readily available. Single-cell transcriptomics provides a powerful unbiased alternative for profiling asynchronous cell populations. Here, we applied single-cell RNA sequencing to 3 Babesia species (B. divergens, B. bovis, and B. bigemina). We used analytical approaches and algorithms to map the replication cycle and construct pseudo-synchronized time-course gene expression profiles. We identify clusters of co-expressed genes showing “just-in-time” expression profiles, with gradually cascading peaks throughout asexual development. Moreover, clustering analysis of reconstructed gene curves reveals coordinated timing of peak expression in epigenetic markers and transcription factors. Using a regularized Gaussian graphical model, we reconstructed co-expression networks and identified conserved and species-specific nodes. Motif analysis of a co-expression interactome of AP2 transcription factors identified specific motifs previously reported to play a role in DNA replication in Plasmodium species. Finally, we present an interactive web application to visualize and interactively explore the datasets.

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Puchades, Maja A., Gergely Csucs, Debora Ledergerber, Trygve B. Leergaard, and Jan G. Bjaalie. "Spatial registration of serial microscopic brain images to three-dimensional reference atlases with the QuickNII tool." PLOS ONE 14, no. 5 (May 29, 2019): e0216796. http://dx.doi.org/10.1371/journal.pone.0216796.

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Luecken, Malte D., M. Büttner, K. Chaichoompu, A. Danese, M. Interlandi, M. F. Mueller, D. C. Strobl, et al. "Benchmarking atlas-level data integration in single-cell genomics." Nature Methods 19, no. 1 (December 23, 2021): 41–50. http://dx.doi.org/10.1038/s41592-021-01336-8.

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AbstractSingle-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, scANVI, Scanorama, scVI and scGen perform well, particularly on complex integration tasks, while single-cell ATAC-sequencing integration performance is strongly affected by choice of feature space. Our freely available Python module and benchmarking pipeline can identify optimal data integration methods for new data, benchmark new methods and improve method development.

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Xie, Yu, Julien Oster, Emilien Micard, Bailiang Chen, Ioannis K. Douros, Liang Liao, François Zhu, et al. "Impact of Pretreatment Ischemic Location on Functional Outcome after Thrombectomy." Diagnostics 11, no. 11 (November 4, 2021): 2038. http://dx.doi.org/10.3390/diagnostics11112038.

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Pretreatment ischemic location may be an important determinant for functional outcome prediction in acute ischemic stroke. In total, 143 anterior circulation ischemic stroke patients in the THRACE study were included. Ischemic lesions were semi-automatically segmented on pretreatment diffusion-weighted imaging and registered on brain atlases. The percentage of ischemic tissue in each atlas-segmented region was calculated. Statistical models with logistic regression and support vector machine were built to analyze the predictors of functional outcome. The investigated parameters included: age, baseline National Institutes of Health Stroke Scale score, and lesional volume (three-parameter model), together with the ischemic percentage in each atlas-segmented region (four-parameter model). The support vector machine with radial basis functions outperformed logistic regression in prediction accuracy. The support vector machine three-parameter model demonstrated an area under the curve of 0.77, while the four-parameter model achieved a higher area under the curve (0.82). Regions with marked impacts on outcome prediction were the uncinate fasciculus, postcentral gyrus, putamen, middle occipital gyrus, supramarginal gyrus, and posterior corona radiata in the left hemisphere; and the uncinate fasciculus, paracentral lobule, temporal pole, hippocampus, inferior occipital gyrus, middle temporal gyrus, pallidum, and anterior limb of the internal capsule in the right hemisphere. In conclusion, pretreatment ischemic location provided significant prognostic information for functional outcome in ischemic stroke.

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Mathiessen, A., P. S. Robinson, I. K. Haugen, and H. B. Hammer. "FRI0572 Ultrasound Features of Hand Osteoarthritis (OA) Can be Reliably Scored by Use of Reference Atlases: Table 1." Annals of the Rheumatic Diseases 74, Suppl 2 (June 2015): 634.3–635. http://dx.doi.org/10.1136/annrheumdis-2015-eular.4022.

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Piro, Rosario M., Ivan Molineris, Ugo Ala, and Ferdinando Di Cunto. "Evaluation of Candidate Genes from Orphan FEB and GEFS+ Loci by Analysis of Human Brain Gene Expression Atlases." PLoS ONE 6, no. 8 (August 17, 2011): e23149. http://dx.doi.org/10.1371/journal.pone.0023149.

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Klepikova, Anna V., Artem S. Kasianov, Margarita A. Ezhova, Aleksey A. Penin, and Maria D. Logacheva. "Transcriptome atlas of Phalaenopsis equestris." PeerJ 9 (December 10, 2021): e12600. http://dx.doi.org/10.7717/peerj.12600.

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The vast diversity of Orchidaceae together with sophisticated adaptations to pollinators and other unique features make this family an attractive model for evolutionary and functional studies. The sequenced genome of Phalaenopsis equestris facilitates Orchidaceae research. Here, we present an RNA-seq-based transcriptome map of P. equestris that covers 19 organs of the plant, including leaves, roots, floral organs and the shoot apical meristem. We demonstrated the high quality of the data and showed the similarity of the P. equestris transcriptome map with the gene expression atlases of other plants. The transcriptome map can be easily accessed through our database Transcriptome Variation Analysis (TraVA) for visualizing gene expression profiles. As an example of the application, we analyzed the expression of Phalaenopsis “orphan” genes–those that do not have recognizable similarity with the genes of other plants. We found that approximately half of these genes were not expressed; the ones that were expressed were predominantly expressed in reproductive structures.

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Ahmad, Sahar, Ye Wu, Zhengwang Wu, Kim-Han Thung, Siyuan Liu, Weili Lin, Gang Li, Li Wang, and Pew-Thian Yap. "Multifaceted atlases of the human brain in its infancy." Nature Methods, December 30, 2022. http://dx.doi.org/10.1038/s41592-022-01703-z.

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AbstractBrain atlases are spatial references for integrating, processing, and analyzing brain features gathered from different individuals, sources, and scales. Here we introduce a collection of joint surface–volume atlases that chart postnatal development of the human brain in a spatiotemporally dense manner from two weeks to two years of age. Our month-specific atlases chart normative patterns and capture key traits of early brain development and are therefore conducive to identifying aberrations from normal developmental trajectories. These atlases will enhance our understanding of early structural and functional development by facilitating the mapping of diverse features of the infant brain to a common reference frame for precise multifaceted quantification of cortical and subcortical changes.

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Young, David M., Siavash Fazel Darbandi, Grace Schwartz, Zachary Bonzell, Deniz Yuruk, Mai Nojima, Laurent C. Gole, John LR Rubenstein, Weimiao Yu, and Stephan J. Sanders. "Constructing and optimizing 3D atlases from 2D data with application to the developing mouse brain." eLife 10 (February 11, 2021). http://dx.doi.org/10.7554/elife.61408.

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3D imaging data necessitate 3D reference atlases for accurate quantitative interpretation. Existing computational methods to generate 3D atlases from 2D-derived atlases result in extensive artifacts, while manual curation approaches are labor-intensive. We present a computational approach for 3D atlas construction that substantially reduces artifacts by identifying anatomical boundaries in the underlying imaging data and using these to guide 3D transformation. Anatomical boundaries also allow extension of atlases to complete edge regions. Applying these methods to the eight developmental stages in the Allen Developing Mouse Brain Atlas (ADMBA) led to more comprehensive and accurate atlases. We generated imaging data from 15 whole mouse brains to validate atlas performance and observed qualitative and quantitative improvement (37% greater alignment between atlas and anatomical boundaries). We provide the pipeline as the MagellanMapper software and the eight 3D reconstructed ADMBA atlases. These resources facilitate whole-organ quantitative analysis between samples and across development.

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Ramirez Flores, Ricardo Omar, Jan David Lanzer, Daniel Dimitrov, Britta Velten, and Julio Saez-Rodriguez. "Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease." eLife 12 (November 22, 2023). http://dx.doi.org/10.7554/elife.93161.

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Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.

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Ye, Fang, Jingjing Wang, Jiaqi Li, Yuqing Mei, and Guoji Guo. "Mapping Cell Atlases at the Single‐Cell Level." Advanced Science, December 25, 2023. http://dx.doi.org/10.1002/advs.202305449.

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AbstractRecent advancements in single‐cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single‐cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.

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Barrière, D. A., R. Magalhães, A. Novais, P. Marques, E. Selingue, F. Geffroy, F. Marques, et al. "The SIGMA rat brain templates and atlases for multimodal MRI data analysis and visualization." Nature Communications 10, no. 1 (December 2019). http://dx.doi.org/10.1038/s41467-019-13575-7.

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AbstractPreclinical imaging studies offer a unique access to the rat brain, allowing investigations that go beyond what is possible in human studies. Unfortunately, these techniques still suffer from a lack of dedicated and standardized neuroimaging tools, namely brain templates and descriptive atlases. Here, we present two rat brain MRI templates and their associated gray matter, white matter and cerebrospinal fluid probability maps, generated from ex vivo $${\mathrm{T}}_2^ \ast$$T2*-weighted images (90 µm isotropic resolution) and in vivo T2-weighted images (150 µm isotropic resolution). In association with these templates, we also provide both anatomical and functional 3D brain atlases, respectively derived from the merging of the Waxholm and Tohoku atlases, and analysis of resting-state functional MRI data. Finally, we propose a complete set of preclinical MRI reference resources, compatible with common neuroimaging software, for the investigation of rat brain structures and functions.

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Manco, Rita, Inna Averbukh, Ziv Porat, Keren Bahar Halpern, Ido Amit, and Shalev Itzkovitz. "Clump sequencing exposes the spatial expression programs of intestinal secretory cells." Nature Communications 12, no. 1 (May 24, 2021). http://dx.doi.org/10.1038/s41467-021-23245-2.

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AbstractSingle-cell RNA sequencing combined with spatial information on landmark genes enables reconstruction of spatially-resolved tissue cell atlases. However, such approaches are challenging for rare cell types, since their mRNA contents are diluted in the spatial transcriptomics bulk measurements used for landmark gene detection. In the small intestine, enterocytes, the most common cell type, exhibit zonated expression programs along the crypt-villus axis, but zonation patterns of rare cell types such as goblet and tuft cells remain uncharacterized. Here, we present ClumpSeq, an approach for sequencing small clumps of attached cells. By inferring the crypt-villus location of each clump from enterocyte landmark genes, we establish spatial atlases for all epithelial cell types in the small intestine. We identify elevated expression of immune-modulatory genes in villus tip goblet and tuft cells and heterogeneous migration patterns of enteroendocrine cells. ClumpSeq can be applied for reconstructing spatial atlases of rare cell types in other tissues and tumors.

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Kang, Joyce B., Aparna Nathan, Kathryn Weinand, Fan Zhang, Nghia Millard, Laurie Rumker, D. Branch Moody, Ilya Korsunsky, and Soumya Raychaudhuri. "Efficient and precise single-cell reference atlas mapping with Symphony." Nature Communications 12, no. 1 (October 7, 2021). http://dx.doi.org/10.1038/s41467-021-25957-x.

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AbstractRecent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony (https://github.com/immunogenomics/symphony), an algorithm for building large-scale, integrated reference atlases in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.

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Lynch, Allen W., Myles Brown, and Clifford A. Meyer. "Multi-batch single-cell comparative atlas construction by deep learning disentanglement." Nature Communications 14, no. 1 (July 12, 2023). http://dx.doi.org/10.1038/s41467-023-39494-2.

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AbstractCell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield new insights into cell state and trajectory alterations. Perturbation experiments often require that single-cell assays be carried out in multiple batches, which can introduce technical distortions that confound the comparison of biological quantities between different batches. Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. We demonstrate CODAL’s capacity for batch-confounded cell type discovery when applied to simulated datasets and embryonic development atlases with gene knockouts. CODAL improves the representation of RNA-seq and ATAC-seq modalities, yields interpretable modules of biological variation, and enables the generalization of other count-based generative models to multi-batched data.

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Claudi, Federico, Adam L. Tyson, Luigi Petrucco, Troy W. Margrie, Ruben Portugues, and Tiago Branco. "Visualizing anatomically registered data with brainrender." eLife 10 (March 19, 2021). http://dx.doi.org/10.7554/elife.65751.

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Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

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Sunba, Khaleel Anass I., Sonya E. Van Nuland, and Kem A. Rogers. "Anatomy Students: The Difference between their Opinions and their Practice in Studying Anatomy." FASEB Journal 31, S1 (April 2017). http://dx.doi.org/10.1096/fasebj.31.1_supplement.lb16.

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Postsecondary institutions are choosing to use digital resources to teach the anatomical sciences, however, studies remain divided about whether these digital resources benefit students over traditional methods such as cadaveric dissection and textbooks/atlases. Recent trends in a systematic human anatomy course at the University of Western Ontario show more students registering in the online section of the course than the traditional face to face (F2F) section. As more students register for online courses, it is important to understand which resources traditional F2F students rely upon to ensure that they do not become disadvantaged should a future anatomy course change to completely online. Students in this systematic human anatomy course (n=127) were surveyed about the usefulness of several anatomy resources, including cadaveric dissection, textbooks/atlases and e‐learning tools, as well as which resources they used while studying. Students were also asked about their preference for digital and printed resources as well as the perceived value of lectures and laboratories. In this undergraduate anatomy course, F2F students attend a weekly 1‐hour cadaveric laboratory where they viewed and manipulated prosections as they interacted with teaching assistants. We hypothesized that F2F students would favor traditional study methods such as printed materials and dissection over the use of digital resources. Furthermore, because of the hand‐on experience of the prosection laboratory, we hypothesized students would value the lab experience over the anatomy lectures.Results showed that students' opinions of resource usefulness did not reflect the resources they chose to study with. When asked about the usefulness of dissection, textbook/atlases, and e‐learning tools students reported all resources as equally useful. However, despite their positive perceptions, they used lecture slides (92%) significantly more than the PowerPoint lab slides (66%), textbooks/atlases (62%), and e‐learning tools (29%; p<0.01). The overwhelming preference for lecture slides may be due to the professor's narrative that accompanies the slides. Student experience suggests that theory assessments are most often based on the material delivered in the slides. When students were surveyed about using traditional printed or digital resources, results showed that students do not follow a consistent style. Chi Square analysis indicated that printed textbooks and atlases were used significantly more often by students than electronic versions of these resources (p<0.01). Conversely, digital PowerPoint lecture and lab slides were used significantly more than their hardcopy counterparts (p<0.01). These results suggest that students prefer to use the resources supplied or suggested to them by their educators. Furthermore, students valued lectures significantly more than laboratories (p<0.001), possible because they perceived that the simple identification style of the laboratory assessment would not reflect the higher order material taught in the lab.Our results suggest that F2F students are more likely to depend on lectures and abstain from using novel methods such as e‐learning tools and thus may be disadvantaged if this course is taught in a fully online fashion using e‐learning tools.

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De Donno, Carlo, Soroor Hediyeh-Zadeh, Amir Ali Moinfar, Marco Wagenstetter, Luke Zappia, Mohammad Lotfollahi, and Fabian J. Theis. "Population-level integration of single-cell datasets enables multi-scale analysis across samples." Nature Methods, October 9, 2023. http://dx.doi.org/10.1038/s41592-023-02035-2.

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AbstractThe increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.

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Liu, Shu‐Wei. "Establishment of fetal brain atlases during the early second trimester." FASEB Journal 31, S1 (April 2017). http://dx.doi.org/10.1096/fasebj.31.1_supplement.91.2.

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ObjectiveThe use of spatiotemporal atlases can significantly improve the results and efficiency of automated analysis of fetal brain MRI data. The current study aims to establish age‐specific fetal brain atlases of the early second trimester (15–22 gestational weeks).Materials and MethodsThe thirty‐four postmortem fetal specimens of 15–22 weeks GW were collected and performed in a 7.0T Micro‐MRI. Both the data conversion and bias field correction were implemented as Pipeline workflows developed by Laboratory of Neuroimaging of University of Southern California. Manual removal of the non‐brain tissue was performed using BrainSuite software. For each cortex, 4 complementary global shape metrics were computed using LONI ShapeTools pipeline library – volume, surface area, shape index and curvedness. The script buildtemplateparallel.sh of Advanced Normalization Tools (ANTs) developed by University of Pennsylvania was run in each week to build the average template of each week. Three‐Dimension surface reconstruction of these templates were performed by BrainSuite. The general template was built from these weekly templates.ResultsFrom 15 to 22 gestational weeks, growth trajectories of brain area and volume is fitted well by linear regression model. The whole brain increased in volume by approximately 4‐fold and about 2.5‐fold in area. To reduce the bias effects of different demographic distributions of different numbers within each week, we built the templates each week first. After 3D surface reconstruction of these templates, we found that the 15th week still has some trails of neural folding in the early stages. The brain surface of 22nd week still looks smooth, but the whole brain's general shape looks more mature. The aggregated general template was then built based on these weekly templates. Four layers of lamination structures are displayed, from outer to inner, these layers are: cortical plate, subplate zone, intermediate zone, ventricular zone. The composed parts of basal ganglia could also be distinguished.ConclusionOur study first established the spatial‐temporal atlas of the early second trimester. And we also obtained the general developmental trajectories of the early fetal brain development. These atlases based on the high‐field MRI data provide good resolution and contrast, which will enable to characterize the dynamic anatomical changes of fetal brain development.Support or Funding InformationThe National Natural Science Foundation of China(No. 31071050, No. 31571237)

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Chon, Uree, Daniel J. Vanselow, Keith C. Cheng, and Yongsoo Kim. "Enhanced and unified anatomical labeling for a common mouse brain atlas." Nature Communications 10, no. 1 (November 7, 2019). http://dx.doi.org/10.1038/s41467-019-13057-w.

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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.

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Goubran, Maged, Christoph Leuze, Brian Hsueh, Markus Aswendt, Li Ye, Qiyuan Tian, Michelle Y. Cheng, et al. "Multimodal image registration and connectivity analysis for integration of connectomic data from microscopy to MRI." Nature Communications 10, no. 1 (December 2019). http://dx.doi.org/10.1038/s41467-019-13374-0.

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Abstract3D histology, slice-based connectivity atlases, and diffusion MRI are common techniques to map brain wiring. While there are many modality-specific tools to process these data, there is a lack of integration across modalities. We develop an automated resource that combines histologically cleared volumes with connectivity atlases and MRI, enabling the analysis of histological features across multiple fiber tracts and networks, and their correlation with in-vivo biomarkers. We apply our pipeline in a murine stroke model, demonstrating not only strong correspondence between MRI abnormalities and CLARITY-tissue staining, but also uncovering acute cellular effects in areas connected to the ischemic core. We provide improved maps of connectivity by quantifying projection terminals from CLARITY viral injections, and integrate diffusion MRI with CLARITY viral tracing to compare connectivity maps across scales. Finally, we demonstrate tract-level histological changes of stroke through this multimodal integration. This resource can propel investigations of network alterations underlying neurological disorders.

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Gaublomme, Jellert T., Bo Li, Cristin McCabe, Abigail Knecht, Yiming Yang, Eugene Drokhlyansky, Nicholas Van Wittenberghe, et al. "Nuclei multiplexing with barcoded antibodies for single-nucleus genomics." Nature Communications 10, no. 1 (July 2, 2019). http://dx.doi.org/10.1038/s41467-019-10756-2.

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Abstract Single-nucleus RNA-seq (snRNA-seq) enables the interrogation of cellular states in complex tissues that are challenging to dissociate or are frozen, and opens the way to human genetics studies, clinical trials, and precise cell atlases of large organs. However, such applications are currently limited by batch effects, processing, and costs. Here, we present an approach for multiplexing snRNA-seq, using sample-barcoded antibodies to uniquely label nuclei from distinct samples. Comparing human brain cortex samples profiled with or without hashing antibodies, we demonstrate that nucleus hashing does not significantly alter recovered profiles. We develop DemuxEM, a computational tool that detects inter-sample multiplets and assigns singlets to their sample of origin, and validate its accuracy using sex-specific gene expression, species-mixing and natural genetic variation. Our approach will facilitate tissue atlases of isogenic model organisms or from multiple biopsies or longitudinal samples of one donor, and large-scale perturbation screens.

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Tarashansky, Alexander J., Jacob M. Musser, Margarita Khariton, Pengyang Li, Detlev Arendt, Stephen R. Quake, and Bo Wang. "Mapping single-cell atlases throughout Metazoa unravels cell type evolution." eLife 10 (May 4, 2021). http://dx.doi.org/10.7554/elife.66747.

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Comparing single-cell transcriptomic atlases from diverse organisms can elucidate the origins of cellular diversity and assist the annotation of new cell atlases. Yet, comparison between distant relatives is hindered by complex gene histories and diversifications in expression programs. Previously, we introduced the self-assembling manifold (SAM) algorithm to robustly reconstruct manifolds from single-cell data (Tarashansky et al., 2019). Here, we build on SAM to map cell atlas manifolds across species. This new method, SAMap, identifies homologous cell types with shared expression programs across distant species within phyla, even in complex examples where homologous tissues emerge from distinct germ layers. SAMap also finds many genes with more similar expression to their paralogs than their orthologs, suggesting paralog substitution may be more common in evolution than previously appreciated. Lastly, comparing species across animal phyla, spanning sponge to mouse, reveals ancient contractile and stem cell families, which may have arisen early in animal evolution.

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Xu, Xiangmin. "Single-cell Spatial Transcriptomics Analysis of Alzheimer's Disease Pathogenesis in Mouse Models." International Journal of Biomedical Science 19, no. 4 (December 15, 2023). http://dx.doi.org/10.59566/iabs.2023.p016.

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Understanding molecular mechanisms of Alzheimer’s disease (AD) has proven challenging as disease effects occur at multiple scales in different brain regions. We apply multiplexed error-robust fluorescence in situ hybridization (MERFISH) to generate spatially resolved single-cell RNA atlases from multiple cortical and subcortical brain regions in 5xFAD and age-matched C57 wild-type mice. We also investigate the effect of the TREM2R47H mutation, a strong risk factor for the development of AD in humans, producing similar atlases for Trem2R47H and Trem2R47H-5xFAD mice. We identify amyloid- beta plaque proximal molecular alterations in microglia and astrocytes, but also in five neuronal cell types. Spatial analysis of microglia and astrocyte concentrations reveals Trem2R47H -dependent regional variations, as well as regional transcriptional variation independent of either 5xFAD or Trem2R47H mutations. Cortical excitatory neurons exhibit a consistent Trem2R47H -induced expression increase in Ntrk2, and other MAPK signaling- associated genes, and thalamic excitatory neurons exhibit both 5xFAD and Trem2R47H induced gene program alterations. Additionally, nearly every neuronal cell type exhibits subclusters with decreased 5xFAD populations. Taken together, our MERFISH analysis of 5xFAD and Trem2R47H Alzheimer’s mouse models reveals spatially localized, cell-type-specific, plaque and Trem2R47H induced transcriptome dysregulations in cortical and subcortical brain regions.

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Skuhersky, Michael, Tailin Wu, Eviatar Yemini, Amin Nejatbakhsh, Edward Boyden, and Max Tegmark. "Toward a more accurate 3D atlas of C. elegans neurons." BMC Bioinformatics 23, no. 1 (May 28, 2022). http://dx.doi.org/10.1186/s12859-022-04738-3.

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Abstract Background Determining cell identity in volumetric images of tagged neuronal nuclei is an ongoing challenge in contemporary neuroscience. Frequently, cell identity is determined by aligning and matching tags to an “atlas” of labeled neuronal positions and other identifying characteristics. Previous analyses of such C. elegans datasets have been hampered by the limited accuracy of such atlases, especially for neurons present in the ventral nerve cord, and also by time-consuming manual elements of the alignment process. Results We present a novel automated alignment method for sparse and incomplete point clouds of the sort resulting from typical C. elegans fluorescence microscopy datasets. This method involves a tunable learning parameter and a kernel that enforces biologically realistic deformation. We also present a pipeline for creating alignment atlases from datasets of the recently developed NeuroPAL transgene. In combination, these advances allow us to label neurons in volumetric images with confidence much higher than previous methods. Conclusions We release, to the best of our knowledge, the most complete full-body C. elegans 3D positional neuron atlas, incorporating positional variability derived from at least 7 animals per neuron, for the purposes of cell-type identity prediction for myriad applications (e.g., imaging neuronal activity, gene expression, and cell-fate).

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Sitek, Kevin R., Omer Faruk Gulban, Evan Calabrese, G. Allan Johnson, Agustin Lage-Castellanos, Michelle Moerel, Satrajit S. Ghosh, and Federico De Martino. "Mapping the human subcortical auditory system using histology, postmortem MRI and in vivo MRI at 7T." eLife 8 (August 1, 2019). http://dx.doi.org/10.7554/elife.48932.

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Studying the human subcortical auditory system non-invasively is challenging due to its small, densely packed structures deep within the brain. Additionally, the elaborate three-dimensional (3-D) structure of the system can be difficult to understand based on currently available 2-D schematics and animal models. Wfe addressed these issues using a combination of histological data, post mortem magnetic resonance imaging (MRI), and in vivo MRI at 7 Tesla. We created anatomical atlases based on state-of-the-art human histology (BigBrain) and postmortem MRI (50 µm). We measured functional MRI (fMRI) responses to natural sounds and demonstrate that the functional localization of subcortical structures is reliable within individual participants who were scanned in two different experiments. Further, a group functional atlas derived from the functional data locates these structures with a median distance below 2 mm. Using diffusion MRI tractography, we revealed structural connectivity maps of the human subcortical auditory pathway both in vivo (1050 µm isotropic resolution) and post mortem (200 µm isotropic resolution). This work captures current MRI capabilities for investigating the human subcortical auditory system, describes challenges that remain, and contributes novel, openly available data, atlases, and tools for researching the human auditory system.

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Kynast, Josef Paul, Felix Schwägerl, and Birte Höcker. "ATLIGATOR: Editing protein interactions with an atlas-based approach." Bioinformatics, October 19, 2022. http://dx.doi.org/10.1093/bioinformatics/btac685.

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Abstract Motivation Recognition of specific molecules by proteins is a fundamental cellular mechanism and relevant for many applications. Being able to modify binding is a key interest and can be achieved by repurposing established interaction motifs. We were specifically interested in a methodology for the design of peptide binding modules. By leveraging interaction data from known protein structures, we plan to accelerate the design of novel protein or peptide binders. Results We developed ATLIGATOR—a computational method to support the analysis and design of a protein’s interaction with a single side chain. Our program enables the building of interaction atlases based on structures from the PDB. From these atlases pocket definitions are extracted that can be searched for frequent interactions. These searches can reveal similarities in unrelated proteins as we show here for one example. Such frequent interactions can then be grafted onto a new protein scaffold as a starting point of the design process. The ATLIGATOR tool is made accessible through a python API as well as a CLI with python scripts. Availability and Implementation Source code can be downloaded at github (https://www.github.com/Hoecker-Lab/atligator), installed from PyPI (“atligator”) and is implemented in Python 3.

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Xing, Wang, Chen Nan, Zuo ZhenTao, Xue Rong, Jing Luo, Yan Zhuo, Shen DingGang, and Li KunCheng. "Correction: Probabilistic MRI Brain Anatomical Atlases Based on 1,000 Chinese Subjects." PLoS ONE 8, no. 4 (April 22, 2013). http://dx.doi.org/10.1371/annotation/c4d2aff9-0c5c-4ebb-b1ed-efa69fc84d78.

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"Digital brain atlases reveal postnatal development to 2 years of age in human infants." Nature Methods, December 30, 2022. http://dx.doi.org/10.1038/s41592-022-01704-y.

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Carey, Harry, Michael Pegios, Lewis Martin, Chris Saleeba, Anita J. Turner, Nicholas A. Everett, Ingvild E. Bjerke, Maja A. Puchades, Jan G. Bjaalie, and Simon McMullan. "DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas." Nature Communications 14, no. 1 (September 21, 2023). http://dx.doi.org/10.1038/s41467-023-41645-4.

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AbstractRegistration of data to a common frame of reference is an essential step in the analysis and integration of diverse neuroscientific data. To this end, volumetric brain atlases enable histological datasets to be spatially registered and analyzed, yet accurate registration remains expertise-dependent and slow. In order to address this limitation, we have trained a neural network, DeepSlice, to register mouse brain histological images to the Allen Brain Common Coordinate Framework, retaining registration accuracy while improving speed by >1000 fold.

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Jiang, Yonghui, Xueying Gao, Yue Liu, Xueqi Yan, Huangcong Shi, Rusong Zhao, Zi-Jiang Chen, Fei Gao, Han Zhao, and Shigang Zhao. "Cellular atlases of ovarian microenvironment alterations by diet and genetically-induced obesity." Science China Life Sciences, September 15, 2023. http://dx.doi.org/10.1007/s11427-023-2360-3.

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Rhodes, James A., and Robert W. Baer. "Computer‐based “self‐assessment” quizzes for anatomy and histology which reference multiple textbooks and atlases." FASEB Journal 21, no. 5 (April 2007). http://dx.doi.org/10.1096/fasebj.21.5.a215.

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Journal articles on the topic 'Biochemistry – atlases'

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