Author
Staci A. Sorensen
Other affiliations: University of Washington
Bio: Staci A. Sorensen is an academic researcher from Allen Institute for Brain Science. The author has contributed to research in topics: Neocortex & Neuroscience. The author has an hindex of 20, co-authored 38 publications receiving 4252 citations. Previous affiliations of Staci A. Sorensen include University of Washington.
Topics: Neocortex, Neuroscience, Cell type, Visual cortex, Biology
Papers
More filters
••
TL;DR: A brain-wide, cellular-level, mesoscale connectome for the mouse, using enhanced green fluorescent protein-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain.
Abstract: Comprehensive knowledge of the brain's wiring diagram is fundamental for understanding how the nervous system processes information at both local and global scales. However, with the singular exception of the C. elegans microscale connectome, there are no complete connectivity data sets in other species. Here we report a brain-wide, cellular-level, mesoscale connectome for the mouse. The Allen Mouse Brain Connectivity Atlas uses enhanced green fluorescent protein (EGFP)-expressing adeno-associated viral vectors to trace axonal projections from defined regions and cell types, and high-throughput serial two-photon tomography to image the EGFP-labelled axons throughout the brain. This systematic and standardized approach allows spatial registration of individual experiments into a common three dimensional (3D) reference space, resulting in a whole-brain connectivity matrix. A computational model yields insights into connectional strength distribution, symmetry and other network properties. Virtual tractography illustrates 3D topography among interconnected regions. Cortico-thalamic pathway analysis demonstrates segregation and integration of parallel pathways. The Allen Mouse Brain Connectivity Atlas is a freely available, foundational resource for structural and functional investigations into the neural circuits that support behavioural and cognitive processes in health and disease.
2,051 citations
••
TL;DR: This work constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing and identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types.
Abstract: Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type-specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.
1,388 citations
••
TL;DR: Using mouse lines in which subsets of neurons are genetically labelled, the authors provide generalized anatomical rules for connections within and between the cortex and thalamus, showing that cell-class-specific connections are organized in a shallow hierarchy within the mouse corticothalamic network.
Abstract: The mammalian cortex is a laminar structure containing many areas and cell types that are densely interconnected in complex ways, and for which generalizable principles of organization remain mostly unknown. Here we describe a major expansion of the Allen Mouse Brain Connectivity Atlas resource1, involving around a thousand new tracer experiments in the cortex and its main satellite structure, the thalamus. We used Cre driver lines (mice expressing Cre recombinase) to comprehensively and selectively label brain-wide connections by layer and class of projection neuron. Through observations of axon termination patterns, we have derived a set of generalized anatomical rules to describe corticocortical, thalamocortical and corticothalamic projections. We have built a model to assign connection patterns between areas as either feedforward or feedback, and generated testable predictions of hierarchical positions for individual cortical and thalamic areas and for cortical network modules. Our results show that cell-class-specific connections are organized in a shallow hierarchy within the mouse corticothalamic network. Using mouse lines in which subsets of neurons are genetically labelled, the authors provide generalized anatomical rules for connections within and between the cortex and thalamus.
379 citations
••
TL;DR: A high-throughput in situ hybridization (ISH), imaging and data processing pipeline to describe whole brain gene expression patterns in Cre driver mice is established and publicly available via the Allen Institute's Transgenic Characterization database.
Abstract: Significant advances in circuit-level analyses of the brain require tools that allow for labeling, modulation of gene expression, and monitoring and manipulation of cellular activity in specific cell types and/or anatomical regions. Large-scale projects and individual laboratories have produced hundreds of gene-specific promoter-driven Cre mouse lines invaluable for enabling genetic access to subpopulations of cells in the brain. However, the potential utility of each line may not be fully realized without systematic whole brain characterization of transgene expression patterns. We established a high-throughput in situ hybridization, imaging and data processing pipeline to describe whole brain gene expression patterns in Cre driver mice. Currently, anatomical data from over 100 Cre driver lines are publicly available via the Allen Institute’s Transgenic Characterization database, which can be used to assist researchers in choosing the appropriate Cre drivers for functional, molecular, or connectional studies of different regions and/or cell types in the brain.
376 citations
••
TL;DR: A single-cell characterization pipeline is established using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions to establish a morpho-electrical taxonomy of cell types for the mouse visual cortex via unsupervised clustering analysis of multiple quantitative features.
Abstract: Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.
328 citations
Cited by
More filters
••
TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.
7,892 citations
••
TL;DR: This work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets, and demonstrates how anchoring can harmonize in-situ gene expression and scRNA-seq datasets.
Abstract: Single cell transcriptomics (scRNA-seq) has transformed our ability to discover and annotate cell types and states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, including high-dimensional immunophenotypes, chromatin accessibility, and spatial positioning, a key analytical challenge is to integrate these datasets into a harmonized atlas that can be used to better understand cellular identity and function. Here, we develop a computational strategy to "anchor" diverse datasets together, enabling us to integrate and compare single cell measurements not only across scRNA-seq technologies, but different modalities as well. After demonstrating substantial improvement over existing methods for data integration, we anchor scRNA-seq experiments with scATAC-seq datasets to explore chromatin differences in closely related interneuron subsets, and project single cell protein measurements onto a human bone marrow atlas to annotate and characterize lymphocyte populations. Lastly, we demonstrate how anchoring can harmonize in-situ gene expression and scRNA-seq datasets, allowing for the transcriptome-wide imputation of spatial gene expression patterns, and the identification of spatial relationships between mapped cell types in the visual cortex. Our work presents a strategy for comprehensive integration of single cell data, including the assembly of harmonized references, and the transfer of information across datasets. Availability: Installation instructions, documentation, and tutorials are available at: https://www.satijalab.org/seurat
2,037 citations
••
TL;DR: RNA sequencing of half a million single cells was used to create a detailed census of cell types in the mouse nervous system and mapped cell types spatially and derived a hierarchical, data-driven taxonomy.
1,735 citations
••
[...]
Howard Hughes Medical Institute1, Broad Institute2, Massachusetts Institute of Technology3, University of Cambridge4, Wellcome Trust Sanger Institute5, European Bioinformatics Institute6, Harvard University7, Weizmann Institute of Science8, University of Zurich9, Laboratory of Molecular Biology10, Utrecht University11, École Polytechnique Fédérale de Lausanne12, University of Pennsylvania13, German Cancer Research Center14, Heidelberg University15, Ludwig Maximilian University of Munich16, John Radcliffe Hospital17, Newcastle University18, Stanford University19, University of Oxford20, University of California, San Francisco21, Allen Institute for Brain Science22, Karolinska Institutet23, Royal Institute of Technology24, Icahn School of Medicine at Mount Sinai25, University of Cape Town26, University Medical Center Groningen27, Radboud University Nijmegen28, Kettering University29, University of Edinburgh30, Babraham Institute31, New York University32, Netherlands Cancer Institute33, Ragon Institute of MGH, MIT and Harvard34, University of Texas Health Science Center at Houston35, Technische Universität München36, Technical University of Denmark37, University of California, Berkeley38, King's College London39, California Institute of Technology40
TL;DR: An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease.
Abstract: The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.
1,391 citations
••
TL;DR: This work constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing and identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types.
Abstract: Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type-specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.
1,388 citations