scholarly journals Molecular Atlas of the Adult Mouse Brain

2019 ◽  
Author(s):  
Cantin Ortiz ◽  
Jose Fernandez Navarro ◽  
Aleksandra Jurek ◽  
Antje Märtin ◽  
Joakim Lundeberg ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Spatial Organization ◽  
Adult Mouse ◽  
Brain Regions ◽  
Adult Brain ◽  
Adult Mouse Brain ◽  
Whole Brain ◽  
Systematic Classification ◽  
Molecular Code

AbstractBrain maps are essential for integrating information and interpreting the structure-function relationship of circuits and behavior. We aimed to generate a systematic classification of the adult mouse brain organization based on unbiased extraction of spatially-defining features. Applying whole-brain spatial transcriptomics, we captured the gene expression signatures to define the spatial organization of molecularly discrete subregions. We found that the molecular code contained sufficiently detailed information to directly deduce the complex spatial organization of the brain. This unsupervised molecular classification revealed new area- and layer-specific subregions, for example in isocortex and hippocampus, and a new division of striatum. The whole-brain molecular atlas further supports the identification of the spatial origin of single neurons using their gene expression profile, and forms the foundation to define a minimal gene set - a brain palette – that is sufficient to spatially annotate the adult brain. In summary, we have established a new molecular atlas to formally define the identity of brain regions, and a molecular code for mapping and targeting of discrete neuroanatomical domains.

scholarly journals Molecular atlas of the adult mouse brain

2020 ◽  
Vol 6 (26) ◽  
pp. eabb3446 ◽  
Author(s):  
Cantin Ortiz ◽  
Jose Fernandez Navarro ◽  
Aleksandra Jurek ◽  
Antje Märtin ◽  
Joakim Lundeberg ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Spatial Organization ◽  
Adult Mouse ◽  
Brain Regions ◽  
Adult Mouse Brain ◽  
Systematic Classification ◽  
Function Relationship ◽  
And Behavior ◽  
Relationship Of ◽  
The Brain

Brain maps are essential for integrating information and interpreting the structure-function relationship of circuits and behavior. We aimed to generate a systematic classification of the adult mouse brain based purely on the unbiased identification of spatially defining features by employing whole-brain spatial transcriptomics. We found that the molecular information was sufficient to deduce the complex and detailed neuroanatomical organization of the brain. The unsupervised (non-expert, data-driven) classification revealed new area- and layer-specific subregions, for example in isocortex and hippocampus, and new subdivisions of striatum. The molecular atlas further supports the characterization of the spatial identity of neurons from their single-cell RNA profile, and provides a resource for annotating the brain using a minimal gene set—a brain palette. In summary, we have established a molecular atlas to formally define the spatial organization of brain regions, including the molecular code for mapping and targeting of discrete neuroanatomical domains.

scholarly journals Replicability of spatial gene expression atlas data from the adult mouse brain

2020 ◽  
Author(s):  
Shaina Lu ◽  
Cantin Ortiz ◽  
Daniel Fürth ◽  
Stephan Fischer ◽  
Konstantinos Meletis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Spatial Data ◽  
Adult Mouse ◽  
Brain Area ◽  
Physical Distance ◽  
Mammalian Brain ◽  
Data Types ◽  
Adult Mouse Brain ◽  
Whole Brain

AbstractBackgroundSpatial gene expression is particularly interesting in the mammalian brain, with the potential to serve as a link between many data types. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain sub-divisions, between the Allen Institute’s in situ hybridization data from the adult mouse brain (ABA) and a similar dataset collected using Spatial Transcriptomics (ST). With the advent of tractable spatial techniques, for the first time we are able to benchmark the Allen Institute’s whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome.ResultsWe use LASSO, linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen reference atlas labels are classifiable using transcription, but that performance is higher in the ABA than ST. Further, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bi-directionally. Finally, while an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset.ConclusionsIn general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability.

scholarly journals Assessing the replicability of spatial gene expression using atlas data from the adult mouse brain

PLoS Biology ◽  
2021 ◽  
Vol 19 (7) ◽  
pp. e3001341
Author(s):  
Shaina Lu ◽  
Cantin Ortiz ◽  
Daniel Fürth ◽  
Stephan Fischer ◽  
Konstantinos Meletis ◽  
...  
Keyword(s):  
Gene Expression ◽  
Linear Regression ◽  
Mouse Brain ◽  
Cell Biology ◽  
Adult Mouse ◽  
Brain Area ◽  
Adult Mouse Brain ◽  
Whole Brain ◽  
Spatial Expression ◽  
The Brain

High-throughput, spatially resolved gene expression techniques are poised to be transformative across biology by overcoming a central limitation in single-cell biology: the lack of information on relationships that organize the cells into the functional groupings characteristic of tissues in complex multicellular organisms. Spatial expression is particularly interesting in the mammalian brain, which has a highly defined structure, strong spatial constraint in its organization, and detailed multimodal phenotypes for cells and ensembles of cells that can be linked to mesoscale properties such as projection patterns, and from there, to circuits generating behavior. However, as with any type of expression data, cross-dataset benchmarking of spatial data is a crucial first step. Here, we assess the replicability, with reference to canonical brain subdivisions, between the Allen Institute’s in situ hybridization data from the adult mouse brain (Allen Brain Atlas (ABA)) and a similar dataset collected using spatial transcriptomics (ST). With the advent of tractable spatial techniques, for the first time, we are able to benchmark the Allen Institute’s whole-brain, whole-transcriptome spatial expression dataset with a second independent dataset that similarly spans the whole brain and transcriptome. We use regularized linear regression (LASSO), linear regression, and correlation-based feature selection in a supervised learning framework to classify expression samples relative to their assayed location. We show that Allen Reference Atlas labels are classifiable using transcription in both data sets, but that performance is higher in the ABA than in ST. Furthermore, models trained in one dataset and tested in the opposite dataset do not reproduce classification performance bidirectionally. While an identifying expression profile can be found for a given brain area, it does not generalize to the opposite dataset. In general, we found that canonical brain area labels are classifiable in gene expression space within dataset and that our observed performance is not merely reflecting physical distance in the brain. However, we also show that cross-platform classification is not robust. Emerging spatial datasets from the mouse brain will allow further characterization of cross-dataset replicability ultimately providing a valuable reference set for understanding the cell biology of the brain.

scholarly journals Expression of α-subunit of α-glucosidase II in adult mouse brain regions and selected organs

2014 ◽  
Vol 93 (1) ◽  
pp. 82-93 ◽  
Author(s):  
Antje Anji ◽  
Hayley Miller ◽  
Chandrasekar Raman ◽  
Mathew Phillips ◽  
Gary Ciment ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Adult Mouse ◽  
Brain Regions ◽  
Adult Mouse Brain ◽  
Α Subunit ◽  
Glucosidase Ii

scholarly journals Periaxonal and nodal plasticity modulate action potential conduction in the adult mouse brain

2019 ◽  
Author(s):  
Carlie L Cullen ◽  
Renee E Pepper ◽  
Mackenzie T Clutterbuck ◽  
Kimberley A Pitman ◽  
Viola Oorschot ◽  
...  
Keyword(s):  
Action Potential ◽  
Conduction Velocity ◽  
Mouse Brain ◽  
Adult Mouse ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Brain Regions ◽  
Adult Mouse Brain ◽  
Arrival Times ◽  
Structural Modifications ◽  
Periaxonal Space

SummaryCentral nervous system myelination increases action potential conduction velocity, however, it is unclear how myelination is coordinated to ensure the temporally precise arrival of action potentials, and facilitate information processing within cortical and associative circuits. Here, we show that mature myelin remains plastic in the adult mouse brain and can undergo subtle structural modifications to influence action potential arrival times. Repetitive transcranial magnetic stimulation and spatial learning, two stimuli that modify neuronal activity, alter the length of the nodes of Ranvier and the size of the periaxonal space within active brain regions. This change in the axon-glial configuration is independent of oligodendrogenesis and tunes conduction velocity to increase the synchronicity of action potential transit.

scholarly journals Expression of Heparan Sulfate Endosulfatases in the Adult Mouse Brain: Co-expression of Sulf1 and Dopamine D1/D2 Receptors

2021 ◽  
Vol 15 ◽  
Author(s):  
Ken Miya ◽  
Kazuko Keino-Masu ◽  
Takuya Okada ◽  
Kenta Kobayashi ◽  
Masayuki Masu
Keyword(s):  
Heparan Sulfate ◽  
Mouse Brain ◽  
Adult Mouse ◽  
Extracellular Enzymes ◽  
Expression Patterns ◽  
D2 Receptor ◽  
Adult Brain ◽  
Nucleus Accumbens Shell ◽  
Adult Mouse Brain ◽  
Double Labeling

The heparan sulfate 6-O-endosulfatases, Sulfatase 1 (Sulf1), and Sulfatase 2 (Sulf2), are extracellular enzymes that regulate cellular signaling by removing 6-O-sulfate from the heparan sulfate chain. Although previous studies have revealed that Sulfs are essential for normal development, their functions in the adult brain remain largely unknown. To gain insight into their neural functions, we used in situ hybridization to systematically examine Sulf1/2 mRNA expression in the adult mouse brain. Sulf1 and Sulf2 mRNAs showed distinct expression patterns, which is in contrast to their overlapping expression in the embryonic brain. In addition, we found that Sulf1 was distinctly expressed in the nucleus accumbens shell, the posterior tail of the striatum, layer 6 of the cerebral cortex, and the paraventricular nucleus of the thalamus, all of which are target areas of dopaminergic projections. Using double-labeling techniques, we showed that Sulf1-expressing cells in the above regions coincided with cells expressing the dopamine D1 and/or D2 receptor. These findings implicate possible roles of Sulf1 in modulation of dopaminergic transmission and dopamine-mediated behaviors.

scholarly journals Regional and strain-specific gene expression mapping in the adult mouse brain

2000 ◽  
Vol 97 (20) ◽  
pp. 11038-11043 ◽  
Author(s):  
R. Sandberg ◽  
R. Yasuda ◽  
D. G. Pankratz ◽  
T. A. Carter ◽  
J. A. Del Rio ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Adult Mouse ◽  
Specific Gene ◽  
Adult Mouse Brain ◽  
Specific Gene Expression ◽  
Gene Expression Mapping
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