Layers 3 and 4 Neurons of the Bilateral Whisker-Barrel Cortex

Reveal the mechanism of brain function with fluorescence microscopy at single-cell resolution: from neural decoding to encoding

As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms-quantity encoding, spatial encoding, temporal encoding, and frequency encoding-enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.

Annotation Comparison Explorer (ACE): connecting brain cell types across studies of health and Alzheimer's Disease

Single-cell multiomic technologies have allowed unprecedented access to gene profiles of individual cells across species and organ systems, including >1000 papers focused on brain cell types alone. The Allen Institute has created foundational atlases characterizing mammalian brain cell types in the adult mouse brain and the neocortex of aged humans with and without Alzheimer's disease (AD). With so many public cell type classifications (or 'taxonomies') available and many groups choosing to define their own, linking cell types and associated knowledge between studies remains a major challenge. Here, we introduce Annotation Comparison Explorer (ACE), a web application for comparing cell type assignments and other cell-based annotations (e.g., donor demographics, anatomic locations, batch variables, and quality control metrics). ACE allows filtering of cells and includes an interactive set of tools for comparing two or more taxonomy annotations alongside collected knowledge (e.g., increased abundance in disease conditions, cell type aliases, or other information about a specific cell type). We present three primary use cases for ACE. First, we demonstrate how a user can assign cell type labels from the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) taxonomy to cells from their own study and compare these cell type mappings to existing cell type assignments and cell metadata. Second, we extend this approach to ten published human AD studies which we previously reprocessed through a common data analysis pipeline. This allowed us to compare brain taxonomies across otherwise incomparable studies and identify congruent cell type abundance changes in AD, including a decrease in abundance of subsets of somatostatin interneurons. Finally, ACE includes translation tables between different mouse and human brain cell type taxonomies publicly accessible on Allen Brain Map, from initial studies in individual neocortical areas to more recent studies spanning the whole brain. ACE can be freely and publicly accessed as a web application (https://sea-ad.shinyapps.io/ACEapp/) and on GitHub (github.com/AllenInstitute/ACE).

CircSATB2 modulates fear extinction memory via Robo3-driven synaptic plasticity

Circular RNAs (circRNAs) are novel class of stable regulatory RNAs abundantly expressed in the brain. However, their role in fear extinction (EXT) memory remains largely unexplored. To investigate the mechanisms of Circular Special AT-rich Sequence Binding Protein 2 (circSatb2) in EXT memory, we constructed a lentivirus overexpressing circSatb2 and injected it into the infralimbic prefrontal cortex (ILPFC) of the mouse brain. Following extinction training and subsequent testing, we observed an essential role of circSatb2 in this dynamic process. RNA sequencing (RNA-seq) and bioinformatics analyses revealed that circSatb2 enhances the transcription of Roundabout Guidance Receptor 3 (Robo3), a key gene implicated in axon guidance and synaptic plasticity, which was validated by RT-qPCR. Neuronal morphology was assessed using confocal microscopy to determine changes in dendritic spine density. Our results demonstrated that circSatb2 significantly enhances Robo3 transcription, leading to increased dendritic spine formation and improved synaptic plasticity. In conclusion, circSatb2 promotes the formation of EXT memory by upregulating Robo3 transcription and enhancing synaptic plasticity. These findings position circSatb2 as a potential therapeutic target for disorders associated with memory impairment.

Bilateral Whisker Representations in the Primary Somatosensory Cortex in Robo3cKO Mice Are Reflected in the Primary Motor Cortex

In Robo3cKO mice, midline crossing defects of the trigeminothalamic projections from the trigeminal principal sensory nucleus result in bilateral whisker maps in the somatosensory thalamus and consequently in the face representation area of the primary somatosensory (S1) cortex (Renier et al., 2017; Tsytsarev et al., 2017). We investigated whether this bilateral sensory representation in the whisker-barrel cortex is also reflected in the downstream projections from the S1 to the primary motor (M1) cortex. To label these projections, we injected anterograde viral axonal tracer in S1 cortex. Corticocortical projections from the S1 distribute to similar areas across the ipsilateral hemisphere in control and Robo3cKO mice. Namely, in both genotypes they extend to the M1, premotor/prefrontal cortex (PMPF), secondary somatosensory (S2) cortex. Next, we performed voltage-sensitive dye imaging (VSDi) in the left hemisphere following ipsilateral and contralateral single whisker stimulation. While controls showed only activation in the contralateral whisker barrel cortex and M1 cortex, the Robo3cKO mouse left hemisphere was activated bilaterally in both the barrel cortex and the M1 cortex. We conclude that the midline crossing defect of the trigeminothalamic projections leads to bilateral whisker representations not only in the thalamus and the S1 cortex but also downstream from the S1, in the M1 cortex.

Separation of bimodal fMRI responses in mouse somatosensory areas into V1 and non-V1 contributions

Multisensory integration is necessary for the animal to survive in the real world. While conventional methods have been extensively used to investigate the multisensory integration process in various brain areas, its long-range interactions remain less explored. In this study, our goal was to investigate interactions between visual and somatosensory networks on a whole-brain scale using 15.2-T BOLD fMRI. We compared unimodal to bimodal BOLD fMRI responses and dissected potential cross-modal pathways with silencing of primary visual cortex (V1) by optogenetic stimulation of local GABAergic neurons. Our data showed that the influence of visual stimulus on whisker activity is higher than the influence of whisker stimulus on visual activity. Optogenetic silencing of V1 revealed that visual information is conveyed to whisker processing via both V1 and non-V1 pathways. The first-order ventral posteromedial thalamic nucleus (VPM) was functionally affected by non-V1 sources, while the higher-order posterior medial thalamic nucleus (POm) was predominantly modulated by V1 but not non-V1 inputs. The primary somatosensory barrel field (S1BF) was influenced by both V1 and non-V1 inputs. These observations provide valuable insights for into the integration of whisker and visual sensory information.