bioRxiv ScienceSearch

bioRxiv · 10.1101/2021.05.21.445202

Imaging sensory transmission and neuronal plasticity in primary sensory neurons with genetically-encoded voltage indicators

Abstract

Detection of somatosensory inputs requires conversion of external stimuli into electrical signals by activation of primary sensory neurons. The mechanisms by which heterogeneous primary sensory neurons encode different somatosensory inputs remains unclear. In vivo dorsal root ganglia (DRG) imaging using genetically-encoded Ca2+ indicators (GECIs) is currently the best technique for this purpose by providing an unprecedented spatial and populational resolution. It permits the simultaneous imaging of >1800 neurons/DRG in live mice. However, this approach is not ideal given that Ca2+ is a second messenger and has inherently slow response kinetics. In contrast, genetically-encoded voltage indicators (GEVIs) have the potential to track voltage changes in multiple neurons in real time but often lack the brightness and dynamic range required for in vivo use. Here, we used soma-targeted ASAP4.4-Kv, a novel GEVI, to dissect the temporal dynamics of noxious and non-noxious neuronal signals during mechanical, thermal, or chemical stimulation in DRG of live mice. ASAP4.4-Kv is sufficiently bright and fast enough to optically characterize individual neuron coding dynamics. Notably, using ASAP4.4-Kv, we uncovered cell-to-cell electrical synchronization between adjacent DRG neurons and robust dynamic transformations in sensory coding following tissue injury. Finally, we found that a combination of GEVI and GECI imaging empowered in vivo optical studies of sensory signal processing and integration mechanisms with optimal spatiotemporal analysis. HighlightsO_LIIn vivo ultra fast and sensitive dynamic voltage imaging of peripheral primary sensory neurons by a newly generated genetically-encoded voltage indicator. C_LIO_LIIdentification of mechanical, thermal, or chemical stimuli-evoked voltage signals with superior temporal resolution. C_LIO_LISingle-cell detection of changes in sub- and suprathreshold voltage dynamics across different disease conditions. C_LIO_LICombination of voltage (by ASAP4.4-Kv) and Ca2+ (by Pirt-GCaMP3) signals to facilitate the understanding of signal processing and integration of primary sensory neurons, especially for noxious versus non-noxious sensation. C_LI

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Zhang, Y., Shannonhouse, J., Gomez, R., Son, H., Ishida, H., Lin, M., KIM, Y. S.. 2021-05-23. Imaging sensory transmission and neuronal plasticity in primary sensory neurons with genetically-encoded voltage indicators. https://doi.org/10.1101/2021.05.21.445202

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience