Sunday, April 28th, 2024

Time	Event
11:16a	An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and infer the expression of these patterns in the SRT data. Using this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications. (Comment on this)
12:33p	Disentangling signal and noise in neural responses through generative modeling Measurements of neural responses to identically repeated experimental events often exhibit large amounts of variability. This noise is distinct from signal, operationally defined as the average expected response across repeated trials for each given event. Accurately distinguishing signal from noise is important, as each is a target that is worthy of study (many believe noise reflects important aspects of brain function) and it is important not to confuse one for the other. Here, we introduce a principled modeling approach in which response measurements are explicitly modeled as the sum of samples from multivariate signal and noise distributions. In our proposed method termed Generative Modeling of Signal and Noise (GSN)- the signal distribution is estimated by subtracting the estimated noise distribution from the estimated data distribution. We validate GSN using ground-truth simulations and demonstrate the application of GSN to empirical fMRI data. In doing so, we illustrate a simple consequence of GSN: by disentangling signal and noise components in neural responses, GSN denoises principal components analysis and improves estimates of dimensionality. We end by discussing other situations that may benefit from GSN's characterization of signal and noise, such as estimation of noise ceilings for computational models of neural activity. A code toolbox for GSN is provided with both MATLAB and Python implementations. (Comment on this)
1:47p	Single-Cell Transcriptomics Reveals the Molecular Logic Underlying Ca2+ Signaling Diversity in Human and Mouse Brain The calcium ion (Ca2+) is a ubiquitous intracellular signaling molecule that plays a critical role in the adult and developing brain. However, the principles governing the specificity of Ca2+ signaling remain unresolved. In this work, we comprehensively analyzed the Ca2+ signaling transcriptome in the adult mouse brain and developing human brain. We found that neurons form non-stochastic Ca2+-states that are reflective of their cell types and functionality, with evidence suggesting that the diversity is driven by lineage-specific developmental changes. Focusing on the neocortical development, we reveal that an unprecedented number of Ca2+ genes are tightly regulated and evolutionarily conserved, capturing functionally driven differences within radial glia and neuronal progenitors. In summary, our study provides an in-depth understanding of the cellular and temporal diversity of Ca2+ signaling and suggests that Ca2+ signaling is dynamically tailored to specific cell states. (Comment on this)
5:31p	RIPK3 promotes neuronal survival by suppressing excitatory neurotransmission during CNS viral infection While recent work has identified roles for immune mediators in the regulation of neural activity, the capacity for cell intrinsic innate immune signaling within neurons to influence neurotransmission remains poorly understood. However, the existing evidence linking immune signaling with neuronal function suggests that modulation of neurotransmission may serve previously undefined roles in host protection during infection of the central nervous system. Here, we identify a specialized function for RIPK3, a kinase traditionally associated with necroptotic cell death, in preserving neuronal survival during neurotropic flavivirus infection through the suppression of excitatory neurotransmission. We show that RIPK3 coordinates transcriptomic changes in neurons that suppress neuronal glutamate signaling, thereby desensitizing neurons to excitotoxic cell death. These effects occur independently of the traditional functions of RIPK3 in promoting necroptosis and inflammatory transcription. Instead, RIPK3 promotes phosphorylation of the key neuronal regulatory kinase CaMKII, which in turn activates the transcription factor CREB to drive a neuroprotective transcriptional program and suppress deleterious glutamatergic signaling. These findings identify an unexpected function for a canonical cell death protein in promoting neuronal survival during viral infection through the modulation of neuronal activity, highlighting new mechanisms of neuroimmune crosstalk. (Comment on this)
8:16p	A Computational Framework for Understanding the Impact of Prior Experiences on Pain Perception and Neuropathic Pain Pain perception is influenced not only by sensory input from afferent neurons but also by cognitive factors such as prior expectations. It has been suggested that overly precise priors may be a key contributing factor to chronic pain states such as neuropathic pain. However, it remains an open question how overly precise priors in favor of pain might arise. Here, we first verify that a Bayesian approach can describe how statistical integration of prior expectations and sensory input results in pain phenomena such as placebo hypoalgesia, nocebo hyperalgesia, chronic pain, and spontaneous neuropathic pain. Our results indicate that the value of the prior, which is determined by generative model parameters, may be a key contributor to these phenomena. Next, we apply a hierarchical Bayesian approach to update the parameters of the generative model based on the difference between the predicted and the perceived pain, to reflect that people integrate prior experiences in their future expectations. In contrast with simpler approaches, this hierarchical model structure is able to show for placebo hypoalgesia and nocebo hyperalgesia how these phenomena can arise from prior experiences in the form of a classical conditioning procedure. We also demonstrate the phenomenon of offset analgesia, in which a disproportionally large pain decrease is obtained following a minor reduction in noxious stimulus intensity. Finally, we turn to simulations of neuropathic pain, where our hierarchical model corroborates that persistent non-neuropathic pain is a risk factor for developing neuropathic pain following denervation, and additionally offers an interesting prediction that complete absence of informative painful experiences could be a similar risk factor. Taken together, these results provide insight to how prior experiences may contribute to pain perception, in both experimental and neuropathic pain, which in turn might be informative for improving strategies of pain prevention and relief. (Comment on this)

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