Dynamical properties of gene regulatory networks involved in long-term potentiation

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data

Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing and allow for the interrogation of activity-dependent transcription at cellular resolution. Here, we present NEUROeSTIMator, a deep learning model that integrates transcriptomic signals to estimate neuronal activation in a way that we demonstrate is associated with Patch-seq electrophysiological features and that is robust against differences in species, cell type, and brain region. We demonstrate this method's ability to accurately detect neuronal activity in previously published studies of single cell activity-induced gene expression. Further, we applied our model in a spatial transcriptomic study to identify unique patterns of learning-induced activity across different brain regions in male mice. Altogether, our findings establish NEUROeSTIMator as a powerful and broadly applicable tool for measuring neuronal activation, whether as a critical covariate or a primary readout of interest.

Cognitive function: holarchy or holacracy?

Cognition is the most complex function of the brain. When exploring the inner workings of cognitive processes, it is crucial to understand the complexity of the brain's dynamics. This paper aims to describe the integrated framework of the cognitive function, seen as the result of organization and interactions between several systems and subsystems. We briefly describe several organizational concepts, spanning from the reductionist hierarchical approach, up to the more dynamic theory of open complex systems. The homeostatic regulation of the mechanisms responsible for cognitive processes is showcased as a dynamic interplay between several anticorrelated mechanisms, which can be found at every level of the brain's organization, from molecular and cellular level to large-scale networks (e.g., excitation-inhibition, long-term plasticity-long-term depression, synchronization-desynchronization, segregation-integration, order-chaos). We support the hypothesis that cognitive function is the consequence of multiple network interactions, integrating intricate relationships between several systems, in addition to neural circuits.

Bridging Synaptic and Epigenetic Maintenance Mechanisms of the Engram

How memories are maintained, and how memories are lost during aging or disease, are intensely investigated issues. Arguably, the reigning theory is that synaptic modifications allow for the formation of engrams during learning, and sustaining engrams sustains memory. Activity-regulated gene expression profiles have been shown to be critical to these processes, and their control by the epigenome has begun to be investigated in earnest. Here, we propose a novel theory as to how engrams are sustained. We propose that many of the genes that are currently believed to underlie long-term memory are actually part of a “plasticity transcriptome” that underpins structural and functional modifications to neuronal connectivity during the hours to days following learning. Further, we hypothesize that a “maintenance transcriptome” is subsequently induced that includes epigenetic negative regulators of gene expression, particularly histone deacetylases. The maintenance transcriptome negatively regulates the plasticity transcriptome, and thus the plastic capability of a neuron, after learning. In this way, the maintenance transcriptome would act as a metaplasticity mechanism that raises the threshold for change in neurons within an engram, helping to ensure the connectivity is stabilized and memory is maintained.