Elucidating memory in the brain via single-cell transcriptomics

The cell-type-specific spatial organization of the anterior thalamic nuclei of the mouse brain

Understanding the cell-type composition and spatial organization of brain regions is crucial for interpreting brain computation and function. In the thalamus, the anterior thalamic nuclei (ATN) are involved in a wide variety of functions, yet the cell-type composition of the ATN remains unmapped at a single-cell and spatial resolution. Combining single-cell RNA sequencing, spatial transcriptomics, and multiplexed fluorescent in situ hybridization, we identify three discrete excitatory cell-type clusters that correspond to the known nuclei of the ATN and uncover marker genes, molecular pathways, and putative functions of these cell types. We further illustrate graded spatial variation along the dorsomedial-ventrolateral axis for all individual nuclei of the ATN and additionally demonstrate that the anteroventral nucleus exhibits spatially covarying protein products and long-range inputs. Collectively, our study reveals discrete and continuous cell-type organizational principles of the ATN, which will help to guide and interpret experiments on ATN computation and function.

Spatially Resolved Transcriptomic Signatures of Hippocampal Subregions and Arc-Expressing Ensembles in Active Place Avoidance Memory

The rodent hippocampus is a spatially organized neuronal network that supports the formation of spatial and episodic memories. We conducted bulk RNA sequencing and spatial transcriptomics experiments to measure gene expression changes in the dorsal hippocampus following the recall of active place avoidance (APA) memory. Through bulk RNA sequencing, we examined the gene expression changes following memory recall across the functionally distinct subregions of the dorsal hippocampus. We found that recall induced differentially expressed genes (DEGs) in the CA1 and CA3 hippocampal subregions were enriched with genes involved in synaptic transmission and synaptic plasticity, while DEGs in the dentate gyrus (DG) were enriched with genes involved in energy balance and ribosomal function. Through spatial transcriptomics, we examined gene expression changes following memory recall across an array of spots encompassing putative memory-associated neuronal ensembles marked by the expression of the IEGs Arc, Egr1, and c-Jun. Within samples from both trained and untrained mice, the subpopulations of spatial transcriptomic spots marked by these IEGs were transcriptomically and spatially distinct from one another. DEGs detected between Arc+ and Arc- spots exclusively in the trained mouse were enriched in several memory-related gene ontology terms, including "regulation of synaptic plasticity" and "memory." Our results suggest that APA memory recall is supported by regionalized transcriptomic profiles separating the CA1 and CA3 from the DG, transcriptionally and spatially distinct IEG expressing spatial transcriptomic spots, and biological processes related to synaptic plasticity as a defining the difference between Arc+ and Arc- spatial transcriptomic spots.

Neuronal types in the mouse amygdala and their transcriptional response to fear conditioning

The amygdala is a brain region primarily associated with emotional response. The use of genetic markers and single-cell transcriptomics can provide insights into behavior-associated cell state changes. Here we present a detailed cell-type taxonomy of the adult mouse amygdala during fear learning and memory consolidation. We perform single-cell RNA sequencing on naïve and fear-conditioned mice, identify 130 neuronal cell types and validate their spatial distributions. A subset of all neuronal types is transcriptionally responsive to fear learning and memory retrieval. The activated engram cells upregulate activity-response genes and coordinate the expression of genes associated with neurite outgrowth, synaptic signaling, plasticity and development. We identify known and previously undescribed candidate genes responsive to fear learning. Our molecular atlas may be used to generate hypotheses to unveil the neuron types and neural circuits regulating the emotional component of learning and memory.

The elusive transcriptional memory trace

Memory is the brain faculty to store and remember information. It is a sequential process in which four different phases can be distinguished: encoding or learning, consolidation, storage and reactivation. Since the discovery of the first Drosophila gene essential for memory formation in 1976, our knowledge of its mechanisms has progressed greatly. The current view considers the existence of engrams, ensembles of neuronal populations whose activity is temporally coordinated and represents the minimal correlate of experience in brain circuits. In order to form and maintain the engram, protein synthesis and, probably, specific transcriptional program(s) is required. The immediate early gene response during learning process has been extensively studied. However, a detailed description of the transcriptional response for later memory phases was technically challenging. Recent advances in transcriptomics have allowed us to tackle this biological problem. This review summarizes recent findings in this field, and discusses whether or not it is possible to identify a transcriptional trace for memory.

Post-learning micro- and macro-structural neuroplasticity changes with time and sleep

Neuroplasticity refers to the fact that our brain can partially modify both structure and function to adequately respond to novel environmental stimulations. Neuroplasticity mechanisms are not only operating during the acquisition of novel information (i.e., online) but also during the offline periods that take place after the end of the actual learning episode. Structural brain changes as a consequence of learning have been consistently demonstrated on the long term using non-invasive neuroimaging methods, but short-term changes remained more elusive. Fortunately, the swift development of advanced MR methods over the last decade now allows tracking fine-grained cerebral changes on short timescales beyond gross volumetric modifications stretching over several days or weeks. Besides a mere effect of time, post-learning sleep mechanisms have been shown to play an important role in memory consolidation and promote long-lasting changes in neural networks. Sleep was shown to contribute to structural modifications over weeks of prolonged training, but studies evidencing more rapid post-training sleep structural effects linked to memory consolidation are still scarce in human. On the other hand, animal studies convincingly show how sleep might modulate synaptic microstructure. We aim here at reviewing the literature establishing a link between different types of training/learning and the resulting structural changes, with an emphasis on the role of post-training sleep and time in tuning these modifications. Open questions are raised such as the role of post-learning sleep in macrostructural changes, the links between different MR structural measurement-related modifications and the underlying microstructural brain processes, and bidirectional influences between structural and functional brain changes.