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      Spatial Coding Dysfunction and Network Instability in the Aging Medial Entorhinal Cortex

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          Summary

          Across species, spatial memory declines with age, possibly reflecting altered hippocampal and medial entorhinal cortex (MEC) function. However, the integrity of cellular and network-level spatial coding in aged MEC is unknown. Here, we leveraged in vivo electrophysiology to assess MEC function in young, middle-aged, and aged mice navigating virtual environments. In aged grid cells, we observed impaired stabilization of context-specific spatial firing, correlated with spatial memory deficits. Additionally, aged grid networks shifted firing patterns often but with poor alignment to context changes. Aged spatial firing was also unstable in an unchanging environment. In these same mice, we identified 458 genes differentially expressed with age in MEC, 61 of which had expression correlated with spatial firing stability. These genes were enriched among interneurons and related to synaptic transmission. Together, these findings identify coordinated transcriptomic, cellular, and network changes in MEC implicated in impaired spatial memory in aging.

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          Most cited references137

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              Near-optimal probabilistic RNA-seq quantification.

              We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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                Author and article information

                Contributors
                Role: conceptualizationRole: behavioral and neural recording data methodologyRole: collectionRole: formal analysisRole: writing – original draftRole: writing – review & editingRole: visualization
                Role: transcriptomic data methodologyRole: collectionRole: formal analysisRole: writing – original draftRole: visualization
                Role: transcriptomic data methodologyRole: collectionRole: formal analysisRole: writing – original draftRole: visualization
                Role: conceptualizationRole: writing – review & editingRole: supervisionRole: funding
                Role: conceptualizationRole: writing – review & editingRole: supervisionRole: funding
                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                17 April 2024
                : 2024.04.12.588890
                Affiliations
                [1 ]Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, 94305, USA
                [2 ]Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, 94305, USA
                [3 ]Department of Anatomy, University of California San Francisco, 513 Parnassus Avenue, Box 0452, San Francisco, CA, 94143, USA
                [4 ]Bakar Aging Research Institute, San Francisco, CA, 94143, USA
                [5 ]These authors contributed equally.
                [6 ]Lead contact.
                Author notes
                [* ]Corresponding authors: Lisa M. Giocomo, giocomo@ 123456stanford.edu and Charlotte S. Herber, csh47@ 123456stanford.edu .
                Author information
                http://orcid.org/0000-0002-6038-6799
                http://orcid.org/0000-0002-3040-4829
                http://orcid.org/0000-0003-1965-543X
                http://orcid.org/0000-0002-2726-9582
                http://orcid.org/0000-0003-0416-2528
                Article
                10.1101/2024.04.12.588890
                11042240
                38659809
                f278d9a6-79cd-47fa-951e-2945d9bd7b87

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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                aging,medial entorhinal cortex,grid cell,spatial memory,transcriptomics

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