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Abstract Background Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. Results Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles. Conclusion We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.

Background: Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments. Results: Here, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles. Conclusion: We show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases. [ABSTRACT FROM AUTHOR]

The concentrations of Pro-Gly-Pro-Leu (PGPL) tetrapeptide and its metabolites (Gly-Pro-Leu, Pro-Gly-Pro, Gly-Pro, and Pro-Gly) in various rat-brain compartments (hippocampus, cerebellum, cortex) were shown to be time-dependent after intranasal administration. The maximum concentrations of [H]-labeled PGPL and its degradation products in rat brain were observed after 40 min and amounted to 0.07% of the administered dose. The maximum concentrations of PGPL in the cerebellum, hippocampus, and cortex were 2.37, 1.93, and 0.67 pmol/g, respectively. The distribution in these brain compartments of PGPL proteolysis products differed from that of the initial peptide. The greatest amount of Gly-Pro-Leu was observed in the hippocampus with less in the cerebellum and cortex; of glyprolines (Pro-Gly-Pro, Pro-Gly, and Gly-Pro), in the cerebellum with less in the hippocampus and cortex. The contribution of one PGPL metabolic pathway or another to the formation from it of the set of shorter peptides in various rat-brain compartments was estimated based on the results. [ABSTRACT FROM AUTHOR]

Gap junctions are transcellular pathways that enable a dynamic metabolic coupling and a selective exchange of biological signaling mediators. Throughout the course of the brain development these intercellular channels are assembled into regionally and temporally defined patterns. The present review summarizes the possibilities of heterocellular gap junctional pairing in the brain parenchyma, involving glial cells, neurons and neural precursors as well as it highlights on the meaningfulness of these coupled arrays to the concept of brain functional compartments. [Copyright &y& Elsevier]