Spatially charting molecular cell types at single-cell resolution across the three-dimensional (3D) volume of the brain is critical for illustrating the molecular basis of the brain anatomy and functions. Single-cell RNA sequencing (scRNA-seq) has profiled molecular cell types in the mouse brain, but cannot capture their spatial organization. Here, we employed an in situ sequencing technique, STARmap PLUS, to map more than one million high-quality cells across the whole adult mouse brain and the spinal cord, profiling 1,022 genes at subcellular resolution with a voxel size of 194 X 194 X 345 nm in 3D. We developed computational pipelines to segment, cluster, and annotate molecularly defined cell types and tissue regions with single-cell resolution. To create a transcriptome-wide spatial atlas, we further integrated the STARmap PLUS measurements with a published scRNA-seq atlas, imputing 11,844 genes at the single-cell level. Finally, we engineered a highly expressed RNA barcoding system to delineate the tropism of a brain-wide transgene delivery tool, AAV-PHP.eB, revealing its single-cell resolved transduction efficiency across the molecular cell types and tissue regions of the whole mouse brain. Together, our datasets and annotations provide a comprehensive, high-resolution single-cell resource that integrates spatial molecular atlas, cell taxonomy, brain anatomy, and genetic manipulation accessibility of the mammalian central nervous system (CNS).

These revised guidelines for contemporary endodontic education in Australia and New Zealand (version 2021) propose the minimum criteria for the training of dentistry students. The document contains a definition of endodontics and a description of the scope of endodontics. It proposes a general outline for education programmes in endodontics as part of general dental practice.

The methodology reliably monitored fluid flow during cyclic loading. There was no difference between a 4-mm MTA apical plug and full-length MTA root filling after cyclic loading using a dynamic fluid-flow monitoring technique.

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Several computational methods have recently been developed for characterizing molecular tissue regions in spatially resolved transcriptomics (SRT) data. However, each method fundamentally relies on spatially smoothing transcriptomic features across neighboring cells. Here, we demonstrate that smoothing increases autocorrelation between neighboring cells, causing latent space to encode physical adjacency rather than spatial transcriptomic patterns. We find that randomly subsampling neighbors before smoothing mitigates autocorrelation, improving the performance of existing methods and further enabling a simpler, more efficient approach that we call spatial integration (SPIN). SPIN leverages the conventional single-cell toolkit, yielding spatial analogies to each tool: clustering identifies molecular tissue regions; differentially expressed gene analysis calculates region marker genes; trajectory inference reveals continuous, molecularly defined anatomical axes; and integration allows joint analysis across multiple SRT datasets, regardless of tissue morphology, spatial resolution, or experimental technology. We apply SPIN to SRT datasets from mouse and marmoset brains to calculate shared and species-specific region marker genes as well as a molecularly defined neocortical depth axis along which several genes and cell types differ across species.