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Imaging technologies are used throughout the life and biomedical sciences to understand mechanisms in biology and diagnosis and therapy in animal and human medicine. We present criteria for globally applicable guidelines for open image data tools and resources for the rapidly developing fields of biological and biomedical imaging.
As single-cell omics continue to advance, the field of spatially resolved transcriptomics has emerged with a set of experimental and computational methods to map out the positions of cells and their gene expression profiles in space. Here we summarize current transcriptome-wide and sequencing-based methodologies and their applications in genomics research.
One major challenge in neuroscience is to gain a systematic understanding of the extraordinary diversity of brain cell types and how they contribute to brain function. Spatially resolved transcriptomics holds unmatched promise in unraveling the organization of brain cell types and their relationship with connectivity, circuit dynamics, behavior and disease. Here we discuss neuroscience applications of various spatially resolved transcriptomics methods, as well as technical challenges that need to be overcome to realize their full potentials.
The recent advent of genome-scale imaging has enabled single-cell omics analysis in a spatially resolved manner in intact cells and tissues. These advances allow gene expression profiling of individual cells, and hence in situ identification and spatial mapping of cell types, in complex tissues. The high spatial resolution of these approaches further allows determination of the spatial organizations of the genome and transcriptome inside cells, both of which are key regulatory mechanisms for gene expression.
Computing plays a critical role in the biological sciences but faces increasing challenges of scale and complexity. Quantum computing, a computational paradigm exploiting the unique properties of quantum mechanical analogs of classical bits, seeks to address many of these challenges. We discuss the potential for quantum computing to aid in the merging of insights across different areas of biological sciences.
