Spatial transcriptomics enables the simultaneous measurement of morphological features and transcriptional profiles of the same cells or regions in tissues.Here we present multi-modal structured embedding (MUSE), an approach to characterize cells and tissue regions by integrating morphological and spatially resolved transcriptional data. We demonstrate that MUSE can discover tissue subpopulations missed by either modality as well as compensate for modality-specific noise. We apply MUSE to diverse datasets containing spatial transcriptomics (seqFISH+, STARmap or Visium) and imaging (hematoxylin and eosin or fluorescence microscopy) modalities. MUSE identified biologically meaningful tissue subpopulations and stereotyped spatial patterning in healthy brain cortex and intestinal tissues. In diseased tissues, MUSE revealed gene biomarkers for proximity to tumor region and heterogeneity of amyloid precursor protein processing across Alzheimer brain regions. MUSE enables the integration of multi-modal data to provide insights into the states, functions and organization of cells in complex biological tissues.

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The flow structures of Jupiter's Great Red Spot (GRS) are studied based on a highresolution velocity field extracted from the Galileo 1996 cloud images of the GRS by using the physics-based optical flow method. The mean transverse velocity profile across the anti-cyclonic near-elliptical collar of the GRS is obtained. The flow structures in the relatively quiescent inner region enclosed by the high-speed collar are revealed at a coarse-grained level. The cyclonic source node in the inner region is identified that is directly associated with the observed cyclonic rotation near the center of the GRS, and its significance in the maintenance of the GRS is explored by using a topological constraint. C 2012 American Institute of Physics.