RESUMEN
Adipogenin (Adig) is an evolutionarily conserved microprotein and is highly expressed in adipose tissues and testis. Here, we identify Adig as a critical regulator for lipid droplet formation in adipocytes. We determine that Adig interacts directly with seipin, leading to the formation of a rigid complex. We solve the structure of the seipin/Adig complex by Cryo-EM at 2.98Å overall resolution. Surprisingly, seipin can form two unique oligomers, undecamers and dodecamers. Adig selectively binds to the dodecameric seipin complex. We further find that Adig promotes seipin assembly by stabilizing and bridging adjacent seipin subunits. Functionally, Adig plays a key role in generating lipid droplets in adipocytes. In mice, inducible overexpression of Adig in adipocytes substantially increases fat mass, with enlarged lipid droplets. It also elevates thermogenesis during cold exposure. In contrast, inducible adipocyte-specific Adig knockout mice manifest aberrant lipid droplet formation in brown adipose tissues and impaired cold tolerance.
RESUMEN
Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the single-cell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (I) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps.