Mass spectrometry imaging (MSI), which localizes molecules in a tag-free, spatially resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate regions of interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A deep learning–based algorithm is proposed to extract “histomorphological feature spectra” across an entire hematoxylin and eosin image. Clustering is then similarly performed to produce histology segmentation. Since ROIs originating from instrumental noise or artifacts would not be reproduced cross-modally, the consistency between histology and MSI segmentation becomes an effective measure of the biological validity of the results. So, #Clusters that maximize the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.
Gut microbes play critical roles in host nutrition, physiology, and behavior. Periplaneta americana is a famous urban pest which is widely distributed in the tropics and subtropics, but very few information is available on the gut microbiome of Periplaneta americana, particularly in its different life stages. Here, we characterized the diversity and structure of gut microbiome in eggs, nymph and adult life stages of Periplaneta americana using high-throughput 16S rRNA genes sequencing. Both the results of Alpha- and Beta-diversity analysis showed the diversity and structure of gut microbiome were significant different among the eggs, nymph and adult stages. The result of species distribution showed the predominant phyla in three life stages were Bacteroidetes , Firmicutes and Proteobacteria , but the relative abundances of these bacteria were significant different among each life stage. 1,169 operational taxonomic units were shared by three stages, which indicating the gut microbiome may be inherited to offspring from parents of Periplaneta americana. According to the prediction of functional genes in metabolic pathways, most of them were distributed in the metabolic pathways of basic physiology such as nutrition, growth, development and immunity, etc. The relative abundances of functional genes in metabolic pathways were significant different among life stages of Periplaneta americana, indicating the gut microbiome might play an important role in the physiology across its different life stages. This study revealed the diversity and structure of gut microbiome in different life stages of Periplaneta americana, which may contribute to us to understand it’s physiology and behaviors.
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