DataSheet_1_Metabolomic and transcriptomic analyses of the flavonoid biosynthetic pathway in blueberry (Vaccinium spp.).zip

As a highly economic small fruit crop, blueberry is enjoyed by most people in terms of color, taste, and rich nutrition. To better understand its coloring mechanism on the process of ripening, an integrative analysis of the metabolome and transcriptome profiles was performed in three blueberry varieties at three developmental stages. In this study, 41 flavonoid metabolites closely related to the coloring in blueberry samples were analyzed. It turned out that the most differential metabolites in the ripening processes were delphinidin-3-O-arabinoside (dpara), peonidin-3-O-glucoside (pnglu), and delphinidin-3-O-galactoside (dpgal), while the most differential metabolites among different varieties were flavonols. Furthermore, to obtain more accurate and comprehensive transcripts of blueberry during the developmental stages, PacBio and Illumina sequencing technology were combined to obtain the transcriptome of the blueberry variety Misty, for the very first time. Finally, by applying the gene coexpression network analysis, the darkviolet and bisque4 modules related to flavonoid synthesis were determined, and the key genes related to two flavonoid 3′, 5′-hydroxylase (F3′5′H) genes in the darkviolet module and one bHLH transcription factor in the bisque4 module were predicted. It is believed that our findings could provide valuable information for the future study on the molecular mechanism of flavonoid metabolites and flavonoid synthesis pathways in blueberries.</p

Data_Sheet_1_Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning.pdf

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.</p

Data_Sheet_2_Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning.pdf

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.</p