Journal of bioscience and bioengineering最新文献

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in complex biological systems. While this technology has been widely applied to eukaryotic cells, its adaptation to bacterial systems has been challenging due to the unique characteristics of bacterial transcripts. This review surveys the recent developments in bacterial scRNA-seq techniques, highlighting the technical challenges, methodological innovations, and emerging applications in microbiology. We discuss the key differences between eukaryotic and bacterial RNA-seq approaches, focusing on the strategies to overcome limitations such as the lack of poly-A tails in bacterial mRNAs and the low RNA content in individual bacterial cells. The review covers various bacterial scRNA-seq methods, including plate-based, split-pool barcoding, and droplet-based techniques, comparing their strengths and limitations in terms of sensitivity, throughput, and applicability to different bacterial species. Furthermore, we explore the biological insights gained from these techniques, such as identifying rare cell states, characterization of antibiotic responses, and analysis of bacterial communities. Finally, we discuss future perspectives and potential applications of bacterial scRNA-seq in understanding microbial physiology, host-pathogen interactions, and complex microbial ecosystems. This comprehensive overview aims to provide researchers with a clear understanding of the current state and future directions of single-cell transcriptomics in bacteria.

For the safe use of chemicals widely used in human activities, it is crucial to assess their ecological impacts when released into the environment. Daphnia, a well-established environmental indicator species, is commonly used to evaluate the biological effects of chemicals and testing methods have been established. Among various indicators, the growth rate is one of the important parameters, but it requires significant time and effort to measure. In this study, we applied deep learning-based image recognition techniques to extract images of Daphnia from live imaging and assess their size. The estimated size of Daphnia, derived from images processed through deep learning, showed a high correlation with measured values, demonstrating the capability to measure Daphnia size from the images while they are swimming. This approach enables non-invasive measurements of Daphnia size without complicated procedures, which not only streamlines ecological impact assessments but also presents a valuable technique for ecological studies, such as analyzing the size distribution of zooplankton.

Advances in regenerative medicine highlighted the need for label-free cell image analysis to replace conventional microscopic observation for non-invasive cell quality evaluation. Image-based evaluation provides an efficient, quantitative, and automated approach to cell analysis, but segmentation remains a critical and challenging step. In this study, we investigated how training dataset design influenced the robustness of U-Net models for cell segmentation, focusing on challenges posed by limited data availability in cell culture. Using 2592 image pairs from four cell types representing key morphological categories, we constructed 42 investigation patterns to evaluate the effects of dataset size, dataset content, and morphological diversity on model performance. Our results showed that robust segmentation models could be developed with approximately 10 raw images captured using a 4× objective lens, a much smaller dataset than typically assumed. The dataset content was found to be crucial: training dataset images that captured commonly observed cell patterns yielded more robust models compared to those capturing rare or irregular cell patterns, which often impaired model performance with large deviations. Additionally, including both spindle and round cell morphologies in the training datasets improved model robustness when tested across all four cell types, while datasets restricted to a single morphology type could not achieve robust models. These findings highlight the importance of curating datasets that capture representative yet diverse cell morphologies. By addressing critical questions about dataset design, this study provides actionable guidance for the effective use of deep learning-based cell segmentation models in manufacturing and research applications.

As an important crop, bananas still encounter fruit quality and shelf-life problems that are affected by the ripening process. Improving postharvest technologies may effectively address these challenges, such as by studying the ripening mechanism of banana cultivars with a slow ripening process. A banana cultivar that exhibits this characteristic is Musa balbisiana cv. 'Pisang Klutuk Wulung' (BB Group) or Pisang Klutuk Wulung (PKW), which has a ripening duration of 14-28 days. However, the metabolomics study on the ripening mechanism of this banana is still limited. This study aimed to analyze metabolite changes in ethylene-treated Pisang Klutuk Wulung during ripening in comparison to commercial bananas (Cavendish). Both bananas were subjected to exogenous ethylene treatment and analyzed using gas chromatography-mass spectrometry to perform metabolite profiling throughout the ripening process. The principal component analysis showed sample separation based on the ripening stages and banana species in pulp and peel. Orthogonal projection to latent structure analysis suggested that metabolite changes accompanied the ripening stages. Potential metabolite markers that distinguished the ripening of PKW and Cavendish were found, such as quinic acid, inositol, and 2-aminoethanol. This study shows differences in metabolite profiles between these bananas, especially the metabolites involved in sugar metabolism, cell wall metabolism, stress response, and biosynthesis of aromatic compounds. This study provides novel insights into the metabolic changes occurring during PKW ripening, contributing to the improvement of banana postharvest strategies.

The filamentous fungus Penicillium purpurogenum IAM15392 produces a nitrogen-containing azaphilone pigment, (10Z)-12-carboxyl-monascorubramine (PP-V), which is a potentially valuable natural food colorant. Because ammonium is used as a nitrogen source, and because ammonium uptake is the first step in the synthesis of PP-V, ammonium transporters of P. purpurogenum were identified and characterized. The P. purpurogenum genome was found to contain four putative ammonium transporter genes, designated amtA, amtB, amtC, and amtD, which encode 11 transmembrane proteins of 479, 567, 452, and 475 amino acid residues, respectively. These genes were tested for their ability to complement mutations in the ammonium transporter genes of the fission yeast Schizosaccharomyces pombe. The phenotypes of mutants included defects in growth on low ammonium medium, methylammonium sensitivity, ammonium uptake from the culture medium, and ammonium limitation-induced invasive growth. Furthermore, the transcription of the amt genes was examined in P. purpurogenum grown under different ammonium concentrations. The results suggest that AmtB plays a major role in growth using ammonium as a nitrogen source, whereas AmtA and possibly AmtD function at low ammonium concentrations. Because a medium used for the production of PP-V contains a high concentration of ammonium, our functional characterization of the P. purpurogenum ammonium transporters suggests that AmtB is a potential target of bioengineering for increased PP-V production.

Sake contains unique glycosides produced by the transglycosylation action of α-glucosidase from Aspergillus oryzae. Besides influencing the taste of sake, some transglycosylation products in sake exhibit beneficial biological activities. In this study, we searched for new transglycosylation products in sake. Liquid chromatography-mass spectrometry (LC-MS) revealed that peaks with m/z values corresponding to the glycosides, diglucopyranosylglycerol, triglucopyranosylglycerol, and tetraglucopyranosylglycerol, are present in sake. The presence of glycosides containing up to four polymerized glucose units is the first observation in sake. The peaks of the compounds were not observed in the sake that was brewed with a rice-koji made by α-glucosidase A (AgdA) gene disruption A. oryzae strain. The in vitro transglycosylation experiment using maltose, glycerol and a recombinant AgdA suggested that the compounds in sake were transglycosylation products composed of glycerol and one to four units of glucose. Nuclear magnetic resonance (NMR) analysis revealed that one of the major products of in vitro synthesis was α-isomaltosylglycerol (α-iMG). α-iMG was detected in commercial sake as a common component at an average of 1095 ppm (mg/L). This is the first study to report the presence of α-iMG in foods and beverages.