The combined application of electron microscopy (EM) is frequently used for the microstructural investigation of biological specimens and plays two important roles in the quantification and in gaining an improved understanding of biological phenomena by making use of the highest resolution capability provided by EM. The possibility of imaging wet specimens in their "native" states in the environmental scanning electron microscope (ESEM) at high resolution and large depth of focus in real time is discussed in this paper. It is demonstrated here that new features can be discovered by the elimination of even the least hazardous approaches in some preparation techniques, that destroy the samples. Since the analysis conditions may influence the morphology and the extreme surface sensitivity of living biological systems, the results obtained from the same cultured cell with two different ESEM modes (Lvac mode and wet mode) were compared. This offers new opportunities compared with ESEM-wet/Lvac-mode imaging, since wet-mode imaging involves a real contrast and gives an indication of the changes in cell morphology and structure required for cell viability. In this study, wet-mode imaging was optimized using the unique ability of cell quantities for microcharacterization in situ giving very fine features of topological effects. Accordingly, the progress is reported by comparing the results of these two modes, which demonstrate interesting application details. In general, the functional comparisons have revealed that the fresh unprocessed Saccharomyces cerevisiae cells (ESEM-wet mode) were essentially unaltered with improved and minimal specimen preparation timescales, and the optimal cell viability degree was visualized and also measured quantitatively while the cell size remained unchanged with continuous images.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/sca.20005 | DOI Listing |
Sci Rep
January 2025
School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, 300072, China.
Yeast sex-hormone whole-cell biosensors are analytical tools characterized by long-time storage and low production cost. We engineered compact β-estradiol biosensors in S. cerevisiae cells by leveraging short (20-nt long) operators bound by the fusion protein LexA-ER-VP64-where ER is the human estrogen receptor and VP64 a strong viral activation domain.
View Article and Find Full Text PDFMol Cell
January 2025
Department of Biochemistry & Structural Biology and Greehey Children's Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
In this issue of Molecular Cell, studies by Xu et al., Kimble et al., and Elango et al.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Chemistry and Biochemistry, The University of Texas at Dallas, Richardson, TX 75080.
The TRAMP complex contains two enzymatic activities essential for RNA processing upstream of the nuclear exosome. Within TRAMP, RNA is 3' polyadenylated by a subcomplex of Trf4/5 and Air1/2 and unwound 3' to 5' by Mtr4, a DExH helicase. The molecular mechanisms of TRAMP assembly and RNA shuffling between the two TRAMP catalytic sites are poorly understood.
View Article and Find Full Text PDFPLoS One
January 2025
Research Centre for Plant Conservation, Botanic Gardens and Forestry, National Research and Innovation Agency, Bogor, Indonesia.
One way to treat diabetes mellitus type II is by using α-glucosidase inhibitor, that will slow down the postprandial glucose intake. Metabolomics analysis of Artabotrys sumatranus leaf extract was used in this research to predict the active compounds as α-glucosidase inhibitors from this extract. Both multivariate statistical analysis and machine learning approaches were used to improve the confidence of the predictions.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Cleveland Clinic Lou Ruvo Center for Brain Health, Cleveland, OH, USA.
Background: The emerging tools of protein-protein interactome network offer a platform to explore not only the molecular complexity of human diseases, but also to identify risk genes and drug targets. Integration of the genome, transcriptome, proteome, and the interactome networks are essential for such identification, including Alzheimer's disease (AD), Parkinson disease (PD), and Amyotrophic lateral sclerosis (ALS) METHOD: In this study, we performed multi-modal analyses of cross-species protein interactome networks and human brain functional genomics data to identify risk genes and drug targets for neurodegenerative diseases. We presented a multi-view topology-based deep learning framework to identify disease-associated genes for cross-species interactome (TAG-X).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!