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Abstract Detection and remediation of stress in crops is vital to ensure agricultural productivity. Conventional forms of assessing stress in plants are limited by feasibility, delayed phenotypic responses, inadequate specificity, and lack of sensitivity during initial phases of stress. While mass spectrometry is remarkably precise and achieves high-resolution, complex samples, such as plant tissues, require time-consuming and biased depletion strategies to effectively identify low-abundant stress biomarkers. Here, we bypassed these reduction methods via a nano-omics approach, where gold nanoparticles were used to enrich time- and temperature-dependent stress-related proteins through biomolecular corona formation that were subsequently analyzed by ultra-high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). This nano-omic approach was more effective than a conventional proteomic analysis using UHPLC- MS/MS for resolving biotic-stress induced responses at early stages of pathogen infection inArabidopsis thaliana, well before the development of visible phenotypic symptoms, as well as in distal tissues of pathogen infected plants at early timepoints. The enhanced sensitivity of this nano-omic approach enables the identification of stress-related proteins at early critical timepoints, providing a more nuanced understanding of plant-pathogen interactions that can be leveraged for the development of early intervention strategies for sustainable agriculture. 

Abstract Lipid nanoparticles (LNPs) are the most clinically advanced nonviral RNA-delivery vehicles, though challenges remain in fully understanding how LNPs interact with biological systems.In vivo, proteins form an associated corona on LNPs that redefines their physicochemical properties and influences delivery outcomes. Despite its importance, the LNP protein corona is challenging to study owing to the technical difficulty of selectively recovering soft nanoparticles from biological samples. Herein, we developed a quantitative, label-free mass spectrometry-based proteomics approach to characterize the protein corona on LNPs. Critically, this protein corona isolation workflow avoids artifacts introduced by the presence of endogenous nanoparticles in human biofluids. We applied continuous density gradient ultracentrifugation for protein-LNP complex isolation, with mass spectrometry for protein identification normalized to protein composition in the biofluid alone. With this approach, we quantify proteins consistently enriched in the LNP corona including vitronectin, C-reactive protein, and alpha-2-macroglobulin. We explore the impact of these corona proteins on cell uptake and mRNA expression in HepG2 human liver cells, and find that, surprisingly, increased levels of cell uptake do not correlate with increased mRNA expression in part likely due to protein corona-induced lysosomal trafficking of LNPs. Our results underscore the need to consider the protein corona in the design of LNP-based therapeutics. Abstract Figure 

BackgroundWe performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous system (CNS). These CNS papers contain tens of thousands of immunofluorescent neural images supporting the findings of over 50,000 associated researchers. While many existing reviews discuss different aspects of immunofluorescent microscopy, such as image acquisition and staining protocols, few papers discuss immunofluorescent imaging from an image-processing perspective. We analyzed the literature to determine the image processing methods that were commonly published alongside the associated CNS cell, microscopy technique, and animal model, and highlight gaps in image processing documentation and reporting in the CNS research field. MethodsWe completed a comprehensive search of PubMed publications using Medical Subject Headings (MeSH) terms and other general search terms for CNS cells and common fluorescent microscopy techniques. Publications were found on PubMed using a combination of column description terms and row description terms. We manually tagged the comma-separated values file (CSV) metadata of each publication with the following categories: animal or cell model, quantified features, threshold techniques, segmentation techniques, and image processing software. ResultsOf the almost 9,000 immunofluorescent imaging papers identified in our search, only 856 explicitly include image processing information. Moreover, hundreds of the 856 papers are missing thresholding, segmentation, and morphological feature details necessary for explainable, unbiased, and reproducible results. In our assessment of the literature, we visualized current image processing practices, compiled the image processing options from the top twelve software programs, and designed a road map to enhance image processing. We determined that thresholding and segmentation methods were often left out of publications and underreported or underutilized for quantifying CNS cell research. DiscussionLess than 10% of papers with immunofluorescent images include image processing in their methods. A few authors are implementing advanced methods in image analysis to quantify over 40 different CNS cell features, which can provide quantitative insights in CNS cell features that will advance CNS research. However, our review puts forward that image analysis methods will remain limited in rigor and reproducibility without more rigorous and detailed reporting of image processing methods. ConclusionImage processing is a critical part of CNS research that must be improved to increase scientific insight, explainability, reproducibility, and rigor.