ACS Measurement Science Au最新文献

High-density droplet arrays are emerging as a powerful tool for high-throughput bioanalytical applications. These arrays are formed of thousands of nanoliter droplets, which can be analyzed by various optical and spectroscopic methods as well as label-free matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). However, special precautions are required for the massive number of small droplets, particularly in the deposition of matrix compounds. Here, we introduce a new workflow for the analytical preparation of an array comprising 6048 droplets, which significantly improves the intensity of the MALDI-MS signals. We deposited matrix compounds in a custom-made sublimation chamber followed by a recrystallization step to achieve significant signal intensity increases for three model proteins with low, medium, and large masses, respectively. Furthermore, selective removal of the matrix before recrystallization enhanced the spatial resolution and increased the signal intensity by an average of 57%. This method can be easily standardized and upscaled for the preparation of an even larger number of droplets per array for MS analysis.

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. “Shotgun proteomics” or “bottom-up proteomics” is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Frequent monitoring of glycan patterns is a critical step in studying glycan-mediated cellular processes. However, the current glycan analysis tools are resource-intensive and less suitable for routine use in standard laboratories. We developed a novel glycan detection platform by integrating surface-enhanced Raman spectroscopy (SERS), boronic acid (BA) receptors, and machine learning tools. This sensor monitors the molecular fingerprint spectra of BA binding to cis-diol-containing glycans. Different types of BA receptors could yield different stereoselective reactions toward different glycans and exhibit unique vibrational spectra. By integration of the Raman spectra collected from different BA receptors, the structural information can be enriched, eventually improving the accuracy of glycan classification and quantification. Here, we established a SERS-based sensor incorporating multiple different BA receptors. This sensing platform could directly analyze the biological samples, including whole milk and intact glycoproteins (fetuin and asialofetuin), without tedious glycan release and purification steps. The results demonstrate the platform’s ability to classify milk oligosaccharides with remarkable classification accuracy, despite the presence of other non-glycan constituents in the background. This sensor could also directly quantify sialylation levels of a fetuin/asialofetuin mixture without glycan release procedures. Moreover, by selecting appropriate BA receptors, the sensor exhibits an excellent performance of differentiating between α2,3 and α2,6 linkages of sialic acids. This low-cost, rapid, and highly accessible sensor will provide the scientific community with an invaluable tool for routine glycan screening in standard laboratories.

DNA glycosylase dysregulation is implicated in carcinogenesis and therapeutic resistance of cancers. Thus, various DNA-based detection platforms have been developed by leveraging the base excision activity of DNA glycosylases. However, the efficacy of DNA-based methods is hampered due to nonspecific degradation by nucleases commonly present in cancer cells and during preparations of cell lysates. In this report, we describe a fluorescence-based assay using a specific and nuclease-resistant three-dimensional DNAzyme walker to investigate the activity of DNA glycosylases from cancer cell lysates. We focus on DNA glycosylases that excise uracil from deoxyuridine (dU) lesions, namely, uracil DNA glycosylase (UDG) and single-stranded monofunctional uracil DNA glycosylase (SMUG1). The limits of detection for detecting UDG and SMUG1 in the buffer were 3.2 and 3.0 pM, respectively. The DNAzyme walker detected uracil excision activity in diluted cancer cell lysate from as few as 48 A549 cells. The results of the UDG inhibitor experiments demonstrate that UDG is the predominant uracil-excising glycosylase in A549 cells. Approximately 500 nM of UDG is present in each A549 cell on average. No fluorescence was generated in the samples lacking DNAzyme activation, indicating that there was no nonspecific nuclease interference. The ability of the DNAzyme walker to respond to glycosylase activity illustrates the potential use of DNAzyme walker technology to monitor and study biochemical processes involving glycosylases.

Accurately quantifying high analyte concentrations poses a challenge due to the common occurrence of the prozone or hook effect within sandwich assays utilized in plasmonic nanoparticle-based lateral flow devices (LFDs). As a result, LFDs are often underestimated compared to other biosensors with concerns surrounding their specificity and sensitivity toward the target analyte. To address this limitation, here we develop an analytical model capable of predicting the prozone effect and subsequently the dynamic range of the biosensor based on the concentration of the capture antibody. To support our model, we conduct a sandwich immunoassay to detect C-reactive protein (CRP) in a phosphate-buffered saline (PBS) buffer using an LFD. Within the experiment, we investigate the relationship between the CRP dynamic range and the prozone effect as a function of the capture antibody concentration, which is increased from 0.1 to 2 mg/mL. The experimental results, while supporting the developed analytical model, show that increasing the capture antibody concentration increases the dynamic range. The developed model therefore holds the potential to expand the measurable range and reduce costs associated with quantifying biomarkers in diverse diagnostic assays. This will ultimately allow LFDs to have better clinical significance before the prozone effect becomes dominant.