Analytical Chemistry最新文献

The Tn antigen, an immature truncated O-glycosylation, is a promising biomarker for cancer detection and diagnosis. However, reliable methods for analyzing O-GalNAcylation and complex O-glycosylation are lacking. Here, we develop a novel method, MOTAI, for the sequential analysis of O-glycosylation using different O-glycoproteases. MOTAI conjugates glycopeptides on a solid support and releases different types of O-glycosylation through sequential enzymatic digestion by O-glycoproteases, including OpeRATOR and IMPa. Because OpeRATOR has less activity on O-GalNAcylation, MOTAI enriches O-GalNAcylation for subsequent analysis. We demonstrate the effectiveness of MOTAI by analyzing fetuin O-glycosylation and Jurkat cell lines. We then apply MOTAI to analyze colorectal cancer and benign colorectal polyps. We identify 32 Tn/sTn-glycoproteins and 43 T/sT-glycoproteins that are significantly increased in tumor tissues. Gene Ontology analysis reveals that most of these proteins are ECM proteins involved in the adhesion process of the intercellular matrix. Additionally, the protein disulfide isomerase CRELD2 has a significant difference in Tn expression, and the abnormally glycosylated T345 and S349 O-glycosylation sites in cancer group samples may promote the secretion of CRELD2 and ultimately tumorigenesis through ECM reshaping. In summary, MOTAI provides a powerful new tool for the in-depth analysis of O-GalNAcylation and complex O-glycosylation. It also reveals the upregulation of Tn/sTn-glycoproteins in colorectal cancer, which may provide new insights into cancer biology and biomarker discovery.

Long noncoding RNAs (lncRNAs) act as the dynamic regulatory molecules that control the expression of genes and affect numerous biological processes, and their dysregulation is associated with tumor progression. Herein, we develop a fluorescent light-up aptasensor to simultaneously measure multiple lncRNAs in living cells and breast tissue samples based on the DNAzyme-mediated cleavage reaction and transcription-driven synthesis of light-up aptamers. When target lncRNAs are present, they can be recognized by template probes to form the active DNAzyme structures, initiating the T4 PNK-catalyzed dephosphorylation-triggered extension reaction to generate double-strand DNAs with the T7 promoter sequences. The corresponding T7 promoters can initiate the transcription amplification catalyzed by the T7 RNA polymerase to generate abundant Broccoli aptamers and malachite green aptamers, which can bind DFHBI-1T and MG to generate strong fluorescence signals. Taking advantage of the good selectivity of DNAzyme-mediated cleavage of lncRNAs, high amplification efficiency of T7 transcription-driven amplification reaction, and bright fluorescence of the RNA aptamer-fluorophore complex, this method exhibits high sensitivity with a detection limit of 21.4 aM for lncRNA HOTAIR and 18.47 aM for lncRNA MALAT1, and it can accurately measure multiple lncRNAs in both tumor cell lines and breast tissue samples, providing a powerful paradigm for biomedical research and early clinic diagnostics.

Glycans on proteins and lipids play important roles in maturation and cellular interactions, contributing to a variety of biological processes. Aberrant glycosylation has been associated with various human diseases including cancer; however, elucidating the distribution and heterogeneity of glycans in complex tissue samples remains a major challenge. Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is routinely used to analyze the spatial distribution of a variety of molecules including N-glycans directly from tissue surfaces. Sialic acids are nine carbon acidic sugars that often exist as the terminal sugars of glycans and are inherently difficult to analyze using MALDI-MSI due to their instability prone to in- and postsource decay. Here, we report on a rapid and robust method for stabilizing sialic acid on N-glycans in FFPE tissue sections. The established method derivatizes and identifies the spatial distribution of α2,3- and α2,6-linked sialic acids through complete methylamidation using methylamine and PyAOP ((7-azabenzotriazol-1-yloxy)tripyrrolidinophosphonium hexafluorophosphate). Our in situ approach increases the glycans detected and enhances the coverage of sialylated species. Using this streamlined, sensitive, and robust workflow, we rapidly characterize and spatially localize N-glycans in human tumor tissue sections. Additionally, we demonstrate this method's applicability in imaging mammalian cell suspensions directly on slides, achieving cellular resolution with minimal sample processing and cell numbers. This workflow reveals the cellular locations of distinct N-glycan species, shedding light on the biological and clinical significance of these biomolecules in human diseases.

26% of the world's population lacks access to clean drinking water; clean water and sanitation are major global challenges highlighted by the UN Sustainable Development Goals, indicating water security in public water systems is at stake today. Water monitoring using precise instruments by skilled operators is one of the most promising solutions. Despite decades of research, the professionalism-convenience trade-off when monitoring ubiquitous metal ions remains the major challenge for public water safety. Thus, to overcome these disadvantages, an easy-to-use and highly sensitive visual method is desirable. Herein, an innovative strategy for one-to-nine metal detection is proposed, in which a novel thiourea spectroscopic probe with high 9-metal affinity is synthesized, acting as "one", and is detected based on the 9 metal-thiourea complexes within portable spectrometers in the public water field; this is accomplished by nonspecialized personnel as is also required. During the processing of multimetal analysis, issues arise due to signal overlap and reproducibility problems, leading to constrained sensitivity. In this innovative endeavor, machine learning (ML) algorithms were employed to extract key features from the composite spectral signature, addressing multipeak overlap, and completing the detection within 30-300 s, thus achieving a detection limit of 0.01 mg/L and meeting established conventional water quality standards. This method provides a convenient approach for public drinking water safety testing.

Ferroptosis is an iron-dependent programmed cell death that is characterized by the dysregulation of lipid reactive oxygen species (ROS) production, causing abnormal changes in hypochlorous acid (HClO) levels in lysosomes. Super-resolution imaging can observe the fine structure of the lysosome at the nanometer level; therefore, it can be used to detect lysosome HClO levels during ferroptosis at the suborganelle level. Herein, we utilize a ratiometric fluorescent probe, SRF-HClO, for super-resolution imaging of lysosome HClO. Structured-illumination microscopy (SIM) improves the accuracy of lysosome targeting and enables the probe SRF-HClO to be successfully applied to rapidly monitor the up-regulated lysosome HClO at the nanoscale during inflammation and ferroptosis. Importantly, the probe SRF-HClO can also detect HClO changes in inflammatory and ferroptosis mice and evaluate the inhibitory effect of ferroptosis on mice tumors.

The number of possible candidate formulas for high molecular weight unknown compounds (e.g., 7000-8000 Da for common 20-mer oligonucleotides) by high-resolution mass spectrometry is in the order of several hundred thousand even at the highest level of experimental accuracy. In demanding analytical applications involving new chemistries and synthetic routes where little is known about the chemical nature or mechanisms of formation of the unknown compounds (e.g., impurities), the generation of a short list of the most plausible formulas would be highly desirable. Such an approach has been developed in the current work. The concept of mass difference from a reference compound is introduced to simplify the approach and greatly reduce the number of possible formulas. The approach allows for the generation of candidate formulas by both the addition and subtraction of atoms to account for all possible molecular changes from the parent compound. A reduction of 3 orders of magnitude in the number of possible formulas has been achieved by the approach. Ranking of the formulas by the product of the sums of the absolute changes in the total number of all atoms and all heteroatoms in the proposed difference formula successfully ranked the correct formula within the top 10 from a list of 200-250 best candidate formulas. There is a tendency for the impurities to be formed involving the least change in the number of atoms and heteroatoms. Δ_fH^o and Δ_fG'^o values can be used as a complementary ranking system of the top candidates. The approach is applicable to unknowns in any other systems of high MW compounds.

Applications involving two-photon activation, including two-photon fluorescence imaging, photodynamic therapy, and 3D data storage, require precise knowledge of the two-photon absorption (2PA) spectra of target chromophores. Broadband pump-probe spectroscopy using femtosecond laser pulses provides wavelength-dependent 2PA spectra with absolute cross sections, but the measurements are sometimes complicated by cross-phase modulation effects and dispersion of the broadband probe. Here, we introduce a single-shot approach that eliminates artifacts from cross-phase modulation and enables more rapid measurements by avoiding the need to scan the time delay between the pump and the probe pulses. The approach uses counterpropagating beams to automatically integrate over the full interaction between the two pulses as they cross. We demonstrate this single-shot approach for a common 2PA reference, coumarin 153 (C153), in three different solvents using the output from a Yb:KGW laser. This approach provides accurate 2PA cross sections that are more reliable and easier to obtain compared with scanning pump-probe methods using copropagating laser beams. The single-shot method for broadband two-photon absorption (BB-2PA) spectroscopy also has significant advantages compared with single-wavelength measurements, such as z-scan and two-photon fluorescence.

Fluorescence detection has always suffered from high background fluorescence from real samples such as milk. Therefore, cumbersome pretreatments of samples were necessary to remove the fluorescent substances but led to long processing times and low efficiency. Time-resolved luminescence detection is a powerful technique for eliminating short-lived background fluorescence without additional pretreatments. However, the related instruments are usually equipped with high-speed excitation sources and detectors, which are always bulky and expensive. Herein, we developed a low-cost and miniaturized imaging system for high-throughput time-gated luminescence detection. An UV LED array was used to excite multiple samples, the luminescence of which could be detected by a smartphone simultaneously. An analog circuit was designed to synchronize the LED to the mechanical chopper to eliminate the background signals resulting from scattering and short-lived autofluorescence. Compared to other synchronous circuits based on FPGAs and microcontrollers, this analog circuit required no programming and memory. For the first time, high-throughput time-resolved luminescence detection of tetracycline in milk without any separation or enrichment was achieved by utilizing a smartphone as a camera, and the scattered signals and the background fluorescence were eliminated efficiently. The limit of detection reached as low as 53 nM (∼0.024 ppm), lower than the residue limit set by the European Union. This high-throughput time-gated luminescence detection method can be used for quantitative analysis of many real samples with high background fluorescence.

Apurinic/apyrimidinic endonuclease 1 (APE1), as a vital base excision repair enzyme, is essential for maintaining genomic integrity and stability, and its abnormal expression is closely associated with malignant tumors. Herein, we constructed an electrochemiluminescence (ECL) biosensor for detecting APE1 activity by combining nanoconfined ECL silver nanoclusters (Ag NCs) with X-shaped DNA recognizer-triggered cascade amplification. Specifically, the Ag NCs were prepared and confined in the glutaraldehyde-cross-linked chitosan hydrogel network using the one-pot method, resulting in a strong ECL response and exceptional stability in comparison with discrete Ag NCs. Furthermore, the self-assembled X-shaped DNA recognizers were designed for APE1 detection, which not only improved reaction kinetics due to the ordered arrangement of recognition sites but also achieved high sensitivity by utilizing the recognizer-triggered cascade amplification of strand displacement amplification (SDA) and DNAzyme catalysis. As expected, this biosensor achieved sensitive ECL detection of APE1 in the range of 1.0 × 10^-3 U·μL^-1 to 1.0 × 10^-10 U·μL^-1 with the detection limit of 2.21 × 10^-11 U·μL^-1, rendering it a desirable approach for biomarker detection.

The determination of pH values is crucial in various fields, such as analytical chemistry, medical diagnostics, and biochemical research. pH test strips, renowned for their convenience and cost-effectiveness, are commonly utilized for pH qualitative estimation. Recently, quantitative methods for determining pH values using pH test strips have been developed. However, these methods can be prone to errors due to environmental factors, such as lighting conditions, which affect the imaging quality of the pH test strips. To address these challenges, we developed an innovative approach that combines machine learning techniques with pH test strips for the quantitative determination of pH values. Our method involves extracting artificial features from the pH test strip images and combining them across multiple dimensions for comprehensive analysis. To ensure optimal feature selection, we developed a feature selection strategy based on SHAP importance. This strategy helps in identifying the most relevant features that contribute to accurate pH prediction. Furthermore, we integrated multiple machine learning algorithms, employing a robust stacking fusion strategy to establish a highly reliable pH value prediction model. Our proposed method automates the determination of pH values through pH test strips, effectively overcoming the limitations associated with environmental lighting interference. Experimental results demonstrate that this method is convenient, effective, and highly reliable for the determination of pH values.