Visualizing Low-Abundance Proteins and Post-Translational Modifications in Living Drosophila Embryos via Fluorescent Antibody Injection.

Visualization of proteins in living cells using GFP (Green Fluorescent Protein) and other fluorescent tags has greatly improved understanding of protein localization, dynamics, and function. Compared to immunofluorescence, live imaging more accurately reflects protein localization without potential artifacts arising from tissue fixation. Importantly, live imaging enables quantitative and temporal characterization of protein levels and localization, crucial for understanding dynamic biological processes such as cell movement or division. However, a major limitation of fluorescent tagging approaches is the need for sufficiently high protein expression levels to achieve successful visualization. Consequently, many endogenously tagged fluorescent proteins with relatively low expression levels cannot be detected. On the other hand, ectopic expression using viral promoters can sometimes lead to protein mislocalization or functional alterations in physiological contexts. To address these limitations, an approach is presented that utilizes highly sensitive antibody-mediated protein detection in living embryos, essentially performing immunofluorescence without the need for tissue fixation. As proof of principle, endogenously GFP-tagged Notch receptor that is barely detectable in living embryos can be successfully visualized after antibody injection. Furthermore, this approach was adapted to visualize post-translational modifications (PTMs) in living embryos, allowing the detection of temporal changes in tyrosine phosphorylation patterns during early embryogenesis and revealing a novel subpopulation of phosphotyrosine (p-Tyr) underneath apical membranes. This approach can be modified to accommodate other protein-specific, tag-specific, or PTM-specific antibodies and should be compatible with other injection-amenable model organisms or cell lines. This protocol opens new possibilities for live imaging of low-abundance proteins or PTMs that were previously challenging to detect using traditional fluorescent tagging methods.

A Systematic Investigation of Complement and Coagulation-Related Protein in Autism Spectrum Disorder Using Multiple Reaction Monitoring Technology.

Autism spectrum disorder (ASD) is one of the common neurodevelopmental disorders in children. Its etiology and pathogenesis are poorly understood. Previous studies have suggested potential changes in the complement and coagulation pathways in individuals with ASD. In this study, using multiple reactions monitoring proteomic technology, 16 of the 33 proteins involved in this pathway were identified as differentially-expressed proteins in plasma between children with ASD and controls. Among them, CFHR3, C4BPB, C4BPA, CFH, C9, SERPIND1, C8A, F9, and F11 were found to be altered in the plasma of children with ASD for the first time. SERPIND1 expression was positively correlated with the CARS score. Using the machine learning method, we obtained a panel composed of 12 differentially-expressed proteins with diagnostic potential for ASD. We also reviewed the proteins changed in this pathway in the brain and blood of patients with ASD. The complement and coagulation pathways may be activated in the peripheral blood of children with ASD and play a key role in the pathogenesis of ASD.

The use of data independent acquisition based proteomic analysis and machine learning to reveal potential biomarkers for autism spectrum disorder.

Autism spectrum disorder (ASD) is a complex neurological developmental disorder in children, and is associated with social isolation and restricted interests. The etiology of this disorder is still unknown. There is neither any confirmed laboratory test nor any effective therapeutic strategy to diagnose or cure it. We performed data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis of plasma from children with ASD and controls. The result showed that 45 differentially expressed proteins (DEPs) were identified between autistic subjects and controls. Among these, only one DEP was down-regulated in ASD; other DEPs were up-regulated in ASD children's plasma. These proteins are found associated with complement and coagulation cascades, vitamin digestion and absorption, cholesterol metabolism, platelet degranulation, selenium micronutrient network, extracellular matrix organization and inflammatory pathway, which have been reported to be related to ASD. After MRM verification, five key proteins in complement pathway (PLG, SERPINC1, and A2M) and inflammatory pathway (CD5L, ATRN, SERPINC1, and A2M) were confirmed to be significantly up-regulated in ASD group. Through the screening of machine learning model and MRM verification, we found that two proteins (biotinidase and carbonic anhydrase 1) can be used as early diagnostic markers of ASD (AUC = 0.8, p = 0.0001). SIGNIFICANCE: ASD is the fastest growing neurodevelopmental disorder in the world and has become a major public health problem worldwide. Its prevalence has been steadily increasing, with a global prevalence rate of 1%. Early diagnosis and intervention can achieve better prognosis. In this study, data independent acquisition (DIA) and multiple reaction monitoring (MRM) analysis was applied to analyze the plasma proteome of ASD patients (31 (±5) months old), and 378 proteins were quantified. 45 differentially expressed proteins (DEPs) were identified between the ASD group and the control group. They mainly were associated with platelet degranulation, ECM proteoglycar, complement and coagulation cascades, selenium micronutrient network, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), cholesterol metabolism, vitamin metabolism, and inflammatory pathway. Through the integrated machine learning methods and the MRM verification of independent samples, it is considered that biotinidase and carbon anhydrase 1 have the potential to become biomarkers for the early diagnosis of ASD. These results complement proteomics database of the ASD patients, broaden our understanding of ASD, and provide a panel of biomarkers for the early diagnosis of ASD.

Potential biomarkers identified in plasma of patients with gestational diabetes mellitus.

INTRODUCTION: Gestational diabetes mellitus (GDM) is a common complication during pregnancy. Looking for reliable diagnostic markers for early diagnosis can reduce the impact of the disease on the fetus OBJECTIVE: The present study is designed to find plasma metabolites that can be used as potential biomarkers for GDM, and to clarify GDM-related mechanisms METHODS: By non-target metabolomics analysis, compared with their respective controls, the plasma metabolites of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy were analyzed. Multiple reaction monitoring (MRM) analysis was performed to verify the potential marker RESULTS: One hundred and seventy-two (172) and 478 metabolites were identified as differential metabolites in the plasma of GDM pregnant women at 12-16 weeks and 24-28 weeks of pregnancy, respectively. Among these, 40 metabolites were overlapped. Most of them are associated with the mechanism of diabetes, and related to short-term and long-term complications in the perinatal period. Among them, 7 and 10 differential metabolites may serve as potential biomarkers at the 12-16 weeks and 24-28 weeks of pregnancy, respectively. By MRM analysis, compared with controls, increased levels of 17(S)-HDoHE and sebacic acid may serve as early prediction biomarkers of GDM. At 24-28 weeks of pregnancy, elevated levels of 17(S)-HDoHE and L-Serine may be used as auxiliary diagnostic markers for GDM CONCLUSION: Abnormal amino acid metabolism and lipid metabolism in patients with GDM may be related to GDM pathogenesis. Several differential metabolites identified in this study may serve as potential biomarkers for GDM prediction and diagnosis.

Metabolic characteristics of plasma bile acids in patients with intrahepatic cholestasis of pregnancy-mass spectrometric study.

INTRODUCTION: Intrahepatic cholestasis of pregnancy (ICP) is one of the more common complications in the middle and late stages of pregnancy, which requires early detection and intervention. OBJECTIVE: The aim of the study is to investigate the changes in the metabolic profile of bile acids (BAs) in plasma of pregnant women with ICP and to look biomarkers for the diagnosis and grading of ICP, and to explore the disease mechanism. METHODS: The targeted metabolomics based on high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was used to analyze plasma BAs. RESULTS: Twenty-seven BAs can be quantified in all participants. Among them, 22 BAs were identified as differential BAs between ICP and control groups. Five BAs include 3ß-CA, 3ß-DCA, CDCA-3Gln, NCA, and Tß-MCA, were found to be associated with ICP for the first time. Nine BAs include NCA, GCA, GCDCA, GHCA, GUDCA, HCA, TCA, TCDCA and THCA, can be used as possible ICP diagnostic biomarkers. Four BAs, i.e., GLCA, THCA, GHCA and TLCA-3S may be used as potential biomarkers for ICP grading. CONCLUSION: There were significant differences in plasma BA profiles between ICP patients and the control. The BA profiles of mild ICP group and severe ICP group partially overlapped. Potential diagnostic and grading BA markers were identified. A significant characteristic of ICP group was the increase of conjugated BAs. A mechanism to sustain the equilibrium of BA metabolism and adaptive response has been developed in ICP patients to accelerate excretion and detoxification.

Ultrasensitive Detection of Exosome Using Biofunctionalized Gold Nanorods on a Silver-Island Film.

Exosomes are often a promising source of biomarkers for cancer diagnosis in the early stages. Therefore, it is important to develop a sensitive and low-cost detection method. Here, we introduce a new substrate using gold nanorods (GNRs) on a silver-island film that produces a 360-fold AF647 molecule fluorescence enhancement compared to glass. The amplified fluorescence was proven theoretically by using finite difference time-domain simulation (FDTD). Utilizing the enhanced fluorescence from the substrate, GNRs attached with the biomolecules and created a sandwich immunoassay that can significantly detect human CD63 antigen on the exosome. By applying the method, the detection limit of mouse IgG goes down to 0.3 ng/mL, which is considerably better than the existing methods. Moreover, the sensitivity and accuracy for clinical plasma from six patients confirm its diagnostic feasibility. The proposed substrate can be uniformly extended to the identification of other biomarkers by modifying the antibodies on the surfaces of the GNRs.