Investigating on sensing mechanism of MoS2-FET biosensors in response to proteins.

Field-effect transistor (FET) biosensors based on two-dimensional materials have gained extensive attention due to their high sensitivity, label-free detection capability, and fast response. Molybdenum disulfide (MoS2), with tunable bandgap, high surface-to-volume ratio, and smooth surface without dangling bonds, is a promising material for FET biosensors. Previous reports have demonstrated the fabrication of MoS2-FET biosensors and their high sensitivity detection of proteins. However, most prior research has focused on the realization of MoS2-FETs for detecting different kinds of proteins or molecules, while comprehensive analysis of the sensing mechanism and dominant device factors of MoS2-FETs in response to proteins is yet to investigate. In this study, we first fabricated MoS2-FET biosensor and detected different types of proteins (immunoglobulin G (IgG),ß-actin, and prostate-specific antigen (PSA)). Secondly, we built the model of the device and analyzed the sensing mechanism of MoS2-FETs in response to proteins. Experimental and modeling results showed that the induced doping effect and gating effect caused by the target protein binding to the device surface were the major influential factors. Specifically, the channel doping concentration and gate voltage (Vg) offset exhibited monotonic change as the concentration of the protein solution increases. For example, the channel doping concentration increased up to ∼37.9% and theVgoffset was ∼-1.3 V with 10-7µgµl-1IgG. The change was less affected by the device size. We also investigated the effects of proteins with opposite acid-base properties (ß-actin and PSA) to IgG on the device sensing mechanism.ß-actin and PSA exhibited behavior opposite to that of IgG. Additionally, we studied the response behavior of MoS2-FETs with different dimensions and dielectric materials (channel length, MoS2thickness, dielectric layer thickness, dielectric layer material) to proteins. The underlying mechanisms were discussed in details. This study provides valuable guidelines for the design and application of MoS2-FET biosensors.

Integrated Proteomics and Single-Cell Mass Cytometry Analysis Dissects the Immune Landscape of Ankylosing Spondylitis.

Mass cytometry is a powerful single-cell technology widely adopted to depict immune cell heterogeneity in different contexts. However, this method is only capable of examining several dozens of proteins simultaneously and requires a prior knowledge of the markers to be analyzed. Here we propose that the integration of mass cytometry with shot-gun proteomics may serve as a valuable tool to achieve an in-depth understanding of the immune system. By implementing such a strategy, we investigated the immune landscape of ankylosing spondylitis (AS), a chronic inflammatory arthritis with unclear etiology. The proteome alteration in peripheral blood mononuclear cells (PBMCs) was investigated by quantitative proteomics, and then mass cytometry analysis was conducted to decipher the immunome by considering the signaling molecules identified with differential expression by proteomics. As a result, we identified a wide spectrum of proteins dysregulated in AS, e.g., upregulation of glycolytic enzymes, downregulation of lipid transporters, and dysregulation of chemokine signaling molecules involved in proinflammatory cytokine production and leucocyte migration. Moreover, the single-cell analysis showed the upregulation of chemokine signaling regulators in subclusters of both innate and adaptive immune cells in AS. In addition, correlation analysis unveiled the interplay among Phenograph-identified subclusters of monocytes, CD4+ T cells, and CD8+ T cells. Taken together, our findings demonstrated that the integration of mass spectrometry-based proteomics and single-cell mass cytometry may serve as a useful tool to reveal clinically relevant information regarding useful targets and cellular phenotypes that could be further exploited to develop novel therapeutic strategies.

In situgeneration of FeOOH/NiOOH interface in FeS2/NiS2nanosheets through deep reconstruction for efficient oxygen evolution reaction.

Transition metal sulfides (TMSs) for electrochemical water splitting undergo significant self-reconstruction to form actual active species favorable for high oxygen evolution reaction (OER) performance. However, the complete self-reconstruction of most reported TMSs in alkaline media is unfrequent and the active species cannot be efficiently used. Herein, self-supported FeS2/NiS2nanosheet arrays (FeNiS) are deliberately fabricated as pre-catalysts and then accomplished deep phase transformation into low-crystalline and ultrathin FeOOH/NiOOH (FeNiS-R) nanosheets favorable to alkaline OER. Variousex situcharacterization studies uncover that the FeNiS-R with abundant interfaces is generated via complete reconstruction during electrolysis and the high-valence Fe and Ni in the FeNiS-R interface are the real active sites for high OER activity. The reconstructed FeNiS-R exhibits a small overpotential of 290 mV at 100 mA cm-2and favorable durability (≥80 h), much superior to commercial benchmark IrO2. This work provides a promising avenue to achieve the deep reconstruction of TMSs and the targeted design of OER catalysts in energy devices.

A data-independent acquisition (DIA)-based quantification workflow for proteome analysis of 5000 cells.

Data independent acquisition (DIA) has emerged as a powerful proteomic technique with exceptional reproducibility and throughput, and has been widely applied to clinical sample analysis. DIA approaches normally rely on project-specific spectral libraries generated by data dependent acquisition (DDA), requiring extensive off-line fractionation and large amounts of input material. In this study, we aimed to explore the utility of DIA for the analysis of samples with limited quantities. We employed three software tools (DIA-NN, Spectronaut, and EncyclopeDIA) for data analysis and generated three types of libraries, including an experiment-specific library built by DDA analysis of off-line fractions, a FASTA sequence database, and a library generated by gas-phase fractionation (GPF), resulting in eight analysis pipelines. Then we systematically compared the performance of the eight strategies by analyzing the DIA data from HEK293T cell tryptic peptides with sample loads of 500 ng, 100 ng, 20 ng, and 4 ng. The results showed that DIA-NN with GPF-based libraries outperformed the others in protein identification and retention time calibration. Next, we further evaluated the optimized workflow by analyzing the proteome alteration in 5000 peripheral blood mononuclear cells (PBMCs) induced by lipopolysaccharide (LPS) stimulation. As a result, 3179 protein groups were quantified, and functional analysis revealed activation of multiple signaling pathways, e. g., endocytosis, NF-kappa B signaling, and T cell receptor signaling. The results showed the practicability of using DIA for scarce samples, and the established workflow of PBMC analysis could be easily adapted for biomarker discovery, immune status evaluation, and drug response monitoring, especially for diseases involved with dysfunction of the immune system.

[Phosphoproteomic analysis of peripheral blood mononuclear cells of ankylosing spondylitis patients].

Objective To investigate the changes of protein phosphorylation in peripheral blood mononuclear cells (PBMCs) of patients with ankylosing spondylitis (AS) and its potential regulatory role in AS. Methods PBMCs were obtained from 20 cases of AS without treatment, 15 cases of AS with drug treatment, and 20 matched healthy controls. Protein extraction, trypsin digestion, phosphorylated peptide enrichment and mass spectrometric analyses were performed. AS-related phosphorylated proteins were screened by bioinformatics analysis, and the changes of expression levels of these proteins after treatment were analyzed. Results A total of 1561 significantly differential phosphorylated peptides were detected, of which 756 peptides from 472 proteins were up-regulated, and 805 from 363 proteins were down-regulated. GO enrichment analysis showed that the proteins with altered phosphorylation were mainly involved in biological processes such as neutrophil activation and platelet aggregation. The enrichment analysis of KEGG pathway showed infection, platelet activation, T cell receptors and B cell receptor signaling pathways might be closely related to AS. Conclusion This study systematically depicted the change of protein phosphorylation in PBMCs of AS patients, providing important information to understand the pathogenesis and disease progression mechanism of AS.

Deciphering the protein-protein interaction network regulating hepatocellular carcinoma metastasis.

Hepatocellular carcinoma (HCC) is one of the leading causes of mortality related to cancer all over the world. To better understand the molecular mechanisms of HCC metastasis, we analyzed the proteome of three HCC cell lines with different metastasis potentials by quantitative proteomics and bioinformatics analysis. As a result, we identified 378 cellular proteins potentially associated to HCC metastasis, and constructed a highly connected protein-protein interaction (PPI) network. Functional annotation of the network uncovered prominent pathways and key roles of these proteins, suggesting that the metabolism and cytoskeleton biological processes are greatly involved with HCC metastasis. Furthermore, the integrative network analysis revealed a rich-club organization within the PPI network, indicating a hub center of connections. The rich-club nodes include several well-known cancer-related proteins, such as proto-oncogene non-receptor tyrosine kinase (SRC) and pyruvate kinase M2 (PKM2). Moreover, the differential expressions of two identified proteins, including PKM2 and actin-related protein 2/3 complex subunit 4 (ARPC4), were validated using Western blotting. These two proteins were revealed as potential prognostic markers for HCC as shown by survival rate analysis.