Generation of an Aptamer Targeting Receptor-Type Tyrosine-Protein Phosphatase F.

Cell-SELEX is a powerful tool to generate aptamers that specifically bind the native molecules on living cells. Here, we report an aptamer ZAJ4a generated by cell-SELEX. The molecular target of ZAJ4a was pulled down by the enriched cell-SELEX pool and identified to be the receptor-type tyrosine-protein phosphatase F (PTPRF) through a stable isotope labeling using amino acids in cell culture (SILAC)-based quantitative proteomic method. ZAJ4a showed high binding affinity with nanomolar range to cancer cells expressing PTPRF. Meanwhile, PTPRF was proven to highly express on several cancer cell lines using ZAJ4a as a molecular probe and to highly express in many kinds of cancer samples using gene expression profiling interactive analysis (GEPIA2) from the TCGA and GTEx databases. These results indicate that the aptamer generated by cell-SELEX showed good specificity at the molecular level. This cell-SELEX and target identification strategies show great potential for identifying biomarkers on the cell surface.

Characterization and Identification of Aptamers against CD49c for the Detection, Capture, and Release of Cancer Cells.

As a kind of recognition molecule, aptamers can be inserted into some regulatory sequences for the smart response of their targets. However, the molecular engineering might lead to the change of the binding affinity. Here, we present a stable aptamer ZAJ-2c and an environmentally sensitive aptamer ZAJ-2d optimized from an original cell-binding aptamer ZAJ-2, and the molecular target was further identified as CD49c on the cell membrane. ZAJ-2c was characterized with high binding ability independent of the presence of divalent cations at a temperature range from 4 to 37 °C, showing promise for measuring the expression of CD49c on cancer cells. Moreover, ZAJ-2d had a nanomolar binding affinity in the binding buffer at 4 °C, the same as ZAJ-2c, but lost the binding ability in a PBS buffer supplemented with 5 mM EDTA at 37 °C. This aptamer variant proved to selectively capture and release the CD49c positive cells by simply adjusting the temperatures and divalent cations. This set of aptamers might provide a toolbox for monitoring and operating of a wide range of cancer cells with CD49c expression on the surface, which will be helpful for the studying the heterogeneity of rare cells.

Rapid Detection of Single Viable Escherichia coli O157 Cells in Fresh Lettuce and Strawberry by Immunomagnetic Flow Cytometry in Combination with Pre-Enrichment.

Enterohemorrhagic Escherichia coli are an important pathogen causing food poisoning. The rapid detection of viable E. coli O157 in vegetables and fruits at single-cell level is critical because of the low infective dose of this pathogen. In this study, an immunomagnetic flow cytometry (IMFC)-based method was developed to detect E. coli O157 in lettuce and strawberries inoculated with 1 CFU/25 g. This method developed immunomagnetic (IM)-beads to capture E. coli O157 cells. The pre-enrichment of E. coli O157 and IM-bead separation rapidly increased the concentration of cells to a detectable range for flow cytometry. Compared with the plate-based method, the diagnostic sensitivity and specificity of the IMFC-based method were 100% in 166 samples, including 100 artificially contaminated samples, 60 retail samples, and six O157-positive samples for proficiency testing. The developed IMFC-based method was found to be effective in detecting E. coli O157 at single-cell level in 25 g of lettuce or strawberry with relatively shorter associated time to results of 5.7 h. Therefore, the IMFC-based method could improve detection efficiency and also make early warnings in a short time.

Depression Speech Recognition With a Three-Dimensional Convolutional Network.

Depression has become one of the main afflictions that threaten people's mental health. However, the current traditional diagnosis methods have certain limitations, so it is necessary to find a method of objective evaluation of depression based on intelligent technology to assist in the early diagnosis and treatment of patients. Because the abnormal speech features of patients with depression are related to their mental state to some extent, it is valuable to use speech acoustic features as objective indicators for the diagnosis of depression. In order to solve the problem of the complexity of speech in depression and the limited performance of traditional feature extraction methods for speech signals, this article suggests a Three-Dimensional Convolutional filter bank with Highway Networks and Bidirectional GRU (Gated Recurrent Unit) with an Attention mechanism (in short 3D-CBHGA), which includes two key strategies. (1) The three-dimensional feature extraction of the speech signal can timely realize the expression ability of those depression signals. (2) Based on the attention mechanism in the GRU network, the frame-level vector is weighted to get the hidden emotion vector by self-learning. Experiments show that the proposed 3D-CBHGA can well establish mapping from speech signals to depression-related features and improve the accuracy of depression detection in speech signals.

Faster Detection of Staphylococcus aureus in Milk and Milk Powder by Flow Cytometry.

A flow cytometry (FCM)-based method was developed for the faster detection of Staphylococcus aureus in milk and milk powder. Viable S. aureus cells were recognized by highly selective, fluorescently labeled antibodies and Propidium Iodide, and then analyzed by FCM. Using a 5-h pre-enrichment period, the method could detect low numbers of S. aureus cells in 6 h, with a limit of detection of 7.50 cells/mL in milk and 8.30 cells/g in milk powder. The established method was compared with the plate-based method using 75 ultra-high-temperature-treated milk samples, 25 pasteurized milk samples, 66 raw milk samples, and 123 milk powder samples. The two methods yielded similar results for the detection of the pathogen in all sample types. The FCM-based method allows effective and faster monitoring of S. aureus contamination and can be applied to the rapid detection of microorganisms in milk and dairy products.

Detection of Circulating Tumor-Related Materials by Aptamer Capturing and Endogenous Enzyme-Signal Amplification.

Circulating tumor-related materials (CTRMs) shed from original or metastatic tumors, carry a lot of tumor information and are considered as important markers for cancer diagnosis and metastasis prognosis. Herein, we report a colorimetric detection strategy for CTRMs based on aptamer-based magnetic isolation and endogenous alkaline phosphatase (AP)-signal amplification. This strategy exhibited high sensitivity and selectivity toward the CTRMs that express AP heterodimers (the target of aptamer, a potential tumor marker). For clinical samples, this CTRM assay significantly discriminated colorectal cancer patients (n = 50) from healthy individuals (n = 39, p < 0.0001). The receiver operating characteristic (ROC) analysis indicated the sensitivity and specificity reached 92% and 82%, respectively, at the optimal cutoff point, the area under the curve of ROC reached 0.93, suggesting great potential for colorectal cancer diagnosis and therapeutic monitoring. Compared with CTC assays, this strategy is simple and has the potential for point-of-care testing.