A novel ESIPT fluorescent probe for early detection and assessment of ferroptosis-mediated acute kidney injury via peroxynitrite fluctuation.

BACKGROUND: Acute kidney injury (AKI) poses a severe risk to public health, mostly manifested by damage and death of renal tubular epithelial cells. However, routine blood examination, a conventional approach for clinical detection of AKI, is not available for identifying early-stage AKI. Plenty of reported methods were lack of early biomarkers and real time evaluation tools, which resulted in a vital challenge for early diagnosis of AKI. Therefore, developing novel probes for early detection and assessment of AKI is exceedingly crucial. RESULTS: Based on ESIPT mechanism, a new fluorescent probe (MEO-NO) with 2-(2'-hydroxyphenyl) benzothiazole (HBT) derivatives as fluorophore has been synthesized for dynamic imaging peroxynitrite (ONOO-) levels in ferroptosis-mediated AKI. Upon the addition of ONOO-, MEO-NO exhibited obvious fluorescence changes, a significant Stokes shift (130 nm) and rapid response (approximately 45 s), and featured exceptional sensitivity (LOD = 7.28 nM) as well as high selectivity from the competitive species at physiological pH. In addition, MEO-NO was conducive to the biological depth imaging ONOO- in cells, zebrafish, and mice. Importantly, MEO-NO could monitor ONOO- levels during sorafenib-induced ferroptosis and CP-induced AKI. With the assistance of MEO-NO, we successfully visualized and tracked ONOO- variations for early detection and assessment of ferroptosis-mediated AKI in cells, zebrafish and mice models. SIGNIFICANCE AND NOVELTY: Benefiting from the superior performance of MEO-NO, experimental results further demonstrated that the levels of ONOO- was overexpressed during ferroptosis-mediated AKI in cells, zebrafish, and mice models. The developed novel probe MEO-NO provided a strong visualization tool for imagining ONOO-, which might be a potential method for the prevention, diagnosis, and treatment of ferroptosis-mediated AKI.

Using Multi-Scale Convolutional Neural Network Based on Multi-Instance Learning to Predict the Efficacy of Neoadjuvant Chemoradiotherapy for Rectal Cancer.

BACKGROUND: At present, radical total mesorectal excision after neoadjuvant chemoradiotherapy is crucial for locally advanced rectal cancer. Therefore, the use of histopathological images analysis technology to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer is of great significance for the subsequent treatment of patients. METHODS: In this study, we propose a new pathological images analysis method based on multi-instance learning to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Specifically, we proposed a gated attention normalization mechanism based on the multilayer perceptron, which accelerates the convergence of stochastic gradient descent optimization and can speed up the training process. We also proposed a bilinear attention multi-scale feature fusion mechanism, which organically fuses the global features of the larger receptive fields and the detailed features of the smaller receptive fields and alleviates the problem of pathological images context information loss caused by block sampling. At the same time, we also designed a weighted loss function to alleviate the problem of imbalance between cancerous instances and normal instances. RESULTS: We evaluated our method on a locally advanced rectal cancer dataset containing 150 whole slide images. In addition, to verify our method's generalization performance, we also tested on two publicly available datasets, Camelyon16 and MSKCC. The results show that the AUC values of our method on the Camelyon16 and MSKCC datasets reach 0.9337 and 0.9091, respectively. CONCLUSION: Our method has outstanding performance and advantages in predicting the efficacy of neoadjuvant chemoradiotherapy for rectal cancer. Clinical and Translational Impact Statement -This study aims to predict the efficacy of neoadjuvant chemoradiotherapy for rectal cancer to assist clinicians quickly diagnose and formulate personalized treatment plans for patients.

Binding screen for cystic fibrosis transmembrane conductance regulator correctors finds new chemical matter and yields insights into cystic fibrosis therapeutic strategy.

The most common mutation in cystic fibrosis (CF) patients is deletion of F508 (ΔF508) in the first nucleotide binding domain (NBD1) of the CF transmembrane conductance regulator (CFTR). ΔF508 causes a decrease in the trafficking of CFTR to the cell surface and reduces the thermal stability of isolated NBD1; it is well established that both of these effects can be rescued by additional revertant mutations in NBD1. The current paradigm in CF small molecule drug discovery is that, like revertant mutations, a path may exist to ΔF508 CFTR correction through a small molecule chaperone binding to NBD1. We, therefore, set out to find small molecule binders of NBD1 and test whether it is possible to develop these molecules into potent binders that increase CFTR trafficking in CF-patient-derived human bronchial epithelial cells. Several fragments were identified that bind NBD1 at either the CFFT-001 site or the BIA site. However, repeated attempts to improve the affinity of these fragments resulted in only modest gains. Although these results cannot prove that there is no possibility of finding a high-affinity small molecule binder of NBD1, they are discouraging and lead us to hypothesize that the nature of these two binding sites, and isolated NBD1 itself, may not contain the features needed to build high-affinity interactions. Future work in this area may, therefore, require constructs including other domains of CFTR in addition to NBD1, if high-affinity small molecule binding is to be achieved.

Separation of fluorescently labeled phosphoinositides and sphingolipids by capillary electrophoresis.

Phosphoinositides (PIs) and sphingolipids regulate many aspects of cell behavior and are often involved in disease processes such as oncogenesis. Capillary electrophoresis with laser induced fluorescence detection (CE-LIF) is emerging as an important tool for enzymatic assays of the metabolism of these lipids, particularly in cell-based formats. Previous separations of phosphoinositide lipids by CE required a complex buffer with polymer additives which had the disadvantages of high cost and/or short shelf life. Further a simultaneous separation of these classes of lipids has not been demonstrated in a robust buffer system. In the current work, a simple separation buffer based on NaH(2)PO(4) and 1-propanol was optimized to separate two sphingolipids and multiple phosphoinositides by CE. The NaH(2)PO(4) concentration, pH, 1-propanol fraction, and a surfactant additive to the buffer were individually optimized to achieve simultaneous separation of the sphingolipids and phosphoinositides. Fluorescein-labeled sphingosine (SFL) and sphingosine 1-phosphate (S1PFL), fluorescein-labeled phosphatidyl-inositol 4,5-bisphosphate (PIP2) and phosphatidyl-inositol 3,4,5-trisphosphate (PIP3), and bodipy-fluorescein (BFL)-labeled PIP2 and PIP3 were separated pairwise and in combination to demonstrate the generalizability of the method. Theoretical plate numbers achieved were as high as 2×10(5) in separating fluorophore-labeled PIP2 and PIP3. Detection limits for the 6 analytes were in the range of 10(-18)-10(-20)mol. The method also showed high reproducibility, as the relative standard deviation of the normalized migration time for each analyte in the simultaneous separation of all 6 compounds was less than 1%. The separation of a mixture composed of diacylglycerol (DAG) and multiple phosphoinositides was also demonstrated. As a final test, fluorescent lipid metabolites formed within cells loaded with BFLPIP2 were separated from a cell lysate as well as a single cell. This simple and robust separation method for SFL and S1PFL and various metabolites of phosphoinositide-related signal transduction is expected to enable improved enzymatic assays for biological and clinical applications.

Photon-regulated DNA-enzymatic nanostructures by molecular assembly.

Future smart nanostructures will have to rely on molecular assembly for unique or advanced desired functions. For example, the evolved ribosome in nature is one example of functional self-assembly of nucleic acids and proteins employed in nature to perform specific tasks. Artificial self-assembled nanodevices have also been developed to mimic key biofunctions, and various nucleic acid- and protein-based functional nanoassemblies have been reported. However, functionally regulating these nanostructures is still a major challenge. Here we report a general approach to fine-tune the catalytic function of DNA-enzymatic nanosized assemblies by taking advantage of the trans-cis isomerization of azobenzene molecules. To the best of our knowledge, this is the first study to precisely modulate the structures and functions of an enzymatic assembly based on light-induced DNA scaffold switching. Via photocontrolled DNA conformational switching, the proximity of multiple enzyme catalytic centers can be adjusted, as well as the catalytic efficiency of cofactor-mediated DNAzymes. We expect that this approach will lead to the advancement of DNA-enzymatic functional nanostructures in future biomedical and analytical applications.

Kinetic analysis of PI3K reactions with fluorescent PIP2 derivatives.

Phosphatidylinositol 3-kinase (PI3K) signaling plays important roles in cell differentiation, proliferation, and migration. Increased mutations and expression levels of PI3K are hallmarks for the development of certain cancers. Pharmacological targeting of PI3K activity has also been actively pursued as a novel cancer therapeutic. Consequently, measurement of PI3K activity in different cell types or patient samples holds the promise as being a novel diagnostic tool. However, the direct measurement of cellular PI3K activity has been a challenging task. We report here the characterization of two fluorescent PIP(2) derivatives as reporters for PI3K enzymatic activity. The reporters are efficiently separated from their corresponding PI3K enzymatic products through either thin layer chromatography (TLC) or capillary electrophoresis (CE), and can be detected with high sensitivity by fluorescence. The biophysical and kinetic properties of the two probes are measured, and their suitability to characterize PI3K inhibitors is explored. Both probes show similar capacity as PI3K substrates for inhibitor characterization, yet also possess distinct properties that may suggest their different applications. These characterizations have laid the groundwork to systematically measure cellular PI3K activity, and have the potential to generate molecular fingerprints for diagnostic and therapeutic applications.

The effects of flow type on aptamer capture in differential mobility cytometry cell separations.

In this work, differential mobility cytometry (DMC) was used to monitor cell separation based on aptamer recognition for target cells. In this device, open-tubular capillaries coated with Sgc8 aptamers were used as affinity chromatography columns for separation. After cells were injected into the columns, oscillating flow was generated to allow for long-term cell adhesion studies. This process was monitored by optical microscopy, and differential imaging was used to analyze the cells as they adhered to the affinity surface. We investigated the capture time, capture efficiency, purity of target and control cells, as well as the reusability of the affinity columns. Capture time for both CCRF-CEM cells and Jurkat T cells was 0.4+/-0.2 s, which demonstrated the high separation affinity between aptamers and target cells. The capture efficiency for CCRF-CEM cells was 95% and purity was 99% in a cell mixture. With the advantage of both high cell capture efficiency and purity, DMC combined with aptamer-based separation emerges as a powerful tool for rare cell enrichment. In addition, aptamer-based DMC channels were found to be more robust than antibody based channels with respect to reuse of the separation device.

In silico method for systematic analysis of feature importance in microRNA-mRNA interactions.

BACKGROUND: MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions. RESULTS: Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC) values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNA-mRNA interactions and in designing experiments. CONCLUSIONS: Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.

Rapid data analysis method for differential mobility cytometry.

Differential mobility cytometry (DMC) has recently been established as a powerful method to capture cells and study adhesion processes. DMC uses an oscillation system and cell affinity chromatography to monitor cells as they adhere to a surface. In the past, differential images had to be created individually which limited the throughput of the method. A new method to create differential images is presented. The method involves the subtraction of short movies from each other to create a stack of differential images that can be easily analyzed. In the future, this method will make DMC more accessible and improve throughput.

Simultaneous cell capture and induction of apoptosis using an anti-CD95 affinity microdevice.

A microfluidic device is designed and demonstrated for the simultaneous capture and induction of apoptosis in Jurkat cells. In this unique case, the cell capture event initiates a biological process. The device features a single channel made from poly(dimethylsiloxane) sealed to a glass substrate. The channel is coated with a series of reagents used in affinity chromatography separations of cells. In this case, the antibody used to capture the cells is functional anti-CD95 which captures the cells by binding to the Fas receptor on the cell membrane and, at the same time, inducing apoptosis via the caspase 8 pathway. Cells retained on the surface of the channel are known to be induced to undergo apoptosis. Medium is flowed slowly through the channel to maintain cell viability while the cells undergo apoptosis. After 3 h, staining with Annexin V-PE and 7-AAD revealed that 43.5% of the cells bound to the anti-CD95 coated channel are apoptotic, whereas 7.9% of cultured Jurkat cells induced with anti-CD95 for 3 h and stained in the same way were determined to be apoptotic by flow cytometry. The device provides a method of determining when apoptosis is induced, maintaining cell viability for long-term analysis and observing cells in real time as they are exposed to reagents that affect apoptosis. In the future, the device will be an invaluable tool for the study of the temporal dynamics of apoptosis.

Differential mobility cytometry.

A new cell analysis method, differential mobility cytometry (DMC), was developed to monitor cells spatially and temporally or to separate cells based on affinity interactions. DMC combines an oscillation system with open-tubular capillary cell affinity chromatography (OT-CAC), although any separation volume (capillaries, channels, etc.) can be used. This unique separation approach uses oscillating flow and differential imaging to analyze cells as they retard and adhere to an affinity surface. Three main factors of the oscillation system were studied: the pump speed, oscillation frequency, and cell velocity at different oscillation speeds. The optimized oscillation frequency and intensity were determined. Cell-surface interactions were used to estimate the number of bonds formed during cell capture. An average of 200 bonds (standard deviation of 150 bonds) were formed during cell capture. The variability was due to differences in cell-capture times (0.8 +/- 0.6 s). Cells expressing the target protein on the surface oscillated slower and were captured by the corresponding ligand on the capillary surface. Cells were detected by differential imaging of a charge-coupled device camera. DMC measurements were optimized with respect to the camera frame difference. Cells were observed to slow as they reached the surface and could be observed to sway in the oscillating flow as they were tethered to the surface by a capture antibody. With the advantage of high cell-capture efficiency and temporally monitoring cell adhesion by the differential mobility of cells, DMC has proven to be a useful tool in cell analysis for basic biological studies and biomedical research.

Open-tubular capillary cell affinity chromatography: single and tandem blood cell separation.

In this paper, an open-tubular capillary cell affinity chromatography (OT-CAC) method to enrich and separate target cells is described. Open tubular capillaries coated with anti-CD4, anti-CD14, or anti-CD19 antibodies were used as affinity chromatography columns to separate target blood cells. Cells were eluted using either shear force or bubbles. Bubbles were used to elute the captured cells without diluting the captured cells appreciably, while maintaining viability (the viability of the recovered cells was 85.83 +/- 7.34%; the viability of the cells was 90.41 +/- 3.49% before separation). Several aspects of the OT-CAC method were studied, such as the affinity of one antibody between two different cell lines, the effect of shear force, and the recovery of captured cells. Single- and multicell type separations were demonstrated by isolating CD4+ cells with antiCD4 coated capillary and isolating CD4+ and CD19+ cells with two capillaries in tandem from blood samples. In the one cell type isolation test, an average of 87.7% of the recovered cells from antiCD4 capillary were lymphocytes and an average of 97.7% of those lymphocytes were CD4+ cells. In the original blood sample, only 14.2% of the leukocytes were CD4+ cells. Two capillary columns were also run in tandem, separating two blood cell types from a single sample with high purity. The use of different elution shear forces was demonstrated to selectively elute one cell type. This method is an inexpensive, rapid, and effective method to separate target cells from blood samples.

Isolation and counting of multiple cell types using an affinity separation device.

A simple device for the separation of cells by phenotype is described. Cells are separated/isolated using capture antibodies on a glass chip. Unlike other "sandwich" type assays, the readout is performed without labels using transmission microscopy, simplifying cell enumeration. T and B lymphocytes from cell culture or whole blood were separated using antibodies for the CD4, CD19, and CD71 antigens. The separation slides were found to reproducibly bind cells by antigen expression, allowing for accurate enumeration of mixed cell samples. Inter- and intra-device variability was evaluated, and the issue of nonspecific binding is addressed. We envision that this type of cell separation technique could be used in remote settings, as sample preparation is minimal and the analysis time is rapid (20 min from sample acquisition to final readout).

Cellular separations: a review of new challenges in analytical chemistry.

The ability to generate a sample of cells of a given phenotype is a prerequisite for many cellular assays. In response to this growing need, numerous methods for cell separation have been developed in recent years. This Review covers recent progress in the field of cell separations and cell chromatography. Cell separation principles-such as size and affinity capture-are discussed, as well as conventional methods such as fluorescence-activated cell sorting and magnetic sorting. Planar flow cell arrays, dielectrophoresis, field-flow methods, and column separation devices are reviewed, as well as applications of these methods to medicine and biotechnology. Cell attachment and adhesion strategies and a comparison of techniques are also presented.

Local sequence information-based support vector machine to classify voltage-gated potassium channels.

In our previous work, we developed a computational tool, PreK-ClassK-ClassKv, to predict and classify potassium (K+) channels. For K+ channel prediction (PreK) and classification at family level (ClassK), this method performs well. However, it does not perform so well in classifying voltage-gated potassium (Kv) channels (ClassKv). In this paper, a new method based on the local sequence information of Kv channels is introduced to classify Kv channels. Six transmembrane domains of a Kv channel protein are used to define a protein, and the dipeptide composition technique is used to transform an amino acid sequence to a numerical sequence. A Kv channel protein is represented by a vector with 2000 elements, and a support vector machine algorithm is applied to classify Kv channels. This method shows good performance with averages of total accuracy (Acc), sensitivity (SE), specificity (SP), reliability (R) and Matthews correlation coefficient (MCC) of 98.0%, 89.9%, 100%, 0.95 and 0.94 respectively. The results indicate that the local sequence information-based method is better than the global sequence information-based method to classify Kv channels.

Prediction of mitochondrial proteins using discrete wavelet transform.

A new method was proposed for prediction of mitochondrial proteins by the discrete wavelet transform, based on the sequence-scale similarity measurement. This sequence-scale similarity, revealing more information than other conventional methods, does not rely on subcellular location information and can directly predict protein sequences with different length. In our experiments, 499 mitochondrial protein sequences, constituting a mitochondria database, were used as training dataset, and 681 non-mitochondrial protein sequences were tested. The system can predict these sequences with sensitivity, specificity, accuracy and MCC of 50.30%, 95.74%, 76.53% and 0.54, respectively. Source code of the new program is available on request from the authors.

Fast fourier transform-based support vector machine for prediction of G-protein coupled receptor subfamilies.

Although the sequence information on G-protein coupled receptors (GPCRs) continues to grow, many GPCRs remain orphaned (i.e. ligand specificity unknown) or poorly characterized with little structural information available, so an automated and reliable method is badly needed to facilitate the identification of novel receptors. In this study, a method of fast Fourier transform-based support vector machine has been developed for predicting GPCR subfamilies according to protein's hydrophobicity. In classifying Class B, C, D and F subfamilies, the method achieved an overall Matthe's correlation coefficient and accuracy of 0.95 and 93.3%, respectively, when evaluated using the jackknife test. The method achieved an accuracy of 100% on the Class B independent dataset. The results show that this method can classify GPCR subfamilies as well as their functional classification with high accuracy. A web server implementing the prediction is available at http://chem.scu.edu.cn/blast/Pred-GPCR.

Analyzing functional similarity of protein sequences with discrete wavelet transform.

This paper applies discrete wavelet transform (DWT) with various protein substitution models to find functional similarity of proteins with low identity. A new metric, 'S' function, based on the DWT is proposed to measure the pair-wise similarity. We also develop a segmentation technique, combined with DWT, to handle long protein sequences. The results are compared with those using the pair-wise alignment and PSI-BLAST.