Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Design, synthesis and biological evaluation of acyl hydrazones-based derivatives as RXRα-targeted anti-mitotic agents.

RXRα, a unique and important nuclear receptor, plays a vital role in various biological and pathological pathways, including growth, differentiation, and apoptosis. We recently reported a transcription-independent function of RXRα in cancer cells in which RXRα is phosphorylated by Cdk1 at the onset of mitosis, resulting in its translocation to the centrosome, where the phosphorylated RXRα (p-RXRα) interacts with polo-like kinase 1 (PLK1) to promote centrosome maturation and mitotic progression. Significantly, we also identified that a small molecule XS-060 binds to RXRα and selectively inhibits the p-RXRα/PLK1 interaction to induce mitotic arrest and catastrophe in cancer cells. Here, we report our design, synthesis, and biological evaluation of a series of XS-060 analogs as RXRα-targeted anti-mitotic agents. Our results identified B10 as an improved anti-mitotic agent. B10 bound to RXRα (Kd = 3.04 ± 0.58 µM) and inhibited the growth of cervical cancer cells (HeLa, IC50 = 1.46 ± 0.10 µM) and hepatoma cells (HepG2, IC50 = 3.89 ± 0.45 µM and SK-hep-1, IC50 = 5.74 ± 0.50 µM) with low cytotoxicity to nonmalignant cells(LO2, IC50 > 50 µM). Furthermore, our mechanistic studies confirmed that B10 acted as an anticancer agent by inhibiting the p-RXRα/PLK1 pathway. These results provide a basis for further investigation and optimization of RXRα-targeted anti-mitotic molecules for cancer therapy.

Protocol to identify centrosome-associated transcription factors during mitosis in mammalian cell lines.

During eukaryotic cell mitosis, the nuclear envelope disintegrates and transcription factors are dissociated from condensed chromosomes. Here, we describe a protocol to study centrosomal translocation of nuclear receptor RXRα. We detail procedures for HeLa cell synchronization followed by immunofluorescence, in situ proximity ligation assay, and centrosome isolation. This protocol can be used to identify other transcription factors associated with the centrosome or other subcellular structures during mitotic progression. For complete details on the use and execution of this protocol, please refer to Xie et al. (2020).

LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation.

Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, which are often unavailable in clinical practices. Besides, top-performing methods usually have a vast number of parameters, which result in high computation complexity for model training and testing. This study addresses cardiac image segmentation in scenarios where few labeled data are available with a lightweight cross-consistency network named LCC-Net. Specifically, to reduce the risk of overfitting on small labeled datasets, we substitute computationally intensive standard convolutions with a lightweight module. To leverage plenty of unlabeled data, we introduce extreme consistency learning, which enforces equivariant constraints on the predictions of different perturbed versions of the input image. Cutting and mixing different training images, as an extreme perturbation on both the labeled and unlabeled data, are utilized to enhance the robust representation learning. Extensive comparisons demonstrate that the proposed model shows promising performance with high annotation- and computation-efficiency. With only two annotated subjects for model training, the LCC-Net obtains a performance gain of 14.4% in the mean Dice over the baseline U-Net trained from scratch.

HIVE-Net: Centerline-aware hierarchical view-ensemble convolutional network for mitochondria segmentation in EM images.

BACKGROUND AND OBJECTIVE: With the advancement of electron microscopy (EM) imaging technology, neuroscientists can investigate the function of various intracellular organelles, e.g, mitochondria, at nano-scale. Semantic segmentation of electron microscopy (EM) is an essential step to efficiently obtain reliable morphological statistics. Despite the great success achieved using deep convolutional neural networks (CNNs), they still produce coarse segmentations with lots of discontinuities and false positives for mitochondria segmentation. METHODS: In this study, we introduce a centerline-aware multitask network by utilizing centerline as an intrinsic shape cue of mitochondria to regularize the segmentation. Since the application of 3D CNNs on large medical volumes is usually hindered by their substantial computational cost and storage overhead, we introduce a novel hierarchical view-ensemble convolution (HVEC), a simple alternative of 3D convolution to learn 3D spatial contexts using more efficient 2D convolutions. The HVEC enables both decomposing and sharing multi-view information, leading to increased learning capacity. RESULTS: Extensive validation results on two challenging benchmarks show that, the proposed method performs favorably against the state-of-the-art methods in accuracy and visual quality but with a greatly reduced model size. Moreover, the proposed model also shows significantly improved generalization ability, especially when training with quite limited amount of training data. Detailed sensitivity analysis and ablation study have also been conducted, which show the robustness of the proposed model and effectiveness of the proposed modules. CONCLUSIONS: The experiments highlighted that the proposed architecture enables both simplicity and efficiency leading to increased capacity of learning spatial contexts. Moreover, incorporating shape cues such as centerline information is a promising approach to improve the performance of mitochondria segmentation.

Novel Delivery of Mitoxantrone with Hydrophobically Modified Pullulan Nanoparticles to Inhibit Bladder Cancer Cell and the Effect of Nano-drug Size on Inhibition Efficiency.

Reducing the dosage of chemotherapeutic drugs via enhancing the delivery efficiency using novel nanoparticles has great potential for cancer treatment. Here, we focused on improving mitoxantrone delivery by using cholesterol-substituted pullulan polymers (CHPs) and selected a suitable nano-drug size to inhibit the growth of bladder cancer cells. We synthesized three kinds of CHPs, named CHP-1, CHP-2, CHP-3. Their chemical structures were identified by NMR, and the degree of cholesterol substitution was 6.82%, 5.78%, and 2.74%, respectively. Their diameters were 86.4, 162.30, and 222.28 nm. We tested the release rate of mitoxantrone in phosphate-buffered saline for 48 h: the release rate was 38.73%, 42.35%, and 58.89% for the three CHPs. The hydrophobic substitution degree in the polymer was associated with the self-assembly process of the nanoparticles, which affected their size and therefore drug release rate. The release of the three drug-loaded nanoparticles was significantly accelerated in acid release media. The larger the nanoparticle, the greater the drug release velocity. At 24 h, the IC50 value was 0.25 M, for the best inhibition of mitoxantrone on bladder cancer cells.3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) experiments demonstrated that drug-loaded CHP-3 nanoparticles with the largest size were the most toxic to bladder cancer cells. Immunofluorescence and flow cytometry revealed that drug-loaded CHP-3 nanoparticles with the largest size had the strongest effect on promoting apoptosis of bladder cancer cells. Also, the three drug-loaded nanoparticles could all inhibit the migration of MB49 cells, with large-size CHP-3 nanoparticles having the most powerful inhibition.