Microsnoop: A generalist tool for microscopy image representation.

Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological research. Here, we present Microsnoop, a novel deep learning-based representation tool trained on large-scale microscopy images using masked self-supervised learning. Microsnoop can process various complex and heterogeneous images, and we classified images into three categories: single-cell, full-field, and batch-experiment images. Our benchmark study on 10 high-quality evaluation datasets, containing over 2,230,000 images, demonstrated Microsnoop's robust and state-of-the-art microscopy image representation ability, surpassing existing generalist and even several custom algorithms. Microsnoop can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal analysis. Furthermore, Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing platforms. We will regularly retrain and reevaluate the model using community-contributed data to consistently improve Microsnoop.

Multimodal Identification by Transcriptomics and Multiscale Bioassays of Active Components in Xuanfeibaidu Formula to Suppress Macrophage-Mediated Immune Response.

Xuanfeibaidu Formula (XFBD) is a Chinese medicine used in the clinical treatment of coronavirus disease 2019 (COVID-19) patients. Although XFBD has exhibited significant therapeutic efficacy in clinical practice, its underlying pharmacological mechanism remains unclear. Here, we combine a comprehensive research approach that includes network pharmacology, transcriptomics, and bioassays in multiple model systems to investigate the pharmacological mechanism of XFBD and its bioactive substances. High-resolution mass spectrometry was combined with molecular networking to profile the major active substances in XFBD. A total of 104 compounds were identified or tentatively characterized, including flavonoids, terpenes, carboxylic acids, and other types of constituents. Based on the chemical composition of XFBD, a network pharmacology-based analysis identified inflammation-related pathways as primary targets. Thus, we examined the anti-inflammation activity of XFBD in a lipopolysaccharide-induced acute inflammation mice model. XFBD significantly alleviated pulmonary inflammation and decreased the level of serum proinflammatory cytokines. Transcriptomic profiling suggested that genes related to macrophage function were differently expressed after XFBD treatment. Consequently, the effects of XFBD on macrophage activation and mobilization were investigated in a macrophage cell line and a zebrafish wounding model. XFBD exerts strong inhibitory effects on both macrophage activation and migration. Moreover, through multimodal screening, we further identified the major components and compounds from the different herbs of XFBD that mediate its anti-inflammation function. Active components from XFBD, including Polygoni cuspidati Rhizoma, Phragmitis Rhizoma, and Citri grandis Exocarpium rubrum, were then found to strongly downregulate macrophage activation, and polydatin, isoliquiritin, and acteoside were identified as active compounds. Components of Artemisiae annuae Herba and Ephedrae Herba were found to substantially inhibit endogenous macrophage migration, while the presence of ephedrine, atractylenolide I, and kaempferol was attributed to these effects. In summary, our study explores the pharmacological mechanism and effective components of XFBD in inflammation regulation via multimodal approaches, and thereby provides a biological illustration of the clinical efficacy of XFBD.

Discovery of Natural Small Molecules Promoting Collagen Secretion by High-Throughput Screening in Caenorhabditis elegans.

Advancing approaches for drug screening are in great demand to explore natural small molecules that may play important roles in collagen biogenesis, secretion, and assembly, which may find novel lead compounds for treating collagen-related diseases or preventing skin aging. In this study, we generated a single copy insertion transgenic Pcol-19- COL-12::GFP Caenorhabditis elegans (C. elegans) strain to label epidermis collagen XII (COL-12), a cuticle structure component, and established an efficient high-content screening techniques to discover bioactive natural products in this worm strain through quantification of fluorescence imaging. We performed a preliminary screening of 614 compounds from the laboratory's library of natural small molecule compounds on the COL-12 labeling worm model, which was tested once at a single concentration of 100 µM to screen for compounds that promoted COL-12 protein amount. Besides col-12, the transcriptional levels of worm-associated collagen coding genes col-19 and sqt-3 were also examined, and none of the compounds affected their transcriptional levels. Meanwhile, the protein levels of COL-12 were significantly upregulated after treating with Danshensu, Lawsone, and Sanguinarine. The effects of these drugs on COL-12 overexpressing worms occur mainly after collagen transcription. Through various validation methods, Danshensu, Lawsone, and Sanguinarine were more effective in promoting the synthesis or secretion of COL-12.

Scellseg: A style-aware deep learning tool for adaptive cell instance segmentation by contrastive fine-tuning.

Deep learning-based cell segmentation is increasingly utilized in cell biology due to the massive accumulation of large-scale datasets and excellent progress in model architecture and instance representation. However, the development of specialist algorithms has long been hampered by a paucity of annotated training data, whereas the performance of generalist algorithms is limited without experiment-specific calibration. Here, we present Scellseg, an adaptive pipeline that utilizes a style-aware pre-trained model coupled to a contrastive fine-tuning strategy that also learns from unlabeled data. Scellseg achieves state-of-the-art transferability in average precision and Aggregated Jaccard Index on disparate datasets containing microscopy images at three biological levels, from organelle, cell to organism. Interestingly, when fine-tuning Scellseg, we show that performance plateaued after approximately eight images, implying that a specialist model can be obtained with few manual efforts. For convenient dissemination, we develop a graphical user interface that allows biologists to easily specialize their self-adaptive segmentation model.

DiHuangYin decoction protects dopaminergic neurons in a Parkinson's disease model by alleviating peripheral inflammation.

BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disease, which brings increasing threaten for human health and is still lacking of satisfied treatment. Recently, numerous studies have also demonstrated the effect of particular subsets of CD4+ T cells on PD pathology. Th17 cells played an important role in the pathogenesis of PD. Traditional Chinese medicine has been widely used to treat PD clinically, and has a tremendous potential in clinical drug development. PURPOSE: The aim of this study was to verify the therapeutic effects of DHY on PD mice model, and investigate the underlying molecular mechanisms. METHODS: Herein, we verified the effects of a traditional Chinese medicine formula, named DiHuangYin (DHY), on the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) induced mouse model of PD through behavioral and histopathological tests. High-resolution mass spectrometry combined with molecular networking was applied for substance profiling of DHY. Based on the chemical compositions of DHY, network pharmacology was performed. Immunofluorescence and ELISA were used to evaluate the expressions of cytokines in peripheral immune system. qPCR and immunofluorescence were used to detect the inflammation infiltration of central nervous system. RESULTS: DHY improves the motor function and prevents the loss of dopaminergic neurons in the MPTP induced mouse model of PD. 118 components of DHY were identified or tentatively characterized based on the MS/MS data and molecular networking. Network pharmacology suggested IL-17 signaling pathway and neuroactive ligand-receptor interaction as the important targets. Compared to the MPTP-intoxicated mice, the DHY group showed a decreased number of Th17 cells from splenocytes and a decreased level of IL-17A in the serum. On the other hand, less inflammatory infiltration was found in the midbrain of DHY treatment mice which might be associated with the attenuated peripheral inflammation. CONCLUSIONS: Though the underlying pharmacological mechanism of DHY is still lacking, we provided evidence that DHY decoction could protect dopaminergic neurons by mitigating peripheral inflammation.

Si@Ag@PEI substrate-based SERS sensor for rapid detection of illegally adulterated sulfur dioxide in traditional Chinese medicine.

The illegal adulteration of sulfur dioxide in natural healthcare products may lead to serious health problems, which raise an urgent demand of straightforward approach for detecting sulfur dioxide. In this paper, surface-enhanced Raman scattering (SERS) sensor with sample preparation apparatus for headspace adsorption of SO2 has been developed, which was successfully applied to detect illegal adulteration of sulfur dioxide in traditional Chinese medicine (TCM). Functional membrane substrate of Si@Ag@PEI composite was synthesized to enhance the adsorption and Raman signal of SO2. A 3D-printed headspace extraction device was designed to adsorbed SO2 by Si@Ag@PEI membrane after micro-extraction of TCM samples in 15 min. The content of sulfur dioxide was subsequently quantitatively measured by SERS sensor. The linear range of sensor is between 2.5 and 250 mg/kg with limit of detection of 0.25 mg/kg, which is lower than the strictest standard of Chinese Pharmacopoeia (10 mg/kg). The proposed approach was used to detect the SO2 residue in TCMs including ginseng, Salvia miltiorrhiza, and bitter almonds. The fabricated sensor exhibited satisfied sensitivity and stability, which provide a simple approach for on-site detection of illegal adulteration of sulfur dioxide.

Deep-Learning-Assisted Assessment of DNA Damage Based on Foci Images and Its Application in High-Content Screening of Lead Compounds.

DNA damage is one of major culprits in many complex diseases; thus, there is great interest in the discovery of novel lead compounds regulating DNA damage. However, there remain plenty of challenges to evaluate DNA damage through counting the amount of intranuclear foci. Herein, a deep-learning-based open-source pipeline, FociNet, was developed to automatically segment full-field fluorescent images and dissect DNA damage of each cell. We annotated 6000 single-nucleus images to train the classification ability of the proposed computational pipeline. Results showed that FociNet achieved satisfying performance in classifying a single cell into a normal, damaged, or nonsignaling (no fusion-protein expression) state and exhibited excellent compatibility in the assessment of DNA damage based on fluorescent foci images from various imaging platforms. Furthermore, FociNet was employed to analyze a data set of over 5000 foci images from a high-content screening of 315 natural compounds from traditional Chinese medicine. It was successfully applied to identify several novel active compounds including evodiamine, isoliquiritigenin, and herbacetin, which were found to reduce 53BP1 foci for the first time. Among them, isoliquiritigenin from Glycyrrhiza uralensis Fisch. exerts a significant effect on attenuating double strand breaks as indicated by the comet assay. In conclusion, this work provides an artificial intelligence tool to evaluate DNA damage on the basis of microscopy images as well as a potential strategy for high-content screening of active compounds.