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We demonstrate that a deep neural network can be trained to virtually refocus a two-dimensional fluorescence image onto user-defined three-dimensional (3D) surfaces within the sample. Using this method, termed Deep-Z, we imaged the neuronal activity of a Caenorhabditis elegans worm in 3D using a time sequence of fluorescence images acquired at a single focal plane, digitally increasing the depth-of-field by 20-fold without any axial scanning, additional hardware or a trade-off of imaging resolution and speed. Furthermore, we demonstrate that this approach can correct for sample drift, tilt and other aberrations, all digitally performed after the acquisition of a single fluorescence image. This framework also cross-connects different imaging modalities to each other, enabling 3D refocusing of a single wide-field fluorescence image to match confocal microscopy images acquired at different sample planes. Deep-Z has the potential to improve volumetric imaging speed while reducing challenges relating to sample drift, aberration and defocusing that are associated with standard 3D fluorescence microscopy.

Macrophage/microglia polarization acts as an important part in regulating inflammatory responses in spinal cord injury (SCI). However, the regulation of inflammation of Schwann cell-derived exosomes (SCDEs) for SCI repair is still unclear. Therefore, we intend to find out the effect of SCDEs on regulating the inflammation related to macrophage polarization during the recovery of SCI. Firstly, the thesis demonstrated that SCDEs could attenuate the LPS- inflammation in BMDMs by suppressing M1 polarization and stimulating M2 polarization. Similarly, SCDEs improved functional recovery of female Wistar rats of the SCI contusion model according to BBB (Basso, Beattie, and Bresnahan) score, electrophysiological assay, and the gait analysis system of CatWalk XT. Moreover, MFG-E8 was verified as the main component of SCDEs to improve the inflammatory response by proteomic sequencing and lentiviral transfection. Improvement of the inflammatory microenvironment also inhibited neuronal apoptosis. The knockout of MFG-E8 in SCs can reverse the anti-inflammatory effects of SCDEs treatment. The SOCS3/STAT3 signaling pathway was identified to participate in upregulating M2 polarization induced by MFG-E8. In conclusion, our findings will enrich the mechanism of SCDEs in repairing SCI and provide potential applications and new insights for the clinical translation of SCDEs treatment for SCI.

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.

The abnormal activation of epidermal growth factor receptor （EGFR） is strongly associated with a variety of human cancers but the underlying molecular mechanism is not fully understood. By using direct stochastic optical reconstruction microscopy （dSTORM）, we find that EGFR proteins form nanoclusters in the cell membrane of both normal lung epithelial cells and lung cancer cells, but the number and size of clusters significantly increase in lung cancer cells. The formation of EGFR clusters is mediated by the ionic interaction between the anionic lipid phosphatidylinositol-4,5-bisphosphate （PIP2） in the plasma membrane and the juxtamembrane （JM） region of EGFR. Disruption of EGFR clustering by PIP2 depletion or JM region mutation impairs EGFR activation and downstream signaling. Furthermore, JM region mutation in constitutively active EGFR mutant attenuates its capability of cell transformation. Collectively, our findings highlight the key roles of anionic phospholipids in EGFR signaling and function, and reveal a novel mechanism to explain the aberrant activation of EGFR in cancers.

Atomic force microscopy (AFM) has evolved from the originally morphological imaging technique to a powerful and multifunctional technique for manipulating and detecting the interactions between molecules at nanometer resolution. However, AFM cannot provide the precise information of synchronized molecular groups and has many shortcomings in the aspects of determining the mechanism of the interactions and the elaborate structure due to the limitations of the technology, itself, such as non-specificity and low imaging speed. To overcome the technical limitations, it is necessary to combine AFM with other complementary techniques, such as fluorescence microscopy. The combination of several complementary techniques in one instrument has increasingly become a vital approach to investigate the details of the interactions among molecules and molecular dynamics. In this review, we reported the principles of AFM and optical microscopy, such as confocal microscopy and single-molecule localization microscopy, and focused on the development and use of correlative AFM and optical microscopy.

Background Long non-coding RNAs (LncRNAs) have been extensively studied to play essential roles in tumor progression. However, more in-depth studies are waiting to be solved on how lncRNAs regulate the progression of hepatocellular carcinoma (HCC). Methods Different expression levels of lncRNAs in HCC cells were compared by analysis of Gene Expression Omnibus and The Cancer Genome Atlas databases. The effects of lncRNA FTO Intronic Transcript 1 (FTO-IT1) on HCC cells were assessed by gain- and loss-of-function experiments. Colony formation assay, Edu assay, glucose uptake and lactic acid production assay were performed to evaluate the regulation of proliferation and glycolysis of HCC cells by FTO-IT1. The binding between protein interleukin enhancer binding factor 2/3 (ILF2/ILF3) and FTO-IT1 was determined by RNA pull-down, mass spectroscopy and RNA immunoprecipitation experiments. RNA stability assay, quantitative reverse transcription PCR and Western blot were employed to determine the regulatory mechanisms of FTO-IT1 on fat mass and obesity-associated (FTO). Methylated RNA immunoprecipitation assay was used to assessed the regulation of key enzymes of glycolysis by FTO. The role of FTO-IT1/FTO in vivo was confirmed via xenograft tumor model. Results LncRNA FTO-IT1, an intronic region transcript of FTO gene, was highly expressed in HCC and associated with poor prognosis of patients with HCC. FTO-IT1 was related to proliferation and glycolysis of HCC cells, and contributed to the malignant progression of HCC by promoting glycolysis. Mechanistically, FTO-IT1 induced stabilization of FTO mRNA by recruiting ILF2/ILF3 protein complex to 3’UTR of FTO mRNA. As a demethylase for N 6 -methyladenosine (m 6 A), FTO decreased m 6 A modification on mRNAs of glycolysis associated genes including GLUT1, PKM2, and c-Myc which alleviated the YTH N6-methyladenosine RNA binding protein 2 (YTHDF2)-mediated mRNA degradation. Therefore, the upregulated expression of FTO-IT1 leaded to overexpression of GLUT1, PKM2, and c-Myc by which enhanced glycolysis of HCC. Meanwhile, it was found that c-Myc transcriptional regulated expression of FTO-IT1 by binding to its promoter area under hypo-glucose condition, forming a reciprocal loop between c-Myc and FTO-IT1. Conclusions This study identified an important role of the FTO-IT1/FTO axis mediated m 6 A modification of glycolytic genes contributed to glycolysis and tumorigenesis of HCC, and FTO-IT1 might be served as a new therapeutic target for HCC.

An invasive biopsy followed by histological staining is the benchmark for pathological diagnosis of skin tumors. The process is cumbersome and time-consuming, often leading to unnecessary biopsies and scars. Emerging noninvasive optical technologies such as reflectance confocal microscopy (RCM) can provide label-free, cellular-level resolution, in vivo images of skin without performing a biopsy. Although RCM is a useful diagnostic tool, it requires specialized training because the acquired images are grayscale, lack nuclear features, and are difficult to correlate with tissue pathology. Here, we present a deep learning-based framework that uses a convolutional neural network to rapidly transform in vivo RCM images of unstained skin into virtually-stained hematoxylin and eosin-like images with microscopic resolution, enabling visualization of the epidermis, dermal-epidermal junction, and superficial dermis layers. The network was trained under an adversarial learning scheme, which takes ex vivo RCM images of excised unstained/label-free tissue as inputs and uses the microscopic images of the same tissue labeled with acetic acid nuclear contrast staining as the ground truth. We show that this trained neural network can be used to rapidly perform virtual histology of in vivo, label-free RCM images of normal skin structure, basal cell carcinoma, and melanocytic nevi with pigmented melanocytes, demonstrating similar histological features to traditional histology from the same excised tissue. This application of deep learning-based virtual staining to noninvasive imaging technologies may permit more rapid diagnoses of malignant skin neoplasms and reduce invasive skin biopsies.

Early identification of pathogenic bacteria in food, water, and bodily fluids is very important and yet challenging, owing to sample complexities and large sample volumes that need to be rapidly screened. Existing screening methods based on plate counting or molecular analysis present various tradeoffs with regard to the detection time, accuracy/sensitivity, cost, and sample preparation complexity. Here, we present a computational live bacteria detection system that periodically captures coherent microscopy images of bacterial growth inside a 60-mm-diameter agar plate and analyses these time-lapsed holograms using deep neural networks for the rapid detection of bacterial growth and the classification of the corresponding species. The performance of our system was demonstrated by the rapid detection of Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and Klebsiella pneumoniae subsp. pneumoniae) in water samples, shortening the detection time by >12 h compared to the Environmental Protection Agency (EPA)-approved methods. Using the preincubation of samples in growth media, our system achieved a limit of detection (LOD) of ~1 colony forming unit (CFU)/L in ≤9 h of total test time. This platform is highly cost-effective (~$0.6/test) and has high-throughput with a scanning speed of 24 cm2/min over the entire plate surface, making it highly suitable for integration with the existing methods currently used for bacteria detection on agar plates. Powered by deep learning, this automated and cost-effective live bacteria detection platform can be transformative for a wide range of applications in microbiology by significantly reducing the detection time and automating the identification of colonies without labelling or the need for an expert.Neural networks to automatically detect bacteria in waterA novel automated system quickly detects and classifies live bacteria in water by using deep neural networks to analyze holographic images. Water-borne pathogens affect billions of people, but current gold standard methods for counting and identifying live bacteria in water take 24 hours or more, highlighting the need for fast, accurate, automatic methods that can handle large sample sizes. Aydogan Ozcan at the University of California Los Angeles, USA, and co-workers developed a system that analyzes lensfree holographic microscopy images of bacteria growing on agar plates. After training and testing their algorithms with >16000 bacterial colonies from three different species, the team was able to detect bacterial growth and classify species in <12 hours. The system will not only improve monitoring of food and water quality, but also provides a powerful tool for microbiology research.

The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wide-field autofluorescence images of unlabelled tissue sections into images that are equivalent to the bright-field images of histologically stained versions of the same samples. A blind comparison, by board-certified pathologists, of this virtual staining method and standard histological staining using microscopic images of human tissue sections of the salivary gland, thyroid, kidney, liver and lung, and involving different types of stain, showed no major discordances. The virtual-staining method bypasses the typically labour-intensive and costly histological staining procedures, and could be used as a blueprint for the virtual staining of tissue images acquired with other label-free imaging modalities. Deep learning can be used to virtually stain autofluorescence images of unlabelled tissue sections, generating images that are equivalent to the histologically stained versions.

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.