Immunofluorescence Image Feature Analysis and Phenotype Scoring Pipeline for Distinguishing Epithelial-Mesenchymal Transition

A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFβ dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFβ dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.

Surgical Selection of T1 Stage Renal Tumor Resection Based on Imaging MAP Score under Smart Medical Care

Smart medical uses the medical information platform and the current technological means to enable the process of sharing information between medical staff and medical equipment. The combination of current technology and the medical field has become the norm. In the future, more artificial intelligence technologies will be integrated into the medical field to promote the development of medical care. At present, the information on the Internet is very large and complex, and general search engines often do not have knowledge in certain professional fields and can only perform shallow keyword searches. Therefore, it is difficult to meet people's medical diagnosis needs, and smart medical care can solve these needs. Medical imaging refers to the technology or process of obtaining internal tissue images of a certain part of the human body for medical research, including medical imaging systems and medical image processing. Medical image processing refers to the further processing of the obtained images, the purpose of which is either to restore the original image that was not clear enough or to highlight some characteristic information in the image. The purpose of this paper is to study the research on the selection of T1 stage renal tumor resection based on the imaging MAP score under smart medical care. It is hoped that through smart medicine and medical imaging technology, it can help renal tumor resection, reduce the sequelae of renal tumor resection, and promote the development of medical services. This paper proposes applying natural language processing technology to the medical field, creating an intelligent diagnosis assistance system, and using the existing medical record data to realize the corresponding medical assistance functions. It studies the class imbalance problem prevalent in medical datasets and provides better solutions through ensemble learning techniques to improve classifier performance when the number of positive and negative samples is unbalanced. The experimental results in this paper show that the creatinine of patients undergoing renal tumor resection combined with smart medicine and imaging technology is stable at 75 mol/L, while the creatinine is stable at 71 mol/L in other methods. It shows that the postoperative effect of smart medical treatment and imaging technology is better.

Adenoviral Transduction of Dickkopf-1 Alleviates Silica-Induced Silicosis Development in Lungs of Mice

Silicosis is an occupational disease caused by inhalation of silica dust, which is hallmarked by progressive pulmonary fibrosis associated with poor prognosis. Wnt/β-catenin signaling is implicated in the development of fibrosis and is a therapeutic target for fibrotic diseases. Previous clinical studies of patients with pneumoconiosis, including silicosis, revealed an increased concentration of circulating WNT3A and DKK1 proteins and inflammatory cells in bronchoalveolar lavage compared with healthy subjects. The present study evaluated the effects of adenovirus-mediated transduction of Dickkopf-1 (Dkk1), a Wnt/β-catenin signaling inhibitor, on the development of pulmonary silicosis in mice. Consistent with previous human clinical studies, our experimental studies in mice demonstrated an aberrant Wnt/β-catenin signaling activity coinciding with increased Wnt3a and Dkk1 proteins and inflammation in lungs of silica-induced silicosis mice compared with controls. Intratracheal delivery of adenovirus expressing murine Dkk1 (AdDkk1) inhibited Wnt/β-catenin activity in mouse lungs. The adenovirus-mediated Dkk1 gene transduction demonstrated the potential to prevent silicosis development and ameliorate silica-induced lung fibrogenesis in mice, accompanied by the reduced expression of epithelia--mesenchymal transition markers and deposition of extracellular matrix proteins compared with mice treated with "null" adenoviral vector. Mechanistically, AdDkk1 is able to attenuate the lung silicosis by inhibiting a silica-induced spike in TGF-β/Smad signaling. In addition, the forced expression of Dkk1 suppressed silica-induced epithelial cell proliferation in polarized human bronchial epithelial cells. This study provides insight into the underlying role of Wnt/β-catenin signaling in promoting the pathogenesis of silicosis and is proof-of-concept that targeting Wnt/β-catenin signaling by Dkk1 gene transduction may be an alternative approach in the prevention and treatment of silicosis lung disease.

Interpretable, Scalable, and Transferrable Functional Projection of Large-Scale Transcriptome Data Using Constrained Matrix Decomposition

Large-scale transcriptome data, such as single-cell RNA-sequencing data, have provided unprecedented resources for studying biological processes at the systems level. Numerous dimensionality reduction methods have been developed to visualize and analyze these transcriptome data. In addition, several existing methods allow inference of functional variations among samples using gene sets with known biological functions. However, it remains challenging to analyze transcriptomes with reduced dimensions that are interpretable in terms of dimensions’ directionalities, transferrable to new data, and directly expose the contribution or association of individual genes. In this study, we used gene set non-negative principal component analysis (gsPCA) and non-negative matrix factorization (gsNMF) to analyze large-scale transcriptome datasets. We found that these methods provide low-dimensional information about the progression of biological processes in a quantitative manner, and their performances are comparable to existing functional variation analysis methods in terms of distinguishing multiple cell states and samples from multiple conditions. Remarkably, upon training with a subset of data, these methods allow predictions of locations in the functional space using data from experimental conditions that are not exposed to the models. Specifically, our models predicted the extent of progression and reversion for cells in the epithelial-mesenchymal transition (EMT) continuum. These methods revealed conserved EMT program among multiple types of single cells and tumor samples. Finally, we demonstrate this approach is broadly applicable to data and gene sets beyond EMT and provide several recommendations on the choice between the two linear methods and the optimal algorithmic parameters. Our methods show that simple constrained matrix decomposition can produce to low-dimensional information in functionally interpretable and transferrable space, and can be widely useful for analyzing large-scale transcriptome data.