Long-term exposure to dietary emulsifier Tween 80 promotes liver lipid accumulation and induces different-grade inflammation in young and aged mice.

With the advent of industrialization, there has been a substantial increase in the production and consumption of ultra-processed foods (UPFs). These processed foods often contain artificially synthesized additives, such as emulsifiers. Emulsifiers constitute approximately half of the total amount of food additives, with Tween 80 being a commonly used emulsifier in the food industry. Concurrently, China is undergoing significant demographic changes, transitioning into an aging society. Despite this demographic shift, there is insufficient research on the health implications of food emulsifiers, particularly on the elderly population. In this study, we present novel findings indicating that even at low concentrations, Tween 80 suppressed the viability of multiple cell types. Prolonged in vivo exposure to 1 % Tween 80 in drinking water induced liver lipid accumulation and insulin resistance in young adult mice under a regular chow diet. Intriguingly, in mice with high-fat diet (HFD) induced metabolic dysfunction-associated steatotic liver disease (MASLD), this inductive effect was masked. In aged mice, liver lipid accumulation was replicated under prolonged Tween 80 exposure. We further revealed that Tween 80 induced inflammation in both adult and aged mice, with a more pronounced inflammation in aged mice. In conclusion, our study provides compelling evidence that Tween 80 could contribute to a low-grade inflammation and liver lipid accumulation. These findings underscore the need for increasing attention regarding the consumption of UPFs with Tween 80 as the emulsifier, particularly in the elderly consumers.

An Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry (UPLC-MS/MS) Method for Qualifying DAPB in Rat Plasma and Application to Pharmacokinetic Studies.

DAPB, a new molecule including danshensu, borneol, and a mother nucleus of ACEI (Angiotensin-converting enzyme inhibitors), is being developed as an antihypertensive candidate compound. A rapid, accurate, and sensitive ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was established and validated for the determination of DAPB in rat plasma. Chromatographic separation was performed on an Agilent SB-C18 column after protein precipitation by acetonitrile with a mobile phase consisting of acetonitrile and deionized water with 0.02% formic acid and 5 mM NH4F (v/v) at a flow rate of 0.2 mL/min. Quantification was performed using electrospray positive ionization mass spectrometry in the multiple reaction monitoring (MRM) mode. The method was linear over the range of 2-1000 ng/mL. The intra- and inter-day precision was within 12%, with accuracies less than 7%. Stability was within the acceptable limits under various storage and processing conditions. No apparent matrix effect was detected. The validated method was applied to the pre-clinical pharmacokinetic study of DAPB after oral administration of 30 mg/kg and intravenous administration of 6 mg/kg in rats.

Chemical Synthesis, Safety and Efficacy of Antihypertensive Candidate Drug 221s (2,9).

Hypertension is the main risk factor of cardiovascular and cerebrovascular diseases. In this paper, a novel compound known as 221s (2,9), which includes tanshinol, borneol and a mother nucleus of ACEI, was synthesized by condensation esterification, deprotection, amidation, deprotection, and amidation, with borneol as the initial raw material, using the strategy of combinatorial molecular chemistry. The structure of the compound was confirmed by 1H NMR, 13C NMR, and high-resolution mass spectrometry, with a purity of more than 99.5%. The compound 221s (2,9) can significantly reduce the systolic and diastolic blood pressure of SHR rats by about 50 mmHg and 35 mmHg after 4 weeks of administration. The antihypertensive effect of 221s (2,9) is equivalent to that of captopril. The use of 221s (2,9) can reduce the content of Ren, Ang II and ACE in the serum of SHR rats, inhibit the RAAS and enhance the vascular endothelial function by upregulating the level of NO. Pathological studies in this area have shown that high dosage of 221s (2,9) can notably protect myocardial fibrosis in rats and reduce the degeneration and necrosis of myocardial fibers, inflammatory cell infiltration, and proliferation of fibrous tissue in the heart of rat. Therefore, the existing work provided a foundation for preclinical research and follow-up clinical research of 221s (2,9) as a new drug.

Transcription Factor Forkhead Box O1 Mediates Transforming Growth Factor-ß1-Induced Apoptosis in Hepatocytes.

Dysregulation of hepatocyte apoptosis is associated with several types of chronic liver diseases. Transforming growth factor-ß1 (TGF-ß1) is a well-known pro-apoptotic factor in the liver, which constitutes a receptor complex composed of TGF-ß receptor I and II, along with transcription factor Smad proteins. As a member of the forkhead box O (Foxo) class of transcription factors, Foxo1 is a predominant regulator of hepatic glucose production and apoptosis. This study investigated the potential relationship between TGF-ß1 signaling and Foxo1 in control of apoptosis in hepatocytes. TGF-ß1 induced hepatocyte apoptosis in a Foxo1-dependent manner in hepatocytes isolated from both wild-type and liver-specific Foxo1 knockout mice. TGF-ß1 activated protein kinase A through TGF-ß receptor I-Smad3, followed by phosphorylation of Foxo1 at Ser273 in promotion of apoptosis in hepatocytes. Moreover, Smad3 overexpression in the liver of mice promoted the levels of phosphorylated Foxo1-S273, total Foxo1, and a Foxo1-target pro-apoptotic gene Bim, which eventually resulted in hepatocyte apoptosis. The study further demonstrated a crucial role of Foxo1-S273 phosphorylation in the pro-apoptotic effect of TGF-ß1 by using hepatocytes isolated from Foxo1-S273A/A knock-in mice, in which the phosphorylation of Foxo1-S273 was disrupted. Taken together, this study established a novel role of TGF-ß1→protein kinase A→Foxo1 signaling cascades in control of hepatocyte survival.

Reciprocal Regulation of Hepatic TGF-ß1 and Foxo1 Controls Gluconeogenesis and Energy Expenditure.

Obesity and insulin resistance are risk factors for the pathogenesis of type 2 diabetes (T2D). Here, we report that hepatic TGF-ß1 expression positively correlates with obesity and insulin resistance in mice and humans. Hepatic TGF-ß1 deficiency decreased blood glucose levels in lean mice and improved glucose and energy dysregulations in diet-induced obese (DIO) mice and diabetic mice. Conversely, overexpression of TGF-ß1 in the liver exacerbated metabolic dysfunctions in DIO mice. Mechanistically, hepatic TGF-ß1 and Foxo1 are reciprocally regulated: fasting or insulin resistance caused Foxo1 activation, increasing TGF-ß1 expression, which, in turn, activated protein kinase A, stimulating Foxo1-S273 phosphorylation to promote Foxo1-mediated gluconeogenesis. Disruption of TGF-ß1→Foxo1→TGF-ß1 looping by deleting TGF-ß1 receptor II in the liver or by blocking Foxo1-S273 phosphorylation ameliorated hyperglycemia and improved energy metabolism in adipose tissues. Taken together, our studies reveal that hepatic TGF-ß1→Foxo1→TGF-ß1 looping could be a potential therapeutic target for prevention and treatment of obesity and T2D. ARTICLE HIGHLIGHTS: Hepatic TGF-ß1 levels are increased in obese humans and mice. Hepatic TGF-ß1 maintains glucose homeostasis in lean mice and causes glucose and energy dysregulations in obese and diabetic mice. Hepatic TGF-ß1 exerts an autocrine effect to promote hepatic gluconeogenesis via cAMP-dependent protein kinase-mediated Foxo1 phosphorylation at serine 273, endocrine effects on brown adipose tissue action, and inguinal white adipose tissue browning (beige fat), causing energy imbalance in obese and insulin-resistant mice. TGF-ß1→Foxo1→TGF-ß1 looping in hepatocytes plays a critical role in controlling glucose and energy metabolism in health and disease.

Dynamic low-count PET image reconstruction using spatio-temporal primal dual network.

Objective.Dynamic positron emission tomography (PET) imaging, which can provide information on dynamic changes in physiological metabolism, is now widely used in clinical diagnosis and cancer treatment. However, the reconstruction from dynamic data is extremely challenging due to the limited counts received in individual frame, especially in ultra short frames. Recently, the unrolled model-based deep learning methods have shown inspiring results for low-count PET image reconstruction with good interpretability. Nevertheless, the existing model-based deep learning methods mainly focus on the spatial correlations while ignore the temporal domain.Approach.In this paper, inspired by the learned primal dual (LPD) algorithm, we propose the spatio-temporal primal dual network (STPDnet) for dynamic low-count PET image reconstruction. Both spatial and temporal correlations are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative learning process of the network, which provides the physical constraints and enhances interpretability.Main results.The experiments of both simulation data and real rat scan data have shown that the proposed method can achieve substantial noise reduction in both temporal and spatial domains and outperform the maximum likelihood expectation maximization, spatio-temporal kernel method, LPD and FBPnet.Significance.Experimental results show STPDnet better reconstruction performance in the low count situation, which makes the proposed method particularly suitable in whole-body dynamic imaging and parametric PET imaging that require extreme short frames and usually suffer from high level of noise.

Test development, optimization and validation of a WGS pipeline for genetic disorders.

BACKGROUND: With advances in massive parallel sequencing (MPS) technology, whole-genome sequencing (WGS) has gradually evolved into the first-tier diagnostic test for genetic disorders. However, deployment practice and pipeline testing for clinical WGS are lacking. METHODS: In this study, we introduced a whole WGS pipeline for genetic disorders, which included the entire process from obtaining a sample to clinical reporting. All samples that underwent WGS were constructed using polymerase chain reaction (PCR)-free library preparation protocols and sequenced on the MGISEQ-2000 platform. Bioinformatics pipelines were developed for the simultaneous detection of various types of variants, including single nucleotide variants (SNVs), insertions and deletions (indels), copy number variants (CNVs) and balanced rearrangements, mitochondrial (MT) variants, and other complex variants such as repeat expansion, pseudogenes and absence of heterozygosity (AOH). A semiautomatic pipeline was developed for the interpretation of potential SNVs and CNVs. Forty-five samples (including 14 positive commercially available samples, 23 laboratory-held positive cell lines and 8 clinical cases) with known variants were used to validate the whole pipeline. RESULTS: In this study, a whole WGS pipeline for genetic disorders was developed and optimized. Forty-five samples with known variants (6 with SNVs and Indels, 3 with MT variants, 5 with aneuploidies, 1 with triploidy, 23 with CNVs, 5 with balanced rearrangements, 2 with repeat expansions, 1 with AOHs, and 1 with exon 7-8 deletion of SMN1 gene) validated the effectiveness of our pipeline. CONCLUSIONS: This study has been piloted in test development, optimization, and validation of the WGS pipeline for genetic disorders. A set of best practices were recommended using our pipeline, along with a dataset of positive samples for benchmarking.

Facial nerve function training in patients with peripheral facial paralysis: an expert consensus.

Facial nerve training can prevent facial expression muscle atrophy and promote the recovery of facial para-lysis in patients with peripheral facial paralysis. However, there is still a lack of specific and unified technical standards for facial nerve training, which results in a variety of clinical training methods and uneven levels. In order to standardize the application of facial nerve function training technology for nursing staff, the study convened relevant domestic experts, based on evidence-based combination with the disease characteristics of peripheral facial paralysis and expert clinical experience, conducted in-depth interviews with experts, expert correspondence and expert meetings, and finally formulated the expert consensus on facial nerve function training in patients with peripheral facial paralysis. Overall, suggestions for standardizing the timing, training methods, evaluation methods, health education and other aspects were provided for clinical reference.

Human obstructive (postvasectomy) and nonobstructive azoospermia - Insights from scRNA-Seq and transcriptome analysis.

A substantial number of male infertility is caused by azoospermia. However, the underlying etiology and the molecular basis remain largely unknown. Through single-cell (sc)RNA sequencing, we had analyzed testis biopsy samples from two patients with obstructive azoospermia (OA) and nonobstructive azoospermia (NOA). We found only somatic cells in the NOA samples and explored the transcriptional changes in Sertoli cells in response to a loss of interactions with germ cells. Moreover, we observed a germ cell population discrepancy between an OA (postvasectomy) patient and a healthy individual. We confirmed this observation in a secondary study with two datasets at GSM3526588 and GSE124263 for detailed analysis wherein the regulatory mechanisms at the transcriptional level were identified. These findings thus provide valuable information on human spermatogenesis, and we also identified insightful information for further research on reproduction-related diseases.

Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction.

Learned optimization algorithms are promising approaches to inverse problems by leveraging advanced numerical optimization schemes and deep neural network techniques in machine learning. In this paper, we propose a novel deep neural network architecture imitating an extra proximal gradient algorithm to solve a general class of inverse problems with a focus on applications in image reconstruction. The proposed network features learned regularization that incorporates adaptive sparsification mappings, robust shrinkage selections, and nonlocal operators to improve solution quality. Numerical results demonstrate the improved efficiency and accuracy of the proposed network over several state-of-the-art methods on a variety of test problems.

An optimal control framework for joint-channel parallel MRI reconstruction without coil sensitivities.

GOAL: This work aims at developing a novel calibration-free fast parallel MRI (pMRI) reconstruction method incorporate with discrete-time optimal control framework. The reconstruction model is designed to learn a regularization that combines channels and extracts features by leveraging the information sharing among channels of multi-coil images. We propose to recover both magnitude and phase information by taking advantage of structured convolutional networks in image and Fourier spaces. METHODS: We develop a novel variational model with a learnable objective function that integrates an adaptive multi-coil image combination operator and effective image regularization in the image and Fourier spaces. We cast the reconstruction network as a structured discrete-time optimal control system, resulting in an optimal control formulation of parameter training where the parameters of the objective function play the role of control variables. We demonstrate that the Lagrangian method for solving the control problem is equivalent to back-propagation, ensuring the local convergence of the training algorithm. RESULTS: We conduct a large number of numerical experiments of the proposed method with comparisons to several state-of-the-art pMRI reconstruction networks on real pMRI datasets. The numerical results demonstrate the promising performance of the proposed method evidently. CONCLUSION: The proposed method provides a general deep network design and training framework for efficient joint-channel pMRI reconstruction. SIGNIFICANCE: By learning multi-coil image combination operator and performing regularizations in both image domain and k-space domain, the proposed method achieves a highly efficient image reconstruction network for pMRI.

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Nitrogen reduction combined with organic materials is an important measure to achieve or even increase crop yield retention at the background of fertilizer reduction. We conducted a pot experiment to explore the effects of nitrogen reduction combined with organic materials on yield, photosynthetic characteristics, and product quality of agricultural products of maize-cabbage rotation system in yellow soil area of Guizhou. There were five treatments, including no fertilizer (CK), conventional fertilizer (CF), nitrogen reduction (20%, the same below) combined with biochar (RF+B), nitrogen reduction combined with rapeseed cake (RF+O), and nitrogen reduction combined with both biochar and rapeseed cake (RF+BO). Leaf photosynthetic characteristics were measured in maize (seedling stage, jointing stage, heading stage, and mature stage) and cabbage (seedling stage, growing stage and harvest stage). The biological characters, yield and quality indices were investigated in the harvest period. Compared with CF, RF+BO significantly enhanced the yield of corn and cabbage by 9.7% and 39.2%, respectively, while RF+O had no effect, and RF+B did not affet maize yield. RF+BO improved the biological properties of maize and cabbage, including the 100-kernel weight of maize, and plant height, maximum leaf length and total biomass of cabbage. Furthermore, the green holding period and high photosynthetic duration of maize and cabbage were prolonged, among which, maize leaf SPAD was increased respectively by 42.7%, 11.0%, 12.8%, and 30.3% at seedling, jointing, heading, and mature stages, the cabbage leaf SPAD was increased by 13.5%, 9.2%, and 30.3% in seedling, growing and harvest stages, respectively. The net photosynthetic rate (Pn) of maize was increased by 11.1%, 10.9%, and 119.8% in seedling, jointing, and mature stages, while that of cabbage was increased by 12.7% and 14.6% in growing and harvest stages, respectively. The stomatal conductance (gs) of maize was increased by 58.3% and 41.7% in jointing and harvest stages, while that of cabbage was increased by 10%, 64.7%, and 19.2% in seedling, growing, and harvest stages, respectively. The transpiration rate (Tr) of maize was increased by 55.0%, 10.6%, 14.0%, and 143.9% in seedling, jointing, heading, and mature stages, respectively, while that of cabbage was increased by 26.1% in growing stage. The nutritional quality of maize and cabbage was significantly improved. The contents of reducing sugar, starch, and crude protein in maize were increased by 16.2%, 3.5% and 20.3%. The contents of Vc, amino acid, and reducing sugar in cabbage were increased by 26.3%, 21.0% and 27.8%, separately. In conclusion, 20% nitrogen reduction combined with biochar and rapeseed cake had positive effects on crop growth, yield increase, green retention period, high photosynthetic duration, and agricultural product quality improvement in Guizhou yellow soil maize-cabbage rotation system, the overall effect of which was the best. Nitrogen reduction combined with single organic material overally did not affect crop yield, photosynthetic characteristics, and quality.

An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset.

This work aims at developing a generalizable Magnetic Resonance Imaging (MRI) reconstruction method in the meta-learning framework. Specifically, we develop a deep reconstruction network induced by a learnable optimization algorithm (LOA) to solve the nonconvex nonsmooth variational model of MRI image reconstruction. In this model, the nonconvex nonsmooth regularization term is parameterized as a structured deep network where the network parameters can be learned from data. We partition these network parameters into two parts: a task-invariant part for the common feature encoder component of the regularization, and a task-specific part to account for the variations in the heterogeneous training and testing data. We train the regularization parameters in a bilevel optimization framework which significantly improves the robustness of the training process and the generalization ability of the network. We conduct a series of numerical experiments using heterogeneous MRI data sets with various undersampling patterns, ratios, and acquisition settings. The experimental results show that our network yields greatly improved reconstruction quality over existing methods and can generalize well to new reconstruction problems whose undersampling patterns/trajectories are not present during training.

Unraveling epigenomic abnormality in azoospermic human males by WGBS, RNA-Seq, and transcriptome profiling analyses.

PURPOSE: To determine associations between genomic DNA methylation in testicular cells and azoospermia in human males. METHODS: This was a case-control study investigating the differences and conservations in DNA methylation, genome-wide DNA methylation, and bulk RNA-Seq for transcriptome profiling using testicular biopsy tissues from NOA and OA patients. Differential methylation and different conserved methylation regions associated with azoospermia were identified by comparing genomic DNA methylation of testicular seminiferous cells derived from NOA and OA patients. RESULTS: The genome methylation modification of testicular cells from NOA patients was disordered, and the reproductive-related gene expression was significantly different. CONCLUSION: Our findings not only provide valuable knowledge of human spermatogenesis but also paved the way for the identification of genes/proteins involved in male germ cell development. The approach presented in this report provides a powerful tool to identify responsible biomolecules, and/or cellular changes (e.g., epigenetic abnormality) that induce male reproductive dysfunction such as OA and NOA.

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition.

Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples, and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is first proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification. Moreover, a statistical measure is introduced to quantify the stability of representation-based classification methods. Based on the IPRC technique, a robust tumor recognition framework is presented by interpreting microarray gene expression data, where a two-stage hybrid gene selection method is introduced to select informative genes. Finally, the functional analysis of candidate's pathogenicity-related genes is given. Extensive experiments on six public tumor microarray gene expression datasets demonstrate the proposed technique is competitive with state-of-the-art methods.

On Kernel Method-Based Connectionist Models and Supervised Deep Learning Without Backpropagation.

We propose a novel family of connectionist models based on kernel machines and consider the problem of learning layer by layer a compositional hypothesis class (i.e., a feedforward, multilayer architecture) in a supervised setting. In terms of the models, we present a principled method to "kernelize" (partly or completely) any neural network (NN). With this method, we obtain a counterpart of any given NN that is powered by kernel machines instead of neurons. In terms of learning, when learning a feedforward deep architecture in a supervised setting, one needs to train all the components simultaneously using backpropagation (BP) since there are no explicit targets for the hidden layers (Rumelhart, Hinton, & Williams, 1986). We consider without loss of generality the two-layer case and present a general framework that explicitly characterizes a target for the hidden layer that is optimal for minimizing the objective function of the network. This characterization then makes possible a purely greedy training scheme that learns one layer at a time, starting from the input layer. We provide instantiations of the abstract framework under certain architectures and objective functions. Based on these instantiations, we present a layer-wise training algorithm for an l-layer feedforward network for classification, where l≥2 can be arbitrary. This algorithm can be given an intuitive geometric interpretation that makes the learning dynamics transparent. Empirical results are provided to complement our theory. We show that the kernelized networks, trained layer-wise, compare favorably with classical kernel machines as well as other connectionist models trained by BP. We also visualize the inner workings of the greedy kernelized models to validate our claim on the transparency of the layer-wise algorithm.

3D Tensor Based Nonlocal Low Rank Approximation in Dynamic PET Reconstruction.

Reconstructing images from multi-view projections is a crucial task both in the computer vision community and in the medical imaging community, and dynamic positron emission tomography (PET) is no exception. Unfortunately, image quality is inevitably degraded by the limitations of photon emissions and the trade-off between temporal and spatial resolution. In this paper, we develop a novel tensor based nonlocal low-rank framework for dynamic PET reconstruction. Spatial structures are effectively enhanced not only by nonlocal and sparse features, but momentarily by tensor-formed low-rank approximations in the temporal realm. Moreover, the total variation is well regularized as a complementation for denoising. These regularizations are efficiently combined into a Poisson PET model and jointly solved by distributed optimization. The experiments demonstrated in this paper validate the excellent performance of the proposed method in dynamic PET.

Glycosaminoglycan from Apostichopus japonicus Improves Glucose Metabolism in the Liver of Insulin Resistant Mice.

Holothurian glycosaminoglycan isolated from Apostichopus japonicus (named AHG) can suppress hepatic glucose production in insulin resistant hepatocytes, but its effects on glucose metabolism in vivo are unknown. The present study was conducted to investigate the effects of AHG on hyperglycemia in the liver of insulin resistant mice induced by a high-fat diet (HFD) for 12 weeks. The results demonstrated that AHG supplementation apparently reduced body weight, blood glucose level, and serum insulin content in a dose-dependent manner in HFD-fed mice. The protein levels and gene expression of gluconeogenesis rate-limiting enzymes G6Pase and PEPCK were remarkedly suppressed in the insulin resistant liver. In addition, although the total expression of IRS1, Akt, and AMPK in the insulin resistant liver was not affected by AHG supplementation, the phosphorylation of IRS1, Akt, and AMPK were clearly elevated by AHG treatment. These results suggest that AHG could be a promising natural marine product for the development of an antihyperglycemic agent.

Glycosaminoglycan from Apostichopus japonicus inhibits hepatic glucose production via activating Akt/FoxO1 and inhibiting PKA/CREB signaling pathways in insulin resistant hepatocytes.

The aim of this study was to elucidate the effect and the underlying mechanism of glycosaminoglycan from Apostichopus japonicus (AHG) on hepatic glucose production (HGP) in insulin resistant hepatocytes. Insulin resistance was induced with high glucose (HG) for 24 h in primary hepatocytes. The results showed that AHG exhibited hypoglycemic activity at a relatively low concentration (1 µg mL-1) and revealed non-toxic activity to insulin resistant hepatocytes even at 500 µg mL-1 concentration. The HGP test showed that the treatment of AHG (10 µg mL-1) for 3 h decreased HGP by 25% in insulin resistant hepatocytes. Quantitative PCR and western blot analysis revealed that AHG also ameliorated phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pase). The data revealed the mechanism of AHG in alleviating HGP via activating the Akt/FoxO1 signaling pathway and suppressing the PKA/CREB signaling pathway in insulin resistant hepatocytes. This finding suggests that AHG could be a potential marine natural product for the treatment of dysregulating glucose homeostasis.

Long Read Single-Molecule Real-Time Sequencing Elucidates Transcriptome-Wide Heterogeneity and Complexity in Esophageal Squamous Cells.

Esophageal squamous cell carcinoma is a leading cause of cancer death. Mapping the transcriptional landscapes such as isoforms, fusion transcripts, as well as long noncoding RNAs have played a central role to understand the regulating mechanism during malignant processes. However, canonical methods such as short-read RNA-seq are difficult to define the entire polyadenylated RNA molecules. Here, we combined single-molecule real-time sequencing with RNA-seq to generate high-quality long reads and to survey the transcriptional program in esophageal squamous cells. Compared with the recent annotations of human transcriptome (Ensembl 38 release 91), single-molecule real-time data identified many unannotated transcripts, novel isoforms of known genes and an expanding repository of long intergenic noncoding RNAs (lincRNAs). By integrating with annotation of lincRNA catalog, 1,521 esophageal-cancer-specific lincRNAs were defined from single-molecule real-time reads. Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicated that these lincRNAs and their target genes are involved in a variety of cancer signaling pathways. Isoform usage analysis revealed the shifted alternative splicing patterns, which can be recaptured from clinical samples or supported by previous studies. Utilizing vigorous searching criteria, we also detected multiple transcript fusions, which are not documented in current gene fusion database or readily identified from RNA-seq reads. Two novel fusion transcripts were verified based on real-time PCR and Sanger sequencing. Overall, our long-read single-molecule sequencing largely expands current understanding of full-length transcriptome in esophageal cells and provides novel insights on the transcriptional diversity during oncogenic transformation.