Deep learning tackles single-cell analysis-a survey of deep learning for scRNA-seq analysis.

Since its selection as the method of the year in 2013, single-cell technologies have become mature enough to provide answers to complex research questions. With the growth of single-cell profiling technologies, there has also been a significant increase in data collected from single-cell profilings, resulting in computational challenges to process these massive and complicated datasets. To address these challenges, deep learning (DL) is positioned as a competitive alternative for single-cell analyses besides the traditional machine learning approaches. Here, we survey a total of 25 DL algorithms and their applicability for a specific step in the single cell RNA-seq processing pipeline. Specifically, we establish a unified mathematical representation of variational autoencoder, autoencoder, generative adversarial network and supervised DL models, compare the training strategies and loss functions for these models, and relate the loss functions of these models to specific objectives of the data processing step. Such a presentation will allow readers to choose suitable algorithms for their particular objective at each step in the pipeline. We envision that this survey will serve as an important information portal for learning the application of DL for scRNA-seq analysis and inspire innovative uses of DL to address a broader range of new challenges in emerging multi-omics and spatial single-cell sequencing.

CeCaFLUX: the first web server for standardized and visual instationary 13C metabolic flux analysis.

SUMMARY: The number of instationary 13C-metabolic flux (INST-MFA) studies grows every year, making it more important than ever to ensure the clarity, standardization and reproducibility of each study. We proposed CeCaFLUX, the first user-friendly web server that derives metabolic flux distribution from instationary 13C-labeled data. Flux optimization and statistical analysis are achieved through an evolutionary optimization in a parallel manner. It can visualize the flux optimizing process in real-time and the ultimate flux outcome. It will also function as a database to enhance the consistency and to facilitate sharing of flux studies. AVAILABILITY AND IMPLEMENTATION: CeCaFLUX is freely available at https://www.cecaflux.net, the source code can be downloaded at https://github.com/zhzhd82/CeCaFLUX. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Unmasking the biological function and regulatory mechanism of NOC2L: a novel inhibitor of histone acetyltransferase.

NOC2 like nucleolar associated transcriptional repressor (NOC2L) was recently identified as a novel inhibitor of histone acetyltransferase (INHAT). NOC2L is found to have two INHAT function domains and regulates histone acetylation in a histone deacetylases (HDAC) independent manner, which is distinct from other INHATs. In this review, we summarize the biological function of NOC2L in histone acetylation regulation, P53-mediated transcription, ribosome RNA processing, certain development events and carcinogenesis. We propose that NOC2L may be explored as a potential biomarker and a therapeutic target in clinical practice.

Molecular and immune signatures, and pathological trajectories of fatal COVID-19 lungs defined by in situ spatial single-cell transcriptome analysis.

Despite intensive studies during the last 3 years, the pathology and underlying molecular mechanism of coronavirus disease 2019 (COVID-19) remain poorly defined. In this study, we investigated the spatial single-cell molecular and cellular features of postmortem COVID-19 lung tissues using in situ sequencing (ISS). We detected 10 414 863 transcripts of 221 genes in whole-slide tissues and segmented them into 1 719 459 cells that were mapped to 18 major parenchymal and immune cell types, all of which were infected by SARS-CoV-2. Compared with the non-COVID-19 control, COVID-19 lungs exhibited reduced alveolar cells (ACs) and increased innate and adaptive immune cells. We also identified 19 differentially expressed genes in both infected and uninfected cells across the tissues, which reflected the altered cellular compositions. Spatial analysis of local infection rates revealed regions with high infection rates that were correlated with high cell densities (HIHD). The HIHD regions expressed high levels of SARS-CoV-2 entry-related factors including ACE2, FURIN, TMPRSS2 and NRP1, and co-localized with organizing pneumonia (OP) and lymphocytic and immune infiltration, which exhibited increased ACs and fibroblasts but decreased vascular endothelial cells and epithelial cells, mirroring the tissue damage and wound healing processes. Sparse nonnegative matrix factorization (SNMF) analysis of niche features identified seven signatures that captured structure and immune niches in COVID-19 tissues. Trajectory inference based on immune niche signatures defined two pathological routes. Trajectory A primarily progressed with increased NK cells and granulocytes, likely reflecting the complication of microbial infections. Trajectory B was marked by increased HIHD and OP, possibly accounting for the increased immune infiltration. The OP regions were marked by high numbers of fibroblasts expressing extremely high levels of COL1A1 and COL1A2. Examination of single-cell RNA-seq data (scRNA-seq) from COVID-19 lung tissues and idiopathic pulmonary fibrosis (IPF) identified similar cell populations consisting mainly of myofibroblasts. Immunofluorescence staining revealed the activation of IL6-STAT3 and TGF-ß-SMAD2/3 pathways in these cells, likely mediating the upregulation of COL1A1 and COL1A2 and excessive fibrosis in the lung tissues. Together, this study provides a spatial single-cell atlas of cellular and molecular signatures of fatal COVID-19 lungs, which reveals the complex spatial cellular heterogeneity, organization, and interactions that characterized the COVID-19 lung pathology.

A glycolysis-related two-gene risk model that can effectively predict the prognosis of patients with rectal cancer.

BACKGROUND: Aerobic glycolysis is an emerging hallmark of cancer. Although some studies have constructed glycolysis-related prognostic models of colon adenocarcinoma (COAD) based on The Cancer Genome Atlas (TCGA) database, whether the COAD glycolysis-related prognostic model is appropriate for distinguishing the prognosis of rectal adenocarcinoma (READ) patients remains unknown. Exploring critical and specific glycolytic genes related to READ prognosis may help us discover new potential therapeutic targets for READ patients. RESULTS: Three gene sets, HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS and REACTOME_REGULATION_OF_GLYCOLYSIS_BY_FRUCTOSE_2_6_BISPHOSPHATE_METABOLISM, were both significantly enriched in both COAD and READ through glycolysis-related gene set enrichment analysis (GSEA). We found that six genes (ANKZF1, STC2, SUCLG2P2, P4HA1, GPC1 and PCK1) were independent prognostic genes in COAD, while TSTA3 and PKP2 were independent prognostic genes in READ. Glycolysis-related prognostic model of COAD and READ was, respectively, constructed and assessed in COAD and READ. We found that the glycolysis-related prognostic model of COAD was not appropriate for READ, while glycolysis-related prognostic model of READ was more appropriate for READ than for COAD. PCA and t-SNE analysis confirmed that READ patients in two groups (high and low risk score groups) were distributed in discrete directions based on the glycolysis-related prognostic model of READ. We found that this model was an independent prognostic indicator through multivariate Cox analysis, and it still showed robust effectiveness in different age, gender, M stage, and TNM stage. A nomogram combining the risk model of READ with clinicopathological characteristics was established to provide oncologists with a practical tool to evaluate the rectal cancer outcomes. GO enrichment and KEGG analyses confirmed that differentially expressed genes (DEGs) were enriched in several glycolysis-related molecular functions or pathways based on glycolysis-related prognostic model of READ. CONCLUSIONS: We found that a glycolysis-related prognostic model of COAD was not appropriate for READ, and we established a novel glycolysis-related two-gene risk model to effectively predict the prognosis of rectal cancer patients.

Neutralizing antibodies to SARS-CoV-2 variants of concern including Delta and Omicron in subjects receiving mRNA-1273, BNT162b2, and Ad26.COV2.S vaccines.

SARS-CoV-2 vaccines have contributed to the control of COVID-19 in some parts of the world. However, the constant emergence of variants of concern (VOCs) challenges the effectiveness of SARS-CoV-2 vaccines over time. In particular, Omicron contains a high number of mutations in the spike (S) protein gene, on which most vaccines were developed. In this study, we quantitated neutralizing antibodies in vaccine recipients at various times postvaccination using S protein-based pseudoviruses derived from wild type (WT) SARS-CoV-2 and five VOCs including Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), and Omicron (B.1.1.529). We found that two-dose mRNA-1273 and BNT162b2 vaccines elicited robust neutralizing antibodies against WT, Alpha, Beta, Gamma, and Delta, but wanned after 6 months with a faster decline observed for BNT162b2. Both mRNA-1273 and BNT162b2 elicited weak neutralizing antibodies against Omicron. One dose of Ad26.COV2.S vaccine induced weaker neutralizing antibodies against WT and most VOCs than mRNA-1273 and BNT162b2 did but moderate neutralizing antibodies against Delta and Omicron, which lasted for 6 months. These results support current recommendations of the Centers for Disease Control and Prevention for a booster 5 months after full immunization with an mRNA-based vaccine and the use of an mRNA-based vaccine 2 months after Ad26.COV2.S vaccination.

Spectral polarization camera based on ghost imaging via sparsity constraints.

A spectral polarization camera based on ghost imaging via sparsity constraints (GISC) is presented. The proposed imager modulates three-dimensional spatial and spectral information of the target into two-dimensional speckle patterns using a spatial random phase modulator and then acquires the speckle patterns at four linear polarization channels through a polarized CCD. The experimental results verify the feasibility of the system structure and reconstruction algorithm. The GISC spectral polarization camera, which has a simple structure and achieves compressive sampling during the imaging acquisition process, provides a simple scheme for obtaining multi-dimensional information of the light field.

Speech Emotion Recognition Based on Selective Interpolation Synthetic Minority Over-Sampling Technique in Small Sample Environment.

Speech emotion recognition often encounters the problems of data imbalance and redundant features in different application scenarios. Researchers usually design different recognition models for different sample conditions. In this study, a speech emotion recognition model for a small sample environment is proposed. A data imbalance processing method based on selective interpolation synthetic minority over-sampling technique (SISMOTE) is proposed to reduce the impact of sample imbalance on emotion recognition results. In addition, feature selection method based on variance analysis and gradient boosting decision tree (GBDT) is introduced, which can exclude the redundant features that possess poor emotional representation. Results of experiments of speech emotion recognition on three databases (i.e., CASIA, Emo-DB, SAVEE) show that our method obtains average recognition accuracy of 90.28% (CASIA), 75.00% (SAVEE) and 85.82% (Emo-DB) for speaker-dependent speech emotion recognition which is superior to some state-of-the-arts works.

EPHA2 blockade reverses acquired resistance to afatinib induced by EPHA2-mediated MAPK pathway activation in gastric cancer cells and avatar mice.

Afatinib is a pan-HER inhibitor approved for specific types of lung cancer. We explored antitumor activity, predictive biomarkers and the potential mechanisms underlying antitumor effect and acquired resistance of afatinib in gastric cancer (GC) in vitro and in vivo. Five human GC cell lines and eight patient-derived xenograft (PDX) models with clear molecular profiling were used to evaluate the antitumor activity and mechanisms of afatinib. The ErbB family and downstream PI3K/AKT/mTOR and mitogen-activated protein kinase (MAPK) pathways were evaluated before and after afatinib treatment. An afatinib-resistant PDX model was established to explore both the potential mechanisms of drug resistance and reversal strategies. We found that afatinib exerted a strong tumor suppression in EGFR/HER2 highly amplified (copy number >6) or overexpressed (IHC 3+) PDX models and a moderate tumor suppression in EGFR/HER2 moderately expressed (IHC 2+) PDX models. Afatinib selectively inhibited the proliferation of HER2 highly amplified GC cells in a dose-dependent manner in vitro. Afatinib also exerted its antitumor effect by inducing cell apoptosis and cell arrest at G1 phase. Diminished activation of the ErbB family and downstream PI3K/AKT/mTOR and MAPK pathways was also observed. Erythropoietin-producing hepatocellular receptor A2 (EPHA2) upregulation and phosphorylation might be involved in afatinib-acquired resistance, and EPHA2 blockade could restore afatinib sensitivity. GC patients with amplification (copy number >6) or overexpression (IHC 3+) of EGFR/HER2 were most likely to benefit from afatinib treatment and EPHA2 blockade reversed acquired resistance to afatinib treatment, which could provide solid evidences for future clinical trials.

Optimization of light fields in ghost imaging using dictionary learning.

Ghost imaging (GI) is a novel imaging technique based on the second-order correlation of light fields. Due to limited number of samplings in practice, traditional GI methods often reconstruct objects with unsatisfactory quality. To improve the imaging results, many reconstruction methods have been developed, yet the reconstruction quality is still fundamentally restricted by the modulated light fields. In this paper, we propose to improve the imaging quality of GI by optimizing the light fields, which is realized via matrix optimization for a learned dictionary incorporating the sparsity prior of objects. A closed-form solution of the sampling matrix, which enables successive sampling, is derived. Through simulation and experimental results, it is shown that the proposed scheme leads to better imaging quality compared to the state-of-the-art optimization methods for light fields, especially at a low sampling rate.

Ghost imaging LiDAR via sparsity constraints using push-broom scanning.

Ghost imaging LiDAR via sparsity constraints using push-broom scanning is proposed. It can image the stationary target scene continuously along the scanning direction by taking advantage of the relative movement between the platform and the target scene. Compared to conventional ghost imaging LiDAR that requires multiple speckle patterns staring the target, ghost imaging LiDAR via sparsity constraints using push-broom scanning not only simplifies the imaging system, but also reduces the sampling number. Numerical simulations and experiments have demonstrated its efficiency.

Expansion of the FOV in speckle autocorrelation imaging by spatial filtering.

Optical imaging through inhomogeneous media based on autocorrelations suffers from a limited field of view (FOV), since the optical memory effect (ME) of a scattering medium has its inherent angular extent. Here we successfully expand the angular ME range by exploiting a spatial filtering technique to select low-frequency components, mainly ballistic light and less scattered light, thereby increasing the FOV of the speckle autocorrelation imaging. Both a simulation and experimental verifications are presented. This technique, which is not limited to the discussed 4f structure, can provide a guideline for the design of an optical system to image through scattering media.

Establishment and genomic characterizations of patient-derived esophageal squamous cell carcinoma xenograft models using biopsies for treatment optimization.

BACKGROUND: Squamous cell carcinoma is the dominant type of esophageal cancer in China with many patients initially diagnosed at advanced stage. Patient-derived xenografts (PDX) models have been developed to be an important platform for preclinical research. This study aims to establish and characterize PDX models using biopsy tissue from advanced esophageal cancer patients to lay the foundation of preclinical application. METHODS: Fresh endoscopic biopsy tissues were harvested from patients with advanced esophageal cancer and implanted subcutaneously into NOD/SCID mice. Then, the PDXs were serially passaged for up to four generations. Transplantation was analyzed and genomic characteristics of xenografts were profiled using next-generation sequencing. RESULTS: Twenty-five PDX models were established (13.3%, 25/188). The latency period was 75.12 ± 19.87 days (50-120 days) for the first passage and it decreased with increasing passaging. Other than tumor stages, no differences were found between transplantations of xenografts and patient characteristics, irrespective of chemotherapy. Histopathological features and chemosensitivity of PDXs were in great accordance with primary patient tumors. Each PDX was assessed for molecular characteristics including copy number variations, somatic mutations, and signaling pathway abnormalities and these were similar to patient results. CONCLUSIONS: Our PDX models were established from real time biopsies and molecularly profiled. They might be promising for drug development and individualized therapy.

Hyperspectral ghost imaging camera based on a flat-field grating.

A spectral camera based on ghost imaging via sparsity constraints (GISC) acquires a three-dimensional (3D) spatial-spectral data cube of the target through a two-dimensional (2D) detector in a single snapshot. However, the spectral and spatial resolution are interrelated because both of them are modulated by the same spatial random phase modulator. In this paper, we theoretically and experimentally demonstrate a system by equipping the GISC spectral camera with a flat-field grating to disperse the light fields before the spatial random phase modulator, hence consequently decoupling the spatial and spectral resolution. By theoretical derivation of the imaging process we obtain the spectral resolution 1nm and spatial resolution 50µm about the new system which are verified by the experiment. The new system can not only modulate the spatial and spectral resolution separately, but also provide a possibility of optimizing the light field fluctuations of different wavelengths according to the imaging scene.

Activation of the BMP-BMPR pathway conferred resistance to EGFR-TKIs in lung squamous cell carcinoma patients with EGFR mutations.

The empirical criteria for defining a clinical subtype of lung cancer are gradually transiting from histopathology to genetic variations in driver genes. Targeting these driver mutations, such as sensitizing epidermal growth factor receptor (EGFR) mutations, has dramatically improved the prognosis of advanced non-small cell lung cancer (NSCLC). However, the clinical benefit of molecularly targeted therapy on NSCLC appears to be different between lung adenocarcinomas and squamous cell carcinomas (SqCCs). We report here that the resistance of lung SqCC harboring EGFR mutations to EGFR tyrosine kinase inhibitors (EGFR-TKIs) was due to the activation of BMP-BMPR-Smad1/5-p70S6K. The combined treatment of these tumor cells with EGFR-TKI, together with inhibitors specific to BMPR or downstream mTOR, effectively reversed the resistance to EGFR-TKI. Moreover, blocking the whole PI3K-AKT-mTOR pathway with the PI3K/mTOR dual inhibitor BEZ235 also showed efficacy in treating this subtype of lung SqCC. This study details the empirical basis for a feasible clinical solution for squamous cell carcinomas with EGFR mutations.

Gimatecan exerts potent antitumor activity against gastric cancer in vitro and in vivo via AKT and MAPK signaling pathways.

BACKGROUND: We investigated antitumor activity and underlying mechanisms of DNA topoisomerase I (TopI) inhibitor gimatecan and irinotecan in gastric cancer (GC) in vitro cell lines and in vivo patient-derived xenograft (PDX) models. METHODS: GC cell lines SNU-1, HGC27, MGC803 and NCI-N87 were used to evaluate cell viability and apoptosis after gimatecan or irinotecan treatment, using a cell proliferation assay and flow cytometry, respectively. DNA TopI expression and critical molecules of PI3K/AKT, MAPK and apoptosis signaling pathways were analyzed with western blot. For in vivo studies, five PDXs models were treated with gimatecan or irinotecan to assess its antitumor activity. Immunohistochemistry staining of Ki-67 was performed after mice were sacrificed. RESULTS: Gimatecan inhibited the proliferation of GC cells in vitro in a dose- and time-dependent manner by inducing apoptosis, and gimatecan had greater inhibitory effects than irinotecan. In addition, both gimatecan and irinotecan demonstrated significant tumor growth inhibition in in vivo PDX models. Gimatecan treatment significantly inhibited the expression of DNA TopI, phosphorylated AKT (pAKT), phosphorylated MEK (pMEK) and phosphorylated ERK (pERK). Meanwhile, gimatecan could also activate the JNK2 and p38 MAPK pathway as indicated by upregulation of phosphorylated p38 MAPK (p-p38) and phosphorylated JNK2 (pJNK2). CONCLUSIONS: For the first time, we have shown that the antitumor activity of gimatecan in GC via suppressing AKT and ERK pathway and activating JNK2 and p38 MAPK pathway, which indicated that gimatecan might be an alternative to irinotecan in the treatment of GC.

CDK4/6 inhibitor-SHR6390 exerts potent antitumor activity in esophageal squamous cell carcinoma by inhibiting phosphorylated Rb and inducing G1 cell cycle arrest.

BACKGROUND: Cell cycle dysregulation is common in human malignancies, and CDK4/6 inhibitors targeting cell cycle have potential antitumor activity. SHR6390 is a novel small molecule inhibitor specifically targeting the CDK4/6 pathway. However, the role of SHR6390 in esophageal squamous cell carcinoma (ESCC) remains unknown, which will be investigated in our study. METHODS: Eca 109, Eca 9706, and KYSE-510 ESCC cell lines were chosen for further analysis. The effect of SHR6390 on cell viability, cell cycle and cell apoptosis, the status of kinases in Cyclin D1-CDK4/6-Rb pathway were determined by MTS assay, flow cytometry, and western blotting, respectively. Cell-derived and patient-derived xenografts were established to investigate the effects of drugs in vivo. RESULTS: SHR6390 could suppress cell proliferation in vitro cell lines and inhibit tumor growth in vivo PDX models with different drug susceptibility. The effective treatment of SHR6390 induced the inhibition of phosphorylated Rb and cell cycle arrest at G1 phase both in cell lines and in xenografts. SHR6390 combined with paclitaxel or cisplatin offered synergistic inhibitory effects in cell-derived xenografts especially in Eca 9706 xenografts which showed relative lower sensitivity of SHR6390 single. Moreover, low expression of CDK6 and/or high expression of Cyclin D1 might be associated with high sensitivity of SHR6390, which would be validated in the future. CONCLUSIONS: CDK4/6 inhibitor-SHR6390 exerted potential antitumor activity against ESCC cell lines and xenografts, and evaluation of CDK6 and Cyclin D1 expressions might be helpful to select patients beneficial from SHR6390, which provided evidences for future clinical trials.

A Metastatic Cancer Expression Generator (MetGen): A Generative Contrastive Learning Framework for Metastatic Cancer Generation.

Despite significant advances in tumor biology and clinical therapeutics, metastasis remains the primary cause of cancer-related deaths. While RNA-seq technology has been used extensively to study metastatic cancer characteristics, challenges persist in acquiring adequate transcriptomic data. To overcome this challenge, we propose MetGen, a generative contrastive learning tool based on a deep learning model. MetGen generates synthetic metastatic cancer expression profiles using primary cancer and normal tissue expression data. Our results demonstrate that MetGen generates comparable samples to actual metastatic cancer samples, and the cancer and tissue classification yields performance rates of 99.8 ± 0.2% and 95.0 ± 2.3%, respectively. A benchmark analysis suggests that the proposed model outperforms traditional generative models such as the variational autoencoder. In metastatic subtype classification, our generated samples show 97.6% predicting power compared to true metastatic samples. Additionally, we demonstrate MetGen's interpretability using metastatic prostate cancer and metastatic breast cancer. MetGen has learned highly relevant signatures in cancer, tissue, and tumor microenvironments, such as immune responses and the metastasis process, which can potentially foster a more comprehensive understanding of metastatic cancer biology. The development of MetGen represents a significant step toward the study of metastatic cancer biology by providing a generative model that identifies candidate therapeutic targets for the treatment of metastatic cancer.

Nanorod-like Bimetallic Oxide for Enhancing the Performance of Supercapacitor Electrodes.

Supercapacitors are widely used in many fields owing to their advantages, such as high power, good cycle performance, and fast charging speed. Among the many metal-oxide cathode materials reported for supercapacitors, NiMoO4 is currently the most promising electrode material for high-specific-energy supercapacitors. We have employed a rational design approach to create a nanorod-like NiMoO4 structure, which serves as a conductive scaffold for supercapacitors; the straightforward layout has led to outstanding results, with nanorod-shaped NiMoO4 exhibiting a remarkable capacity of 424.8 F g-1 at 1 A g-1 and an impressive stability of 80.2% capacity preservation even after 3500 cycles, which surpasses those of the majority of previously reported NiMoO4 materials. NiMoO4//AC supercapacitors demonstrate a remarkable energy density of 46.31 W h kg-1 and a power density of 0.75 kW kg-1. This synthesis strategy provides a facile method for the fabrication of bimetallic oxide materials for high-performance supercapacitors.

Is milk fat globule size correlated with milk fat content in Ruminants?

In milk, fat exists in the form of milk fat globules (MFGs). The average size (average fat globules of different particle sizes) is the most common parameter when describing MFG size. There are different views on whether there is a correlation between MFG size and milk fat content. Is the MFG size correlated with milk fat content in ruminants? To address this question, we conducted two experiments. In experiment Ⅰ, dairy cows (n = 40) and dairy goats (n = 30) were each divided into a normal group and a low-fat group according to the milk fat content. In experiment Ⅱ, dairy cows (n = 16) and dairy goats (n = 12) were each divided into a normal group and a conjugated linoleic acid (CLA)-induced low-fat group. The normal groups were fed a basal diet, and the CLA-induced low-fat groups were fed the basal diet + 300 g/d CLA (cows) or the basal diet + 90 g/d CLA (goats). In both experiments, we determined the correlation between MFG size and milk composition and MFG distribution. The results showed that in the normal and low-fat groups of cows and goats, MFG size was not correlated with milk fat, protein, or lactose content or fat-to-protein ratio. Additionally, there was no difference in the distribution of large, medium, and small MFGs (P > 0.05). However, in the CLA-induced low-fat groups, we found a correlation between MFG size and milk fat content and fat-to-protein ratio (R2 > 0.3). Moreover, there was a significant change in the size distribution of MFGs. Therefore, in natural milk, MFG size was not correlated with milk fat content. Following CLA supplementation, MFG size was correlated with milk fat content. Our findings revealed that CLA and not milk fat affects MFG distribution and size.