Transcriptomic profiling across human serotonin neuron differentiation via the FEV reporter system.

BACKGROUND: The detailed transcriptomic profiles during human serotonin neuron (SN) differentiation remain elusive. The establishment of a reporter system based on SN terminal selector holds promise to produce highly-purified cells with an early serotonergic fate and help elucidate the molecular events during human SN development process. METHODS: A fifth Ewing variant (FEV)-EGFP reporter system was established by CRISPR/Cas9 technology to indicate SN since postmitotic stage. FACS was performed to purify SN from the heterogeneous cell populations. RNA-sequencing analysis was performed for cells at four key stages of differentiation (pluripotent stem cells, serotonergic neural progenitors, purified postmitotic SN and purifed mature SN) to explore the transcriptomic dynamics during SN differentiation. RESULTS: We found that human serotonergic fate specification may commence as early as day 21 of differentiation from human pluripotent stem cells. Furthermore, the transcriptional factors ZIC1, HOXA2 and MSX2 were identified as the hub genes responsible for orchestrating serotonergic fate determination. CONCLUSIONS: For the first time, we exposed the developmental transcriptomic profiles of human SN via FEV reporter system, which will further our understanding for the development process of human SN.

Corticosterone Impairs Hippocampal Neurogenesis and Behaviors through p21-Mediated ROS Accumulation.

Stress is known to induce a reduction in adult hippocampal neurogenesis (AHN) and anxiety-like behaviors. Glucocorticoids (GCs) are secreted in response to stress, and the hippocampus possesses the greatest levels of GC receptors, highlighting the potential of GCs in mediating stress-induced hippocampal alterations and behavior deficits. Herein, RNA-sequencing (RNA-seq) analysis of the hippocampus following corticosterone (CORT) exposure revealed the central regulatory role of the p21 (Cdkna1a) gene, which exhibited interactions with oxidative stress-related differentially expressed genes (DEGs), suggesting a potential link between p21 and oxidative stress-related pathways. Remarkably, p21-overexpression in the hippocampal dentate gyrus partially recapitulated CORT-induced phenotypes, including reactive oxygen species (ROS) accumulation, diminished AHN, dendritic atrophy, and the onset of anxiety-like behaviors. Significantly, inhibiting ROS exhibited a partial rescue of anxiety-like behaviors and hippocampal alterations induced by p21-overexpression, as well as those induced by CORT, underscoring the therapeutic potential of targeting ROS or p21 in the hippocampus as a promising avenue for mitigating anxiety disorders provoked by chronic stress.

Uncovering the role of FOXA2 in the Development of Human Serotonin Neurons.

Directed differentiation of serotonin neurons (SNs) from human pluripotent stem cells (hPSCs) provides a valuable tool for uncovering the mechanism of human SN development and the associated neuropsychiatric disorders. Previous studies report that FOXA2 is expressed by serotonergic progenitors (SNPs) and functioned as a serotonergic fate determinant in mouse. However, in the routine differentiation experiments, it is accidentally found that less SNs and more non-neuronal cells are obtained from SNP stage with higher percentage of FOXA2-positive cells. This phenomenon prompted them to question the role of FOXA2 as an intrinsic fate determinant for human SN differentiation. Herein, by direct differentiation of engineered hPSCs into SNs, it is found that the SNs are not derived from FOXA2-lineage cells; FOXA2-knockout hPSCs can still differentiate into mature and functional SNs with typical serotonergic identity; FOXA2 overexpression suppresses the SN differentiation, indicating that FOXA2 is not intrinsically required for human SN differentiation. Furthermore, repressing FOXA2 expression by retinoic acid (RA) and dynamically modulating Sonic Hedgehog (SHH) signaling pathway promotes human SN differentiation. This study uncovers the role of FOXA2 in human SN development and improves the differentiation efficiency of hPSCs into SNs by repressing FOXA2 expression.

Physicochemical characterization and antioxidant activity of polysaccharides from Chlorella sp. by microwave-assisted enzymatic extraction.

Microwave-assisted enzymatic extraction (MAEE) was used for the separation of polysaccharides from micro-Chlorella. The extraction condition of MAEE was optimized by Box-Behnken design and response surface methodology. Results showed that the optimal condition for the extraction of Chlorella sp. crude polysaccharides (CSCP) was at 50°C for 2.3 h with 380 W of microwave power and 0.31% of enzyme dosage. Under the optimal extraction condition, the extraction yield of CSCP reached 0.72%. Similarly, the α-amylase modification conditions of the CSCP were also optimized, in which the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging rate was used as the response value. The scavenging rate of DPPH free radicals was 17.58% when enzyme dosage was 271 U/g at 51°C for 14 min. Moreover, the enzyme-modified CSCP presented a typical heteropolysaccharide mainly including glucose (48.84%), ribose (13.57%) and mannose (11.30%). MAEE used in this work achieved a high extraction yield of CSCP, which provides an efficient method for the extraction of CSCP from Chlorella sp.

Generation of a TPH2-EGFP reporter cell line for purification and monitoring of human serotonin neurons in vitro and in vivo.

Generation of serotonin neurons (SNs) from human pluripotent stem cells (hPSCs) provides a promising platform to explore the mechanisms of serotonin-associated neuropsychiatric disorders. However, neural differentiation always yields heterogeneous cell populations, making it difficult to identify and purify SNs in vitro or track them in vivo following transplantation. Herein, we generated a TPH2-EGFP reporter hPSC line with insertion of EGFP into the endogenous tryptophan hydroxylase 2 (TPH2) locus using CRISPR-Cas9-mediated gene editing technology. This TPH2-reporter, which faithfully indicated TPH2 expression during differentiation, enabled us to obtain purified SNs for subsequent transcriptional analysis and study of pharmacological responses to antidepressants. In addition, the reporter system showed strong EGFP expression to indicate SNs, which enabled us to explore in vitro and ex vivo electrophysiological properties of SNs. In conclusion, this TPH2-EGFP reporter cell line might be of great significance for studies on human SN-related development and differentiation, drug screening, disease modeling, and cell replacement therapies.

Corticosterone induces obesity partly via promoting intestinal cell proliferation and survival.

Introduction: A vicious cycle ensues whereby prolonged exposure to social stress causes increased production of glucocorticoids (GCs), leading to obesity even further. Understanding the role of GCs, the key element in the vicious circle, might be helpful to break the vicious circle. However, the mechanism by which GCs induce obesity remains elusive. Methods: Corticosterone (CORT) was administered to mice for 8 weeks. Food and water intake were recorded; obesity was analyzed by body-weight evaluation and magnetic resonance imaging (MRI); intestinal proliferation and survival were evaluated by H&E staining, EdU-progression test, TUNEL assay and immunofluorescence staining of Ki67 and CC3; RNA-seq was performed to analyze transcriptional alterations in small intestines and livers. Results: Chronic CORT treatment induced obesity, longer small intestines, hepatic steatosis and elevated levels of serum insulin and leptin in mice; CORT-treated mice showed increased cell proliferation and decreased apoptosis of small intestines; RNA-seq results indicate that differentially expressed genes (DEGs) were enriched in several cell growth/death-associated signaling pathways. Discussion: Herein we find that administration of CORT to mice promotes the proliferation and survival of intestinal cells, which might contribute to the longer small intestines and the elongated intestinal villi, thus leading to increased nutrient absorption and obesity in mice. Understanding CORT-induced alterations in intestines and associated signaling pathways might provide novel therapeutic clues for GCs or stress-associated obesity.

Elimination of Serotonergic Neurons by Stereotaxic Injection of 5,7-Dihydroxytryptamine in the Dorsal Raphe Nuclei of Mice.

Stereotaxic injection has been widely used for direct delivery of compounds or viruses to targeted brain areas in rodents. Direct targeting of serotonergic neurons in the dorsal raphe nucleus (DRN) can cause excessive bleeding and animal death, due to its location below the superior sagittal sinus (SSS). This protocol describes the generation of a DRN serotonergic neuron-lesioned mouse model (>90% survival rate) with stable loss of >70% 5-HT-positive cells in the DRN. The lesion is induced by stereotaxic injection of a selective serotonergic neurotoxin 5,7-dihydroxytryptamine (5,7-DHT) into the DRN using an angled approach (30° in the anterior/posterior direction) to avoid injury to the SSS. DRN serotonergic neuron-lesioned mice display anxiety-associated behavior alterations, which helps to confirm success of the DRN lesion surgery. This method is used here for DRN lesions, but it can also be used for other stereotaxic injections that require angular injections to avoid midline structures. This DRN serotonergic neuron-lesioned mouse model provides a valuable tool for understanding the role of serotonergic neurons in the pathogenesis of psychiatric disorders, such as generalized anxiety disorder and major depressive disorder.

Real-time detection of mRNA splicing variants with specifically designed reverse-transcription loop-mediated isothermal amplification.

Alternative splicing is a ubiquitous and crucial process in cellular processes and has a specific linkage with diseases. To date, developing cost-effective methods with high sensitivity and specificity for detection of splicing variants has been needed. Herein, we report a novel splicing variant assay based on specifically designed reverse-transcription loop-mediated isothermal amplification. After reverse transcribing the splicing variant into cDNA, four DNA primers are specifically designed to recognize six distinct regions. The four DNA primers can hybridize with corresponding sequences for extension and strand displacement DNA synthesis to form stem-loop DNA and then LAMP amplification is started. The proposed method can detect as low as 100 aM splicing variants in real-time fashion with high specificity, showing great potential in biological function and clinical studies.

Convolutional neural network initialized active contour model with adaptive ellipse fitting for nuclear segmentation on breast histopathological images.

Automated detection and segmentation of nuclei from high-resolution histopathological images is a challenging problem owing to the size and complexity of digitized histopathologic images. In the context of breast cancer, the modified Bloom-Richardson Grading system is highly correlated with the morphological and topological nuclear features are highly correlated with Modified Bloom-Richardson grading. Therefore, to develop a computer-aided prognosis system, automated detection and segmentation of nuclei are critical prerequisite steps. We present a method for automated detection and segmentation of breast cancer nuclei named a convolutional neural network initialized active contour model with adaptive ellipse fitting (CoNNACaeF). The CoNNACaeF model is able to detect and segment nuclei simultaneously, which consist of three different modules: convolutional neural network (CNN) for accurate nuclei detection, (2) region-based active contour (RAC) model for subsequent nuclear segmentation based on the initial CNN-based detection of nuclear patches, and (3) adaptive ellipse fitting for overlapping solution of clumped nuclear regions. The performance of the CoNNACaeF model is evaluated on three different breast histological data sets, comprising a total of 257 H&E-stained images. The model is shown to have improved detection accuracy of F-measure 80.18%, 85.71%, and 80.36% and average area under precision-recall curves (AveP) 77%, 82%, and 74% on a total of 3 million nuclei from 204 whole slide images from three different datasets. Additionally, CoNNACaeF yielded an F-measure at 74.01% and 85.36%, respectively, for two different breast cancer datasets. The CoNNACaeF model also outperformed the three other state-of-the-art nuclear detection and segmentation approaches, which are blue ratio initialized local region active contour, iterative radial voting initialized local region active contour, and maximally stable extremal region initialized local region active contour models.

Gut Microbiota and Relevant Metabolites Analysis in Alcohol Dependent Mice.

Alcohol abuse is a major public health crisis. Relative evidences supported that the gut microbiota (GM) played an important role in central nervous system (CNS) function, and the composition of them had changed after alcohol drinking. We sought to explore the changes of GM in alcohol dependence. In our study, the GM of mice with alcohol administration was detected through analyzed 16S rRNA gene sequencing and the fecal metabolites were analyzed by LC-MS. The microbial diversity was significantly higher in the alcohol administration group, the abundance of phylum Firmicutes and its class Clostridiales were elevated, meanwhile the abundance of Lachnospiraceae, Alistipes, and Odoribacter showed significant differences among the three groups. Based on LC-MS results, bile acid, secondary bile acid, serotonin and taurine level had varying degrees of changes in alcohol model. From paraffin sections, tissue damage was observed in liver and colon. These findings provide direct evidence that alcohol intake affects the composition of GM, enable a better understanding of the function of GM in the microbiota-gut-brain (MGB) axis, and give a new thought for alcohol addiction treatment.

A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images.

Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated segmentation or classification of EP and ST tissues is important when developing computerized system for analyzing the tumor microenvironment. In this paper, a Deep Convolutional Neural Networks (DCNN) based feature learning is presented to automatically segment or classify EP and ST regions from digitized tumor tissue microarrays (TMAs). Current approaches are based on handcraft feature representation, such as color, texture, and Local Binary Patterns (LBP) in classifying two regions. Compared to handcrafted feature based approaches, which involve task dependent representation, DCNN is an end-to-end feature extractor that may be directly learned from the raw pixel intensity value of EP and ST tissues in a data driven fashion. These high-level features contribute to the construction of a supervised classifier for discriminating the two types of tissues. In this work we compare DCNN based models with three handcraft feature extraction based approaches on two different datasets which consist of 157 Hematoxylin and Eosin (H&E) stained images of breast cancer and 1376 immunohistological (IHC) stained images of colorectal cancer, respectively. The DCNN based feature learning approach was shown to have a F1 classification score of 85%, 89%, and 100%, accuracy (ACC) of 84%, 88%, and 100%, and Matthews Correlation Coefficient (MCC) of 86%, 77%, and 100% on two H&E stained (NKI and VGH) and IHC stained data, respectively. Our DNN based approach was shown to outperform three handcraft feature extraction based approaches in terms of the classification of EP and ST regions.

Sparse Non-negative Matrix Factorization (SNMF) based color unmixing for breast histopathological image analysis.

Color deconvolution has emerged as a popular method for color unmixing as a pre-processing step for image analysis of digital pathology images. One deficiency of this approach is that the stain matrix is pre-defined which requires specific knowledge of the data. This paper presents an unsupervised Sparse Non-negative Matrix Factorization (SNMF) based approach for color unmixing. We evaluate this approach for color unmixing of breast pathology images. Compared to Non-negative Matrix Factorization (NMF), the sparseness constraint imposed on coefficient matrix aims to use more meaningful representation of color components for separating stained colors. In this work SNMF is leveraged for decomposing pure stained color in both Immunohistochemistry (IHC) and Hematoxylin and Eosin (H&E) images. SNMF is compared with Principle Component Analysis (PCA), Independent Component Analysis (ICA), Color Deconvolution (CD), and Non-negative Matrix Factorization (NMF) based approaches. SNMF demonstrated improved performance in decomposing brown diaminobenzidine (DAB) component from 36 IHC images as well as accurately segmenting about 1400 nuclei and 500 lymphocytes from H & E images.