Integrative single-cell transcriptomic investigation unveils long non-coding RNAs associated with localized cellular inflammation in psoriasis

Psoriasis is a complex, chronic autoimmune disorder predominantly affecting the skin. Accumulating evidence underscores the critical role of localized cellular inflammation in the development and persistence of psoriatic skin lesions, involving cell types such as keratinocytes, mesenchymal cells, and Schwann cells. However, the underlying mechanisms remain largely unexplored. Long non-coding RNAs (lncRNAs), known to regulate gene expression across various cellular processes, have been particularly implicated in immune regulation. We utilized our neural-network learning pipeline to integrate 106,675 cells from healthy human skin and 79,887 cells from psoriatic human skin. This formed the most extensive cell transcriptomic atlas of human psoriatic skin to date. The robustness of our reclassified cell-types, representing full-layer zonation in human skin, was affirmed through neural-network learning-based cross-validation. We then developed a publicly available website to present this integrated dataset. We carried out analysis for differentially expressed lncRNAs, co-regulated gene patterns, and GO-bioprocess enrichment, enabling us to pinpoint lncRNAs that modulate localized cellular inflammation in psoriasis at the single-cell level. Subsequent experimental validation with skin cell lines and primary cells from psoriatic skin confirmed these lncRNAs’ functional role in localized cellular inflammation. Our study provides a comprehensive cell transcriptomic atlas of full-layer human skin in both healthy and psoriatic conditions, unveiling a new regulatory mechanism that governs localized cellular inflammation in psoriasis and highlights the therapeutic potential of lncRNAs in this disease’s management

Morphological and functional evaluation of an animal model for the retinal degeneration induced by N-methyl-N-nitrosourea

The retinal degeneration (RD) is a general cause of blindness. To study its pathophysiology and evaluate the effects of new therapeutic agents before clinical trials, it is essential to establish reliable and stable animal models. This study evaluated a RD animal model in which blindness was induced by N-methyl-N-nitrosourea (MNU), a potent retinotoxin leading to apoptosis of photoreceptors. MNU was applied to the Sprague-Dawley rats by a single intraperitoneal injection in different doses (40, 50, and 60 mg/kg). The retinal functions were examined at 1 week after MNU injection by electroretinogram (ERG). Afterwards, each retina was examined by hematoxylin and eosin stain and immunohistochemistry with anti-glial fibrillary acidic protein antibody. Upon MNU injection of 40, 50 and 60 mg/kg, the ERG amplitude of a-waves showed significant reductions of 7, 26, and 44%, respectively, when compared to that of normal a-waves. The b-wave amplitudes were about 89, 65, and 58% of normal b-waves in the response to scotopic light stimulus. At 1 week, 2 weeks, and 4 weeks after MNU injection (50 mg/kg), all scotopic ERG components decreased progressively. In addition, degeneration of retinal neurons was observed in a time- and dose-dependent manner after MNU injection. Taken together, functional reduction following RD induced by MNU correlates with morphological changes. Thus, this RD rat model may be a useful model to study its pathophysiology and to evaluate the effects of new therapeutic agents before clinical trials

Identification of Ratholes in Desert Steppe Based on UAV Hyperspectral Remote Sensing

This paper established a mathematical method for the spectral feature extraction of ratholes, based on UAV hyperspectral imaging technology. The degradation of grasslands is a major challenge to terrestrial ecosystems. Rodents not only promote soil erosion and accelerate the process of grassland degradation, but also carry diseases that can easily cause epidemics. The calculation of the number of rodent holes and grassland vegetation cover is an important indicator for monitoring and evaluating grassland degradation. Manual surveys have drawbacks in efficiently monitoring large areas and are human- and material-costly, hardly meeting the current needs of grassland degradation monitoring. Therefore, there is an urgent need to conduct real-time dynamic monitoring of grassland rathole distributions and grassland degradation processes. In this study, a low-altitude remote sensing platform was constructed by integrating a hyperspectral imager with a UAV to collect spectral data of the desert steppes in central Inner Mongolia Autonomous Region, China. Then, the spectral features of ratholes were extracted via radiation correction, noise reduction, and principal component analysis (PCA). Meanwhile, the spectral features of vegetation and bare soil were extracted based on the normalized difference vegetation index (NDVI), which was inputted to calculate the vegetation cover. The results showed that the single-band map extracted based on PCA could effectively determine the location of ratholes, where the overall accuracy and kappa coefficient were 97% and 0.896, respectively. Therefore, the method proposed in this study can accurately identify the location of desert steppe rodent holes. It provides a high-precision technical means for scientific and effective control of grassland rodent infestation and also provides a higher technical means for grassland degradation

Research on Classification of Grassland Degeneration Indicator Objects Based on UAV Hyperspectral Remote Sensing and 3D_RNet-O Model

Real-time and high-precision land cover classification is the foundation for efficient and quantitative research on grassland degradation using remote sensing techniques. In view of the shortcomings of manual surveying and satellite remote sensing, this study focuses on the identification and classification of grass species indicating grassland degradation. We constructed a UAV-based hyperspectral remote sensing system and collected field data in grassland areas. By applying artificial intelligence technology, we developed a 3D_RNet-O model based on convolutional neural networks, effectively addressing technical challenges in hyperspectral remote sensing identification and classification of grassland degradation indicators, such as low reflectance of vegetation, flat spectral curves, and sparse distribution. The results showed that the model achieved a classification accuracy of 99.05% by optimizing hyperparameter combinations based on improving residual block structures. The establishment of the UAV-based hyperspectral remote sensing system and the proposed 3D_RNet-O classification model provide possibilities for further research on low-altitude hyperspectral remote sensing in grassland ecology

Single cell transcriptional zonation of human psoriasis skin identifies an alternative immunoregulatory axis conducted by skin resident cells

Psoriasis is the most common skin disease in adults. Current experimental and clinical evidences suggested the infiltrating immune cells could target local skin cells and thus induce psoriatic phenotype. However, recent studies indicated the existence of a potential feedback signaling loop from local resident skin cells to infiltrating immune cells. Here, we deconstructed the full-thickness human skins of both healthy donors and patients with psoriasis vulgaris at single cell transcriptional level, and further built a neural-network classifier to evaluate the evolutional conservation of skin cell types between mouse and human. Last, we systematically evaluated the intrinsic and intercellular molecular alterations of each cell type between healthy and psoriatic skin. Cross-checking with psoriasis susceptibility gene loci, cell-type based differential expression, and ligand-receptor communication revealed that the resident psoriatic skin cells including mesenchymal and epidermis cell types, which specifically harbored the target genes of psoriasis susceptibility loci, intensively evoked the expression of major histocompatibility complex (MHC) genes, upregulated interferon (INF), tumor necrosis factor (TNF) signalling and increased cytokine gene expression for primarily aiming the neighboring dendritic cells in psoriasis. The comprehensive exploration and pathological observation of psoriasis patient biopsies proposed an uncovered immunoregulatory axis from skin local resident cells to immune cells, thus provided a novel insight for psoriasis treatment. In addition, we published a user-friendly website to exhibit the transcriptional change of each cell type between healthy and psoriatic human skin.Peer reviewe

EGF/EGFR upregulates and cooperates with Netrin-4 to protect glioblastoma cells from DNA damage-induced senescence

BackgroundGlioblastoma multiforme (GBM) is the most malignant central nervous system tumor. Alkylating agent, temozolomide (TMZ), is currently the first-line chemotherapeutic agent for GBM. However, the sensitivity of GBM cells to TMZ is affected by many factors. And, several clinic trials, including co-administration of TMZ with other drugs, have failed in successful treatment of GBM. We have previously reported that Netrin-4 (NTN4), a laminin-like axon guidance protein, plays a protective role in GBM cell senescence upon TMZ-triggered DNA damage. However, the master regulator of NTN4 needs further elucidation. Epidermal growth factor/Epidermal growth factor receptor (EGF/EGFR) can modulate the expression of various extracellular matrix related molecules, and prevent DNA damage in GBM cells. In this study, we investigated the relationship between EGF/EGFR signaling and NTN4, and explored their effect on therapeutic efficacy in GBM cells upon TMZ treatment.MethodsCo-expression analysis were performed by using the RNA sequencing data from NIH 934 cell lines and from single cell RNA sequencing data of GBM tumor. The co-expressing genes were used for GO enrichment and signaling pathway enrichment. mRNA expression of the target genes were quantified by qPCR, and cell senescence were investigated by Senescence-Associated Beta-Galactosidase Staining. Protein phosphorylation were observed and analyzed by immunoblotting. The RNA sequencing data and clinical information of TMZ treated patients were extracted from TCGA-glioblastoma project, and then used for Kaplan-Meier survival analysis.ResultsAnalysis of RNA sequencing data revealed a potential co-expression relationship between NTN4 and EGFR. GO enrichment of EGFR-correlated genes indicated that EGFR regulates GBM cells in a manner similar to that in central nervous system development and neural cell differentiation. Pathway analysis suggested that EGFR and its related genes contribute to cell adhesion, extracellular matrix (ECM) organization and caspase related signaling. We also show that EGF stimulates NTN4 expression in GBM cells and cooperates with NTN4 to attenuate GBM cell senescence induced by DNA damage, possibly via AKT and ERK. Clinical analysis showed that co-expression of EGFR and NTN4 significantly predicts poor survival in TMZ-treated GBM patients.ConclusionsThis study indicates that EGF/EGFR regulates and cooperates with NTN4 in DNA damage resistance in GBM. Therefore, our findings provide a potential therapeutic target for GBM.Peer reviewe

A novel vented tunnel hood with decreasing open ratio to mitigate micro-pressure wave emitted at high-speed maglev tunnel exit

The significant increase in train speed contributes to stronger vehicle/tunnel coupling aerodynamic effect, especially on the intensity of the micro-pressure wave (MPW) emitted at high-speed maglev tunnel exit. Hence when the train speed reaches 600 km/h and more, how to effectively mitigate the MPW becomes a challenge for aerodynamic researchers. In this study, a novel vented tunnel hood with the decreasing open ratio along the enlarged cross-section wall was proposed, while one consistent and two inconsistent layouts of the hoods applied at the tunnel portals were attempted to obtain a better hood combination for the mitigation of MPW. In addition, the sliding mesh technique was used to simulate the train passing through the single-track high-speed maglev tunnel. The validation of the methodology has been carried out to compare with the previous moving model test results. The peak variations of pressure wave and MPW were analysed in combination with the grid-independence study and numerical validation. The new hoods installed consistently at the tunnel portals (entrance and exit), can reduce the maximums of MPWs at required locations, i.e., 20m and 50m from the tunnel exit, by 66.9% and 40.9% respectively, when compared to the existing unvented tunnel hood; however, when the new hood at the tunnel exit is replaced by the existing tunnel hood without vents, the maximums of MPWs at the corresponding 20m and 50m are significantly increased by 79.1% and 71.0%. After changing the set-up of the inconsistent hoods, i.e., the tunnel entrance hood is unvented and this novel hood is installed at the tunnel exit, the corresponding MPW peaks can be reduced by 84.0% and 71.1%. Therefore, the hoods at both of tunnel entrance and exit can affect the variations of MPWs, and a reasonable arrangement of the hood openings at tunnel portals can effectively mitigate the MPW emitted at the high-speed maglev tunnel exit

Partial Least-Squares-Discriminant Analysis Differentiating Chinese Wolfberries by UPLC–MS and Flow Injection Mass Spectrometric (FIMS) Fingerprints

<i>Lycium barbarum</i> L. fruits (Chinese wolfberries) were differentiated for their cultivation locations and the cultivars by ultraperformance liquid chromatography coupled with mass spectrometry (UPLC–MS) and flow injection mass spectrometric (FIMS) fingerprinting techniques combined with chemometrics analyses. The partial least-squares-discriminant analysis (PLS-DA) was applied to the data projection and supervised learning with validation. The samples formed clusters in the projected data. The prediction accuracies by PLS-DA with bootstrapped Latin partition validation were greater than 90% for all models. The chemical profiles of Chinese wolfberries were also obtained. The differentiation techniques might be utilized for Chinese wolfberry authentication

Table_1_Integrative single-cell transcriptomic investigation unveils long non-coding RNAs associated with localized cellular inflammation in psoriasis.xlsx

Psoriasis is a complex, chronic autoimmune disorder predominantly affecting the skin. Accumulating evidence underscores the critical role of localized cellular inflammation in the development and persistence of psoriatic skin lesions, involving cell types such as keratinocytes, mesenchymal cells, and Schwann cells. However, the underlying mechanisms remain largely unexplored. Long non-coding RNAs (lncRNAs), known to regulate gene expression across various cellular processes, have been particularly implicated in immune regulation. We utilized our neural-network learning pipeline to integrate 106,675 cells from healthy human skin and 79,887 cells from psoriatic human skin. This formed the most extensive cell transcriptomic atlas of human psoriatic skin to date. The robustness of our reclassified cell-types, representing full-layer zonation in human skin, was affirmed through neural-network learning-based cross-validation. We then developed a publicly available website to present this integrated dataset. We carried out analysis for differentially expressed lncRNAs, co-regulated gene patterns, and GO-bioprocess enrichment, enabling us to pinpoint lncRNAs that modulate localized cellular inflammation in psoriasis at the single-cell level. Subsequent experimental validation with skin cell lines and primary cells from psoriatic skin confirmed these lncRNAs’ functional role in localized cellular inflammation. Our study provides a comprehensive cell transcriptomic atlas of full-layer human skin in both healthy and psoriatic conditions, unveiling a new regulatory mechanism that governs localized cellular inflammation in psoriasis and highlights the therapeutic potential of lncRNAs in this disease’s management.</p

Coprecipitation Synthesis of Large-Pore-Volume γ‑Alumina Nanofibers by Two Serial Membrane Dispersion Microreactors with a Circulating Continuous Phase

A coprecipitation method was developed for the synthesis of fibrous γ-alumina using serial membrane dispersion microreactors with a circulating continuous phase and high concentrations of NaAlO2 and Al2(SO4)3 as reactants. Owing to the ultra-high mixing intensity and reduction of supersaturation due to the large circular phase ratio, a large pore volume and specific surface area and an extremely narrow pore diameter distribution were realized using the high-concentration and high-viscosity precipitation system. The influence of the phase ratio, dispersion order of reactants, Al2(SO4)3 residence time, and the precipitation reaction pH and time were investigated, and the nanofiber formation mechanism was explored employing theoretical calculations. By controlling the Al2(SO4)3 residence time of 3 s, phase ratio of 16, and pH of 8.0, γ-Al2O3 nanofibers with a pore volume of 1.36 cm3/g, a specific surface area of 376 m2/g, and a length/diameter ratio in the range of 30–54 were obtained without any organic reagents. This study provides an economical and readily scalable method for the synthesis of fibrous γ-Al2O3 with excellent pore properties and a large specific surface area, which can potentially be applied as an excellent catalyst support for diesel and bio-oil hydrogenation