Unveiling inflammatory and prehypertrophic cell populations as key contributors to knee cartilage degeneration in osteoarthritis using multi-omics data integration.

OBJECTIVES: Single-cell and spatial transcriptomics analysis of human knee articular cartilage tissue to present a comprehensive transcriptome landscape and osteoarthritis (OA)-critical cell populations. METHODS: Single-cell RNA sequencing and spatially resolved transcriptomic technology have been applied to characterise the cellular heterogeneity of human knee articular cartilage which were collected from 8 OA donors, and 3 non-OA control donors, and a total of 19 samples. The novel chondrocyte population and marker genes of interest were validated by immunohistochemistry staining, quantitative real-time PCR, etc. The OA-critical cell populations were validated through integrative analyses of publicly available bulk RNA sequencing data and large-scale genome-wide association studies. RESULTS: We identified 33 cell population-specific marker genes that define 11 chondrocyte populations, including 9 known populations and 2 new populations, that is, pre-inflammatory chondrocyte population (preInfC) and inflammatory chondrocyte population (InfC). The novel findings that make this an important addition to the literature include: (1) the novel InfC activates the mediator MIF-CD74; (2) the prehypertrophic chondrocyte (preHTC) and hypertrophic chondrocyte (HTC) are potentially OA-critical cell populations; (3) most OA-associated differentially expressed genes reside in the articular surface and superficial zone; (4) the prefibrocartilage chondrocyte (preFC) population is a major contributor to the stratification of patients with OA, resulting in both an inflammatory-related subtype and a non-inflammatory-related subtype. CONCLUSIONS: Our results highlight InfC, preHTC, preFC and HTC as potential cell populations to target for therapy. Also, we conclude that profiling of those cell populations in patients might be used to stratify patient populations for defining cohorts for clinical trials and precision medicine.

Diagnostic and grading criteria for androgenetic alopecia using dermoscopy.

OBJECTIVES: To establish accurate and objective dermoscopic diagnostic criteria and grading standards for males and females with androgenetic alopecia (AGA). METHODS: Twenty patients each with AGA, diffuse alopecia areata, telogen effluvium, and healthy controls were enrolled in the current study. In addition, 60 patients with grades F1/V1, F2/V2, and F3/V3 AGA (20 cases each) were enrolled. The patients underwent dermoscopic examinations. The sensitivity and specificity of the diagnostic criteria were based on the 60 AGA and 60 non-AGA. In addition, 150 patients diagnosed with AGA clinically and by dermoscopy were enrolled to calculate the accuracy of the grading criteria. RESULTS: The diagnostic criteria included primary, secondary, and exclusion criteria. The grading criteria included three indices, which divided the severity of AGA into grades 1, 2, and 3. The sensitivity and specificity of the diagnostic criteria were 98.3% and 96.7% respectively. The accuracy of grade 1, 2, and 3 dermoscopic grading criteria were 96%, 92%, and 100% respectively, with a total accuracy of 96%. LIMITATIONS: To test the diagnostic and grading criteria, more patients need to be collected. CONCLUSIONS: The dermoscopic diagnostic and grading criteria are objective with good accuracy, which could provide a reasonable basis for the early diagnosis, grading treatment, and improved prognosis for AGA.

Performance of a polymerization-based electrochemically assisted persulfate process on a real coking wastewater treatment.

Industrial wastewater should be treated with caution due to its potential environmental risks. In this study, a polymerization-based cathode/Fe3+/peroxydisulfate (PDS) process was employed for the first time to treat a raw coking wastewater, which can achieve simultaneous organics abatement and recovery by converting organic contaminants into separable solid organic-polymers. The results confirm that several dominant organic contaminants in coking wastewater such as phenol, cresols, quinoline and indole can be induced to polymerize by self-coupling or cross-coupling. The total chemical oxygen demand (COD) abatement from coking wastewater is 46.8% and the separable organic-polymer formed from organic contaminants accounts for 62.8% of the abated COD. Dissolved organic carbon (DOC) abatement of 41.9% is achieved with about 89% less PDS consumption than conventional degradation-based process. Operating conditions such as PDS concentration, Fe3+ concentration and current density can affect the COD/DOC abatement and organic-polymer yield by regulating the generation of reactive radicals. ESI-MS result shows that some organic-polymers are substituted by inorganic ions such as Cl-, Br-, I-, NH4+, SCN- and CN-, suggesting that these inorganic ions may be involved in the polymerization. The specific consumption of this coking wastewater treatment is 27 kWh/kg COD and 95 kWh/kg DOC. The values are much lower than those of the degradation-based processes in treating the same coking wastewater, and also are lower than those of most processes previously reported for coking wastewater treatment.

Efficient removal of tetracycline hydrochloride through novel Fe/BiOBr/Bi2WO6 photocatalyst prepared by dual-strategy under visible-light irradiation.

It is important to investigate whether combining two modification strategies has a synergistic effect on the activity of photocatalysts. In this manuscript, Fe-doped BiOBr/Bi2WO6 heterojunctions were synthesized by a one-pot solvothermal method, and excellent photocatalytic performance was obtained for the degradation of tetracycline hydrochloride (TCH) in water without the addition of surfactant. Combining experiments and characterization, the synergistic effect between Fe ion doping and the BiOBr/Bi2WO6 heterojunction was elucidated. The Fe/BiOBr/Bi2WO6 composite photocatalyst had a beneficial void structure, enhanced visible light response, and could inhibit the recombination of photogenerated support well, which improved the photocatalytic activity. The presented experiments demonstrate that Fe/BiOBr/Bi2WO6 removes 97% of TCH from aqueous solution, while pure BiOBr and Bi2WO6 only remove 56% and 65% of TCH, respectively. Finally, the separation and transfer mechanisms of photoexcited carriers were determined in conjunction with the experimental results. This study provides a new direction for the design of efficient photocatalysts through the use of a dual co-modification strategy.

Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies.

Identifying genes that display spatial expression patterns in spatially resolved transcriptomic studies is an important first step toward characterizing the spatial transcriptomic landscape of complex tissues. Here we present a statistical method, SPARK, for identifying spatial expression patterns of genes in data generated from various spatially resolved transcriptomic techniques. SPARK directly models spatial count data through generalized linear spatial models. It relies on recently developed statistical formulas for hypothesis testing, providing effective control of type I errors and yielding high statistical power. With a computationally efficient algorithm, which is based on penalized quasi-likelihood, SPARK is also scalable to datasets with tens of thousands of genes measured on tens of thousands of samples. Analyzing four published spatially resolved transcriptomic datasets using SPARK, we show it can be up to ten times more powerful than existing methods and disclose biological discoveries that otherwise cannot be revealed by existing approaches.

Aspartate beta-hydroxylase domain containing 1 as a prognostic marker associated with immune infiltration in skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is an extremely malignant tumor and accounts for the majority of skin cancer deaths. Aspartate beta-hydroxylase domain containing 1 (ASPHD1) may participate in cancer progression through controlling α-ketoglutarate-dependent dioxygenases. However, its role in skin cutaneous melanoma (SKCM) has not been well studied. METHODS: The gene expression data of ASPDH1 and differentially expressed genes (DEGs) from TCGA and GTEx were evaluated, and verified via the GEO database. Then, we performed GO/KEGG, GSEA, PPI network analysis to analyze the functional implications of the DEGs related to ASPHD1. Then, the association between the ASPHD1 expression and clinical parameters was investigated by Cox regression analysis. Subsequently, the survival time of SKCM patients was evaluated by plotting Kaplan-Meier curves. Moreover, we investigated the correlation between the ASPHD1 expression and lymphocytic infiltration by using the data from TISIDB and TIMER 2.0. Next, we explored the association between ASPHD1 expression and drug sensitivity. Finally, we validate the expression differences by analyzing the results of qPCR, Western blot from human normal epidermal melanocytes and melanoma cells, and immunohistochemistry (IHC) from non-tumor skin as well as melanoma tissues. RESULTS: The ASPHD1 expression level was significantly upregulated in several cancers, including SKCM especially SKCM-metastasis tissues, and patients with an increased ASPHD1 expression had longer overall survival time than low expression ones. The functional enrichment analysis of ASPHD1-related DEGs showed an association with cell development regulation and tumorigenic pathways. Furthermore, the increased ASPHD1 expression level was associated with the level of immunostimulors, immunoinhibitors, chemokines, and TILs, such as CD4+, CD8+ T cell, mast cell, Th2 cell, and dendritic cell. More interesting, we found that ASPHD1 expression was tightly associated with CTLA4 and CD276 which are immune checkpoint markers. Moreover, the upregulated expression of ASPHD1 exhibited higher IC50 values for 24 chemotherapy drugs, including doxorubicin, and masitinib. Finally, the differential expression of ASPHD1 in SKCM was validated by the results of qPCR, Western blot, and IHC. CONCLUSION: The expression of ASPHD1 in SKCM patients is closely related to patient survival. ASPHD1 may participate in the regulation of tumor immune microenvironment. Additionally, it may serve as a prognostic biomarker for SKCM and future in-depth studies are necessary to explore its value.

Photocatalytic degradation of organic pollutants by carbon quantum dots functionalized g-C3N4: A review.

Graphitic carbon nitride (g-C3N4) has received much attention due to its unique characteristics of stable physicochemical features, facile preparation, and inexpensive cost. However, the bulk g-C3N4 has a weak capacity for pollutant degradation and needs to be modified for real application. Therefore, extensive research has been done on g-C3N4, and the discovery of the novel zero-dimensional nanomaterials known as carbon quantum dots (CQDs) provided it with a unique modification option. In this review, the development for the removal of organic pollutants by g-C3N4/CQDs was discussed. Firstly, the preparation of g-C3N4/CQDs were introduced. Then, the application and the degradation mechanism of g-C3N4/CQDs were briefly described. And the discussion of the influencing factors on g-C3N4/CQDs' ability to degrade organic pollutants came in third. Finally, the conclusions of photocatalytic degradation of organic pollutants by g-C3N4/CQDs and future perspectives followed. This review will strengthen the understanding of the photocatalytic degradation of real organic wastewater by g-C3N4/CQDs, including their preparation, application, mechanism, and influencing factors.

WISP1 Is Involved in the Pathogenesis of Kashin-Beck Disease via the Autophagy Pathway.

OBJECTIVE: Kashin-Beck disease (KBD) is a kind of endemic and chronic osteochondropathy in China. This study aims to explore the functional relevance and potential mechanism of Wnt-inducible signaling pathway protein 1 (WISP1) in the pathogenesis of KBD. DESIGN: KBD and control cartilage specimens were collected for tissue section observation and primary chondrocyte culture. Firstly, the morphological and histopathological observations were made under a light and electron microscope. Then, the expression levels of WISP1 as well as molecular markers related to the autophagy pathway and extracellular matrix (ECM) synthesis were detected in KBD and control chondrocytes by qRT-PCR, Western blot, and immunohistochemistry. Furthermore, the lentiviral transfection technique was applied to make a WISP1 knockdown cell model based on KBD chondrocytes. In vitro intervention experiments were conducted on the C28/I2 human chondrocyte cell line using human recombinant WISP1 (rWISP1). RESULTS: The results showed that the autolysosome appeared in the KBD chondrocytes. The expression of WISP1 was significantly higher in KBD chondrocytes. Additionally, T-2 toxin, a risk factor for KBD onset, could up-regulate the expression of WISP1 in C28/I2. The autophagy markers ATG4C and LC3II were upregulated after the low-concentration treatment of T-2 toxin and downregulated after the high-concentration treatment. After knocking down WISP1 expression in KBD chondrocytes, MAP1LC3B decreased while ATG4C and COL2A1 increased. Moreover, the rWISP1 protein treatment in C28/I2 chondrocytes could upregulate the expression of ATG4C and LC3II at the beginning and downregulate them then. CONCLUSIONS: Our study suggested that WISP1 might play a role in the pathogenesis of KBD through autophagy.

Insights into a novel nitrogen removal process based on simultaneous anammox and denitrification (SAD) following nitritation with in-situ NOB elimination.

Simultaneous anammox and denitrification (SAD) is an efficient approach to treat wastewater having a low C/N ratio; however, few studies have investigated a combination of SAD and partial nitritation (PN). In this study, a lab-scale up-flow blanket filter (UBF) and zeolite sequence batch reactor (ZSBR) were continuously operated to implement SAD and PN advantages, respectively. The UBF achieved a high total nitrogen (TN) removal efficiency of over 70% during the start-up stage (days 1-50), and reached a TN removal efficiency of 96% in the following 90 days (days 51-140) at COD/NH4+-N ratio of 2.5. The absolute abundance of anammox bateria increased to the highest value of 1.58 × 107 copies/µL DNA; Comamonadaceae was predominant in the UBF at the optimal ratio. Meanwhile, ZSBR was initiated on day 115 as fast nitritation process to satisfy the influent requirement for the UBF. The combined process was started on day 140 and then lasted for 30 days, during the combined process, between the two reactors, the UBF was the main contributor for TN (66.5% ± 4.5%) and COD (71.8% ± 4.9%) removal. These results demonstrated that strong SAD occurred in the UBF when following a ZSBR with in-situ NOB elimination. This research presents insights into a novel biological nitrogen removal process for low C/N ratio wastewater treatment.

A novel nonparametric computational strategy for identifying differential methylation regions.

BACKGROUND: DNA methylation has long been known as an epigenetic gene silencing mechanism. For a motivating example, the methylomes of cancer and non-cancer cells show a number of methylation differences, indicating that certain features characteristics of cancer cells may be related to methylation characteristics. Robust methods for detecting differentially methylated regions (DMRs) could help scientists narrow down genome regions and even find biologically important regions. Although some statistical methods were developed for detecting DMR, there is no default or strongest method. Fisher's exact test is direct, but not suitable for data with multiple replications, while regression-based methods usually come with a large number of assumptions. More complicated methods have been proposed, but those methods are often difficult to interpret. RESULTS: In this paper, we propose a three-step nonparametric kernel smoothing method that is both flexible and straightforward to implement and interpret. The proposed method relies on local quadratic fitting to find the set of equilibrium points (points at which the first derivative is 0) and the corresponding set of confidence windows. Potential regions are further refined using biological criteria, and finally selected based on a Bonferroni adjusted t-test cutoff. Using a comparison of three senescent and three proliferating cell lines to illustrate our method, we were able to identify a total of 1077 DMRs on chromosome 21. CONCLUSIONS: We proposed a completely nonparametric, statistically straightforward, and interpretable method for detecting differentially methylated regions. Compared with existing methods, the non-reliance on model assumptions and the straightforward nature of our method makes it one competitive alternative to the existing statistical methods for defining DMRs.

The pyroptosis-related gene signature predicts prognosis and indicates immune activity in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) remains one of the most common malignant tumors with poor survival. Pyroptosis is a kind of programmed cell death that can regulate the proliferation, invasion, and metastasis of tumor cells. However, the expression levels of pyroptosis-related genes (PRGs) in HCC and their relationship with prognosis are still unclear. METHODS: Our study identified 35 PRGs through bioinformatics analysis that were differentially expressed between tumor samples and nontumor samples. According to these differentially expressed genes, HCC patients could be divided into two groups, cluster 1 and cluster 2. The least absolute shrinkage and selection operator (LASSO) Cox regression method was performed to construct a 10-gene signature that classified HCC patients in the cancer genome atlas (TCGA) database into low-risk and high-risk groups. RESULTS: The results showed that the survival rate of HCC patients in the low-risk group was significantly higher than that in the high-risk group (p < 0.001). The validation cohort, the Gene Expression Omnibus (GEO) cohort, was divided into two risk groups based on the median risk score calculated by the TCGA cohort. The overall survival (OS) of the low-risk group was significantly better than that of the high-risk group (p = 0.007). Univariate and multivariate Cox regression analyses revealed that the risk score was an independent factor in predicting OS in HCC patients. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that immune-related high-risk groups were rich in genes and had reduced immune status. CONCLUSIONS: PRGs play a significant role in tumor immunity and have the potential capability to predict the prognosis of HCC patients.

Fabrication of g-C3N4@Bi2MoO6@AgI floating sponge for photocatalytic inactivation of Microcystis aeruginosa under visible light.

In this work, a floating photocatalyst was constructed by loading g-C3N4@Bi2MoO6@AgI (GBA) nanocomposite on a modified polyurethane sponge via a simple dip-coating method and applied for the inactivation of Microcystis aeruginosa under visible light. GBA ternary photocatalyst was fabricated successfully and the morphology, structure, chemical state, and optical properties were characterized systematically. The floating catalyst achieved near 100% removal efficiency of algae cells under 6 h visible light irradiation and also could be retrieved and used at least three times repeatedly. The influences of various conditions on photocatalytic performance such as loading content of nanoparticles, algae density, and concentration of natural organic matters were also studied, which revealed that the GBA floating catalyst exhibited excellent photocatalytic performance of algae removal under different conditions. Furthermore, the physiological characteristics of algae cells during the photocatalytic process, including cell morphology, membrane permeability, Zeta potential, photosynthetic system, antioxidant system, and the metabolic activity were investigated. Results confirmed that the algae cells were severely damaged during the photocatalytic inactivation and the normal physiological functions were significantly affected, which resulted in the death of algae cells at last. Finally, a possible photocatalytic inactivation mechanism of algae cells was proposed. In summary, GBA floating catalyst can effectively inactivate Microcystis aeruginosa under visible light, which confirmed the high efficiency of the novel photocatalytic algae removal technology. Meanwhile, the recyclable floating material also makes the practical application in eutrophic waters of the algae removal technology possible.

Efficient adsorption of tetracycline in aquatic system by thermally-treated sediment.

The disposal of dredged sediment is a considerable challenge for environmental protection and resource utilization. In this study, the dredged sediment was thermally-treated to prepare as adsorbent and utilized for tetracycline adsorption. Sediments based adsorbents under different pyrolysis temperature and atmosphere (N2 and limited oxygen) were obtained and 600 °C and N2 atmosphere (600AN) exhibited maximum TC adsorption capacity (15.45 mg/g). SEM, N2 adsorption-desorption isotherm, XRD, FTIR and XPS analysis suggested larger pore volume, relatively higher surface area, effective pore size distribution and abundant surface functional groups were the main reasons. Moreover, the influence of key adsorption parameters, including adsorbent dosage, initial pH, coexisting ions, ionic strength, contact time, initial TC concentration and ambient temperature had also been investigated. Results revealed that TC adsorption by 600AN were more consistent with pseudo-second order kinetic and Freundlich isothermal models. Combined with characterization results, which reasonably inferred that the adsorption mechanisms of 600AN were mainly involved pore-filling effect, hydrogen bonding interaction and π-π EDA interaction. This work has provided a low-cost, high efficiency and promising method for the dredged sediment reduction and resource recovery.

Identifying discriminative features for diagnosis of Kashin-Beck disease among adolescents.

INTRODUCTION: Diagnosing Kashin-Beck disease (KBD) involves damages to multiple joints and carries variable clinical symptoms, posing great challenge to the diagnosis of KBD for clinical practitioners. However, it is still unclear which clinical features of KBD are more informative for the diagnosis of Kashin-Beck disease among adolescent. METHODS: We first manually extracted 26 possible features including clinical manifestations, and pathological changes of X-ray images from 400 KBD and 400 non-KBD adolescents. With such features, we performed four classification methods, i.e., random forest algorithms (RFA), artificial neural networks (ANNs), support vector machines (SVMs) and linear regression (LR) with four feature selection methods, i.e., RFA, minimum redundancy maximum relevance (mRMR), support vector machine recursive feature elimination (SVM-RFE) and Relief. The performance of diagnosis of KBD with respect to different classification models were evaluated by sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). RESULTS: Our results demonstrated that the 10 out of 26 discriminative features were displayed more powerful performance, regardless of the chosen of classification models and feature selection methods. These ten discriminative features were distal end of phalanges alterations, metaphysis alterations and carpals alterations and clinical manifestations of ankle joint movement limitation, enlarged finger joints, flexion of the distal part of fingers, elbow joint movement limitation, squatting limitation, deformed finger joints, wrist joint movement limitation. CONCLUSIONS: The selected ten discriminative features could provide a fast, effective diagnostic standard for KBD adolescents.

Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies.

Motivation: Genomic sequencing studies, including RNA sequencing and bisulfite sequencing studies, are becoming increasingly common and increasingly large. Large genomic sequencing studies open doors for accurate molecular trait heritability estimation and powerful differential analysis. Heritability estimation and differential analysis in sequencing studies requires the development of statistical methods that can properly account for the count nature of the sequencing data and that are computationally efficient for large datasets. Results: Here, we develop such a method, PQLseq (Penalized Quasi-Likelihood for sequencing count data), to enable effective and efficient heritability estimation and differential analysis using the generalized linear mixed model framework. With extensive simulations and comparisons to previous methods, we show that PQLseq is the only method currently available that can produce unbiased heritability estimates for sequencing count data. In addition, we show that PQLseq is well suited for differential analysis in large sequencing studies, providing calibrated type I error control and more power compared to the standard linear mixed model methods. Finally, we apply PQLseq to perform gene expression heritability estimation and differential expression analysis in a large RNA sequencing study in the Hutterites. Availability and implementation: PQLseq is implemented as an R package with source code freely available at www.xzlab.org/software.html and https://cran.r-project.org/web/packages/PQLseq/index.html. Supplementary information: Supplementary data are available at Bioinformatics online.

Differential expression analysis for RNAseq using Poisson mixed models.

Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is among the most common analyses in genomics. However, RNAseq DE analysis presents several statistical and computational challenges, including over-dispersed read counts and, in some settings, sample non-independence. Previous count-based methods rely on simple hierarchical Poisson models (e.g. negative binomial) to model independent over-dispersion, but do not account for sample non-independence due to relatedness, population structure and/or hidden confounders. Here, we present a Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence. We also develop a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution. With simulations, we show that our method properly controls for type I error and is generally more powerful than other widely used approaches, except in small samples (n <15) with other unfavorable properties (e.g. small effect sizes). We also apply our method to three real datasets that contain related individuals, population stratification or hidden confounders. Our results show that our method increases power in all three data compared to other approaches, though the power gain is smallest in the smallest sample (n = 6). Our method is implemented in MACAU, freely available at www.xzlab.org/software.html.

Higher-order partial least squares for predicting gene expression levels from chromatin states.

BACKGROUND: Extensive studies have shown that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns are still unclear. To further understand the relationship between histone modifications and gene expression levels, here in this paper, we introduce a purely geometric higher-order representation, tensor (also called multidimensional array), which might borrow more unknown interactions in chromatin states to predicting gene expression levels. RESULTS: The prediction models were learned from regions around upstream 10k base pairs and downstream 10k base pairs of the transcriptional start sites (TSSs) on three species (i.e., Human, Rhesus Macaque, and Chimpanzee) with five histone modifications (i.e., H3K4me1, H3K4me3, H3K27ac, H3K27me3, and Pol II). Experimental results demonstrate that the proposed method is more powerful to predicting gene expression levels than several other popular methods. Specifically, our method enable to get more powerful performance on both commonly used criteria, R and RMSE, as high as 1.7% and 11%, respectively. CONCLUSIONS: The overall aim of this work is to show that the higher-order representation is able to include more unknown interaction information between histone modifications across different species.

Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq.

We present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.

Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies.

Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

Enhancing cell pyroptosis with biomimetic nanoparticles for melanoma chemo-immunotherapy.

Despite the fact that immunotherapy has significantly improved the prognosis of melanoma patients, the non-response rate of monoimmunotherapy is considerably high due to insufficient tumor immunogenicity. Therefore, it is necessary to develop alternative methods of combination therapy with enhanced antitumor efficiency and less systemic toxicity. In this study, we reported a cancer cell membrane-coated zeolitic imidazole framework-8 (ZIF-8) encapsulating pyroptosis-inducer oxaliplatin (OXA) and immunomodulator imiquimod (R837) for chemoimmunotherapy. With the assistance of DNA methyltransferase inhibitor decitabine (DCT), upregulated Gasdermin E (GSDME) was cleaved by OXA-activated caspase-3, further inducing tumor cell pyroptosis, then localized antitumor immunity was enhanced by immune adjuvant R837, followed by triggering systemic antitumor immune responses. These results provided a proof-of-concept for the use of cell membrane-coated biomimetic nanoparticles as a promising drug carrier of combination therapy and a potential insight for pyroptosis-based melanoma chemo-immunotherapy.