Characterization of human pluripotent stem cell differentiation by single-cell dual-omics analyses.

In vivo differentiation of human pluripotent stem cells (hPSCs) has unique advantages, such as multilineage differentiation, angiogenesis, and close cell-cell interactions. To systematically investigate multilineage differentiation mechanisms of hPSCs, we constructed the in vivo hPSC differentiation landscape containing 239,670 cells using teratoma models. We identified 43 cell types, inferred 18 cell differentiation trajectories, and characterized common and specific gene regulation patterns during hPSC differentiation at both transcriptional and epigenetic levels. Additionally, we developed the developmental single-cell Basic Local Alignment Search Tool (dscBLAST), an R-based cell identification tool, to simplify the identification processes of developmental cells. Using dscBLAST, we aligned cells in multiple differentiation models to normally developing cells to further understand their differentiation states. Overall, our study offers new insights into stem cell differentiation and human embryonic development; dscBLAST shows favorable cell identification performance, providing a powerful identification tool for developmental cells.

Achieving Color-Tunable Long Persistent Luminescence in Cs2 CdCl4 Ruddlesden-Popper Phase Perovskites.

Two-dimensional (2D)-halide perovskites have been enriched over recent years to offer remarkable features from diverse chemical structures and environmental stability endowed with exciting functionalities in photoelectric detectors and phosphorescence systems. However, the low conversion efficiency of singlet to triplet in 2D hybrid halide perovskites reduces phosphorescence lifetimes. In this study, the long persistent luminescence of 2D all-inorganic perovskites with a self-assembled 2D interlayer galleries structure is investigated. The results show that the decay time of the long persistent luminescence increases from 450 s to 600 s, and the luminescence color changes from cyan to orange, and the thermal stability of photoluminescence enhances dramatically after replacing Cd2+ by appropriate Mn2+ ions in 2D Cs2 CdCl4 Ruddlesden-Popper phase perovskites. Furthermore, diversified anti-counterfeiting modes are fabricated to highlight the promising applications of Cs2 CdCl4 perovskite systems with tunable persistent luminescence in advanced anti-counterfeiting. Therefore, our studies provide a novel model for realizing tunable long persistent luminescence of perovskite with 2D self-assembled layered structure for advanced anti-counterfeiting.

Controlled mechano-luminescence properties of SrGa2O4:Tb3+ co-doping with Dy3+ and Eu3+ ions.

Trap-controlled mechano-luminescence (ML) featuring photon emission under mechanical stimuli provides promising applications such as dynamic imaging of force, integrated optical sensing, information storage, and anti-counterfeiting encryption. However, the corresponding emission with a single color still limits the application of ML materials. Here, a trap-controlled ML phosphor of SrGa2O4:Tb3+ (SGO:Tb3+) with a green-emission is investigated with an adjustable ML color. The relationship between ML and thermoluminescence (TL) is verified by co-doping with Dy3+ and Eu3+ ions for the manipulation of the constructed traps. Accordingly, the as-explored ML phosphor with multicolor output is employed to create encrypted anticounterfeiting patterns, which produces bright and spatially resolvable optical codes under the single-point dynamic pressure of a ballpoint pen. Hence, it provides a new approach to achieve ML with multicolor and gives us an insight into understanding the mechanism of the ML procedure.

Single-cell transcriptomics reveals the role of Macrophage-Naïve CD4 + T cell interaction in the immunosuppressive microenvironment of primary liver carcinoma.

BACKGROUND: Liver carcinoma generally presents as an immunosuppressive microenvironment that promotes tumor evasion. The intercellular crosstalk of immune cells significantly influences the construction of an immunosuppressive microenvironment. This study aimed to investigate the important interactions between immune cells and their targeting drugs in liver carcinoma, by using single-cell and bulk transcriptomic data. METHODS: Single-cell and bulk transcriptomic data were retrieved from Gene Expression Omnibus (GSE159977, GSE136103, and GSE125449) and The Cancer Genome Atlas (TGCA-LIHC), respectively. Quality control, dimension reduction, clustering, and annotation were performed according to the Scanpy workflow based on Python. Cell-cell interactions were explored using the CellPhone database and CellChat. Trajectory analysis was executed using a partition-based graph abstraction method. The transcriptomic factors (TFs) were predicted using single-cell regulatory network inference and clustering (SCENIC). The target genes from TFs were used to establish a related score based on the TCGA cohort; this score was subsequently validated by survival, gene set enrichment, and immune cell infiltration analyses. Drug prediction was performed based on the Cancer Therapeutics Response Portal and PRISM Repurposing datasets. RESULTS: Thirty-one patients at four different states, including health, hepatitis, cirrhosis, and cancer, were enrolled in this study. After dimension reduction and clustering, twenty-two clusters were identified. Cell-cell interaction analyses indicated that macrophage-naive CD4 + T cell interaction significantly affect cancerous state. In brief, macrophages interact with naive CD4 + T cells via different pathways in different states. The results of SCENIC indicated that macrophages present in cancer cells were similar to those present during cirrhosis. A macrophage-naive CD4 + T cell (MNT) score was generated by the SCENIC-derived target genes. Based on the MNT score, five relevant drugs (inhibitor of polo-like kinase 1, inhibitor of kinesin family member 11, dabrafenib, ispinesib, and epothilone-b) were predicted. CONCLUSIONS: This study reveals the crucial role of macrophage-naive CD4 + T cell interaction in the immunosuppressive microenvironment of liver carcinoma. Tumor-associated macrophages may be derived from cirrhosis and can initiate liver carcinoma. Predictive drugs that target the macrophage-naive CD4 + T cell interaction may help to improve the immunosuppressive microenvironment and prevent immune evasion. The relevant mechanisms need to be further validated in experiments and cohort studies.

Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types.

Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Drosophila and earthworm. We then integrated cell landscapes from eight representative metazoan species to study gene regulation across evolution. Using these uniformly constructed cross-species landscapes, we developed a deep-learning-based strategy, Nvwa, to predict gene expression and identify regulatory sequences at the single-cell level. We systematically compared cell-type-specific transcription factors to reveal conserved genetic regulation in vertebrates and invertebrates. Our work provides a valuable resource and offers a new strategy for studying regulatory grammar in diverse biological systems.

Histone methylation modification patterns and relevant M-RiskScore in acute myeloid leukemia.

Objective: We tried to identify novel molecular subtypes of acute myeloid leukemia (AML) associated with histone methylation and established a relevant scoring system to predict treatment response and prognosis of AML. Methods: Gene expression data and clinical characteristics of patients with AML were obtained from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. Molecular subtyping was carried out by consensus clustering analysis, based on the expression of 24 histone methylation modification regulators (HMMRs). The clinical and biological features of each clustered pattern were taken into account. The scoring system was constructed by using differential expression analysis, Cox regression method and lasso regression analysis. Subsequently, the scoring system in the roles of prognostic and chemotherapeutic prediction of AML were explored. Finally, an independent GSE dataset was used for validating the established clustering system. Results: Two distinct subtypes of AML were identified based on the expression of the 24 HMMRs, which exhibited remarkable differences in several clinical and biological characteristics, including HMMRs expression, AML-M0 distribution, NPM1 mutation, tumor mutation burden, somatic mutations, pathway activation, immune cell infiltration and patient survival. The scoring system, M-RiskScore, was established. Integrated analysis demonstrated that patients with the low M-RiskScore displayed a prominent survival advantage and a good response to decitabine treatment, while patients with high M-RiskScore have resistance to decitabine, but they could benefit from IA regimen therapy. Conclusion: Detection of HMMRs expression would be a potential strategy for AML subtyping. Meanwhile, targeting histone methylation would be a preferred strategy for either AML-M0 or NPM1 mutant patients. M-RiskScore was a useful prognostic biomarker and a guide for the choice of appropriate chemotherapy strategy.

Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma.

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC. Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score. Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts. Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

A novel taxane, difluorovinyl-ortataxel, effectively overcomes paclitaxel-resistance in breast cancer cells.

Paclitaxel (PTX) is widely used to treat breast and ovarian cancers, but innate and acquired resistance often compromises its applications. The objective of this study was to screen new-generation taxanes for their efficiency against both PTX-sensitive and PTX-resistant breast cancer cells. From twelve compounds, difluorovinyl-ortataxel (DFV-OTX) displayed potent cytotoxic activities against both PTX-sensitive and PTX-resistant breast cancer cells. Moreover, DFV-OTX effectively induced tubulin/microtubule polymerization and G2/M phase arrest, leading to apoptosis in both PTX-sensitive and PTX-resistant cancer cells. Molecular docking analysis showed that DFV-OTX possesses unique hydrogen-bonding and van der Waals interactions with ß-tubulin. LC-MS/MS analysis also demonstrated that the intracellular drug amount of DFV-OTX was lower than that of PTX, which would be critical to overcome PTX-resistance. Furthermore, DFV-OTX exhibited clear efficacy in the MCF-7R and MDA-MB-231R tumor xenografts in mouse models. Taken together, our results demonstrate that the novel taxane, DFV-OTX, can effectively overcome PTX-resistance in MDA-MB-231R cells, wherein the drug resistance was attributed to ABCB1/ABCG2 upregulation and a distinct mode of action in MCF-7R cells. Our results strongly indicate that DFV-OTX is a promising chemotherapeutic agent for the treatment of PTX-resistant cancers.