m6A-modified cenRNA stabilizes CENPA to ensure centromere integrity in cancer cells.

m6A modification is best known for its critical role in controlling multiple post-transcriptional processes of the mRNAs. Here, we discovered elevated levels of m6A modification on centromeric RNA (cenRNA) in cancerous cells compared with non-cancerous cells. We then identified CENPA, an H3 variant, as an m6A reader of cenRNA. CENPA is localized at centromeres and is essential in preserving centromere integrity and function during mitosis. The m6A-modified cenRNA stabilizes centromeric localization of CENPA in cancer cells during the S phase of the cell cycle. Mutations of CENPA at the Leu61 and the Arg63 or removal of cenRNA m6A modification lead to loss of centromere-bound CENPA during S phase. This in turn results in compromised centromere integrity and abnormal chromosome separation and hinders cancer cell proliferation and tumor growth. Our findings unveil an m6A reading mechanism by CENPA that epigenetically governs centromere integrity in cancer cells, providing potential targets for cancer therapy.

Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes.

It is poorly understood how different cells in a tissue organize themselves to support tissue functions. We describe the CytoCommunity algorithm for the identification of tissue cellular neighborhoods (TCNs) based on cell phenotypes and their spatial distributions. CytoCommunity learns a mapping directly from the cell phenotype space to the TCN space using a graph neural network model without intermediate clustering of cell embeddings. By leveraging graph pooling, CytoCommunity enables de novo identification of condition-specific and predictive TCNs under the supervision of sample labels. Using several types of spatial omics data, we demonstrate that CytoCommunity can identify TCNs of variable sizes with substantial improvement over existing methods. By analyzing risk-stratified colorectal and breast cancer data, CytoCommunity revealed new granulocyte-enriched and cancer-associated fibroblast-enriched TCNs specific to high-risk tumors and altered interactions between neoplastic and immune or stromal cells within and between TCNs. CytoCommunity can perform unsupervised and supervised analyses of spatial omics maps and enable the discovery of condition-specific cell-cell communication patterns across spatial scales.

Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.

Most sequencing-based spatial transcriptomics (ST) technologies do not achieve single-cell resolution where each captured location (spot) may contain a mixture of cells from heterogeneous cell types, and several cell-type decomposition methods have been proposed to estimate cell type proportions of each spot by integrating with single-cell RNA sequencing (scRNA-seq) data. However, these existing methods did not fully consider the effect of distribution difference between scRNA-seq and ST data for decomposition, leading to biased cell-type-specific genes derived from scRNA-seq for ST data. To address this issue, we develop an instance-based transfer learning framework to adjust scRNA-seq data by ST data to correctly match cell-type-specific gene expression. We evaluate the effect of raw and adjusted scRNA-seq data on cell-type decomposition by eight leading decomposition methods using both simulated and real datasets. Experimental results show that data adjustment can effectively reduce distribution difference and improve decomposition, thus enabling for a more precise depiction on spatial organization of cell types. We highlight the importance of data adjustment in integrative analysis of scRNA-seq with ST data and provide guidance for improved cell-type decomposition.

One-Dimensional Crystal-Structure Te-Se Alloy for Flexible Shortwave Infrared Photodetector and Imaging.

Flexible shortwave infrared detectors play a crucial role in wearable devices, bioimaging, automatic control, etc. Commercial shortwave infrared detectors face challenges in achieving flexibility due to the high fabrication temperature and rigid material properties. Herein, we develop a high-performance flexible Te0.7Se0.3 photodetector, resulting from the unique 1D crystal structure and small elastic modulus of Te-Se alloying. The flexible photodetector exhibits a broad-spectrum response ranging from 365 to 1650 nm, a fast response time of 6 µs, a broad linear dynamic range of 76 dB, and a specific detectivity of 4.8 × 1010 Jones at room temperature. The responsivity of the flexible detector remains at 93% of its initial value after bending with a small curvature of 3 mm. Based on the optimized flexible detector, we demonstrate its application in shortwave infrared imaging. These results showcase the great potential of Te0.7Se0.3 photodetectors for flexible electronics.

TexSe1-x Shortwave Infrared Photodiode Arrays with Monolithic Integration.

TexSe1-x shortwave infrared (SWIR) photodetectors show promise for monolithic integration with readout integrated circuits (ROIC), making it a potential alternative to conventional expensive SWIR photodetectors. However, challenges such as a high dark current density and insufficient detection performance hinder their application in large-scale monolithic integration. Herein, we develop a ZnO/TexSe1-x heterojunction photodiode and synergistically address the interfacial elemental diffusion and dangling bonds via inserting a well-selected 0.3 nm amorphous TeO2 interfacial layer. The optimized device achieves a reduced dark current density of -3.5 × 10-5 A cm-2 at -10 mV, a broad response from 300 to 1700 nm, a room-temperature detectivity exceeding 2.03 × 1011 Jones, and a 3 dB bandwidth of 173 kHz. Furthermore, for the first time, we monolithically integrate the TexSe1-x photodiodes on ROIC (64 × 64 pixels) with the largest-scale array among all TexSe1-x-based detectors. Finally, we demonstrate its applications in transmission imaging and substance identification.

Bienzyme-Locked Activatable Fluorescent Probes for Specific Imaging of Tumor-Associated Mast Cells.

Tumor-associated mast cells (TAMCs) have been recently revealed to play a multifaceted role in the tumor microenvironment. Noninvasive optical imaging of TAMCs is thus highly desired to gain insights into their functions in cancer immunotherapy. However, due to the lack of a single enzyme that is specific to mast cells, a common probe design approach based on single-enzyme activation is not applicable. Herein, we reported a bienzyme-locked molecular probe (THCMC) based on a photoinduced electron transfer-intramolecular charge-transfer hybrid strategy for in vivo imaging of TAMCs. The bienzyme-locked activation mechanism ensures that THCMC exclusively turns on near-infrared (NIR) fluorescence only in the presence of both tryptase and chymase specifically coexpressed by mast cells. Thus, THCMC effectively distinguishes mast cells from other leukocytes, including T cells, neutrophils, and macrophages, a capability lacking in single-locked probes. Such a high specificity of THCMC allows noninvasive tracking of the fluctuation of TAMCs in the tumor of living mice during cancer immunotherapy. The results reveal that the decreased intratumoral signal of THCMC after combination immunotherapy correlates well with the reduced population of TAMCs, accurately predicting the inhibition of tumor growth. Thus, this study not only presents the first NIR fluorescent probe specific for TAMCs but also proposes a generic bienzyme-locked probe design approach for in vivo cell imaging.

Dual-Locked Enzyme-Activatable Bioorthogonal Fluorescence Turn-On Imaging of Senescent Cancer Cells.

Bioorthogonal pretargeting optical imaging shows the potential for enhanced diagnosis and prognosis. However, the bioorthogonal handles, known for being "always reactive", may engage in reactions at unintended sites with their counterparts, resulting in nonspecific fluorescence activation and diminishing detection specificity. Meanwhile, despite the importance of detecting senescent cancer cells in cancer therapy, current methods mainly rely on common single senescence-associated biomarkers, which lack specificity for differentiating between various types of senescent cells. Herein, we report a dual-locked enzyme-activatable bioorthogonal fluorescence (DEBOF) turn-on imaging approach for the specific detection of senescent cancer cells. A dual-locked bioorthogonal targeting agent (DBTA) and a bioorthogonally activatable fluorescent imaging probe (BAP) are synthesized as the biorthogonal pair. DBTA is a tetrazine derivative dually caged by two enzyme-cleavable moieties, respectively, associated with senescence and cancer, which ensures that its bioorthogonal reactivity ("clickability") is only triggered in the presence of senescent cancer cells. BAP is a fluorophore caged by trans-cyclooctane (TCO), whose fluorescence is only activated upon bioorthogonal reaction between its TCO and the decaged tetrazine of DBTA. As such, the DEBOF imaging approach differentiates senescent cancer cells from nonsenescent cancer cells or other senescent cells, allowing noninvasive tracking of the population fluctuation of senescent cancer cells in the tumor of living mice to guide cancer therapies. This study thus provides a general molecular strategy for biomarker-activatable in vivo bioorthogonal pretargeting imaging with the potential to be applied to other imaging modalities beyond optics.

Leveraging Long-Distance Singlet-Oxygen Transfer for Bienzyme-Locked Afterglow Imaging of Intratumoral Granule Enzymes.

Dual-locked activatable optical probes, leveraging the orthogonal effects of two biomarkers, hold great promise for the specific imaging of biological processes. However, their design approaches are limited to a short-distance energy or charge transfer mechanism, while the signal readout relies on fluorescence, which inevitably suffers from tissue autofluorescence. Herein, we report a long-distance singlet oxygen transfer approach to develop a bienzyme-locked activatable afterglow probe (BAAP) that emits long-lasting self-luminescence without real-time light excitation for the dynamic imaging of an intratumoral granule enzyme. Composed of an immuno-biomarker-activatable singlet oxygen (1O2) donor and a cancer-biomarker-activatable 1O2 acceptor, BAAP is initially nonafterglow. Only in the presence of both immune and cancer biomarkers can 1O2 be generated by the activated donor and subsequently diffuse toward the activated acceptor, resulting in bright near-infrared afterglow with a high signal-to-background ratio and specificity toward an intratumoral granule enzyme. Thus, BAAP allows for real-time tracking of tumor-infiltrating cytotoxic T lymphocytes, enabling the evaluation of cancer immunotherapy and the differentiation of tumor from local inflammation with superb sensitivity and specificity, which are unachievable by single-locked probes. Thus, this study not only presents the first dual-locked afterglow probe but also proposes a new design way toward dual-locked probes via reactive oxygen species transfer processes.

Single-cell multiomics reveals increased plasticity, resistant populations, and stem-cell-like blasts in KMT2A-rearranged leukemia.

KMT2A-rearranged (KMT2A-r) infant acute lymphoblastic leukemia (ALL) is a devastating malignancy with a dismal outcome, and younger age at diagnosis is associated with increased risk of relapse. To discover age-specific differences and critical drivers that mediate poor outcome in KMT2A-r ALL, we subjected KMT2A-r leukemias and normal hematopoietic cells from patients of different ages to single-cell multiomics analyses. We uncovered the following critical new insights: leukemia cells from patients <6 months have significantly increased lineage plasticity. Steroid response pathways are downregulated in the most immature blasts from younger patients. We identify a hematopoietic stem and progenitor-like (HSPC-like) population in the blood of younger patients that contains leukemic blasts and form an immunosuppressive signaling circuit with cytotoxic lymphocytes. These observations offer a compelling explanation for the ability of leukemias in young patients to evade chemotherapy and immune-mediated control. Our analysis also revealed preexisting lymphomyeloid primed progenitors and myeloid blasts at initial diagnosis of B-ALL. Tracking of leukemic clones in 2 patients whose leukemia underwent a lineage switch documented the evolution of such clones into frank acute myeloid leukemia (AML). These findings provide critical insights into KMT2A-r ALL and have clinical implications for molecularly targeted and immunotherapy approaches. Beyond infant ALL, our study demonstrates the power of single-cell multiomics to detect tumor intrinsic and extrinsic factors affecting rare but critical subpopulations within a malignant population that ultimately determines patient outcome.

Alloy Reconstruction in Pyrolytic Bowknot-like Heteronuclear CoFe Clusters for Electrocatalytic Application.

The construction of doped molecular clusters is an intriguing way to perform bimetallic doping for electrocatalysts. However, efficiently harnessing the benefits of a doping strategy and alloy engineering to create a nanostructure for electrocatalytic application at the molecular level has consistently posed a challenge. Here we propose an in situ reconstruction strategy aimed at producing an alloy nanostructure through a pyrolysis process, originating from bowknot-like heterometallic clusters. The Schiff base, denoted as ligand L1 (o-vanillin ethylenediamine), was introduced as a precursor to coordinate Fe and Co metals, thereby yielding a heteronuclear metal cluster [(FeCo)(L1)2O]CH3CN. Subsequently, a comprehensive investigation of the in situ reconstruction process [(FeCo)(L1)2O](CH3CN) → [(FeCo)(L1)2O] → [M-O-M/M-O] [CH3+/CH3O+/H2C═N/C2H5+/C4H4+] → [FeCo/Fe3O4/Fe2O3/Co3O4][carbon layer] led to the formation of MOx/CoFe@NC-700 during the pyrolysis. This process reveals that the metals Fe and Co in the clusters undergo partly in situ evolution into FeCo alloys, resulting in the successful preparation of MOx/CoFe@NC (M = Fe, Co) nanomaterials that leverage the advantages of both doping strategies and alloy engineering. The synergistic interaction between alloy particles and metal oxides establishes active sites that contribute to the excellent oxygen evolution (OER) and hydrogen evolution (HER) catalytic behaviors. Notably, these materials exhibit outstanding OER and HER properties under alkaline conditions, with overpotentials of 191 and 88 mV for OER and HER, respectively, at 10 mA cm-2. Investigation of the in situ conversion of Schiff base bimetal clusters into alloy materials through pyrolysis offers a novel strategy for advancing electrocatalytic applications.

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of computational models to predict drug clearance. However, concerns persist regarding the reliability of data collected from public sources, and a majority of current in silico quantitative structure-property relationship models tend to neglect the influence of molecular chirality. In this study, we meticulously examined human liver microsome (HLM) data from public databases and constructed two distinct data sets with varying HLM data quantity and quality. Two baseline models (RF and DNN) and three chirality-focused GNNs (DMPNN, TetraDMPNN, and ChIRo) were proposed, and their performance on HLM data was evaluated and compared with each other. The TetraDMPNN model, which leverages chirality from 2D structure, exhibited the best performance with a test R2 of 0.639 and a test root-mean-squared error of 0.429. The applicability domain of the model was also defined by using a molecular similarity-based method. Our research indicates that graph neural networks capable of capturing molecular chirality have significant potential for practical application and can deliver superior performance.

DRAMP 3.0: an enhanced comprehensive data repository of antimicrobial peptides.

Stapled antimicrobial peptides are an emerging class of artificial cyclic peptide molecules which have antimicrobial activity and potent structure stability. We previously published the Data Repository of Antimicrobial Peptides (DRAMP) as a manually annotated and open-access database of antimicrobial peptides (AMPs). In the update of version 3.0, special emphasis was placed on the new development of stapled AMPs, and a subclass of specific AMPs was added to store information on these special chemically modified AMPs. To help design low toxicity AMPs, we also added the cytotoxicity property of AMPs, as well as the expansion of newly discovered AMP data. At present, DRAMP has been expanded and contains 22259 entries (2360 newly added), consisting of 5891 general entries, 16110 patent entries, 77 clinical entries and 181 stapled AMPs. A total of 263 entries have predicted structures, and more than 300 general entries have links to experimentally determined structures in the Protein Data Bank. The update also covers new annotations, statistics, categories, functions and download links. DRAMP is available online at http://dramp.cpu-bioinfor.org/.

Quercetin alleviates difenoconazole-induced growth inhibition in carp through intestinal-brain axis.

Difenoconazole (DIF) is frequently used for the management of fungal infections in fruit and vegetables and excessive residues in the aquatic environment can have adverse effects on fish such as growth inhibition. A treatment based on the dietary additive quercetin (QUE) is a promising approach to positively regulate the state of fish growth. This study focused on whether and how QUE alleviated DIF-induced growth inhibition in fish. In this study, carp were exposed to DIF (0.3906 mg/L) for consecutive 30 d, which showed growth inhibition. Disruption of the intestinal barrier led to elevated levels of intestinal lipopolysaccharide (LPS) and an inflammatory response. Through the intestinal-brain axis, LPS entered the brain where it disrupted the blood-brain barrier, triggered neuroinflammation, caused brain cell apoptosis, and damaged nerves in addition to other things. The dietary supplementation of QUE (400 mg/kg) reduced the levels of LPS in the intestinal and brain, while reducing inflammation and increasing the expression of appetite factors, thereby reducing growth inhibition in carp. This work provided evidence for QUE from the intestinal-brain axis perspective as a potential candidate for alleviating growth inhibition in fish.

Fluorescent and colorimetric dual-mode detection of Cu2+ based on carbon dots.

A fluorescent and colorimetric dual-mode strategy based on carbon dots (CDs) was rationally designed for sensitive determination of Cu2+. Green fluorescent CDs with high absolute quantum yield of 72.9% were synthesized by facile one-step hydrothermal treatment of triethylenetetramine and Rose Bengal. Cu2+ could trigger the oxidative and chromogenic reaction of p-phenylenediamine (PPD) to generate chromogenic PPDox, accompanied by the fluorescence quenching of the CDs. The quenching mechanism was identified as the inner filter effect between PPDox and CDs. Therefore, a colorimetric/fluorescent dual-mode detection method for Cu2+ recognition was constructed. The limits of detection for Cu2+ were 4.14 µM and 1.28 µM for colorimetric and fluorescent mode, respectively. In addition, this method had achieved satisfactory results in the detection of Cu2+ in real serum samples.

In Silico Prediction of Human Organ Toxicity via Artificial Intelligence Methods.

Unpredicted human organ level toxicity remains one of the major reasons for drug clinical failure. There is a critical need for cost-efficient strategies in the early stages of drug development for human toxicity assessment. At present, artificial intelligence methods are popularly regarded as a promising solution in chemical toxicology. Thus, we provided comprehensive in silico prediction models for eight significant human organ level toxicity end points using machine learning, deep learning, and transfer learning algorithms. In this work, our results showed that the graph-based deep learning approach was generally better than the conventional machine learning models, and good performances were observed for most of the human organ level toxicity end points in this study. In addition, we found that the transfer learning algorithm could improve model performance for skin sensitization end point using source domain of in vivo acute toxicity data and in vitro data of the Tox21 project. It can be concluded that our models can provide useful guidance for the rapid identification of the compounds with human organ level toxicity for drug discovery.

Narrowband Fluorescent Emitters Based on BN-Doped Polycyclic Aromatic Hydrocarbons for Efficient and Stable Organic Light-Emitting Diodes.

Organic light-emitting diodes (OLEDs) using conventional fluorescent emitters are currently attracting considerable interests due to outstanding stability and abundant raw materials. To construct high-performance narrowband fluorophores to satisfy requirements of ultra-high-definition displays, a strategy fusing multi-resonance BN-doped moieties to naphthalene is proposed to construct two novel narrowband fluorophores. Green Na-sBN and red Na-dBN, manifest narrow full-width at half-maxima of 31 nm, near-unity photoluminescence quantum yields and molecular horizontal dipole ratios above 90 %. Their OLEDs exhibit the state-of-the-art performances including high external quantum efficiencies (EQE), ultra-low efficiency roll-off and long operational lifetimes. The Na-sBN-based device achieves EQE as high as 28.8 % and remains 19.8 % even at luminance of 100,000 cd m-2 , and Na-dBN-based device acquires a record-high EQE of 25.2 % among all red OLEDs using pure fluorescent emitters.

Peripherally Heavy-Atom-Decorated Strategy Towards High-Performance Pure Green Electroluminescence with External Quantum Efficiency over 40 .

Heavy-atom integration into thermally activated delayed fluorescence (TADF) molecule could significantly promote the reverse intersystem crossing (RISC) process. However, simultaneously achieving high efficiency, small roll-off, narrowband emission and good operational lifetime remains a big challenge for the corresponding organic light-emitting diodes (OLEDs). Herein, we report a pure green multi-resonance TADF molecule BN-STO by introducing a peripheral heavy atom selenium onto the parent BN-Cz molecule. The organic light-emitting diode device based on BN-STO exhibited state-of-the-art performance with a maximum external quantum efficiency (EQE) of 40.1 %, power efficiency (PE) of 176.9 lm W-1 , well-suppressed efficiency roll-off and pure green gamut. This work reveals a feasible strategy to reach a balance between fast RISC process and narrow full width at half maximum (FWHM) of MR-TADF by heavy atom effect.

Smart Nanosensitizers for Activatable Sono-Photodynamic Immunotherapy of Tumors by Redox-Controlled Disassembly.

Tumor-targeted and stimuli-activatable nanosensitizers are highly desirable for cancer theranostics. However, designing smart nanosensitizers with multiple imaging signals and synergistic therapeutic activities switched on is challenging. Herein, we report tumor-targeted and redox-activatable nanosensitizers (1-NPs) for sono-photodynamic immunotherapy of tumors by molecular co-assembly and redox-controlled disassembly. 1-NPs show a high longitudinal relaxivity (r1 =18.7±0.3 mM-1 s-1 ), but "off" dual fluorescence (FL) emission (at 547 and 672 nm), "off" sono-photodynamic therapy and indoleamine 2,3-dioxygenase 1 (IDO1) inhibition activities. Upon reduction by glutathione (GSH), 1-NPs rapidly disassemble and remotely release small molecules 2-Gd, Zn-PPA-SH and NLG919, concurrently switching on (1) dual FL emission, (2) sono-photodynamic therapy and (3) IDO1 inhibition activities. After systemic injection, 1-NPs are effective for bimodal FL and magnetic resonance (MR) imaging-guided sono-photodynamic immunotherapy of orthotropic breast and brain tumors in mice under combined ultrasound (US) and 671-nm laser irradiation.

Gut-Lung Dysbiosis Accompanied by Diabetes Mellitus Leads to Pulmonary Fibrotic Change through the NF-κB Signaling Pathway.

Growing evidence shows that the lungs are an unavoidable target organ of diabetic complications. However, the pathologic mechanisms of diabetic lung injury are still controversial. This study demonstrated the dysbiosis of the gut and lung microbiome, pulmonary alveolar wall thickening, and fibrotic change in streptozotocin-induced diabetic mice and antibiotic-induced gut dysbiosis mice compared with controls. In both animal models, the NF-κB signaling pathway was activated in the lungs. Enhanced pulmonary alveolar well thickening and fibrotic change appeared in the lungs of transgenic mice expressing a constitutively active NF-κB mutant compared with wild type. When lincomycin hydrochloride-induced gut dysbiosis was ameliorated by fecal microbiota transplant, enhanced inflammatory response in the intestine and pulmonary fibrotic change in the lungs were significantly decreased compared with lincomycin hydrochloride-treated mice. Furthermore, the application of fecal microbiota transplant and baicalin could also redress the microbial dysbiosis of the gut and lungs in streptozotocin-induced diabetic mice. Taken together, these data suggest that multiple as yet undefined factors related to microbial dysbiosis of gut and lungs cause pulmonary fibrogenesis associated with diabetes mellitus through an NF-κB signaling pathway.

Evaluation and comparison of multi-omics data integration methods for cancer subtyping.

Computational integrative analysis has become a significant approach in the data-driven exploration of biological problems. Many integration methods for cancer subtyping have been proposed, but evaluating these methods has become a complicated problem due to the lack of gold standards. Moreover, questions of practical importance remain to be addressed regarding the impact of selecting appropriate data types and combinations on the performance of integrative studies. Here, we constructed three classes of benchmarking datasets of nine cancers in TCGA by considering all the eleven combinations of four multi-omics data types. Using these datasets, we conducted a comprehensive evaluation of ten representative integration methods for cancer subtyping in terms of accuracy measured by combining both clustering accuracy and clinical significance, robustness, and computational efficiency. We subsequently investigated the influence of different omics data on cancer subtyping and the effectiveness of their combinations. Refuting the widely held intuition that incorporating more types of omics data always produces better results, our analyses showed that there are situations where integrating more omics data negatively impacts the performance of integration methods. Our analyses also suggested several effective combinations for most cancers under our studies, which may be of particular interest to researchers in omics data analysis.