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Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Through benchmarking with other label transfer methods on a total of 30 single cell omics datasets, scGCN consistently demonstrates superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN . Making sense of the rapidly growing single-cell omics datasets available is limited by difficulties in leveraging disparate datasets in analyses. Here, the authors present scGCN, a graph based convolutional network to allow effective knowledge transfer across omics datasets.

Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27–50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems. Recent advances have pushed spatial transcriptomics to subcellular resolution. Here, the authors propose SiGra, a graph artificial intelligence model designed for high-throughput spatial molecular imaging.

Pepper fluorescent RNAs are a recently reported bright, stable and multicolor fluorogenic aptamer tag that enable imaging of diverse RNAs in live cells. To investigate the molecular basis of the superior properties of Pepper, we determined the structures of complexes of Pepper aptamer bound with its cognate HBC or HBC-like fluorophores at high resolution by X-ray crystallography. The Pepper aptamer folds in a monomeric non-G-quadruplex tuning-fork-like architecture composed of a helix and one protruded junction region. The near-planar fluorophore molecule intercalates in the middle of the structure and is sandwiched between one non-G-quadruplex base quadruple and one noncanonical G·U wobble helical base pair. In addition, structure-based mutational analysis is evaluated by in vitro and live-cell fluorogenic detection. Taken together, our research provides a structural basis for demystifying the fluorescence activation mechanism of Pepper aptamer and for further improvement of its future application in RNA visualization. Structural analysis of the Pepper aptamer in complex with its cognate HBC or HBC-like color variants reveals that it binds fluorophore molecules via one non-G-quadruplex base quadruple and one noncanonical G·U base pair.

Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs). Taking advantage of capturing long-range contextual information and learning more complex relations in the image data, Transformers have been used and applied to histopathological image processing tasks. In this survey, we make an effort to present a thorough analysis of the uses of Transformers in histopathological image analysis, covering several topics, from the newly built Transformer models to unresolved challenges. To be more precise, we first begin by outlining the fundamental principles of the attention mechanism included in Transformer models and other key frameworks. Second, we analyze Transformer-based applications in the histopathological imaging domain and provide a thorough evaluation of more than 100 research publications across different downstream tasks to cover the most recent innovations, including survival analysis and prediction, segmentation, classification, detection, and representation. Within this survey work, we also compare the performance of CNN-based techniques to Transformers based on recently published papers, highlight major challenges, and provide interesting future research directions. Despite the outstanding performance of the Transformer-based architectures in a number of papers reviewed in this survey, we anticipate that further improvements and exploration of Transformers in the histopathological imaging domain are still required in the future. We hope that this survey paper will give readers in this field of study a thorough understanding of Transformer-based techniques in histopathological image analysis, and an up-to-date paper list summary will be provided at https://github.com/S-domain/Survey-Paper .

Malignant pleural effusion (MPE) is indicative of terminal malignancy with a uniformly fatal prognosis. Often, two distinct compartments of tumour microenvironment, the effusion and disseminated pleural tumours, co-exist in the pleural cavity, presenting a major challenge for therapeutic interventions and drug delivery. Clinical evidence suggests that MPE comprises abundant tumour-associated myeloid cells with the tumour-promoting phenotype, impairing antitumour immunity. Here we developed a liposomal nanoparticle loaded with cyclic dinucleotide (LNP-CDN) for targeted activation of stimulators of interferon genes signalling in macrophages and dendritic cells and showed that, on intrapleural administration, they induce drastic changes in the transcriptional landscape in MPE, mitigating the immune cold MPE in both effusion and pleural tumours. Moreover, combination immunotherapy with blockade of programmed death ligand 1 potently reduced MPE volume and inhibited tumour growth not only in the pleural cavity but also in the lung parenchyma, conferring significantly prolonged survival of MPE-bearing mice. Furthermore, the LNP-CDN-induced immunological effects were also observed with clinical MPE samples, suggesting the potential of intrapleural LNP-CDN for clinical MPE immunotherapy.Malignant pleural effusion (MPE) is the terminal stage of cancer and the current standard of care for MPE is largely palliative. Here the authors design a liposomal nanoparticle loaded with cyclic dinucleotide for targeted activation of STING signalling in macrophages and dendritic cells and show that, on intrapleural administration, the nanoparticle effectively mitigates the immune cold MPE and significantly augments the checkpoint blockade immunotherapy in a mouse MPE model and clinical patients’ samples.

Alternative polyadenylation (APA) and alternative splicing (AS) are essential post-transcriptional mechanisms that enhance transcriptome diversity and regulate gene expression across various biological contexts. APA modifies transcript stability, localization and translation efficiency by generating mRNA isoforms with distinct 3′ untranslated regions or coding sequences, while AS alters protein diversity through exon inclusion or exclusion. The advent of high-throughput RNA sequencing has driven the development of computational methods to systematically identify, quantify and analyze APA and AS events, shedding light on their regulatory roles in normal physiology and disease. These methods can be broadly categorized based on their underlying methodologies and the data types they process, with specialized tools designed for both bulk and single-cell RNA sequencing. Here, in this Review, we provide a comprehensive overview of computational strategies for APA and AS detection and differential analysis, highlighting their advantages, limitations and applications. In addition, we explore techniques specifically tailored for single-cell RNA sequencing. We enhance our understanding of APA and AS regulation across diverse biological systems by summarizing recent advancements, offering new insights into gene regulation at both the population and single-cell levels. Exploring post-transcriptional regulation via alternative polyadenylation and splicing This article explores how genes in complex organisms, such as humans, generate different protein isoforms through post-transcriptional mechanisms such as alternative polyadenylation and alternative splicing. The authors discuss the complexity of these regulatory processes and how they are analyzed using high-throughput RNA sequencing technologies. The study reviews various computational methods developed to detect alternative polyadenylation and alternative splicing events from RNA sequencing data. These methods help identify alternative transcript isoforms by detecting variable polyadenylation sites and splicing patterns in various conditions. The research highlights the importance of these methods in understanding gene regulation and their potential role in diseases such as cancer. While considerable progress has been made, challenges remain due to sequencing biases and technical limitations. They suggest future research should focus on integrating new technologies and data types to improve our understanding of post-transcriptional gene regulation. This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.

Pancreatic cancer is one of the most malignant tumours with a poor prognosis. In recent years, the incidence of pancreatic cancer is on the rise. Traditional chemotherapy and radiotherapy for pancreatic cancer have been improved, first-line and second-line palliative treatments have been developed, and adjuvant treatments have also been used in clinical. However, the 5-year survival rate is still less than 10% and new treatment methods such as targeted therapy and immunotherapy need to be investigated. In the past decades, many clinical trials of targeted therapies and immunotherapies for pancreatic cancer were launched and some of them showed an ideal prospect in a subgroup of pancreatic cancer patients. The experience of both success and failure of these clinical trials will be helpful to improve these therapies in the future. Therefore, the current research progress and challenges of selected targeted therapies and immunotherapies for pancreatic cancer are reviewed.

In order to explore the rules for the variation in the adfreeze shear strength at the interface between frozen soil and a pile foundation, and their influencing factors, a measuring system was developed to estimate the freezing strength at the interface by utilizing a pile-pressing method under a cryogenic environment. Experimental results demonstrate that the maximum vertical pressure on the pile top increased significantly with the decrease in temperature under the same moisture content. The shear stress–shear displacement curves, at the bottom part of the interface, presented strain-softening characteristics, while the strain-hardening phenomenon was observed at the upper part of the interface. The strength parameters of the interface decreased with the increase in the pile depth. Moreover, the influence of temperature on the shear strength of the interface was more significant compared with that of the moisture content. The research results can provide references for the construction of pile foundations, structural design optimization, and for frozen damage prevention and treatment in permafrost regions.

Intronic PolyAdenylation (IPA) is an important post-transcriptional mechanism that can alter transcript coding potential by truncating translation regions, thereby increasing transcriptome and proteome diversity. This process generates novel protein isoforms with altered peptide sequences, some of which are implicated in disease progression, including cancer. Truncated proteins may lose tumor-suppressive functions, contributing to oncogenesis. Despite advancements in Alternative PolyAdenylation (APA) analysis using RNA-seq, detecting and quantifying novel IPA events remains challenging. To address this, we developed IPScan, a computational pipeline for precise IPA event identification, quantification, and visualization. IPScan has been benchmarked against existing methods using simulated data, different human and mouse cell lines, and TCGA (The Cancer Genome Atlas) breast cancer datasets. Differential IPA events under different biological conditions were quantified and validated via qPCR.

Liquid biopsies, based on cell free DNA (cfDNA) and proteins, have shown the potential to detect early stage cancers of diverse tissue types. However, most of these studies were retrospective, using individuals previously diagnosed with cancer as cases and healthy individuals as controls. Here, we developed a liquid biopsy assay, named the hepatocellular carcinoma screen (HCCscreen), to identify HCC from the surface antigen of hepatitis B virus (HBsAg) positive asymptomatic individuals in the community population. The training cohort consisted of individuals who had liver nodules and/or elevated serum α-fetoprotein (AFP) levels, and the assay robustly separated those with HCC from those who were non-HCC with a sensitivity of 85% and a specificity of 93%. We further applied this assay to 331 individuals with normal liver ultrasonography and serum AFP levels. A total of 24 positive cases were identified, and a clinical follow-up for 6–8 mo confirmed four had developed HCC. No HCC cases were diagnosed from the 307 test-negative individuals in the follow-up during the same time-scale. Thus, the assay showed 100% sensitivity, 94% specificity, and 17% positive predictive value in the validation cohort. Notably, each of the four HCC cases was at the early stage (<3 cm) when diagnosed. Our study provides evidence that the use of combined detection of cfDNA alterations and protein markers is a feasible approach to identify early stage HCC from asymptomatic community populations with unknown HCC status.