Cell surface RNAs control neutrophil recruitment.

RNAs localizing to the outer cell surface have been recently identified in mammalian cells, including RNAs with glycan modifications known as glycoRNAs. However, the functional significance of cell surface RNAs and their production are poorly known. We report that cell surface RNAs are critical for neutrophil recruitment and that the mammalian homologs of the sid-1 RNA transporter are required for glycoRNA expression. Cell surface RNAs can be readily detected in murine neutrophils, the elimination of which substantially impairs neutrophil recruitment to inflammatory sites in vivo and reduces neutrophils' adhesion to and migration through endothelial cells. Neutrophil glycoRNAs are predominantly on cell surface, important for neutrophil-endothelial interactions, and can be recognized by P-selectin (Selp). Knockdown of the murine Sidt genes abolishes neutrophil glycoRNAs and functionally mimics the loss of cell surface RNAs. Our data demonstrate the biological importance of cell surface glycoRNAs and highlight a noncanonical dimension of RNA-mediated cellular functions.

Spatially exploring RNA biology in archival formalin-fixed paraffin-embedded tissues.

The capability to spatially explore RNA biology in formalin-fixed paraffin-embedded (FFPE) tissues holds transformative potential for histopathology research. Here, we present pathology-compatible deterministic barcoding in tissue (Patho-DBiT) by combining in situ polyadenylation and computational innovation for spatial whole transcriptome sequencing, tailored to probe the diverse RNA species in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for 5 years. Furthermore, genome-wide single-nucleotide RNA variants can be captured to distinguish malignant subclones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis. Single-cell level Patho-DBiT dissects the spatiotemporal cellular dynamics driving tumor clonal architecture and progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to aid in clinical pathology evaluation.

Colorectal Cancer Cells Enter a Diapause-like DTP State to Survive Chemotherapy.

Cancer cells enter a reversible drug-tolerant persister (DTP) state to evade death from chemotherapy and targeted agents. It is increasingly appreciated that DTPs are important drivers of therapy failure and tumor relapse. We combined cellular barcoding and mathematical modeling in patient-derived colorectal cancer models to identify and characterize DTPs in response to chemotherapy. Barcode analysis revealed no loss of clonal complexity of tumors that entered the DTP state and recurred following treatment cessation. Our data fit a mathematical model where all cancer cells, and not a small subpopulation, possess an equipotent capacity to become DTPs. Mechanistically, we determined that DTPs display remarkable transcriptional and functional similarities to diapause, a reversible state of suspended embryonic development triggered by unfavorable environmental conditions. Our study provides insight into how cancer cells use a developmentally conserved mechanism to drive the DTP state, pointing to novel therapeutic opportunities to target DTPs.

DNA-Demethylating Agents Target Colorectal Cancer Cells by Inducing Viral Mimicry by Endogenous Transcripts.

DNA-demethylating agents have shown clinical anti-tumor efficacy via an unknown mechanism of action. Using a combination of experimental and bioinformatics analyses in colorectal cancer cells, we demonstrate that low-dose 5-AZA-CdR targets colorectal cancer-initiating cells (CICs) by inducing viral mimicry. This is associated with induction of dsRNAs derived at least in part from endogenous retroviral elements, activation of the MDA5/MAVS RNA recognition pathway, and downstream activation of IRF7. Indeed, disruption of virus recognition pathways, by individually knocking down MDA5, MAVS, or IRF7, inhibits the ability of 5-AZA-CdR to target colorectal CICs and significantly decreases 5-AZA-CdR long-term growth effects. Moreover, transfection of dsRNA into CICs can mimic the effects of 5-AZA-CdR. Together, our results represent a major shift in understanding the anti-tumor mechanisms of DNA-demethylating agents and highlight the MDA5/MAVS/IRF7 pathway as a potentially druggable target against CICs.

miniSNV: accurate and fast single nucleotide variant calling from nanopore sequencing data.

Nanopore sequence technology has demonstrated a longer read length and enabled to potentially address the limitations of short-read sequencing including long-range haplotype phasing and accurate variant calling. However, there is still room for improvement in terms of the performance of single nucleotide variant (SNV) identification and computing resource usage for the state-of-the-art approaches. In this work, we introduce miniSNV, a lightweight SNV calling algorithm that simultaneously achieves high performance and yield. miniSNV utilizes known common variants in populations as variation backgrounds and leverages read pileup, read-based phasing, and consensus generation to identify and genotype SNVs for Oxford Nanopore Technologies (ONT) long reads. Benchmarks on real and simulated ONT data under various error profiles demonstrate that miniSNV has superior sensitivity and comparable accuracy on SNV detection and runs faster with outstanding scalability and lower memory than most state-of-the-art variant callers. miniSNV is available from https://github.com/CuiMiao-HIT/miniSNV.

Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data.

Structural Variants (SVs) are a crucial type of genetic variant that can significantly impact phenotypes. Therefore, the identification of SVs is an essential part of modern genomic analysis. In this article, we present kled, an ultra-fast and sensitive SV caller for long-read sequencing data given the specially designed approach with a novel signature-merging algorithm, custom refinement strategies and a high-performance program structure. The evaluation results demonstrate that kled can achieve optimal SV calling compared to several state-of-the-art methods on simulated and real long-read data for different platforms and sequencing depths. Furthermore, kled excels at rapid SV calling and can efficiently utilize multiple Central Processing Unit (CPU) cores while maintaining low memory usage. The source code for kled can be obtained from https://github.com/CoREse/kled.

Epigenetic therapy induces transcription of inverted SINEs and ADAR1 dependency.

Cancer therapies that target epigenetic repressors can mediate their effects by activating retroelements within the human genome. Retroelement transcripts can form double-stranded RNA (dsRNA) that activates the MDA5 pattern recognition receptor1-6. This state of viral mimicry leads to loss of cancer cell fitness and stimulates innate and adaptive immune responses7,8. However, the clinical efficacy of epigenetic therapies has been limited. To find targets that would synergize with the viral mimicry response, we sought to identify the immunogenic retroelements that are activated by epigenetic therapies. Here we show that intronic and intergenic SINE elements, specifically inverted-repeat Alus, are the major source of drug-induced immunogenic dsRNA. These inverted-repeat Alus are frequently located downstream of 'orphan' CpG islands9. In mammals, the ADAR1 enzyme targets and destabilizes inverted-repeat Alu dsRNA10, which prevents activation of the MDA5 receptor11. We found that ADAR1 establishes a negative-feedback loop, restricting the viral mimicry response to epigenetic therapy. Depletion of ADAR1 in patient-derived cancer cells potentiates the efficacy of epigenetic therapy, restraining tumour growth and reducing cancer initiation. Therefore, epigenetic therapies trigger viral mimicry by inducing a subset of inverted-repeats Alus, leading to an ADAR1 dependency. Our findings suggest that combining epigenetic therapies with ADAR1 inhibitors represents a promising strategy for cancer treatment.

xQTLatlas: a comprehensive resource for human cellular-resolution multi-omics genetic regulatory landscape.

Understanding how genetic variants influence molecular phenotypes in different cellular contexts is crucial for elucidating the molecular and cellular mechanisms behind complex traits, which in turn has spurred significant advances in research into molecular quantitative trait locus (xQTL) at the cellular level. With the rapid proliferation of data, there is a critical need for a comprehensive and accessible platform to integrate this information. To meet this need, we developed xQTLatlas (http://www.hitxqtl.org.cn/), a database that provides a multi-omics genetic regulatory landscape at cellular resolution. xQTLatlas compiles xQTL summary statistics from 151 cell types and 339 cell states across 55 human tissues. It organizes these data into 20 xQTL types, based on four distinct discovery strategies, and spans 13 molecular phenotypes. Each entry in xQTLatlas is meticulously annotated with comprehensive metadata, including the origin of the tissue, cell type, cell state and the QTL discovery strategies utilized. Additionally, xQTLatlas features multiscale data exploration tools and a suite of interactive visualizations, facilitating in-depth analysis of cell-level xQTL. xQTLatlas provides a valuable resource for deepening our understanding of the impact of functional variants on molecular phenotypes in different cellular environments, thereby facilitating extensive research efforts.

5-Fluorouracil efficacy requires anti-tumor immunity triggered by cancer-cell-intrinsic STING.

5-Fluorouracil (5-FU) is a widely used chemotherapeutic drug, but the mechanisms underlying 5-FU efficacy in immunocompetent hosts in vivo remain largely elusive. Through modeling 5-FU response of murine colon and melanoma tumors, we report that effective reduction of tumor burden by 5-FU is dependent on anti-tumor immunity triggered by the activation of cancer-cell-intrinsic STING. While the loss of STING does not induce 5-FU resistance in vitro, effective 5-FU responsiveness in vivo requires cancer-cell-intrinsic cGAS, STING, and subsequent type I interferon (IFN) production, as well as IFN-sensing by bone-marrow-derived cells. In the absence of cancer-cell-intrinsic STING, a much higher dose of 5-FU is needed to reduce tumor burden. 5-FU treatment leads to increased intratumoral T cells, and T-cell depletion significantly reduces the efficacy of 5-FU in vivo. In human colorectal specimens, higher STING expression is associated with better survival and responsiveness to chemotherapy. Our results support a model in which 5-FU triggers cancer-cell-initiated anti-tumor immunity to reduce tumor burden, and our findings could be harnessed to improve therapeutic effectiveness and toxicity for colon and other cancers.

THItoGene: a deep learning method for predicting spatial transcriptomics from histological images.

Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes, but it is typically cost-prohibitive. Predicting spatial gene expression from histological images via artificial intelligence offers a more affordable option, yet existing methods fall short in extracting deep-level information from pathological images. In this paper, we present THItoGene, a hybrid neural network that utilizes dynamic convolutional and capsule networks to adaptively sense potential molecular signals in histological images for exploring the relationship between high-resolution pathology image phenotypes and regulation of gene expression. A comprehensive benchmark evaluation using datasets from human breast cancer and cutaneous squamous cell carcinoma has demonstrated the superior performance of THItoGene in spatial gene expression prediction. Moreover, THItoGene has demonstrated its capacity to decipher both the spatial context and enrichment signals within specific tissue regions. THItoGene can be freely accessed at https://github.com/yrjia1015/THItoGene.

HNetGO: protein function prediction via heterogeneous network transformer.

Protein function annotation is one of the most important research topics for revealing the essence of life at molecular level in the post-genome era. Current research shows that integrating multisource data can effectively improve the performance of protein function prediction models. However, the heavy reliance on complex feature engineering and model integration methods limits the development of existing methods. Besides, models based on deep learning only use labeled data in a certain dataset to extract sequence features, thus ignoring a large amount of existing unlabeled sequence data. Here, we propose an end-to-end protein function annotation model named HNetGO, which innovatively uses heterogeneous network to integrate protein sequence similarity and protein-protein interaction network information and combines the pretraining model to extract the semantic features of the protein sequence. In addition, we design an attention-based graph neural network model, which can effectively extract node-level features from heterogeneous networks and predict protein function by measuring the similarity between protein nodes and gene ontology term nodes. Comparative experiments on the human dataset show that HNetGO achieves state-of-the-art performance on cellular component and molecular function branches.

MEHunter: transformer-based mobile element variant detection from long reads.

SUMMARY: Mobile genetic elements (MEs) are heritable mutagens that significantly contribute to genetic diseases. The advent of long-read sequencing technologies, capable of resolving large DNA fragments, offers promising prospects for the comprehensive detection of ME variants (MEVs). However, achieving high precision while maintaining recall performance remains challenging mainly brought by the variable length and similar content of MEV signatures, which are often obscured by the noise in long reads. Here, we propose MEHunter, a high-performance MEV detection approach utilizing a fine-tuned transformer model adept at identifying potential MEVs with fragmented features. Benchmark experiments on both simulated and real datasets demonstrate that MEHunter consistently achieves higher accuracy and sensitivity than the state-of-the-art tools. Furthermore, it is capable of detecting novel potentially individual-specific MEVs that have been overlooked in published population projects. AVAILABILITY AND IMPLEMENTATION: MEHunter is available from https://github.com/120L021101/MEHunter.

MCDHGN: heterogeneous network-based cancer driver gene prediction and interpretability analysis.

MOTIVATION: Accurately predicting the driver genes of cancer is of great significance for carcinogenesis progress research and cancer treatment. In recent years, more and more deep-learning-based methods have been used for predicting cancer driver genes. However, deep-learning algorithms often have black box properties and cannot interpret the output results. Here, we propose a novel cancer driver gene mining method based on heterogeneous network meta-paths (MCDHGN), which uses meta-path aggregation to enhance the interpretability of predictions. RESULTS: MCDHGN constructs a heterogeneous network by using several types of multi-omics data that are biologically linked to genes. And the differential probabilities of SNV, DNA methylation, and gene expression data between cancerous tissues and normal tissues are extracted as initial features of genes. Nine meta-paths are manually selected, and the representation vectors obtained by aggregating information within and across meta-path nodes are used as new features for subsequent classification and prediction tasks. By comparing with eight homogeneous and heterogeneous network models on two pan-cancer datasets, MCDHGN has better performance on AUC and AUPR values. Additionally, MCDHGN provides interpretability of predicted cancer driver genes through the varying weights of biologically meaningful meta-paths. AVAILABILITY AND IMPLEMENTATION: https://github.com/1160300611/MCDHGN.

DVA: predicting the functional impact of single nucleotide missense variants.

BACKGROUND: In the past decade, single nucleotide variants (SNVs) have been identified as having a significant relationship with the development and treatment of diseases. Among them, prioritizing missense variants for further functional impact investigation is an essential challenge in the study of common disease and cancer. Although several computational methods have been developed to predict the functional impacts of variants, the predictive ability of these methods is still insufficient in the Mendelian and cancer missense variants. RESULTS: We present a novel prediction method called the disease-related variant annotation (DVA) method that predicts the effect of missense variants based on a comprehensive feature set of variants, notably, the allele frequency and protein-protein interaction network feature based on graph embedding. Benchmarked against datasets of single nucleotide missense variants, the DVA method outperforms the state-of-the-art methods by up to 0.473 in the area under receiver operating characteristic curve. The results demonstrate that the proposed method can accurately predict the functional impact of single nucleotide missense variants and substantially outperforms existing methods. CONCLUSIONS: DVA is an effective framework for identifying the functional impact of disease missense variants based on a comprehensive feature set. Based on different datasets, DVA shows its generalization ability and robustness, and it also provides innovative ideas for the study of the functional mechanism and impact of SNVs.

Tumour-derived exosome SNHG17 induced by oestrogen contributes to ovarian cancer progression via the CCL13-CCR2-M2 macrophage axis.

Oestrogen is known to be strongly associated with ovarian cancer. There was much work to show the importance of lncRNA SNHG17 in ovarian cancer. However, no study has revealed the molecular regulatory mechanism and functional effects between oestrogen and SNHG17 in the development and metastasis of ovarian cancer. In this study, we found that SNHG17 expression was significantly increased in ovarian cancer and positively correlated with oestrogen treatment. Oestrogen could promote M2 macrophage polarization as well as ovarian cancer cells SKOV3 and ES2 cell exosomal SNHG17 expression. When exposure to oestrogen, exosomal SNHG17 promoted ovarian cancer cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) in vitro, and tumour growth and lung metastasis in vivo by accelerating M2-like phenotype of macrophages. Mechanically, exosomal SNHG17 could facilitate the release of CCL13 from M2 macrophage via the PI3K-Akt signalling pathway. Moreover, CCL13-CCR2 axis was identified to be involved in ovarian cancer tumour behaviours driven by oestrogen. There results demonstrate a novel mechanism that exosomal SNHG17 exerts an oncogenic effect on ovarian cancer via the CCL13-CCR2-M2 macrophage axis upon oestrogen treatment, of which SNHG17 may be a potential biomarker and therapeutic target for ovarian cancer responded to oestrogen.

Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington's disease using single-cell RNA sequencing data.

BACKGROUND: Huntington's disease (HD) is a hereditary neurological disorder caused by mutations in HTT, leading to neuronal degeneration. Traditionally, HD is associated with the misfolding and aggregation of mutant huntingtin due to an extended polyglutamine domain encoded by an expanded CAG tract. However, recent research has also highlighted the role of global transcriptional dysregulation in HD pathology. However, understanding the intricate relationship between mRNA expression and HD at the cellular level remains challenging. Our study aimed to elucidate the underlying mechanisms of HD pathology using single-cell sequencing data. RESULTS: We used single-cell RNA sequencing analysis to determine differential gene expression patterns between healthy and HD cells. HD cells were effectively modeled using a residual neural network (ResNet), which outperformed traditional and convolutional neural networks. Despite the efficacy of our approach, the F1 score for the test set was 96.53%. Using the SHapley Additive exPlanations (SHAP) algorithm, we identified genes influencing HD prediction and revealed their roles in HD pathobiology, such as in the regulation of cellular iron metabolism and mitochondrial function. SHAP analysis also revealed low-abundance genes that were overlooked by traditional differential expression analysis, emphasizing its effectiveness in identifying biologically relevant genes for distinguishing between healthy and HD cells. Overall, the integration of single-cell RNA sequencing data and deep learning models provides valuable insights into HD pathology. CONCLUSION: We developed the model capable of analyzing HD at single-cell transcriptomic level.

N6-Methyladenosine-Modified KREMEN2 Promotes Tumorigenesis and Malignant Progression of High-Grade Serous Ovarian Cancer.

High-grade serous ovarian cancer (HGSOC) remains the most lethal female cancer by far. Herein, clinical HGSOC samples had higher N6-methyladenosine (m6A) modification than normal ovarian tissue, and its dysregulation had been reported to drive aberrant transcription and translation programs. However, Kringle-containing transmembrane protein 2 (KREMEN2) and its m6A modification have not been fully elucidated in HGSOC. In this study, the data from the high-throughput messenger RNA (mRNA) sequencing of clinical samples were processed using the weighted correlation network analysis and functional enrichment analysis. Results revealed that KREMEN2 was a driver gene in the tumorigenesis of HGSOC and a potential target of m6A demethylase fat-mass and obesity-associated protein (FTO). KREMEN2 and FTO levels were upregulated and downregulated, respectively, and correlation analysis showed a significant negative correlation in HGSOC samples. Importantly, upregulated KREMEN2 was remarkably associated with lymph node metastasis, distant metastasis, peritoneal metastasis, and high International Federation of Gynecology and Obstetrics stage (Ⅲ/Ⅳ), independent of the age of patients. KREMEN2 promoted the growth of HGSOC in vitro and in vivo, which was dependent on FTO. The methylated RNA immunoprecipitation qPCR and RNA immunoprecipitation assays were performed to verify the m6A level and sites of KREMEN2. FTO overexpression significantly decreased m6A modification in the 3' and 5' untranslated regions of KREMEN2 mRNA and downregulated its expression. In addition, we found that FTO-mediated m6A modification of KREMEN2 mRNA was recognized and stabilized by the m6A reader IGF2BP1 rather than by IGF2BP2 or IGF2BP3. This study highlights the m6A modification of KREMEN2 and extends the importance of RNA epigenetics in HGSOC.

Quantitatively Evaluating Interactions between Patient-Derived Organoids and Autologous Immune Cells by Microfluidic Chip.

The coculture of patient-derived tumor organoids (PDOs) and autologous immune cells has been considered as a useful ex vivo surrogate of in vivo tumor-immune environment. However, the immune interactions between PDOs and autologous immune cells, including immune-mediated killing behaviors and immune-related cytokine variations, have yet to be quantitatively evaluated. This study presents a microfluidic chip for quantifying interactions between PDOs and autologous immune cells (IOI-Chip). A baffle-well structure is designed to ensure efficient trapping, long-term coculturing, and in situ fluorescent observation of a limited amount of precious PDOS and autologous immune cells, while a microbeads-based immunofluorescence assay is designed to simultaneously quantify multiple kinds of immune-related cytokines in situ. The PDO apoptosis and 2 main immune-related cytokines, TNF-α and IFN-γ, are simultaneously quantified using samples from a lung cancer patient. This study provides, for the first time, a capability to quantify interactions between PDOs and autologous immune cells at 2 levels, the immune-mediated killing behavior, and multiple immune-related cytokines, laying the technical foundation of ex vivo assessment of patient immune response.

Microfluidic Chip-Based Automatic System for Sequencing Patient-Derived Organoids at the Single-Cell Level.

Genetically sequencing patient-derived organoids (PDOs) at the single-cell level has emerged as a promising method to infer cell-level heterogeneity of original organs and improve cancer precision medicine. Unfortunately, because of the limited starting quantity and uncontrolled establishing process of PDOs, the existing single-cell sequencing technologies, either manual-operation-based or microfluid-based, are inefficient in processing PDOs originating from clinical tissue samples. To address such issues, this study presents a microfluidic chip-based automatic system for sequencing organoids at the single-cell level, named as MASSO. By performing all required procedures, including PDO establishment/culturing/digesting and single-cell isolation/lysis/whole-genome amplification, in a single microfluidic chip, the possible loss of precious PDO is avoided, and the high quality of on-chip whole-genome amplification of a single PDO cell is ensured. By automating the entire operation process, possible human error is eliminated, and the data repeatability is improved, therefore bridging the technical gap between laboratorial proof-of-concept studies and clinical practices. After characterizing the organoid single-cell whole-genome amplification chip (named as OSA-Chip) and the MASSO, the first successful attempt, to the best of our knowledge, on whole-genome sequencing lung cancer PDO at the single-cell level was performed by MASSO. The results reveal that the MASSO is capable of not only identifying common cancer-related mutations but also discovering specific mutations that affect drug responses, therefore laying the technical foundation for efficiently understanding the cell-level heterogeneities of PDOs and corresponding original organs.

A Microfluidic Chip-Based Automated System for Whole-Course Monitoring the Drug Responses of Organoids.

Tumor patients-derived organoids, as a promising preclinical prediction model, have been utilized to evaluate ex vivo drug responses for formulating optimal therapeutic strategies. Detecting adenosine triphosphate (ATP) has been widely used in existing organoid-based drug response tests. However, all commercial ATP detection kits containing the cell lysis procedure can only be applied for single time point ATP detection, resulting in the neglect of dynamic ATP variations in living cells. Meanwhile, due to the limited number of viable organoids from a single patient, it is impractical to exhaustively test all potential time points in search of optimal ones. In this work, a multifunctional microfluidic chip was developed to perform all procedures of organoid-based drug response tests, including establishment, culturing, drug treatment, and ATP monitoring of organoids. An ATP sensor was developed to facilitate the first successful attempt on whole-course monitoring the growth status of fragile organoids. To realize a clinically applicable automatic system for the drug testing of lung cancer, a microfluidic chip based automated system was developed to perform entire organoid-based drug response test, bridging the gap between laboratorial manipulation and clinical practices, as it outperformed previous methods by improving data repeatability, eliminating human error/sample loss, and more importantly, providing a more accurate and comprehensive evaluation of drug effects.