COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection.

BACKGROUND: Single-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions. RESULT: In this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources. CONCLUSIONS: COMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

Lymphotoxin-ß promotes breast cancer bone metastasis colonization and osteolytic outgrowth.

Bone metastasis is a lethal consequence of breast cancer. Here we used single-cell transcriptomics to investigate the molecular mechanisms underlying bone metastasis colonization-the rate-limiting step in the metastatic cascade. We identified that lymphotoxin-ß (LTß) is highly expressed in tumour cells within the bone microenvironment and this expression is associated with poor bone metastasis-free survival. LTß promotes tumour cell colonization and outgrowth in multiple breast cancer models. Mechanistically, tumour-derived LTß activates osteoblasts through nuclear factor-κB2 signalling to secrete CCL2/5, which facilitates tumour cell adhesion to osteoblasts and accelerates osteoclastogenesis, leading to bone metastasis progression. Blocking LTß signalling with a decoy receptor significantly suppressed bone metastasis in vivo, whereas clinical sample analysis revealed significantly higher LTß expression in bone metastases than in primary tumours. Our findings highlight LTß as a bone niche-induced factor that promotes tumour cell colonization and osteolytic outgrowth and underscore its potential as a therapeutic target for patients with bone metastatic disease.

Tracking-seq reveals the heterogeneity of off-target effects in CRISPR-Cas9-mediated genome editing.

The continued development of novel genome editors calls for a universal method to analyze their off-target effects. Here we describe a versatile method, called Tracking-seq, for in situ identification of off-target effects that is broadly applicable to common genome-editing tools, including Cas9, base editors and prime editors. Through tracking replication protein A (RPA)-bound single-stranded DNA followed by strand-specific library construction, Tracking-seq requires a low cell input and is suitable for in vitro, ex vivo and in vivo genome editing, providing a sensitive and practical genome-wide approach for off-target detection in various scenarios. We show, using the same guide RNA, that Tracking-seq detects heterogeneity in off-target effects between different editor modalities and between different cell types, underscoring the necessity of direct measurement in the original system.

Mapping the chromatin accessibility landscape of zebrafish embryogenesis at single-cell resolution by SPATAC-seq.

Currently, the dynamic accessible elements that determine regulatory programs responsible for the unique identity and function of each cell type during zebrafish embryogenesis lack detailed study. Here we present SPATAC-seq: a split-pool ligation-based assay for transposase-accessible chromatin using sequencing. Using SPATAC-seq, we profiled chromatin accessibility in more than 800,000 individual nuclei across 20 developmental stages spanning the sphere stage to the early larval protruding mouth stage. Using this chromatin accessibility map, we identified 604 cell states and inferred their developmental relationships. We also identified 959,040 candidate cis-regulatory elements (cCREs) and delineated development-specific cCREs, as well as transcription factors defining diverse cell identities. Importantly, enhancer reporter assays confirmed that the majority of tested cCREs exhibited robust enhanced green fluorescent protein expression in restricted cell types or tissues. Finally, we explored gene regulatory programs that drive pigment and notochord cell differentiation. Our work provides a valuable open resource for exploring driver regulators of cell fate decisions in zebrafish embryogenesis.

A single-cell atlas of chromatin accessibility in mouse organogenesis.

Organogenesis is a highly complex and precisely regulated process. Here we profiled the chromatin accessibility in >350,000 cells derived from 13 mouse embryos at four developmental stages from embryonic day (E) 10.5 to E13.5 by SPATAC-seq in a single experiment. The resulting atlas revealed the status of 830,873 candidate cis-regulatory elements in 43 major cell types. By integrating the chromatin accessibility atlas with the previous transcriptomic dataset, we characterized cis-regulatory sequences and transcription factors associated with cell fate commitment, such as Nr5a2 in the development of gastrointestinal tract, which was preliminarily supported by the in vivo experiment in zebrafish. Finally, we integrated this atlas with the previous single-cell chromatin accessibility dataset from 13 adult mouse tissues to delineate the developmental stage-specific gene regulatory programmes within and across different cell types and identify potential molecular switches throughout lineage development. This comprehensive dataset provides a foundation for exploring transcriptional regulation in organogenesis.

Accurate Early Detection and EGFR Mutation Status Prediction of Lung Cancer Using Plasma cfDNA Coverage Patterns: A Proof-of-Concept Study.

Lung cancer is a major global health concern with a low survival rate, often due to late-stage diagnosis. Liquid biopsy offers a non-invasive approach to cancer detection and monitoring, utilizing various features of circulating cell-free DNA (cfDNA). In this study, we established two models based on cfDNA coverage patterns at the transcription start sites (TSSs) from 6X whole-genome sequencing: an Early Cancer Screening Model and an EGFR mutation status prediction model. The Early Cancer Screening Model showed encouraging prediction ability, especially for early-stage lung cancer. The EGFR mutation status prediction model exhibited high accuracy in distinguishing between EGFR-positive and wild-type cases. Additionally, cfDNA coverage patterns at TSSs also reflect gene expression patterns at the pathway level in lung cancer patients. These findings demonstrate the potential applications of cfDNA coverage patterns at TSSs in early cancer screening and in cancer subtyping.

HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction.

Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.

VitTCR: A deep learning method for peptide recognition prediction.

This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.

Dysregulation of Wnt/ß-catenin signaling contributes to intestinal inflammation through regulation of group 3 innate lymphoid cells.

RORγt+ group 3 innate lymphoid cells (ILC3s) are essential for intestinal homeostasis. Dysregulation of ILC3s has been found in the gut of patients with inflammatory bowel disease and colorectal cancer, yet the specific mechanisms still require more investigation. Here we observe increased ß-catenin in intestinal ILC3s from inflammatory bowel disease and colon cancer patients compared with healthy donors. In contrast to promoting RORγt expression in T cells, activation of Wnt/ß-catenin signaling in ILC3s suppresses RORγt expression, inhibits its proliferation and function, and leads to a deficiency of ILC3s and subsequent intestinal inflammation in mice. Activated ß-catenin and its interacting transcription factor, TCF-1, cannot directly suppress RORγt expression, but rather alters global chromatin accessibility and inhibits JunB expression, which is essential for RORγt expression in ILC3s. Together, our findings suggest that dysregulated Wnt/ß-catenin signaling impairs intestinal ILC3s through TCF-1/JunB/RORγt regulation, further disrupting intestinal homeostasis, and promoting inflammation and cancer.

Multiomics-integrated deep language model enables in silico genome-wide detection of transcription factor binding site in unexplored biosamples.

MOTIVATION: Transcription factor binding sites (TFBS) are regulatory elements that have significant impact on transcription regulation and cell fate determination. Canonical motifs, biological experiments, and computational methods have made it possible to discover TFBS. However, most existing in silico TFBS prediction models are solely DNA-based, and are trained and utilized within the same biosample, which fail to infer TFBS in experimentally unexplored biosamples. RESULTS: Here, we propose TFBS prediction by modified TransFormer (TFTF), a multimodal deep language architecture which integrates multiomics information in epigenetic studies. In comparison to existing computational techniques, TFTF has state-of-the-art accuracy, and is also the first approach to accurately perform genome-wide detection for cell-type and species-specific TFBS in experimentally unexplored biosamples. Compared to peak calling methods, TFTF consistently discovers true TFBS in threshold tuning-free way, with higher recalled rates. The underlying mechanism of TFTF reveals greater attention to the targeted TF's motif region in TFBS, and general attention to the entire peak region in non-TFBS. TFTF can benefit from the integration of broader and more diverse data for improvement and can be applied to multiple epigenetic scenarios. AVAILABILITY AND IMPLEMENTATION: We provide a web server (https://tftf.ibreed.cn/) for users to utilize TFTF model. Users can train TFTF model and discover TFBS with their own data.

Glioma-derived ANXA1 suppresses the immune response to TLR3 ligands by promoting an anti-inflammatory tumor microenvironment.

A highly immunosuppressive tumor microenvironment (TME) and the presence of the blood‒brain barrier are the two major obstacles to eliciting an effective immune response in patients with high-grade glioma (HGG). Here, we tried to enhance the local innate immune response in relapsed HGG by intracranially injecting poly(I:C) to establish a robust antitumor immune response in this registered clinical trial (NCT03392545). During the follow-up, 12/27 (44.4%) patients who achieved tumor control concomitant with survival benefit were regarded as responders in our study. We found that the T-cell receptor (TCR) repertoire in the TME was reshaped after poly(I:C) treatment. Based on the RNA-seq analysis of tumor samples, the expression of annexin A1 (ANXA1) was significantly upregulated in the tumor cells of nonresponders, which was further validated at the protein level. In vitro and in vivo experiments showed that ANXA1 could induce the production of M2-like macrophages and microglia via its surface receptor formyl peptide receptor 1 (FPR1) to establish a Treg cell-driven immunosuppressive TME and suppress the antitumor immune response facilitated by poly(I:C). The ANXA1/FPR1 signaling axis can inhibit the innate immune response of glioma patients by promoting an anti-inflammatory and Treg-driven TME. Moreover, ANXA1 could serve as a reliable predictor of response to poly(I:C), with a notable predictive accuracy rate of 92.3%. In light of these notable findings, this study unveils a new perspective of immunotherapy for gliomas.

Aberrant accumulation of Kras-dependent pervasive transcripts during tumor progression renders cancer cells dependent on PAF1 expression.

KRAS is the most commonly mutated oncogene in human cancer, and mutant KRAS is responsible for over 90% of pancreatic ductal adenocarcinoma (PDAC), the most lethal cancer. Here, we show that RNA polymerase II-associated factor 1 complex (PAF1C) is specifically required for survival of PDAC but not normal adult pancreatic cells. We show that PAF1C maintains cancer cell genomic stability by restraining overaccumulation of enhancer RNAs (eRNAs) and promoter upstream transcripts (PROMPTs) driven by mutant Kras. Loss of PAF1C leads to cancer-specific lengthening and accumulation of pervasive transcripts on chromatin and concomitant aberrant R-loop formation and DNA damage, which, in turn, trigger cell death. We go on to demonstrate that the global transcriptional hyperactivation driven by Kras signaling during tumorigenesis underlies the specific demand for PAF1C by cancer cells. Our work provides insights into how enhancer transcription hyperactivation causes general transcription factor addiction during tumorigenesis.

scRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory.

The tumor microenvironment (TME) in gastric cancer (GC) has been shown to be important for tumor control but the specific characteristics for GC are not fully appreciated. We generated an atlas of 166,533 cells from 10 GC patients with matched paratumor tissues and blood. Our results show tumor-associated stromal cells (TASCs) have upregulated activity of Wnt signaling and angiogenesis, and are negatively correlated with survival. Tumor-associated macrophages and LAMP3+ DCs are involved in mediating T cell activity and form intercellular interaction hubs with TASCs. Clonotype and trajectory analysis demonstrates that Tc17 (IL-17+CD8+ T cells) originate from tissue-resident memory T cells and can subsequently differentiate into exhausted T cells, suggesting an alternative pathway for T cell exhaustion. Our results indicate that IL17+ cells may promote tumor progression through IL17, IL22, and IL26 signaling, highlighting the possibility of targeting IL17+ cells and associated signaling pathways as a therapeutic strategy to treat GC.

Rapid screening of TCR-pMHC interactions by the YAMTAD system.

Personalized immunotherapy, such as cancer vaccine and TCR-T methods, demands rapid screening of TCR-pMHC interactions. While several screening approaches have been developed, their throughput is limited. Here, the Yeast Agglutination Mediated TCR antigen Discovery system (YAMTAD) was designed and demonstrated to allow fast and unbiased library-on-library screening of TCR-pMHC interactions. Our proof-of-principle study achieved high sensitivity and specificity in identifying antigens for a given TCR and identifying TCRs recognizing a given pMHC for modest library sizes. Finally, the enrichment of high-affinity TCR-pMHC interactions by YAMTAD in library-on-library screening was demonstrated. Given the high throughput (106-108 × 106-108 in theory) and simplicity (identifying TCR-pMHC interactions without purification of TCR and pMHC) of YAMTAD, this study provides a rapid but effective platform for TCR-pMHC interaction screening, with valuable applications in future personalized immunotherapy.

Tumor-derived Jagged1 promotes cancer progression through immune evasion.

Immune checkpoint inhibitor (ICI) therapy is generating remarkable responses in individuals with cancer, but only a small portion of individuals with breast cancer respond well. Here we report that tumor-derived Jagged1 is a key regulator of the tumor immune microenvironment. Jagged1 promotes tumorigenesis in multiple spontaneous mammary tumor models. Through Jagged1-induced Notch activation, tumor cells increase expression and secretion of multiple cytokines to help recruit macrophages into the tumor microenvironment. Educated macrophages crosstalk with tumor-infiltrating T cells to inhibit T cell proliferation and tumoricidal activity. In individuals with triple-negative breast cancer, a high expression level of Jagged1 correlates with increased macrophage infiltration and decreased T cell activity. Co-administration of an ICI PD-1 antibody with a Notch inhibitor significantly inhibits tumor growth in breast cancer models. Our findings establish a distinct signaling cascade by which Jagged1 promotes adaptive immune evasion of tumor cells and provide several possible therapeutic targets.

SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.

High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using linear models challenging. We present SIGNET, a deep learning-based framework for capturing complex regulatory relationships between genes under the assumption that the expression levels of transcription factors participating in gene regulation are strong predictors of the expression of their target genes. Evaluations based on a variety of real and simulated scRNA-seq datasets showed that SIGNET is more sensitive to ChIP-seq validated regulatory interactions in different types of cells, particularly rare cells. Therefore, this process is more effective for various downstream analyses, such as cell clustering and gene regulatory network inference. We demonstrated that SIGNET is a useful tool for identifying important regulatory modules driving various biological processes.