Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.

Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network.

The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.

Anti-CTLA-4 m2a Antibody Exacerbates Cardiac Injury in Experimental Autoimmune Myocarditis Mice By Promoting Ccl5-Neutrophil Infiltration.

The risk for suffering immune checkpoint inhibitors (ICIs)-associated myocarditis increases in patients with pre-existing conditions and the mechanisms remain to be clarified. Spatial transcriptomics, single-cell RNA sequencing, and flow cytometry are used to decipher how anti-cytotoxic T lymphocyte antigen-4 m2a antibody (anti-CTLA-4 m2a antibody) aggravated cardiac injury in experimental autoimmune myocarditis (EAM) mice. It is found that anti-CTLA-4 m2a antibody increases cardiac fibroblast-derived C-X-C motif chemokine ligand 1 (Cxcl1), which promots neutrophil infiltration to the myocarditic zones (MZs) of EAM mice via enhanced Cxcl1-Cxcr2 chemotaxis. It is identified that the C-C motif chemokine ligand 5 (Ccl5)-neutrophil subpopulation is responsible for high activity of cytokine production, adaptive immune response, NF-κB signaling, and cellular response to interferon-gamma and that the Ccl5-neutrophil subpopulation and its-associated proinflammatory cytokines/chemokines promoted macrophage (Mφ) polarization to M1 Mφ. These altered infiltrating landscape and phenotypic switch of immune cells, and proinflammatory factors synergistically aggravated anti-CTLA-4 m2a antibody-induced cardiac injury in EAM mice. Neutralizing neutrophils, Cxcl1, and applying Cxcr2 antagonist dramatically alleviates anti-CTLA-4 m2a antibody-induced leukocyte infiltration, cardiac fibrosis, and dysfunction. It is suggested that Ccl5-neutrophil subpopulation plays a critical role in aggravating anti-CTLA-4 m2a antibody-induced cardiac injury in EAM mice. This data may provide a strategic rational for preventing/curing ICIs-associated myocarditis.

Neoadjuvant immunochemotherapy improves clinical outcomes of patients with esophageal cancer by mediating anti-tumor immunity of CD8+ T (Tc1) and CD16+ NK cells.

Background: Esophageal cancer (ESCA) is one of the most common tumors in the world, and treatment using neoadjuvant therapy (NT) based on radiotherapy and/or chemotherapy has still unsatisfactory results. Neoadjuvant immunochemotherapy (NICT) has also become an effective treatment strategy nowadays. However, its impact on the tumor microenvironment (TME) and regulatory mechanisms on T cells and NK cells needs to be further elucidated. Methods: A total of 279 cases of ESCA who underwent surgery alone [non-neoadjuvant therapy (NONE)], neoadjuvant chemotherapy (NCT), and NICT were collected, and their therapeutic effect and survival period were compared. Further, RNA sequencing combined with biological information was used to analyze the expression of immune-related genes. Immunohistochemistry, immunofluorescence, and quantitative real-time PCR (qRT-PCR) were used to verify the activation and infiltration status of CD8+ T and CD16+ NK cells, as well as the function and regulatory pathway of killing tumor cells. Results: Patients with ESCA in the NICT group showed better clinical response, median survival, and 2-year survival rates (p < 0.05) compared with the NCT group. Our RNA sequencing data revealed that NICT could promote the expression of immune-related genes. The infiltration and activation of immune cells centered with CD8+ T cells were significantly enhanced. CD8+ T cells activated by PD-1 inhibitors secreted more IFN-γ and cytotoxic effector factor cells through the transcription factor of EOMES and TBX21. At the same time, activated CD8+ T cells mediated the CD16+ NK cell activation and secreted more IFN-γ to kill ESCA cells. In addition, the immunofluorescence co-staining results showed that more CD276+ tumor cells and CD16+ NK cells were existed in pre-NCT and pre-NICT group. However, CD276+ tumor cells were reduced significantly in the post-NICT group, while they still appeared in the post-NCT group, which means that CD16+ NK cells can recognize and kill CD276+ tumor cells after immune checkpoint blocker (ICB) treatment. Conclusion: NICT can improve the therapeutic effect and survival period of resectable ESCA patients. NICT could promote the expression of immune-related genes and activate CD8+ T and CD16+ NK cells to secrete more IFN-γ to kill ESCA cells. It provides a theoretical basis and clinical evidence for its potential as an NT strategy in ESCA.

The Deep Learning Framework iCanTCR Enables Early Cancer Detection Using the T-cell Receptor Repertoire in Peripheral Blood.

T cells recognize tumor antigens and initiate an anticancer immune response in the very early stages of tumor development, and the antigen specificity of T cells is determined by the T-cell receptor (TCR). Therefore, monitoring changes in the TCR repertoire in peripheral blood may offer a strategy to detect various cancers at a relatively early stage. Here, we developed the deep learning framework iCanTCR to identify patients with cancer based on the TCR repertoire. The iCanTCR framework uses TCRß sequences from an individual as an input and outputs the predicted cancer probability. The model was trained on over 2,000 publicly available TCR repertoires from 11 types of cancer and healthy controls. Analysis of several additional publicly available datasets validated the ability of iCanTCR to distinguish patients with cancer from noncancer individuals and demonstrated the capability of iCanTCR for the accurate classification of multiple cancers. Importantly, iCanTCR precisely identified individuals with early-stage cancer with an AUC of 86%. Altogether, this work provides a liquid biopsy approach to capture immune signals from peripheral blood for noninvasive cancer diagnosis. SIGNIFICANCE: Development of a deep learning-based method for multicancer detection using the TCR repertoire in the peripheral blood establishes the potential of evaluating circulating immune signals for noninvasive early cancer detection.

Functional requirement of alternative splicing in epithelial-mesenchymal transition of pancreatic circulating tumor.

Circulating tumor cells (CTCs) that undergo epithelial-to-mesenchymal transition (EMT) can provide valuable information regarding metastasis and potential therapies. However, current studies on the EMT overlook alternative splicing. Here, we used single-cell full-length transcriptome data and mRNA sequencing of CTCs to identify stage-specific alternative splicing of partial EMT and mesenchymal states during pancreatic cancer metastasis. We classified definitive tumor and normal epithelial cells via genetic aberrations and demonstrated dynamic changes in the epithelial-mesenchymal continuum in both epithelial cancer cells and CTCs. We provide the landscape of alternative splicing in CTCs at different stages of EMT, uncovering cell-type-specific splicing patterns and splicing events in cell surface proteins suitable for therapies. We show that MBNL1 governs cell fate through alternative splicing independently of changes in gene expression and affects the splicing pattern during EMT. We found a high frequency of events that contained multiple premature termination codons and were enriched with C and G nucleotides in close proximity, which influence the likelihood of stop codon readthrough and expand the range of potential therapeutic targets. Our study provides insights into the EMT transcriptome's dynamic changes and identifies potential diagnostic and therapeutic targets in pancreatic cancer.

CancerProteome: a resource to functionally decipher the proteome landscape in cancer.

Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.

DeepCCI: a deep learning framework for identifying cell-cell interactions from single-cell RNA sequencing data.

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity. RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI.

Identification of shared characteristics in tumor-infiltrating T cells across 15 cancers.

Tumor-infiltrating T cells are essential players in tumor immunotherapy. Great progress has been achieved in the investigation of T cell heterogeneity. However, little is well known about the shared characteristics of tumor-infiltrating T cells across cancers. In this study, we conduct a pan-cancer analysis of 349,799 T cells across 15 cancers. The results show that the same T cell types had similar expression patterns regulated by specific transcription factor (TF) regulons across cancers. Multiple T cell type transition paths were consistent in cancers. We found that TF regulons associated with CD8+ T cells transitioned to terminally differentiated effector memory (Temra) or exhausted (Tex) states were associated with patient clinical classification. We also observed universal activated cell-cell interaction pathways of tumor-infiltrating T cells in all cancers, some of which specifically mediated crosstalk in certain cell types. Moreover, consistent characteristics of TCRs in the aspect of variable and joining region genes were found across cancers. Overall, our study reveals common features of tumor-infiltrating T cells in different cancers and suggests future avenues for rational, targeted immunotherapies.

Spatial transcriptome analysis of long non-coding RNAs reveals tissue specificity and functional roles in cancer.

Long non-coding RNAs (lncRNAs) play a significant role in maintaining tissue morphology and functions, and their precise regulatory effectiveness is closely related to expression patterns. However, the spatial expression patterns of lncRNAs in humans are poorly characterized. Here, we constructed five comprehensive transcriptomic atlases of human lncRNAs covering thousands of major tissue samples in normal and disease states. The lncRNA transcriptomes exhibited high consistency within the same tissues across resources, and even higher complexity in specialized tissues. Tissue-elevated (TE) lncRNAs were identified in each resource and robust TE lncRNAs were refined by integrative analysis. We detected 1 to 4684 robust TE lncRNAs across tissues; the highest number was in testis tissue, followed by brain tissue. Functional analyses of TE lncRNAs indicated important roles in corresponding tissue-related pathways. Moreover, we found that the expression features of robust TE lncRNAs made them be effective biomarkers to distinguish tissues; TE lncRNAs also tended to be associated with cancer, and exhibited differential expression or were correlated with patient survival. In summary, spatial classification of lncRNAs is the starting point for elucidating the function of lncRNAs in both maintenance of tissue morphology and progress of tissue-constricted diseases.

DeepST: identifying spatial domains in spatial transcriptomics by deep learning.

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

Network analysis between neuron dysfunction and neuroimmune response based on neural single-cell transcriptome of COVID-19 patients.

Despite global vaccination efforts, COVID-19 breakthrough infections caused by variant virus continue to occur frequently, long-term sequelae of COVID-19 infection like neuronal dysfunction emerge as a noteworthy issue. Neuroimmune disorder induced by Inflammatory factor storm was considered as a possible reason, however, little was known about the functional factors affecting neuroimmune response to this virus. Here, using medial prefrontal cortex single cell data of COVID-19 patients, expression pattern analysis indicated that some immune-related pathway genes expressed specifically, including genes associated with T cell receptor, TNF signaling in microglia and Cytokine-cytokine receptor interaction and HIF-1 signaling pathway genes in astrocytes. Besides the well-known immune-related cell type microglia, we also observed immune-related factors like IL17D, TNFRSF1A and TLR4 expressed in Astrocytes. Based on the ligand-receptor relationship of immune-related factors, crosstalk landscape among cell clusters were analyzed. The findings indicated that astrocytes collaborated with microglia and affect excitatory neurons, participating in the process of immune response and neuronal dysfunction. Moreover, subset of astrocytes specific immune factors (hinged neuroimmune genes) were proved to correlate with Covid-19 infection and ventilator-associated pneumonia using multi-tissue RNA-seq and scRNA-seq data. Function characterization clarified that hinged neuroimmune genes were involved in activation of inflammation and hypoxia signaling pathways, which could lead to hyper-responses related neurological sequelae. Finally, a risk model was constructed and testified in RNA-seq and scRNA data of peripheral blood.

MutCov: A pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), is a major threat to public health worldwide. Previous studies have shown that the spike protein of SARS-CoV-2 determines viral infectivity and major antigenicity. However, the spike protein has been undergoing various mutations, which bring a great challenge to the prevention and treatment of COVID-19. Here we present the MutCov, a pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2 by calculating the binding free energy between spike protein and angiotensin-converting enzyme 2 (ACE2) or neutralizing monoclonal antibody (mAb). The predicted infectivity and antigenicity were highly consistent with biologically experimental results, and demonstrated that the MutCov achieved good prediction performance. In conclusion, the MutCov is of high importance for systematically evaluating the effect of novel mutations and improving the prevention and treatment of COVID-19. The source code and installation instruction of MutCov are freely available at http://jianglab.org.cn/MutCov.

Krüppel-like Transcription Factor 7 Is a Causal Gene in Autism Development.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurodevelopmental disease. To date, more than 1000 genes have been shown to be associated with ASD, and only a few of these genes account for more than 1% of autism cases. Klf7 is an important transcription factor of cell proliferation and differentiation in the nervous system, but whether klf7 is involved in autism is unclear. METHODS: We first performed ChIP-seq analysis of klf7 in N2A cells, then performed behavioral tests and RNA-seq in klf7+/- mice, and finally restored mice with adeno-associated virus (AAV)-mediated overexpression of klf7 in klf7+/- mice. RESULTS: Klf7 targeted genes are enriched with ASD genes, and 631 ASD risk genes are also differentially expressed in klf7+/- mice which exhibited the core symptoms of ASD. When klf7 levels were increased in the central nervous system (CNS) in klf7+/- adult mice, deficits in social interaction, repetitive behavior and majority of dysregulated ASD genes were rescued in the adults, suggesting transcriptional regulation. Moreover, knockdown of klf7 in human brain organoids caused dysregulation of 517 ASD risk genes, 344 of which were shared with klf7+/- mice, including some high-confidence ASD genes. CONCLUSIONS: Our findings highlight a klf7 regulation of ASD genes and provide new insights into the pathogenesis of ASD and promising targets for further research on mechanisms and treatments.

Identification of alternative splicing-derived cancer neoantigens for mRNA vaccine development.

Messenger RNA (mRNA) vaccines have shown great potential for anti-tumor therapy due to the advantages in safety, efficacy and industrial production. However, it remains a challenge to identify suitable cancer neoantigens that can be targeted for mRNA vaccines. Abnormal alternative splicing occurs in a variety of tumors, which may result in the translation of abnormal transcripts into tumor-specific proteins. High-throughput technologies make it possible for systematic characterization of alternative splicing as a source of suitable target neoantigens for mRNA vaccine development. Here, we summarized difficulties and challenges for identifying alternative splicing-derived cancer neoantigens from RNA-seq data and proposed a conceptual framework for designing personalized mRNA vaccines based on alternative splicing-derived cancer neoantigens. In addition, several points were presented to spark further discussion toward improving the identification of alternative splicing-derived cancer neoantigens.

Global Characterization of Peripheral B Cells in Parkinson's Disease by Single-Cell RNA and BCR Sequencing.

Immune system plays important roles in the pathogenesis of Parkinson's disease (PD). However, the role of B cells in this complex disease are still not fully understood. B cells produce antibodies but can also regulate immune responses. In order to decode the relative contribution of peripheral B cell subtypes to the etiology of PD, we performed single cell RNA and BCR sequencing for 10,466 B cells from 8 PD patients and 6 age-matched healthy controls. We observed significant increased memory B cells and significant decreased naïve B cells in PD patients compared to healthy controls. Notably, we also discovered increased IgG and IgA isotypes and more frequent class switch recombination events in PD patients. Moreover, we identified preferential V and J gene segments of B cell receptors in PD patients as the evidence of convergent selection in PD. Finally, we found a marked clonal expanded memory B cell population in PD patients, up-regulating both MHC II genes (HLA-DRB5, HLA-DQA2 and HLA-DPB1) and transcription factor activator protein 1 (AP-1), suggesting that the antigen presentation capacity of B cells was enhanced and B cells were activated in PD patients. Overall, this study conducted a comprehensive analysis of peripheral B cell characteristics of PD patients, which provided novel insights into the humoral immune response in the pathogenesis of PD.

N439K Variant in Spike Protein Alter the Infection Efficiency and Antigenicity of SARS-CoV-2 Based on Molecular Dynamics Simulation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), has been undergoing various mutations. The analysis of the structural and energetic effects of mutations on protein-protein interactions between the receptor binding domain (RBD) of SARS-CoV-2 and angiotensin converting enzyme 2 (ACE2) or neutralizing monoclonal antibodies will be beneficial for epidemic surveillance, diagnosis, and optimization of neutralizing agents. According to the molecular dynamics simulation, a key mutation N439K in the SARS-CoV-2 RBD region created a new salt bridge with Glu329 of hACE2, which resulted in greater electrostatic complementarity, and created a weak salt bridge with Asp442 of RBD. Furthermore, the N439K-mutated RBD bound hACE2 with a higher affinity than wild-type, which may lead to more infectious. In addition, the N439K-mutated RBD was markedly resistant to the SARS-CoV-2 neutralizing antibody REGN10987, which may lead to the failure of neutralization. The results show consistent with the previous experimental conclusion and clarify the structural mechanism under affinity changes. Our methods will offer guidance on the assessment of the infection efficiency and antigenicity effect of continuing mutations in SARS-CoV-2.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor.

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles.

Epigenetic aberrations have played a significant role in affecting the pathophysiological state of colorectal cancer, and global DNA hypomethylation mainly occurs in partial methylation domains (PMDs). However, the distribution of PMDs in individual cells and the heterogeneity between cells are still unclear. In this study, the DNA methylation profiles of colorectal cancer detected by WGBS and scBS-seq were used to depict PMDs in individual cells for the first time. We found that more than half of the entire genome is covered by PMDs. Three subclasses of PMDS have distinct characteristics, and Gain-PMDs cover a higher proportion of protein coding genes. Gain-PMDs have extensive epigenetic heterogeneity between different cells of the same tumor, and the DNA methylation in cells is affected by the tumor microenvironment. In addition, abnormally elevated promoter methylation in Gain-PMDs may further promote the growth, proliferation and metastasis of tumor cells through silent transcription. The PMDs detected in this study have the potential as epigenetic biomarkers and provide a new insight for colorectal cancer research based on single-cell methylation data.

Single-cell transcriptome and TCR profiling reveal activated and expanded T cell populations in Parkinson's disease.

Given the chronic inflammatory nature of Parkinson's disease (PD), T cell immunity may be important for disease onset. Here, we performed single-cell transcriptome and TCR sequencing, and conducted integrative analyses to decode composition, function and lineage relationship of T cells in the blood and cerebrospinal fluid of PD. Combined expression and TCR-based lineage tracking, we discovered a large population of CD8+ T cells showing continuous progression from central memory to terminal effector T cells in PD patients. Additionally, we identified a group of cytotoxic CD4+ T cells (CD4 CTLs) remarkably expanded in PD patients, which derived from Th1 cells by TCR-based fate decision. Finally, we screened putative TCR-antigen pairs that existed in both blood and cerebrospinal fluid of PD patients to provide potential evidence for peripheral T cells to participate in neuronal degeneration. Our study provides valuable insights and rich resources for understanding the adaptive immune response in PD.