Integrative analyses of single-cell transcriptome and regulome using MAESTRO

SCInter: A comprehensive single-cell transcriptome integration database for human and mouse

Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive Single-Cell transcriptome integration database for human and mouse (SCInter, https://bio.liclab.net/SCInter/index.php), which aims to provide a manually curated database that supports the provision of gene expression profiles across various cell types at the sample level. The current version of SCInter includes 115 integrated datasets and 1016 samples, covering nearly 150 tissues/cell lines. It contains 8016,646 cell markers in 457 identified cell types. SCInter enabled comprehensive analysis of cataloged single-cell data encompassing quality control (QC), clustering, cell markers, multi-method cell type automatic annotation, predicting cell differentiation trajectories and so on. At the same time, SCInter provided a user-friendly interface to query, browse, analyze and visualize each integrated dataset and single cell sample, along with comprehensive QC reports and processing results. It will facilitate the identification of cell type in different cell subpopulations and explore developmental trajectories, enhancing the study of cell heterogeneity in the fields of immunology and oncology.

Unravelling the genetic basis of Schizophrenia

Neuronal development is a highly regulated mechanism that is central to organismal function in animals. In humans, disruptions to this process can lead to a range of neurodevelopmental phenotypes, including Schizophrenia (SCZ). SCZ has a significant genetic component, whereby an individual with an SCZ affected family member is eight times more likely to develop the disease than someone with no family history of SCZ. By examining a combination of genomic, transcriptomic and epigenomic datasets, large-scale 'omics' studies aim to delineate the relationship between genetic variation and abnormal cellular activity in the SCZ brain. Herein, we provide a brief overview of some of the key omics methods currently being used in SCZ research, including RNA-seq, the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and high-throughput chromosome conformation capture (3C) approaches (e.g., Hi-C), as well as single-cell/nuclei iterations of these methods. We also discuss how these techniques are being employed to further our understanding of the genetic basis of SCZ, and to identify associated molecular pathways, biomarkers, and candidate drug targets.

An integrative pan-cancer analysis of the molecular characteristics of dietary restriction in tumour microenvironment

BACKGROUND

Dietary restriction (DR), a general term for dieting, has been demonstrated as an effective intervention in reducing the occurrence of cancers. Molecular activities associated with DR are crucial in mediating its anti-cancer effects, yet a comprehensive exploration of the landscape of these activities at the pan-cancer level is still lacking.

METHODS

We proposed a computational approach for quantifying DR-related molecular activities and delineating the landscape of these activities across 33 cancer types and 30 normal tissues within 27,320 samples. We thoroughly examined the associations between DR-related molecular activities and various factors, including the tumour microenvironment, immunological phenotypes, genomic features, and clinical prognosis. Meanwhile, we identified two DR genes that show potential as prognostic predictors in hepatocellular carcinoma and verified them by immunohistochemical assays in 90 patients.

FINDINGS

We found that DR-related molecular activities showed a close association with tumour immunity and hold potential for predicting immunotherapy responses in various cancers. Importantly, a higher level of DR-related molecular activities is associated with improved overall survival and cancer-specific survival. FZD1 and G6PD are two DR genes that serve as biomarkers for predicting the prognosis of patients with hepatocellular carcinoma.

INTERPRETATION

This study presents a robust link between DR-related molecular activities and tumour immunity across multiple cancer types. Our research could open the path for further investigation of DR-related molecular processes in cancer treatment.

FUNDING

National Natural Science Foundation of China (Grant No. 82000628) and the Guangdong-Hong Kong-Macao University Joint Laboratory of Interventional Medicine Foundation of Guangdong Province (Grant No. 2023LSYS001).

CAR-engineered lymphocyte persistence is governed by a FAS ligand/FAS auto-regulatory circuit

Chimeric antigen receptor (CAR)-engineered T and NK cells can cause durable remission of B-cell malignancies; however, limited persistence restrains the full potential of these therapies in many patients. The FAS ligand (FAS-L)/FAS pathway governs naturally-occurring lymphocyte homeostasis, yet knowledge of which cells express FAS-L in patients and whether these sources compromise CAR persistence remains incomplete. Here, we constructed a single-cell atlas of diverse cancer types to identify cellular subsets expressing FASLG, the gene encoding FAS-L. We discovered that FASLG is limited primarily to endogenous T cells, NK cells, and CAR-T cells while tumor and stromal cells express minimal FASLG. To establish whether CAR-T/NK cell survival is regulated through FAS-L, we performed competitive fitness assays using lymphocytes modified with or without a FAS dominant negative receptor (ΔFAS). Following adoptive transfer, ΔFAS-expressing CAR-T and CAR-NK cells became enriched across multiple tissues, a phenomenon that mechanistically was reverted through FASLG knockout. By contrast, FASLG was dispensable for CAR-mediated tumor killing. In multiple models, ΔFAS co-expression by CAR-T and CAR-NK enhanced antitumor efficacy compared with CAR cells alone. Together, these findings reveal that CAR-engineered lymphocyte persistence is governed by a FAS-L/FAS auto-regulatory circuit.

Single-cell transcriptomics dissects premalignant progression in proliferative verrucous leukoplakia

OBJECTIVE

Proliferative verrucous leukoplakia (PVL) is characterized by a spectrum of clinicopathological features and a high risk of malignant transformation. In this study, we aimed to delineate the dynamic changes in molecular signature during PVL progression and identify the potential cell subtypes that play a key role in the premalignant evolution of PVL.

METHODS

We performed single-cell RNA sequencing on three biopsy samples from a large PVL lesion. These samples exhibited a histopathological continuum of PVL progression.

RESULTS

By analyzing the transcriptome profiles of 27,611 cells from these samples, we identified ten major cell lineages and revealed that cellular remodeling occurred during the progression of PVL lesions, including epithelial, stromal, and immune cells. Epithelial cells are shifted to tumorigenic states and secretory patterns at the premalignant stage. Immune cells showed growing immunosuppressive phenotypes during PVL progression. Remarkably, two novel cell subtypes INSR+ endothelial cells and ASPN+ fibroblasts, were discovered and may play vital roles in microenvironment remodeling, such as angiogenesis and stromal fibrosis, which are closely involved in malignant transformation.

CONCLUSION

Our work is the first to depict the cellular landscape of PVL and speculate that disease progression may be driven by functional remodeling of multiple cell subtypes.

Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects

The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.

scCorrector: a robust method for integrating multi-study single-cell data

The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space. Specifically, we designed a Study Specific Adaptive Normalization for each study in decoder to implement these features. scCorrector substantially achieves competitive and robust performance compared with state-of-the-art methods and brings novel insights under various circumstances (e.g. various batches, multi-omics, cross-species, and development stages). In addition, the integration of single-cell data and spatial data makes it possible to transfer information between different studies, which greatly expand the narrow range of genes covered by MERFISH technology. In summary, scCorrector can efficiently integrate multi-study single-cell datasets, thereby providing broad opportunities to tackle challenges emerging from noisy resources.

Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients

Purpose

Pyroptosis, a new form of inflammatory programmed cell death, has recently gained attention. However, the impact of the expression levels of pyroptosis-related genes (PRGs) on the overall survival (OS) of osteosarcoma patients remains unclear. This study aims to investigate the impact of the expression levels of PRGs on the OS of pediatric and young adult patients with osteosarcoma.

Patients and Methods

Transcriptome matrix datasets of normal muscle or skeletal tissues from the Genotype-Tissue Expression (GTEx) project and osteosarcoma specimen the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were used to identify pyroptosis-related genes (PRGs) associated with prognosis. The National Center for Biotechnology Information's (NCBI) GSE21257 dataset was employed to validate the predictive value of the pyroptosis-related signature (PRS). Additionally, reverse transcription polymerase chain reaction (RT-qPCR) experiment was performed in normal and osteosarcoma cell lines.

Results

The study identified 18 differentially expressed PRGs (DEPRGs) between normal muscle or skeletal tissues and tumor samples. Multiple machine learning techniques were used to select PRGs, resulting in the identification of four hub PRGs. A PRS-score was calculated for each sample based on the expression of these four hub PRGs, and samples were categorized into low and high PRS-score level groups. It was confirmed that metastatic status and PRS-score level are independent prognostic predictors. A nomogram model for predicting OS of osteosarcoma patients was constructed. Single-cell RNA-sequencing data display the expression patterns of the hub PRGs. RT-qPCR data results were found to be consistent with the differential expression analysis performed on TARGET and GTEx samples.

Conclusion

The study developed a novel pyroptosis-related gene signature that can stratify pediatric and young adult osteosarcoma patients into different risk groups, thus predicting their response to immunotherapy and chemotherapy.

scATAnno: Automated Cell Type Annotation for single-cell ATAC Sequencing Data

The recent advances in single-cell epigenomic techniques have created a growing demand for scATAC-seq analysis. One key task is to determine cell types based on epigenetic profiling. We introduce scATAnno, a workflow designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow can generate scATAC-seq reference atlases from publicly available datasets, and enable accurate cell type annotation by integrating query data with reference atlases, without the aid of scRNA-seq profiling. To enhance annotation accuracy, we have incorporated KNN-based and weighted distance-based uncertainty scores to effectively detect unknown cell populations within the query data. We showcase the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cell (PBMC), basal cell carcinoma (BCC) and Triple Negative Breast Cancer (TNBC), and demonstrate that scATAnno accurately annotates cell types across conditions. Overall, scATAnno is a powerful tool for cell type annotation in scATAC-seq data and can aid in the interpretation of new scATAC-seq datasets in complex biological systems.

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer

Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduce MarsGT: Multi-omics Analysis for Rare population inference using a Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperforms existing tools in identifying rare cells across 550 simulated and four real human datasets. In mouse retina data, it reveals unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detects an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identifies a rare MAIT-like population impacted by a high IFN-I response and reveals the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

Identification of TNFRSF21 as an inhibitory factor of osteosarcoma based on a necroptosis-related prognostic gene signature and molecular experiments

BACKGROUND

Osteosarcoma is one of the most common malignant bone tumors with bad prognosis. Necroptosis is a form of programmed cell death. Recent studies showed that targeting necroptosis was a new promising approach for tumor therapy. This study aimed to establish a necroptosis-related gene signature to evaluated prognosis and explore the relationship between necroptosis and osteosarcoma.

METHODS

Data from The Cancer Genome Atlas was used for developing the signature and the derived necroptosis score (NS). Data from Gene Expression Omnibus served as validation. Principal component analysis (PCA), Cox regression, receiver operating characteristic (ROC) curves and Kaplan-Meier survival analysis were used to assess the performance of signature. The association between the NS and osteosarcoma was analyzed via gene set enrichment analysis, gene set variation analysis and Pearson test. Single-cell data was used for further exploration. Among the genes that constituted the signature, the role of TNFRSF21 in osteosarcoma was unclear. Molecular experiments were used to explore TNFRSF21 function.

RESULTS

Our data revealed that lower NS indicated more active necroptosis in osteosarcoma. Patients with lower NS had a better prognosis. PCA and ROC curves demonstrated NS was effective to predict prognosis. NS was negatively associated with immune infiltration levels and tumor microenvironment scores and positively associated with tumor purity and stemness index. Single-cell data showed necroptosis heterogeneity in osteosarcoma. The cell communication pattern of malignant cells with high NS was positively correlated with tumor progression. The expression of TNFRSF21 was down-regulated in osteosarcoma cell lines. Overexpression of TNFRSF21 inhibited proliferation and motility of osteosarcoma cells. Mechanically, TNFRSF21 upregulated the phosphorylation levels of RIPK1, RIPK3 and MLKL to promote necroptosis in osteosarcoma.

CONCLUSIONS

The necroptosis prognostic signature and NS established in this study could be used as an independent prognostic factor, TNFRSF21 may be a necroptosis target in osteosarcoma therapy.

scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

Uniform quantification of single-nucleus ATAC-seq data with Paired-Insertion Counting (PIC) and a model-based insertion rate estimator

Existing approaches to scoring single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) feature matrices from sequencing reads are inconsistent, affecting downstream analyses and displaying artifacts. We show that, even with sparse single-cell data, quantitative counts are informative for estimating the regulatory state of a cell, which calls for a consistent treatment. We propose Paired-Insertion Counting as a uniform method for snATAC-seq feature characterization and provide a probability model for inferring latent insertion dynamics from snATAC-seq count matrices.

A Bayesian method to infer copy number clones from single-cell RNA and ATAC sequencing

Single-cell RNA and ATAC sequencing technologies enable the examination of gene expression and chromatin accessibility in individual cells, providing insights into cellular phenotypes. In cancer research, it is important to consistently analyze these states within an evolutionary context on genetic clones. Here we present CONGAS+, a Bayesian model to map single-cell RNA and ATAC profiles onto the latent space of copy number clones. CONGAS+ clusters cells into tumour subclones with similar ploidy, rendering straightforward to compare their expression and chromatin profiles. The framework, implemented on GPU and tested on real and simulated data, scales to analyse seamlessly thousands of cells, demonstrating better performance than single-molecule models, and supporting new multi-omics assays. In prostate cancer, lymphoma and basal cell carcinoma, CONGAS+ successfully identifies complex subclonal architectures while providing a coherent mapping between ATAC and RNA, facilitating the study of genotype-phenotype maps and their connection to genomic instability.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references

The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell populations. However, achieving accurate cell-type annotation using public references presents challenges due to inconsistent annotations, batch effects, and rare cell types. Here, we introduce SELINA (single-cell identity navigator), an integrative and automatic cell-type annotation framework based on a pre-curated reference atlas spanning various tissues. SELINA employs a multiple-adversarial domain adaptation network to remove batch effects within the reference dataset. Additionally, it enhances the annotation of less frequent cell types by synthetic minority oversampling and fits query data with the reference data using an autoencoder. SELINA culminates in the creation of a comprehensive and uniform reference atlas, encompassing 1.7 million cells covering 230 distinct human cell types. We substantiate its robustness and superiority across a multitude of human tissues. Notably, SELINA could accurately annotate cells within diverse disease contexts. SELINA provides a complete solution for human single-cell RNA-seq data annotation with both python and R packages.

AtacAnnoR: a reference-based annotation tool for single cell ATAC-seq data

Here, we present AtacAnnoR, a two-round annotation method for scATAC-seq data using well-annotated scRNA-seq data as reference. We evaluate AtacAnnoR's performance against six competing methods on 11 benchmark datasets. Our results show that AtacAnnoR achieves the highest mean accuracy and the highest mean balanced accuracy and performs particularly well when unpaired scRNA-seq data are used as the reference. Furthermore, AtacAnnoR implements a 'Combine and Discard' strategy to further improve annotation accuracy when annotations of multiple references are available. AtacAnnoR has been implemented in an R package and can be directly integrated into currently popular scATAC-seq analysis pipelines.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

Integration of spatial and single-cell data across modalities with weakly linked features

Although single-cell and spatial sequencing methods enable simultaneous measurement of more than one biological modality, no technology can capture all modalities within the same cell. For current data integration methods, the feasibility of cross-modal integration relies on the existence of highly correlated, a priori 'linked' features. We describe matching X-modality via fuzzy smoothed embedding (MaxFuse), a cross-modal data integration method that, through iterative coembedding, data smoothing and cell matching, uses all information in each modality to obtain high-quality integration even when features are weakly linked. MaxFuse is modality-agnostic and demonstrates high robustness and accuracy in the weak linkage scenario, achieving 20~70% relative improvement over existing methods under key evaluation metrics on benchmarking datasets. A prototypical example of weak linkage is the integration of spatial proteomic data with single-cell sequencing data. On two example analyses of this type, MaxFuse enabled the spatial consolidation of proteomic, transcriptomic and epigenomic information at single-cell resolution on the same tissue section.

Integration of eQTL and GWAS analysis uncovers a genetic regulation of natural ionomic variation in Arabidopsis

KEY MESSAGE

This study provided important insights into the genetic architecture of variations in A. thaliana leaf ionome in a cell-type-specific manner. The functional interpretation of traits associated variants by expression quantitative trait loci (eQTL) analysis is usually performed in bulk tissue samples. While the regulation of gene expression is context-dependent, such as cell-type-specific manner. In this study, we estimated cell-type abundances from 728 bulk tissue samples using single-cell RNA-sequencing dataset, and performed cis-eQTL mapping to identify cell-type-interaction eQTL (cis-eQTLs(ci)) in A. thaliana. Also, we performed Genome-wide association studies (GWAS) analyses for 999 accessions to identify the genetic basis of variations in A. thaliana leaf ionome. As a result, a total of 5,664 unique eQTL genes and 15,038 unique cis-eQTLs(ci) were significant. The majority (62.83%) of cis-eQTLs(ci) were cell-type-specific eQTLs. Using colocalization, we uncovered one interested gene AT2G25590 in Phloem cell, encoding a kind of plant Tudor-like protein with possible chromatin-associated functions, which colocalized with the most significant cis-eQTL(ci) of a Mo-related locus (Chr2:10,908,806:A:C; P = 3.27 × 10-27). Furthermore, we prioritized eight target genes associated with AT2G25590, which were previously reported in regulating the concentration of Mo element in A. thaliana. This study revealed the genetic regulation of ionomic variations and provided a foundation for further studies on molecular mechanisms of genetic variants controlling the A. thaliana ionome.

MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer

Rare cell populations are key in neoplastic progression and therapeutic response, offering potential intervention targets. However, their computational identification and analysis often lag behind major cell types. To fill this gap, we introduced MarsGT: Multi-omics Analysis for Rare population inference using Single-cell Graph Transformer. It identifies rare cell populations using a probability-based heterogeneous graph transformer on single-cell multi-omics data. MarsGT outperformed existing tools in identifying rare cells across 400 simulated and four real human datasets. In mouse retina data, it revealed unique subpopulations of rare bipolar cells and a Müller glia cell subpopulation. In human lymph node data, MarsGT detected an intermediate B cell population potentially acting as lymphoma precursors. In human melanoma data, it identified a rare MAIT-like population impacted by a high IFN-I response and revealed the mechanism of immunotherapy. Hence, MarsGT offers biological insights and suggests potential strategies for early detection and therapeutic intervention of disease.

Single-cell dissection of cervical cancer reveals key subsets of the tumor immune microenvironment

The tumor microenvironment (TME) directly determines patients' outcomes and therapeutic efficiencies. An in-depth understanding of the TME is required to improve the prognosis of patients with cervical cancer (CC). This study conducted single-cell RNA and TCR sequencing of six-paired tumors and adjacent normal tissues to map the CC immune landscape. T and NK cells were highly enriched in the tumor area and transitioned from cytotoxic to exhaustion phenotypes. Our analyses suggest that cytotoxic large-clone T cells are critical effectors in the antitumor response. This study also revealed tumor-specific germinal center B cells associated with tertiary lymphoid structures. A high-germinal center B cell proportion in patients with CC is predictive of improved clinical outcomes and is associated with elevated hormonal immune responses. We depicted an immune-excluded stromal landscape and established a joint model of tumor and stromal cells to predict CC patients' prognosis. The study revealed tumor ecosystem subsets linked to antitumor response or prognosis in the TME and provides information for future combinational immunotherapy.

Pan-cancer and single-cell analysis reveal the prognostic value and immune response of NQO1

Background: Overexpression of the NAD(P)H: Quinone Oxidoreductase 1 (NQOI) gene has been linked with tumor progression, aggressiveness, drug resistance, and poor patient prognosis. Most research has described the biological function of the NQO1 in certain types and limited samples, but a comprehensive understanding of the NQO1's function and clinical importance at the pan-cancer level is scarce. More research is needed to understand the role of NQO1 in tumor infiltration, and immune checkpoint inhibitors in various cancers are needed. Methods: The NQO1 expression data for 33 types of pan-cancer and their association with the prognosis, pathologic stage, gender, immune cell infiltration, the tumor mutation burden, microsatellite instability, immune checkpoints, enrichment pathways, and the half-maximal inhibitory concentration (IC50) were downloaded from public databases. Results: Our findings indicate that the NQO1 gene was significantly upregulated in most cancer types. The Cox regression analysis showed that overexpression of the NQO1 gene was related to poor OS in Glioma, uveal melanoma, head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma, and adrenocortical carcinoma. NQO1 mRNA expression positively correlated with infiltrating immune cells and checkpoint molecule levels. The single-cell analysis revealed a potential relationship between the NQO1 mRNA expression levels and the infiltration of immune cells and stromal cells in bladder urothelial carcinoma, invasive breast carcinoma, and colorectal cancer. Conversely, a negative association was noted between various drugs (17-AAG, Lapatinib, Trametinib, PD-0325901) and the NQO1 mRNA expression levels. Conclusion: NQO1 expression was significantly associated with prognosis, immune infiltrates, and drug resistance in multiple cancer types. The inhibition of the NQO1-dependent signaling pathways may provide a promising strategy for developing new cancer-targeted therapies.

SCING: Inference of robust, interpretable gene regulatory networks from single cell and spatial transcriptomics

Gene regulatory network (GRN) inference is an integral part of understanding physiology and disease. Single cell/nuclei RNA-seq (scRNA-seq/snRNA-seq) data has been used to elucidate cell-type GRNs; however, the accuracy and speed of current scRNAseq-based GRN approaches are suboptimal. Here, we present Single Cell INtegrative Gene regulatory network inference (SCING), a gradient boosting and mutual information-based approach for identifying robust GRNs from scRNA-seq, snRNA-seq, and spatial transcriptomics data. Performance evaluation using Perturb-seq datasets, held-out data, and the mouse cell atlas combined with the DisGeNET database demonstrates the improved accuracy and biological interpretability of SCING compared to existing methods. We applied SCING to the entire mouse single cell atlas, human Alzheimer's disease (AD), and mouse AD spatial transcriptomics. SCING GRNs reveal unique disease subnetwork modeling capabilities, have intrinsic capacity to correct for batch effects, retrieve disease relevant genes and pathways, and are informative on spatial specificity of disease pathogenesis.

Dissecting the brain with spatially resolved multi-omics

Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.

Neural cell diversity in the light of single-cell transcriptomics

The development of highly parallel and affordable high-throughput single-cell transcriptomics technologies has revolutionized our understanding of brain complexity. These methods have been used to build cellular maps of the brain, its different regions, and catalog the diversity of cells in each of them during development, aging and even in disease. Now we know that cellular diversity is way beyond what was previously thought. Single-cell transcriptomics analyses have revealed that cell types previously considered homogeneous based on imaging techniques differ depending on several factors including sex, age and location within the brain. The expression profiles of these cells have also been exploited to understand which are the regulatory programs behind cellular diversity and decipher the transcriptional pathways driving them. In this review, we summarize how single-cell transcriptomics have changed our view on the cellular diversity in the human brain, and how it could impact the way we study neurodegenerative diseases. Moreover, we describe the new computational approaches that can be used to study cellular differentiation and gain insight into the functions of individual cell populations under different conditions and their alterations in disease.

Geometry Regularized Autoencoders

A fundamental task in data exploration is to extract low dimensional representations that capture intrinsic geometry in data, especially for faithfully visualizing data in two or three dimensions. Common approaches use kernel methods for manifold learning. However, these methods typically only provide an embedding of the input data and cannot extend naturally to new data points. Autoencoders have also become popular for representation learning. While they naturally compute feature extractors that are extendable to new data and invertible (i.e., reconstructing original features from latent representation), they often fail at representing the intrinsic data geometry compared to kernel-based manifold learning. We present a new method for integrating both approaches by incorporating a geometric regularization term in the bottleneck of the autoencoder. This regularization encourages the learned latent representation to follow the intrinsic data geometry, similar to manifold learning algorithms, while still enabling faithful extension to new data and preserving invertibility. We compare our approach to autoencoder models for manifold learning to provide qualitative and quantitative evidence of our advantages in preserving intrinsic structure, out of sample extension, and reconstruction. Our method is easily implemented for big-data applications, whereas other methods are limited in this regard.

Multimodal single cell analysis infers widespread enhancer co-activity in a lymphoblastoid cell line

Non-coding regulatory elements such as enhancers are key in controlling the cell-type specificity and spatio-temporal expression of genes. To drive stable and precise gene transcription robust to genetic variation and environmental stress, genes are often targeted by multiple enhancers with redundant action. However, it is unknown whether enhancers targeting the same gene display simultaneous activity or whether some enhancer combinations are more often co-active than others. Here, we take advantage of recent developments in single cell technology that permit assessing chromatin status (scATAC-seq) and gene expression (scRNA-seq) in the same single cells to correlate gene expression to the activity of multiple enhancers. Measuring activity patterns across 24,844 human lymphoblastoid single cells, we find that the majority of enhancers associated with the same gene display significant correlation in their chromatin profiles. For 6944 expressed genes associated with enhancers, we predict 89,885 significant enhancer-enhancer associations between nearby enhancers. We find that associated enhancers share similar transcription factor binding profiles and that gene essentiality is linked with higher enhancer co-activity. We provide a set of predicted enhancer-enhancer associations based on correlation derived from a single cell line, which can be further investigated for functional relevance.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

Pan-cancer analysis identifies PD-L2 as a tumor promotor in the tumor microenvironment

BACKGROUND

Programmed cell death protein 1 (PD-1) receptor has two ligands,programmed death-ligand 1 (PD-L1) and PD-L2. When compared with PD-L1, PD-L2 has not received much attention, and its role remains unclear.

METHODS

The expression profiles of pdcd1lg2 (PD-L2-encoding gene) mRNA and PD-L2 protein were analyzed using TCGA, ICGC, and HPA databases. Kaplan-Meier and Cox regression analyses were used to assess the prognostic significance of PD-L2. We used GSEA, Spearman's correlation analysis and PPI network to explore the biological functions of PD-L2. PD-L2-associated immune cell infiltration was evaluated using the ESTIMATE algorithm and TIMER 2.0. The expressions of PD-L2 in tumor-associated macrophages (TAMs) in human colon cancer samples, and in mice in an immunocompetent syngeneic setting were verified using scRNA-seq datasets, multiplex immunofluorescence staining, and flow cytometry. After fluorescence-activated cell sorting, flow cytometry and qRT-PCR and transwell and colony formation assays were used to evaluate the phenotype and functions of PD-L2+TAMs. Immune checkpoint inhibitors (ICIs) therapy prediction analysis was performed using TIDE and TISMO. Last, a series of targeted small-molecule drugs with promising therapeutic effects were predicted using the GSCA platform.

RESULTS

PD-L2 was expressed in all the common human cancer types and deteriorated outcomes in multiple cancers. PPI network and Spearman's correlation analysis revealed that PD-L2 was closely associated with many immune molecules. Moreover, both GSEA results of KEGG pathways and GSEA results for Reactome analysis indicated that PD-L2 expression played an important role in cancer immune response. Further analysis showed that PD-L2 expression was strongly associated with the infiltration of immune cells in tumor tissue in almost all cancer types, among which macrophages were the most positively associated with PD-L2 in colon cancer. According to the results mentioned above, we verified the expression of PD-L2 in TAMs in colon cancer and found that PD-L2+TAMs population was not static. Additionally, PD-L2+TAMs exhibited protumor M2 phenotype and increased the migration, invasion, and proliferative capacity of colon cancer cells. Furthermore, PD-L2 had a substantial predictive value for ICIs therapy cohorts.

CONCLUSION

PD-L2 in the TME, especially expressed on TAMs, could be applied as a potential therapeutic target.

Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha

Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers. The topmost identified regulator, ESRRA, is activated in immunotherapy-resistant tumors. Its inhibition killed tumors by suppressing energy metabolism and activating two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization, and (ii) antigen-presentation stimulation, recruiting CD8+ T cells into tumors. We also demonstrate a wide utility of BipotentR by applying it to angiogenesis and growth suppressor evasion pathways. BipotentR (http://bipotentr.dfci.harvard.edu/) provides a resource for evaluating patient response and discovering drug targets that act simultaneously through multiple mechanisms.

SIGNIFICANCE

BipotentR presents resources for evaluating patient response and identifying targets for drugs that can kill tumors through multiple mechanisms concurrently. Inhibition of the topmost candidate target killed tumors by suppressing energy metabolism and effects on two immune mechanisms. This article is highlighted in the In This Issue feature, p. 517.

Single-cell biological network inference using a heterogeneous graph transformer

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

Matching queried single-cell open-chromatin profiles to large pools of single-cell transcriptomes and epigenomes for reference supported analysis

The true benefits of large single-cell transcriptome and epigenome data sets can be realized only with the development of new approaches and search tools for annotating individual cells. Matching a single-cell epigenome profile to a large pool of reference cells remains a major challenge. Here, we present scEpiSearch, which enables searching, comparison, and independent classification of single-cell open-chromatin profiles against a large reference of single-cell expression and open-chromatin data sets. Across performance benchmarks, scEpiSearch outperformed multiple methods in accuracy of search and low-dimensional coembedding of single-cell profiles, irrespective of platforms and species. Here we also demonstrate the unconventional utilities of scEpiSearch by applying it on single-cell epigenome profiles of K562 cells and samples from patients with acute leukaemia to reveal different aspects of their heterogeneity, multipotent behavior, and dedifferentiated states. Applying scEpiSearch on our single-cell open-chromatin profiles from embryonic stem cells (ESCs), we identified ESC subpopulations with more activity and poising for endoplasmic reticulum stress and unfolded protein response. Thus, scEpiSearch solves the nontrivial problem of amalgamating information from a large pool of single cells to identify and study the regulatory states of cells using their single-cell epigenomes.

Destin2: Integrative and cross-modality analysis of single-cell chromatin accessibility data

We propose Destin2, a novel statistical and computational method for cross-modality dimension reduction, clustering, and trajectory reconstruction for single-cell ATAC-seq data. The framework integrates cellular-level epigenomic profiles from peak accessibility, motif deviation score, and pseudo-gene activity and learns a shared manifold using the multimodal input, followed by clustering and/or trajectory inference. We apply Destin2 to real scATAC-seq datasets with both discretized cell types and transient cell states and carry out benchmarking studies against existing methods based on unimodal analyses. Using cell-type labels transferred with high confidence from unmatched single-cell RNA sequencing data, we adopt four performance assessment metrics and demonstrate how Destin2 corroborates and improves upon existing methods. Using single-cell RNA and ATAC multiomic data, we further exemplify how Destin2's cross-modality integrative analyses preserve true cell-cell similarities using the matched cell pairs as ground truths. Destin2 is compiled as a freely available R package available at https://github.com/yuchaojiang/Destin2.

Integration of scATAC-Seq with scRNA-Seq Data

Single-cell studies are enabling our understanding of the molecular processes of normal cell development and the onset of several pathologies. For instance, single-cell RNA sequencing (scRNA-Seq) measures the transcriptome-wide gene expression at a single-cell resolution, allowing for studying the heterogeneity among the cells of the same population and revealing complex and rare cell populations. On the other hand, single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-Seq) can be used to define transcriptional and epigenetic changes by analyzing the chromatin accessibility at the single-cell level. However, the integration of multi-omics data still remains one of the most difficult tasks in bioinformatics. In this chapter, we focus on the combination of scRNA-Seq and scATACSeq data to perform an integrative analysis of the single-cell transcriptome and chromatin accessibility of human fetal progenitors.

A unified computational framework for single-cell data integration with optimal transport

Single-cell data integration can provide a comprehensive molecular view of cells. However, how to integrate heterogeneous single-cell multi-omics as well as spatially resolved transcriptomic data remains a major challenge. Here we introduce uniPort, a unified single-cell data integration framework that combines a coupled variational autoencoder (coupled-VAE) and minibatch unbalanced optimal transport (Minibatch-UOT). It leverages both highly variable common and dataset-specific genes for integration to handle the heterogeneity across datasets, and it is scalable to large-scale datasets. uniPort jointly embeds heterogeneous single-cell multi-omics datasets into a shared latent space. It can further construct a reference atlas for gene imputation across datasets. Meanwhile, uniPort provides a flexible label transfer framework to deconvolute heterogeneous spatial transcriptomic data using an optimal transport plan, instead of embedding latent space. We demonstrate the capability of uniPort by applying it to integrate a variety of datasets, including single-cell transcriptomics, chromatin accessibility, and spatially resolved transcriptomic data.

AIscEA: unsupervised integration of single-cell gene expression and chromatin accessibility via their biological consistency

MOTIVATION

The integrative analysis of single-cell gene expression and chromatin accessibility measurements is essential for revealing gene regulation, but it is one of the key challenges in computational biology. Gene expression and chromatin accessibility are measurements from different modalities, and no common features can be directly used to guide integration. Current state-of-the-art methods lack practical solutions for finding heterogeneous clusters. However, previous methods might not generate reliable results when cluster heterogeneity exists. More importantly, current methods lack an effective way to select hyper-parameters under an unsupervised setting. Therefore, applying computational methods to integrate single-cell gene expression and chromatin accessibility measurements remains difficult.

RESULTS

We introduce AIscEA-Alignment-based Integration of single-cell gene Expression and chromatin Accessibility-a computational method that integrates single-cell gene expression and chromatin accessibility measurements using their biological consistency. AIscEA first defines a ranked similarity score to quantify the biological consistency between cell clusters across measurements. AIscEA then uses the ranked similarity score and a novel permutation test to identify cluster alignment across measurements. AIscEA further utilizes graph alignment for the aligned cell clusters to align the cells across measurements. We compared AIscEA with the competing methods on several benchmark datasets and demonstrated that AIscEA is highly robust to the choice of hyper-parameters and can better handle the cluster heterogeneity problem. Furthermore, AIscEA significantly outperforms the state-of-the-art methods when integrating real-world SNARE-seq and scMultiome-seq datasets in terms of integration accuracy.

AVAILABILITY AND IMPLEMENTATION

AIscEA is available at https://figshare.com/articles/software/AIscEA_zip/21291135 on FigShare as well as {https://github.com/elhaam/AIscEA} onGitHub.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment

The Tumor Immune Single Cell Hub 2 (TISCH2) is a resource of single-cell RNA-seq (scRNA-seq) data from human and mouse tumors, which enables comprehensive characterization of gene expression in the tumor microenvironment (TME) across multiple cancer types. As an increasing number of datasets are generated in the public domain, in this update, TISCH2 has included 190 tumor scRNA-seq datasets covering 6 million cells in 50 cancer types, with 110 newly collected datasets and almost tripling the number of cells compared with the previous release. Furthermore, TISCH2 includes several new functions that allow users to better utilize the large-scale scRNA-seq datasets. First, in the Dataset module, TISCH2 provides the cell-cell communication results in each dataset, facilitating the analyses of interacted cell types and the discovery of significant ligand-receptor pairs between cell types. TISCH2 also includes the transcription factor analyses for each dataset and visualization of the top enriched transcription factors of each cell type. Second, in the Gene module, TISCH2 adds functions for identifying correlated genes and providing survival information for the input genes. In summary, TISCH2 is a user-friendly, up-to-date and well-maintained data resource for gene expression analyses in the TME. TISCH2 is freely available at http://tisch.comp-genomics.org/.

HUSCH: an integrated single-cell transcriptome atlas for human tissue gene expression visualization and analyses

Understanding gene expression patterns across different human cell types is crucial for investigating mechanisms of cell type differentiation, disease occurrence and progression. The recent development of single-cell RNA-seq (scRNA-seq) technologies significantly boosted the characterization of cell type heterogeneities in different human tissues. However, the huge number of datasets in the public domain also posed challenges in data integration and reuse. We present Human Universal Single Cell Hub (HUSCH, http://husch.comp-genomics.org), an atlas-scale curated database that integrates single-cell transcriptomic profiles of nearly 3 million cells from 185 high-quality human scRNA-seq datasets from 45 different tissues. All the data in HUSCH were uniformly processed and annotated with a standard workflow. In the single dataset module, HUSCH provides interactive gene expression visualization, differentially expressed genes, functional analyses, transcription regulators and cell-cell interaction analyses for each cell type cluster. Besides, HUSCH integrated different datasets in the single tissue module and performs data integration, batch correction, and cell type harmonization. This allows a comprehensive visualization and analysis of gene expression within each tissue based on single-cell datasets from multiple sources and platforms. HUSCH is a flexible and comprehensive data portal that enables searching, visualizing, analyzing, and downloading single-cell gene expression for the human tissue atlas.

PlantPhoneDB: A manually curated pan-plant database of ligand-receptor pairs infers cell-cell communication

Ligand-receptor pairs play important roles in cell-cell communication for multicellular organisms in response to environmental cues. Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) provides unprecedented opportunities to investigate cellular communication based on ligand-receptor expression. However, so far, no reliable ligand-receptor interaction database is available for plant species. In this study, we developed PlantPhoneDB (https://jasonxu.shinyapps.io/PlantPhoneDB/), a pan-plant database comprising a large number of high-confidence ligand-receptor pairs manually curated from seven resources. Also, we developed a PlantPhoneDB R package, which not only provided optional four scoring approaches that calculate interaction scores of ligand-receptor pairs between cell types but also provided visualization functions to present analysis results. At the PlantPhoneDB web interface, the processed datasets and results can be searched, browsed, and downloaded. To uncover novel cell-cell communication events in plants, we applied the PlantPhoneDB R package on GSE121619 dataset to infer significant cell-cell interactions of heat-shocked root cells in Arabidopsis thaliana. As a result, the PlantPhoneDB predicted the actively communicating AT1G28290-AT2G14890 ligand-receptor pair in atrichoblast-cortex cell pair in Arabidopsis thaliana. Importantly, the downstream target genes of this ligand-receptor pair were significantly enriched in the ribosome pathway, which facilitated plants adapting to environmental changes. In conclusion, PlantPhoneDB provided researchers with integrated resources to infer cell-cell communication from scRNA-seq datasets.

Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

Background

Cell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools.

Results

We benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods.

Conclusions

Our study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub (https://github.com/wanglabtongji/CCI).

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02783-y.

Computational Methods for Single-cell Multi-omics Integration and Alignment

Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. The problem of integrating different omics data with very different dimensionality and statistical properties remains, however, quite challenging. A growing body of computational tools is being developed for this task, leveraging ideas ranging from machine translation to the theory of networks, and represents another frontier on the interface of biology and data science. Our goal in this review is to provide a comprehensive, up-to-date survey of computational techniques for the integration of single-cell multi-omics data, while making the concepts behind each algorithm approachable to a non-expert audience.

scATACpipe: A nextflow pipeline for comprehensive and reproducible analyses of single cell ATAC-seq data

Single cell ATAC-seq (scATAC-seq) has become the most widely used method for profiling open chromatin landscape of heterogeneous cell populations at a single-cell resolution. Although numerous software tools and pipelines have been developed, an easy-to-use, scalable, reproducible, and comprehensive pipeline for scATAC-seq data analyses is still lacking. To fill this gap, we developed scATACpipe, a Nextflow pipeline, for performing comprehensive analyses of scATAC-seq data including extensive quality assessment, preprocessing, dimension reduction, clustering, peak calling, differential accessibility inference, integration with scRNA-seq data, transcription factor activity and footprinting analysis, co-accessibility inference, and cell trajectory prediction. scATACpipe enables users to perform the end-to-end analysis of scATAC-seq data with three sub-workflow options for preprocessing that leverage 10x Genomics Cell Ranger ATAC software, the ultra-fast Chromap procedures, and a set of custom scripts implementing current best practices for scATAC-seq data preprocessing. The pipeline extends the R package ArchR for downstream analysis with added support to any eukaryotic species with an annotated reference genome. Importantly, scATACpipe generates an all-in-one HTML report for the entire analysis and outputs cluster-specific BAM, BED, and BigWig files for visualization in a genome browser. scATACpipe eliminates the need for users to chain different tools together and facilitates reproducible and comprehensive analyses of scATAC-seq data from raw reads to various biological insights with minimal changes of configuration settings for different computing environments or species. By applying it to public datasets, we illustrated the utility, flexibility, versatility, and reliability of our pipeline, and demonstrated that our scATACpipe outperforms other workflows.

Single-cell gene regulation network inference by large-scale data integration

Single-cell ATAC-seq (scATAC-seq) has proven to be a state-of-art approach to investigating gene regulation at the single-cell level. However, existing methods cannot precisely uncover cell-type-specific binding of transcription regulators (TRs) and construct gene regulation networks (GRNs) in single-cell. ChIP-seq has been widely used to profile TR binding sites in the past decades. Here, we developed SCRIP, an integrative method to infer single-cell TR activity and targets based on the integration of scATAC-seq and a large-scale TR ChIP-seq reference. Our method showed improved performance in evaluating TR binding activity compared to the existing motif-based methods and reached a higher consistency with matched TR expressions. Besides, our method enables identifying TR target genes as well as building GRNs at the single-cell resolution based on a regulatory potential model. We demonstrate SCRIP's utility in accurate cell-type clustering, lineage tracing, and inferring cell-type-specific GRNs in multiple biological systems. SCRIP is freely available at https://github.com/wanglabtongji/SCRIP.

TBX21 Methylation as a Potential Regulator of Immune Suppression in CMS1 Subtype Colorectal Cancer

Cytotoxic T lymphocyte (CTL) infiltration is associated with survival, recurrence, and therapeutic response in colorectal cancer (CRC). Immune checkpoint inhibitor (ICI) therapy, which requires CTLs for response, does not work for most CRC patients. Therefore, it is critical to improve our understanding of immune resistance in this disease. We utilized 2391 CRC patients and 7 omics datasets, integrating clinical and genomic data to determine how DNA methylation may impact survival and CTL function in CRC. Using comprehensive molecular subtype (CMS) 1 patients as reference, we found TBX21 to be the only gene with altered expression and methylation that was associated with CTL infiltration. We found that CMS1 patients with high TBX21 expression and low methylation had a significant survival advantage. To confirm the role of Tbx21 in CTL function, we utilized scRNAseq data, demonstrating the association of TBX21 with markers of enhanced CTL function. Further analysis using pathway enrichment found that the genes TBX21, MX1, and SP140 had altered expression and methylation, suggesting that the TP53/P53 pathway may modify TBX21 methylation to upregulate TBX21 expression. Together, this suggests that targeting epigenetic modification more specifically for therapy and patient stratification may provide improved outcomes in CRC.

Nonparametric single-cell multiomic characterization of trio relationships between transcription factors, target genes, and cis-regulatory regions

The epigenetic control of gene expression is highly cell-type and context specific. Yet, despite its complexity, gene regulatory logic can be broken down into modular components consisting of a transcription factor (TF) activating or repressing the target gene expression through its binding to a cis-regulatory region. We propose a nonparametric approach, TRIPOD, to detect and characterize the three-way relationships between a TF, its target gene, and the accessibility of the TF's binding site using single-cell RNA and ATAC multiomic data. We apply TRIPOD to interrogate the cell-type-specific regulatory logic in peripheral blood mononuclear cells and contrast our results to detections from enhancer databases, cis-eQTL studies, ChIP-seq experiments, and TF knockdown/knockout studies. We then apply TRIPOD to mouse embryonic brain data and identify regulatory relationships, validated by ChIP-seq and PLAC-seq. Finally, we demonstrate TRIPOD on the SHARE-seq data of differentiating mouse hair follicle cells and identify lineage-specific regulation supported by histone marks and super-enhancer annotations. A record of this paper's transparent peer review process is included in the supplemental information.

Functional inference of gene regulation using single-cell multi-omics

Cells require coordinated control over gene expression when responding to environmental stimuli. Here we apply scATAC-seq and single-cell RNA sequencing (scRNA-seq) in resting and stimulated human blood cells. Collectively, we generate ~91,000 single-cell profiles, allowing us to probe the cis-regulatory landscape of the immunological response across cell types, stimuli, and time. Advancing tools to integrate multi-omics data, we develop functional inference of gene regulation (FigR), a framework to computationally pair scA-TAC-seq with scRNA-seq cells, connect distal cis-regulatory elements to genes, and infer gene-regulatory networks (GRNs) to identify candidate transcription factor (TF) regulators. Utilizing these paired multi-omics data, we define domains of regulatory chromatin (DORCs) of immune stimulation and find that cells alter chromatin accessibility and gene expression at timescales of minutes. Construction of the stimulation GRN elucidates TF activity at disease-associated DORCs. Overall, FigR enables elucidation of regulatory interactions across single-cell data, providing new opportunities to understand the function of cells within tissues.

Single-cell methods for measuring chromatin accessibility (ATAC-seq) and gene expression (RNA-seq) are rapidly evolving, but tools to integrate data and infer gene-regulatory relationships remain limited. Here we generate multi-omics data of resting and stimulated human blood cells and present a new computational framework for constructing gene-regulatory networks (GRNs). Specifically, we describe functional inference of gene regulation (FigR), a workflow to (1) pair scATAC-seq with scRNA-seq, (2) connect cis-regulatory elements to target genes, and (3) identify TF-gene relationships.

Feasibility and Application of Cluster Nursing to the Care of Patients with Acute Oncology

Objective

To probe the utility of cluster nursing for the care of acute oncology clients.

Methods

One hundred fourteen cases of acute oncology pioneers undergoing therapy in our clinic from April 2019 to February 2021 were randomly assigned into two consecutive arms, conventional care and cluster care, in accordance with the nursing modality. Complications, satisfaction, quality of survival, and negative emotions were compared across the two parties.

Results

The comorbidity incidence rate of the subject matter in the research cohort was 7.02%, which was below the comorbidity rate of 17.54% in the reaction cohort (P < 0.05); the percentage of satisfaction in the research cohort was 96.49%, which was higher than the satisfaction rate of 78.95% in the reaction cohort (P < 0.05); aftercare, the quality of survival was significantly higher in both groups, and the SAS and SDS scores were significantly lower, with a more pronounced trend of change in the research cohort than in the reaction cohort (P < 0.05).

Conclusion

Bundled care for casualty oncology is of major value, with a marked reduction in the incidence of postoperative complications, high quality of survival, an excellent prognosis and negative mood, high patient morale and satisfaction and compliance with curative treatment, and good support for subsequent care.

Integration of single-cell multi-omics data by regression analysis on unpaired observations

Despite recent developments, it is hard to profile all multi-omics single-cell data modalities on the same cell. Thus, huge amounts of single-cell genomics data of unpaired observations on different cells are generated. We propose a method named UnpairReg for the regression analysis on unpaired observations to integrate single-cell multi-omics data. On real and simulated data, UnpairReg provides an accurate estimation of cell gene expression where only chromatin accessibility data is available. The cis-regulatory network inferred from UnpairReg is highly consistent with eQTL mapping. UnpairReg improves cell type identification accuracy by joint analysis of single-cell gene expression and chromatin accessibility data.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.