Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts

Single cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets.

Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotype-specific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.

SpatialSort: a Bayesian model for clustering and cell population annotation of spatial proteomics data

MOTIVATION

Recent advances in spatial proteomics technologies have enabled the profiling of dozens of proteins in thousands of single cells in situ. This has created the opportunity to move beyond quantifying the composition of cell types in tissue, and instead probe the spatial relationships between cells. However, most current methods for clustering data from these assays only consider the expression values of cells and ignore the spatial context. Furthermore, existing approaches do not account for prior information about the expected cell populations in a sample.

RESULTS

To address these shortcomings, we developed SpatialSort, a spatially aware Bayesian clustering approach that allows for the incorporation of prior biological knowledge. Our method is able to account for the affinities of cells of different types to neighbour in space, and by incorporating prior information about expected cell populations, it is able to simultaneously improve clustering accuracy and perform automated annotation of clusters. Using synthetic and real data, we show that by using spatial and prior information SpatialSort improves clustering accuracy. We also demonstrate how SpatialSort can perform label transfer between spatial and nonspatial modalities through the analysis of a real world diffuse large B-cell lymphoma dataset.

AVAILABILITY AND IMPLEMENTATION

Source code is available on Github at: https://github.com/Roth-Lab/SpatialSort.

Single-Cell RNA sequencing of leaf sheath cells reveals the mechanism of rice resistance to brown planthopper (Nilaparvata lugens)

The brown planthopper (BPH) (Nilaparvata lugens) sucks rice sap causing leaves to turn yellow and wither, often leading to reduced or zero yields. Rice co-evolved to resist damage by BPH. However, the molecular mechanisms, including the cells and tissues, involved in the resistance are still rarely reported. Single-cell sequencing technology allows us to analyze different cell types involved in BPH resistance. Here, using single-cell sequencing technology, we compared the response offered by the leaf sheaths of the susceptible (TN1) and resistant (YHY15) rice varieties to BPH (48 hours after infestation). We found that the 14,699 and 16,237 cells (identified via transcriptomics) in TN1 and YHY15 could be annotated using cell-specific marker genes into nine cell-type clusters. The two rice varieties showed significant differences in cell types (such as mestome sheath cells, guard cells, mesophyll cells, xylem cells, bulliform cells, and phloem cells) in the rice resistance mechanism to BPH. Further analysis revealed that although mesophyll, xylem, and phloem cells are involved in the BPH resistance response, the molecular mechanism used by each cell type is different. Mesophyll cell may regulate the expression of genes related to vanillin, capsaicin, and ROS production, phloem cell may regulate the cell wall extension related genes, and xylem cell may be involved in BPH resistance response by controlling the expression of chitin and pectin related genes. Thus, rice resistance to BPH is a complicated process involving multiple insect resistance factors. The results presented here will significantly promote the investigation of the molecular mechanisms underlying the resistance of rice to insects and accelerate the breeding of insect-resistant rice varieties.

Single-cell landscape of primary central nervous system diffuse large B-cell lymphoma

Understanding tumor heterogeneity and immune infiltrates within the tumor-immune microenvironment (TIME) is essential for the innovation of immunotherapies. Here, combining single-cell transcriptomics and chromatin accessibility sequencing, we profile the intratumor heterogeneity of malignant cells and immune properties of the TIME in primary central nervous system diffuse large B-cell lymphoma (PCNS DLBCL) patients. We demonstrate diverse malignant programs related to tumor-promoting pathways, cell cycle and B-cell immune response. By integrating data from independent systemic DLBCL and follicular lymphoma cohorts, we reveal a prosurvival program with aberrantly elevated RNA splicing activity that is uniquely associated with PCNS DLBCL. Moreover, a plasmablast-like program that recurs across PCNS/activated B-cell DLBCL predicts a worse prognosis. In addition, clonally expanded CD8 T cells in PCNS DLBCL undergo a transition from a pre-exhaustion-like state to exhaustion, and exhibit higher exhaustion signature scores than systemic DLBCL. Thus, our study sheds light on potential reasons for the poor prognosis of PCNS DLBCL patients, which will facilitate the development of targeted therapy.

Approaches for single-cell RNA sequencing across tissues and cell types

Single-cell sequencing of RNA (scRNA-seq) has advanced our understanding of cellular heterogeneity and signaling in developmental biology and disease. A large number of complementary assays have been developed to profile transcriptomes of individual cells, also in combination with other readouts, such as chromatin accessibility or antibody-based analysis of protein surface markers. As scRNA-seq technologies are advancing fast, it is challenging to establish robust workflows and up-to-date protocols that are best suited to address the large range of research questions. Here, we review scRNA-seq techniques from mRNA end-counting to total RNA in relation to their specific features and outline the necessary sample preparation steps and quality control measures. Based on our experience in dealing with the continuously growing portfolio from the perspective of a central single-cell facility, we aim to provide guidance on how workflows can be best automatized and share our experience in coping with the continuous expansion of scRNA-seq techniques.

macroH2A2 antagonizes epigenetic programs of stemness in glioblastoma

Self-renewal is a crucial property of glioblastoma cells that is enabled by the choreographed functions of chromatin regulators and transcription factors. Identifying targetable epigenetic mechanisms of self-renewal could therefore represent an important step toward developing effective treatments for this universally lethal cancer. Here we uncover an epigenetic axis of self-renewal mediated by the histone variant macroH2A2. With omics and functional assays deploying patient-derived in vitro and in vivo models, we show that macroH2A2 shapes chromatin accessibility at enhancer elements to antagonize transcriptional programs of self-renewal. macroH2A2 also sensitizes cells to small molecule-mediated cell death via activation of a viral mimicry response. Consistent with these results, our analyses of clinical cohorts indicate that high transcriptional levels of this histone variant are associated with better prognosis of high-grade glioma patients. Our results reveal a targetable epigenetic mechanism of self-renewal controlled by macroH2A2 and suggest additional treatment approaches for glioblastoma patients.

scAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets

Undoubtedly, single-cell RNA sequencing (scRNA-seq) has changed the research landscape by providing insights into heterogeneous, complex and rare cell populations. Given that more such data sets will become available in the near future, their accurate assessment with compatible and robust models for cell type annotation is a prerequisite. Considering this, herein, we developed scAnno (scRNA-seq data annotation), an automated annotation tool for scRNA-seq data sets primarily based on the single-cell cluster levels, using a joint deconvolution strategy and logistic regression. We explicitly constructed a reference profile for human (30 cell types and 50 human tissues) and a reference profile for mouse (26 cell types and 50 mouse tissues) to support this novel methodology (scAnno). scAnno offers a possibility to obtain genes with high expression and specificity in a given cell type as cell type-specific genes (marker genes) by combining co-expression genes with seed genes as a core. Of importance, scAnno can accurately identify cell type-specific genes based on cell type reference expression profiles without any prior information. Particularly, in the peripheral blood mononuclear cell data set, the marker genes identified by scAnno showed cell type-specific expression, and the majority of marker genes matched exactly with those included in the CellMarker database. Besides validating the flexibility and interpretability of scAnno in identifying marker genes, we also proved its superiority in cell type annotation over other cell type annotation tools (SingleR, scPred, CHETAH and scmap-cluster) through internal validation of data sets (average annotation accuracy: 99.05%) and cross-platform data sets (average annotation accuracy: 95.56%). Taken together, we established the first novel methodology that utilizes a deconvolution strategy for automated cell typing and is capable of being a significant application in broader scRNA-seq analysis. scAnno is available at https://github.com/liuhong-jia/scAnno.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells. Therefore, the observed signal comes from mixtures of cells of different types. Here, we propose an innovative probabilistic model, Celloscope, that utilizes established prior knowledge on marker genes for cell type deconvolution from spatial transcriptomics data. Celloscope outperforms other methods on simulated data, successfully indicates known brain structures and spatially distinguishes between inhibitory and excitatory neuron types based in mouse brain tissue, and dissects large heterogeneity of immune infiltrate composition in prostate gland tissue.

Widespread sex-dimorphism across single-cell transcriptomes of adult African turquoise killifish tissues

The African turquoise killifish (Nothobranchius furzeri), the shortest-lived vertebrate that can be bred in captivity, is an emerging model organism to study vertebrate aging. Here we describe the first multi-tissue, single-cell gene expression atlas of female and male turquoise killifish tissues comprising immune and metabolic cells from the blood, kidney, liver, and spleen. We were able to annotate 22 distinct cell types, define associated marker genes, and infer differentiation trajectories. Using this dataset, we found pervasive sex-dimorphic gene expression across cell types, especially in the liver. Sex-dimorphic genes tended to be involved in processes related to lipid metabolism, and indeed, we observed clear differences in lipid storage in female vs. male turquoise killifish livers. Importantly, we use machine-learning to predict sex using single-cell gene expression in our atlas and identify potential transcriptional markers for molecular sex identity in this species. As proof-of-principle, we show that our atlas can be used to deconvolute existing liver bulk RNA-seq data in this species to obtain accurate estimates of cell type proportions across biological conditions. We believe that this single-cell atlas can be a resource to the community that could notably be leveraged to identify cell type-specific genes for cell type-specific expression in transgenic animals.

Single-cell Mayo Map ( scMayoMap ): an easy-to-use tool for cell type annotation in single-cell RNA-sequencing data analysis

Single-cell RNA-sequencing (scRNA-seq) has become a widely used tool for both basic and translational biomedical research. In scRNA-seq data analysis, cell type annotation is an essential but challenging step. In the past few years, several annotation tools have been developed. These methods require either labeled training/reference datasets, which are not always available, or a list of predefined cell subset markers, which are subject to biases. Thus, a user-friendly and precise annotation tool is still critically needed. We curated a comprehensive cell marker database named scMayoMapDatabase and developed a companion R package scMayoMap , an easy-to-use single cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of scMayoMap was demonstrated in 48 independent scRNA-seq datasets across different platforms and tissues. scMayoMap performs better than the currently available annotation tools on all the datasets tested. Additionally, the scMayoMapDatabase can be integrated with other tools and further improve their performance. scMayoMap and scMayoMapDatabase will help investigators to define the cell types in their scRNA-seq data in a streamlined and user-friendly way.

Tissue-Resident Memory T Cells in Pancreatic Ductal Adenocarcinoma Coexpress PD-1 and TIGIT and Functional Inhibition Is Reversible by Dual Antibody Blockade

Pancreatic ductal adenocarcinoma (PDAC) has a poor clinical outlook. Responses to immune checkpoint blockade are suboptimal and a much more detailed understanding of the tumor immune microenvironment is needed if this situation is to be improved. Here, we characterized tumor-infiltrating T-cell populations in patients with PDAC using cytometry by time of flight (CyTOF) and single-cell RNA sequencing. T cells were the predominant immune cell subset observed within tumors. Over 30% of CD4+ T cells expressed a CCR6+CD161+ Th17 phenotype and 17% displayed an activated regulatory T-cell profile. Large populations of CD8+ tissue-resident memory (TRM) T cells were also present and expressed high levels of programmed cell death protein 1 (PD-1) and TIGIT. A population of putative tumor-reactive CD103+CD39+ T cells was also observed within the CD8+ tumor-infiltrating lymphocytes population. The expression of PD-1 ligands was limited largely to hemopoietic cells whilst TIGIT ligands were expressed widely within the tumor microenvironment. Programmed death-ligand 1 and CD155 were expressed within the T-cell area of ectopic lymphoid structures and colocalized with PD-1+TIGIT+ CD8+ T cells. Combinatorial anti-PD-1 and TIGIT blockade enhanced IFNγ secretion and proliferation of T cells in the presence of PD-1 and TIGIT ligands. As such, we showed that the PDAC microenvironment is characterized by the presence of substantial populations of TRM cells with an exhausted PD-1+TIGIT+ phenotype where dual checkpoint receptor blockade represents a promising avenue for future immunotherapy.

Cellcano: supervised cell type identification for single cell ATAC-seq data

Computational cell type identification is a fundamental step in single-cell omics data analysis. Supervised celltyping methods have gained increasing popularity in single-cell RNA-seq data because of the superior performance and the availability of high-quality reference datasets. Recent technological advances in profiling chromatin accessibility at single-cell resolution (scATAC-seq) have brought new insights to the understanding of epigenetic heterogeneity. With continuous accumulation of scATAC-seq datasets, supervised celltyping method specifically designed for scATAC-seq is in urgent need. Here we develop Cellcano, a computational method based on a two-round supervised learning algorithm to identify cell types from scATAC-seq data. The method alleviates the distributional shift between reference and target data and improves the prediction performance. After systematically benchmarking Cellcano on 50 well-designed celltyping tasks from various datasets, we show that Cellcano is accurate, robust, and computationally efficient. Cellcano is well-documented and freely available at https://marvinquiet.github.io/Cellcano/ .

deCS: A Tool for Systematic Cell Type Annotations of Single-cell RNA Sequencing Data among Human Tissues

Single-cell RNA sequencing (scRNA-seq) is revolutionizing the study of complex and dynamic cellular mechanisms. However, cell type annotation remains a main challenge as it largely relies on a priori knowledge and manual curation, which is cumbersome and subjective. The increasing number of scRNA-seq datasets, as well as numerous published genetic studies, has motivated us to build a comprehensive human cell type reference atlas.Here, we present decoding Cell type Specificity (deCS), an automatic cell type annotation method augmented by a comprehensive collection of human cell type expression profiles and marker genes. We used deCS to annotate scRNA-seq data from various tissue types and systematically evaluated the annotation accuracy under different conditions, including reference panels, sequencing depth, and feature selection strategies. Our results demonstrate that expanding the references is critical for improving annotation accuracy. Compared to many existing state-of-the-art annotation tools, deCS significantly reduced computation time and increased accuracy. deCS can be integrated into the standard scRNA-seq analytical pipeline to enhance cell type annotation. Finally, we demonstrated the broad utility of deCS to identify trait-cell type associations in 51 human complex traits, providing deep insights into the cellular mechanisms underlying disease pathogenesis. All documents for deCS, including source code, user manual, demo data, and tutorials, are freely available at https://github.com/bsml320/deCS.

scTransSort: Transformers for Intelligent Annotation of Cell Types by Gene Embeddings

Single-cell transcriptomics is rapidly advancing our understanding of the composition of complex tissues and biological cells, and single-cell RNA sequencing (scRNA-seq) holds great potential for identifying and characterizing the cell composition of complex tissues. Cell type identification by analyzing scRNA-seq data is mostly limited by time-consuming and irreproducible manual annotation. As scRNA-seq technology scales to thousands of cells per experiment, the exponential increase in the number of cell samples makes manual annotation more difficult. On the other hand, the sparsity of gene transcriptome data remains a major challenge. This paper applied the idea of the transformer to single-cell classification tasks based on scRNA-seq data. We propose scTransSort, a cell-type annotation method pretrained with single-cell transcriptomics data. The scTransSort incorporates a method of representing genes as gene expression embedding blocks to reduce the sparsity of data used for cell type identification and reduce the computational complexity. The feature of scTransSort is that its implementation of intelligent information extraction for unordered data, automatically extracting valid features of cell types without the need for manually labeled features and additional references. In experiments on cells from 35 human and 26 mouse tissues, scTransSort successfully elucidated its high accuracy and high performance for cell type identification, and demonstrated its own high robustness and generalization ability.

Hierarchical cell-type identifier accurately distinguishes immune-cell subtypes enabling precise profiling of tissue microenvironment with single-cell RNA-sequencing

Single-cell RNA-seq enabled in-depth study on tissue micro-environment and immune-profiling, where a crucial step is to annotate cell identity. Immune cells play key roles in many diseases, whereas their activities are hard to track due to their diverse and highly variable nature. Existing cell-type identifiers had limited performance for this purpose. We present HiCAT, a hierarchical, marker-based cell-type identifier utilising gene set analysis for statistical scoring for given markers. It features successive identification of major-type, minor-type and subsets utilising subset markers structured in a three-level taxonomy tree. Comparison with manual annotation and pairwise match test showed HiCAT outperforms others in major- and minor-type identification. For subsets, we qualitatively evaluated the marker expression profile demonstrating that HiCAT provide the clearest immune-cell landscape. HiCAT was also used for immune-cell profiling in ulcerative colitis and discovered distinct features of the disease in macrophage and T-cell subsets that could not be identified previously.

Heterochronic parabiosis reprograms the mouse brain transcriptome by shifting aging signatures in multiple cell types

Aging is a complex process involving transcriptomic changes associated with deterioration across multiple tissues and organs, including the brain. Recent studies using heterochronic parabiosis have shown that various aspects of aging-associated decline are modifiable or even reversible. To better understand how this occurs, we performed single-cell transcriptomic profiling of young and old mouse brains after parabiosis. For each cell type, we cataloged alterations in gene expression, molecular pathways, transcriptional networks, ligand-receptor interactions and senescence status. Our analyses identified gene signatures, demonstrating that heterochronic parabiosis regulates several hallmarks of aging in a cell-type-specific manner. Brain endothelial cells were found to be especially malleable to this intervention, exhibiting dynamic transcriptional changes that affect vascular structure and function. These findings suggest new strategies for slowing deterioration and driving regeneration in the aging brain through approaches that do not rely on disease-specific mechanisms or actions of individual circulating factors.

Data-Driven and Machine Learning-Based Framework for Image-Guided Single-Cell Mass Spectrometry

Improved throughput of analysis and lowered limits of detection have allowed single-cell chemical analysis to go beyond the detection of a few molecules in such volume-limited samples, enabling researchers to characterize different functional states of individual cells. Image-guided single-cell mass spectrometry leverages optical and fluorescence microscopy in the high-throughput analysis of cellular and subcellular targets. In this work, we propose DATSIGMA (DAta-driven Tools for Single-cell analysis using Image-Guided MAss spectrometry), a workflow based on data-driven and machine learning approaches for feature extraction and enhanced interpretability of complex single-cell mass spectrometry data. Here, we implemented our toolset with user-friendly programs and tested it on multiple experimental data sets that cover a wide range of biological applications, including classifying various brain cell types. Because it is open-source, it offers a high level of customization and can be easily adapted to other types of single-cell mass spectrometry data.

Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network

Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assignment is a crucial step to achieve that. However, the poorly annotated genome and limited known biomarkers hinder us from assigning cell identities for nonmodel species. Here, we design a heterogeneous graph neural network model, CAME, to learn aligned and interpretable cell and gene embeddings for cross-species cell-type assignment and gene module extraction from scRNA-seq data. CAME achieves significant improvements in cell-type characterization across distant species owing to the utilization of non-one-to-one homologous gene mapping ignored by early methods. Our large-scale benchmarking study shows that CAME significantly outperforms five classical methods in terms of cell-type assignment and model robustness to insufficiency and inconsistency of sequencing depths. CAME can transfer the major cell types and interneuron subtypes of human brains to mouse and discover shared cell-type-specific functions in homologous gene modules. CAME can align the trajectories of human and macaque spermatogenesis and reveal their conservative expression dynamics. In short, CAME can make accurate cross-species cell-type assignments even for nonmodel species and uncover shared and divergent characteristics between two species from scRNA-seq data.

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

A novel strategy to dissect multifaceted macrophage function in human diseases

Macrophages are widely distributed immune cells that play central roles in a variety of physiologic and pathologic processes, including obesity and cardiovascular disease (CVD). They are highly plastic cells that execute diverse functions according to a combination of signaling and environmental cues. While macrophages have traditionally been understood to polarize to either proinflammatory M1-like or anti-inflammatory M2-like states, evidence has shown that they exist in a spectrum of states between those 2 phenotypic extremes. In obesity-related disease, M1-like macrophages exacerbate inflammation and promote insulin resistance, while M2-like macrophages reduce inflammation, promoting insulin sensitivity. However, polarization markers are expressed inconsistently in adipose tissue macrophages, and they additionally exhibit phenotypes differing from the M1/M2 paradigm. In atherosclerotic CVD, activated plaque macrophages can also exist in a range of proinflammatory or anti-inflammatory states. Some of these macrophages scavenge lipids, developing into heterogeneous foam cell populations. To better characterize the many actions of macrophages in human disease, we have designed a novel set of computational tools: MacSpectrum and AtheroSpectrum. These tools provide information on the inflammatory polarization status, differentiation, and foaming of macrophages in both human and mouse samples, allowing for better characterization of macrophage subpopulations based on their function. Using these tools, we identified disease-relevant cell states in obesity and CVD, including the novel concept that macrophage-derived foam cell formation can follow homeostatic noninflammatory or pathogenic inflammatory foaming programs.

Ovarian cancer mutational processes drive site-specific immune evasion

High-grade serous ovarian cancer (HGSOC) is an archetypal cancer of genomic instability1-4 patterned by distinct mutational processes5,6, tumour heterogeneity7-9 and intraperitoneal spread7,8,10. Immunotherapies have had limited efficacy in HGSOC11-13, highlighting an unmet need to assess how mutational processes and the anatomical sites of tumour foci determine the immunological states of the tumour microenvironment. Here we carried out an integrative analysis of whole-genome sequencing, single-cell RNA sequencing, digital histopathology and multiplexed immunofluorescence of 160 tumour sites from 42 treatment-naive patients with HGSOC. Homologous recombination-deficient HRD-Dup (BRCA1 mutant-like) and HRD-Del (BRCA2 mutant-like) tumours harboured inflammatory signalling and ongoing immunoediting, reflected in loss of HLA diversity and tumour infiltration with highly differentiated dysfunctional CD8+ T cells. By contrast, foldback-inversion-bearing tumours exhibited elevated immunosuppressive TGFβ signalling and immune exclusion, with predominantly naive/stem-like and memory T cells. Phenotypic state associations were specific to anatomical sites, highlighting compositional, topological and functional differences between adnexal tumours and distal peritoneal foci. Our findings implicate anatomical sites and mutational processes as determinants of evolutionary phenotypic divergence and immune resistance mechanisms in HGSOC. Our study provides a multi-omic cellular phenotype data substrate from which to develop and interpret future personalized immunotherapeutic approaches and early detection research.

Single-cell RNA sequencing reveals intra-tumoral heterogeneity of glioblastoma and a pro-tumor subset of tumor-associated macrophages characterized by EZH2 overexpression

BACKGROUND

Glioblastoma (GBM) is a highly heterogeneous disease with poor clinical outcome.

AIM

To comprehensively dissect molecular landscape of GBM and heterogeneous distribution and potential role of Enhancer of zeste homolog 2 (EZH2) in tumor microenvironment (TME).

METHODS

Single-cell RNA sequencing (scRNA-seq) analysis was performed in GBM samples from 8 patients. Deconvolution analysis, immunofluorescence (IF) microscopy, reverse-transcription quantitative polymerase chain reaction (RT-qPCR), colony formation experiments, and Cell Counting Kit-8 (CCK-8) assays were performed to confirmed the potential role of EZH2 in TME cells.

RESULTS

Malignant cells exhibited remarkable heterogeneity in abnormal metabolic patterns. A mesenchymal-2-like (MES2-like) GBM subcluster with glial-immune dual feature was firstly discovered, which were associated with highly activated hallmark pathways, immune evasion associated transcription factor (IRF8), and poor survival. The oncogene, EZH2, was heterogeneously expressed in malignant cells and immune cells consistent with proliferative genes, cell-cycle transcription factors, and similar activated hallmark pathways. In a tumor-associated macrophages (TAMs) subset (macrophage.3), EZH2 was highly expressed with similar changes of transcriptomic dynamics with cell-cycle genes and macrophages M2-phetotype genes. In addition, the subset tightly interacted with malignant cells. Deconvolution analysis showed increased abundance of the subset in GBM compared to low-grade glioma (LGG) and significant association with worse prognosis. Functional verification experiments confirmed the pro-tumor role of TAMs with EZH2 overexpression in GBM.

CONCLUSIONS

Our study illustrated a MES2-like GBM subcluster characterized by glial-immune dual feature and highlighted the pro-tumor role of a TAMs subset characterized by EZH2 overexpression.

PEAR1 regulates expansion of activated fibroblasts and deposition of extracellular matrix in pulmonary fibrosis

Pulmonary fibrosis is a chronic interstitial lung disease that causes irreversible and progressive lung scarring and respiratory failure. Activation of fibroblasts plays a central role in the progression of pulmonary fibrosis. Here we show that platelet endothelial aggregation receptor 1 (PEAR1) in fibroblasts may serve as a target for pulmonary fibrosis therapy. Pear1 deficiency in aged mice spontaneously causes alveolar collagens accumulation. Mesenchyme-specific Pear1 deficiency aggravates bleomycin-induced pulmonary fibrosis, confirming that PEAR1 potentially modulates pulmonary fibrosis progression via regulation of mesenchymal cell function. Moreover, single cell and bulk tissue RNA-seq analysis of pulmonary fibroblast reveals the expansion of Activated-fibroblast cluster and enrichment of marker genes in extracellular matrix development in Pear1^-/- fibrotic lungs. We further show that PEAR1 associates with Protein Phosphatase 1 to suppress fibrotic factors-induced intracellular signalling and fibroblast activation. Intratracheal aerosolization of monoclonal antibodies activating PEAR1 greatly ameliorates pulmonary fibrosis in both WT and Pear1-humanized mice, significantly improving their survival rate.

Single-cell technologies: From research to application

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry.

Cell therapy attenuates endothelial dysfunction in hypertensive rats with heart failure and preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) is defined by increased left ventricular (LV) stiffness, impaired vascular compliance, and fibrosis. Although systemic inflammation, driven by comorbidities, has been proposed to play a key role, the precise pathogenesis remains elusive. To test the hypothesis that inflammation drives endothelial dysfunction in HFpEF, we used cardiosphere-derived cells (CDCs), which reduce inflammation and fibrosis, improving function, structure, and survival in HFpEF rats. Dahl salt-sensitive rats fed a high-salt diet developed HFpEF, as manifested by diastolic dysfunction, systemic inflammation, and accelerated mortality. Rats were randomly allocated to receive intracoronary infusion of CDCs or vehicle. Two weeks later, inflammation, oxidative stress, and endothelial function were analyzed. Single-cell RNA sequencing of heart tissue was used to assay transcriptomic changes. CDCs improved endothelial-dependent vasodilation while reducing oxidative stress and restoring endothelial nitric oxide synthase (eNOS) expression. RNA sequencing revealed CDC-induced attenuation of pathways underlying endothelial cell leukocyte binding and innate immunity. Exposure of endothelial cells to CDC-secreted extracellular vesicles in vitro reduced VCAM-1 protein expression and attenuated monocyte adhesion and transmigration. Cell therapy with CDCs corrects diastolic dysfunction, reduces oxidative stress, and restores vascular reactivity. These findings lend credence to the hypothesis that inflammatory changes of the vascular endothelium are important, if not central, to HFpEF pathogenesis.NEW & NOTEWORTHY We tested the concept that inflammation of endothelial cells is a major pathogenic factor in HFpEF. CDCs are heart-derived cell products with verified anti-inflammatory therapeutic properties. Infusion of CDCs reduced oxidative stress, restored eNOS abundance, lowered monocyte levels, and rescued the expression of multiple disease-associated genes, thereby restoring vascular reactivity. The salutary effects of CDCs support the hypothesis that inflammation of endothelial cells is a proximate driver of HFpEF.

Multi-level cellular and functional annotation of single-cell transcriptomes using scPipeline

Single-cell RNA-sequencing (scRNA-seq) offers functional insight into complex biology, allowing for the interrogation of cellular populations and gene expression programs at single-cell resolution. Here, we introduce scPipeline, a single-cell data analysis toolbox that builds on existing methods and offers modular workflows for multi-level cellular annotation and user-friendly analysis reports. Advances to scRNA-seq annotation include: (i) co-dependency index (CDI)-based differential expression, (ii) cluster resolution optimization using a marker-specificity criterion, (iii) marker-based cell-type annotation with Miko scoring, and (iv) gene program discovery using scale-free shared nearest neighbor network (SSN) analysis. Both unsupervised and supervised procedures were validated using a diverse collection of scRNA-seq datasets and illustrative examples of cellular transcriptomic annotation of developmental and immunological scRNA-seq atlases are provided herein. Overall, scPipeline offers a flexible computational framework for in-depth scRNA-seq analysis.

Automatic Cell Type Annotation Using Marker Genes for Single-Cell RNA Sequencing Data

Recent advancement in single-cell RNA sequencing (scRNA-seq) technology is gaining more and more attention. Cell type annotation plays an essential role in scRNA-seq data analysis. Several computational methods have been proposed for automatic annotation. Traditional cell type annotation is to first cluster the cells using unsupervised learning methods based on the gene expression profiles, then to label the clusters using the aggregated cluster-level expression profiles and the marker genes’ information. Such procedure relies heavily on the clustering results. As the purity of clusters cannot be guaranteed, false detection of cluster features may lead to wrong annotations. In this paper, we improve this procedure and propose an Automatic Cell type Annotation Method (ACAM). ACAM delineates a clear framework to conduct automatic cell annotation through representative cluster identification, representative cluster annotation using marker genes, and the remaining cells’ classification. Experiments on seven real datasets show the better performance of ACAM compared to six well-known cell type annotation methods.

Unravelling the landscape of skin cancer through single-cell transcriptomics

The human skin is a complex organ that forms the first line of defense against pathogens and external injury. It is composed of a wide variety of cells that work together to maintain homeostasis and prevent disease, such as skin cancer. The exponentially rising incidence of skin malignancies poses a growing public health challenge, particularly when the disease course is complicated by metastasis and therapeutic resistance. Recent advances in single-cell transcriptomics have provided a high-resolution view of gene expression heterogeneity that can be applied to skin cancers to define cell types and states, understand disease evolution, and develop new therapeutic concepts. This approach has been particularly valuable in characterizing the contribution of immune cells in skin cancer, an area of great clinical importance given the increasing use of immunotherapy in this setting. In this review, we highlight recent skin cancer studies utilizing bulk RNA sequencing, introduce various single-cell transcriptomics approaches, and summarize key findings obtained by applying single-cell transcriptomics to skin cancer.

A probabilistic gene expression barcode for annotation of cell types from single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) quantifies gene expression for individual cells in a sample, which allows distinct cell-type populations to be identified and characterized. An important step in many scRNA-seq analysis pipelines is the annotation of cells into known cell types. While this can be achieved using experimental techniques, such as fluorescence-activated cell sorting, these approaches are impractical for large numbers of cells. This motivates the development of data-driven cell-type annotation methods. We find limitations with current approaches due to the reliance on known marker genes or from overfitting because of systematic differences, or batch effects, between studies. Here, we present a statistical approach that leverages public data sets to combine information across thousands of genes, uses a latent variable model to define cell-type-specific barcodes and account for batch effect variation, and probabilistically annotates cell-type identity from a reference of known cell types. The barcoding approach also provides a new way to discover marker genes. Using a range of data sets, including those generated to represent imperfect real-world reference data, we demonstrate that our approach substantially outperforms current reference-based methods, particularly when predicting across studies.

Unsupervised cell functional annotation for single-cell RNA-seq

One of the first steps in the analysis of single-cell RNA sequencing (scRNA-seq) data is the assignment of cell types. Although a number of supervised methods have been developed for this, in most cases such assignment is performed by first clustering cells in low-dimensional space and then assigning cell types to different clusters. To overcome noise and to improve cell type assignments, we developed UNIFAN, a neural network method that simultaneously clusters and annotates cells using known gene sets. UNIFAN combines both low-dimensional representation for all genes and cell-specific gene set activity scores to determine the clustering. We applied UNIFAN to human and mouse scRNA-seq data sets from several different organs. We show, by using knowledge about gene sets, that UNIFAN greatly outperforms prior methods developed for clustering scRNA-seq data. The gene sets assigned by UNIFAN to different clusters provide strong evidence for the cell type that is represented by this cluster, making annotations easier.

Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy

The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.

A machine learning-based method for automatically identifying novel cells in annotating single-cell RNA-seq data

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) has been widely used to decompose complex tissues into functionally distinct cell types. The first and usually the most important step of scRNA-seq data analysis is to accurately annotate the cell labels. In recent years, many supervised annotation methods have been developed and shown to be more convenient and accurate than unsupervised cell clustering. One challenge faced by all the supervised annotation methods is the identification of the novel cell type, which is defined as the cell type that is not present in the training data, only exists in the testing data. Existing methods usually label the cells simply based on the correlation coefficients or confidence scores, which sometimes results in an excessive number of unlabeled cells.

RESULTS

We developed a straightforward yet effective method combining autoencoder with iterative feature selection to automatically identify novel cells from scRNA-seq data. Our method trains an autoencoder with the labeled training data and applies the autoencoder to the testing data to obtain reconstruction errors. By iteratively selecting features that demonstrate a bi-modal pattern and reclustering the cells using the selected feature, our method can accurately identify novel cells that are not present in the training data. We further combined this approach with a support vector machine to provide a complete solution for annotating the full range of cell types. Extensive numerical experiments using five real scRNA-seq datasets demonstrated favorable performance of the proposed method over existing methods serving similar purposes.

AVAILABILITY AND IMPLEMENTATION

Our R software package CAMLU is publicly available through the Zenodo repository (https://doi.org/10.5281/zenodo.7054422) or GitHub repository (https://github.com/ziyili20/CAMLU).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Integrative and comparative single-cell analysis reveals transcriptomic difference between human tumefactive demyelinating lesion and glioma

Tumefactive demyelinating lesion (TDL) is an immune-mediated disease which can be misdiagnosed as glioma. At present, there is no study comparing difference between the two disorders at the cellular level. Here, we perform integrative and comparative single-cell RNA sequencing (ScRNA-seq) transcriptomic analysis on TDL and glioma lesions. At single-cell resolution, TDL is comprised primarily of immune cells, which is completely different from glioma. The integrated analysis reveals a TDL-specific microglial subset involving in B cell activation and proliferation. Comparative analysis highlights remyelination function of glial cells and demyelination function of T cells in TDL. Subclustering and pseudotime trajectory analysis of T cells in TDL reveal their heterogeneity and diverse functions involving in TDL pathogenesis and recovery process. Our study identifies substantial differences between TDL and glioma at single-cell resolution. The observed heterogeneity and potentially diverse functions of cells in TDL may be critical in disease progression.

Integrative and comparative single-cell analysis reveals transcriptomic difference between human tumefactive demyelinating lesion and glioma.

Phenotype clustering in health care: A narrative review for clinicians

Human pathophysiology is occasionally too complex for unaided hypothetical-deductive reasoning and the isolated application of additive or linear statistical methods. Clustering algorithms use input data patterns and distributions to form groups of similar patients or diseases that share distinct properties. Although clinicians frequently perform tasks that may be enhanced by clustering, few receive formal training and clinician-centered literature in clustering is sparse. To add value to clinical care and research, optimal clustering practices require a thorough understanding of how to process and optimize data, select features, weigh strengths and weaknesses of different clustering methods, select the optimal clustering method, and apply clustering methods to solve problems. These concepts and our suggestions for implementing them are described in this narrative review of published literature. All clustering methods share the weakness of finding potential clusters even when natural clusters do not exist, underscoring the importance of applying data-driven techniques as well as clinical and statistical expertise to clustering analyses. When applied properly, patient and disease phenotype clustering can reveal obscured associations that can help clinicians understand disease pathophysiology, predict treatment response, and identify patients for clinical trial enrollment.

Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model

Spatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and can suffer further difficulties identifying cell types in slide regions where transcript capture is low. Here, we describe a conceptually novel methodology that can computationally integrate spatial transcriptomics data with cell-type-informative paired tissue images, obtained from, for example, the reverse side of the same tissue section, to improve inferences of tissue cell type composition in spatial transcriptomics data. The underlying statistical approach is generalizable to any spatial transcriptomics protocol where informative paired tissue images can be obtained. We demonstrate a use case leveraging cell-type-specific immunofluorescence markers obtained on mouse brain tissue sections and a use case for leveraging the output of AI annotated H&E tissue images, which we used to markedly improve the identification of clinically relevant immune cell infiltration in breast cancer tissue. Thus, combining spatial transcriptomics data with paired tissue images has the potential to improve the identification of cell types and hence to improve the applications of spatial transcriptomics that rely on accurate cell type identification.

ASURAT: functional annotation-driven unsupervised clustering of single-cell transcriptomes

MOTIVATION

Single-cell RNA sequencing (scRNA-seq) analysis reveals heterogeneity and dynamic cell transitions. However, conventional gene-based analyses require intensive manual curation to interpret biological implications of computational results. Hence, a theory for efficiently annotating individual cells remains warranted.

RESULTS

We present ASURAT, a computational tool for simultaneously performing unsupervised clustering and functional annotation of disease, cell type, biological process and signaling pathway activity for single-cell transcriptomic data, using a correlation graph decomposition for genes in database-derived functional terms. We validated the usability and clustering performance of ASURAT using scRNA-seq datasets for human peripheral blood mononuclear cells, which required fewer manual curations than existing methods. Moreover, we applied ASURAT to scRNA-seq and spatial transcriptome datasets for human small cell lung cancer and pancreatic ductal adenocarcinoma, respectively, identifying previously overlooked subpopulations and differentially expressed genes. ASURAT is a powerful tool for dissecting cell subpopulations and improving biological interpretability of complex and noisy transcriptomic data.

AVAILABILITY AND IMPLEMENTATION

ASURAT is published on Bioconductor (https://doi.org/10.18129/B9.bioc.ASURAT). The codes for analyzing data in this article are available at Github (https://github.com/keita-iida/ASURATBI) and figshare (https://doi.org/10.6084/m9.figshare.19200254.v4).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Single-cell analysis and functional characterization uncover the stem cell hierarchies and developmental origins of rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is a common childhood cancer that shares features with developing skeletal muscle. Yet, the conservation of cellular hierarchy with human muscle development and the identification of molecularly defined tumor-propagating cells has not been reported. Using single-cell RNA-sequencing, DNA-barcode cell fate mapping and functional stem cell assays, we uncovered shared tumor cell hierarchies in RMS and human muscle development. We also identified common developmental stages at which tumor cells become arrested. Fusion-negative RMS cells resemble early myogenic cells found in embryonic and fetal development, while fusion-positive RMS cells express a highly specific gene program found in muscle cells transiting from embryonic to fetal development at 7-7.75 weeks of age. Fusion-positive RMS cells also have neural pathway-enriched states, suggesting less-rigid adherence to muscle-lineage hierarchies. Finally, we identified a molecularly defined tumor-propagating subpopulation in fusion-negative RMS that shares remarkable similarity to bi-potent, muscle mesenchyme progenitors that can make both muscle and osteogenic cells.

scWizard: a web-based automated tool for classifying and annotating single cells and downstream analysis of single-cell RNA-seq data in cancers

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scWizard provides comprehensive analysis pipeline for integration strategies of cancer scRNA-seq data.

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scWizard enables classification of 47 cell subtypes within the TME based on hierarchical model by deep neural network.

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scWizard gives a higher accuracy for annotation cell subtypes within the TME compared with five methods.

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scWizard packages is a point-and-click tool helping for researchers without proficient programming skills.

The emerging number of single-cell RNA-seq (scRNA-Seq) datasets allows the characterization of cell types across various cancer types. However, there is still lack of effective tools to integrate the various analysis of single-cells, especially for making fine annotation on subtype cells within the tumor microenvironment (TME). We developed scWizard, a point-and-click tool packaging automated process including our developed cell annotation method based on deep neural network learning and 11 downstream analyses methods. scWizard used 113,976 cells across 13 cancer types as a built-in reference dataset for training the hierarchical model enabling to automatedly classify and annotate 7 major cell types and 47 cell subtypes in the TME. scWizard provides a built-in pre-training set for user’s flexible choice, and gives a higher accuracy for annotation subtypes of tumor-derived T-lymphocytes/natural killer cells (T/NK) and myeloid cells from different cancer types compared with the existing five methods. scWizard has good robustness in three independent cancer datasets, with an accuracy of 0.98 in annotating major cell types, 0.85 in annotating myeloid cell subtypes and 0.79 in annotating T/NK cell subtypes, indicting the wide applicability of scWizard in different cell types of cancers. Finally, the automatic analysis and visualization function of scWizard are presented by using the intrahepatic cholangiocarcinoma (ICC) scRNA-Seq dataset as a case. scWizard focuses on decoding TME and covers various analysis flows for cancer scRNA-Seq study, and provides an easy-to-use tool and a user-friendly interface for researchers widely, to further accelerate the biological discovery of cancer research.

Integration of computational analysis and spatial transcriptomics in single-cell study

Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology. Meanwhile, pioneering explorations in spatial transcriptomics have opened avenues to address fundamental biological questions in health and diseases. Here, we review the technical attributes of single-cell RNA sequencing and spatial transcriptomics, and the core concepts of computational data analysis. We further highlight the challenges in the application of data integration methodologies and the interpretation of the biological context of the findings.

Practical bioinformatics pipelines for single-cell RNA-seq data analysis

Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool to explore cells. With an increasing number of scRNA-seq data analysis tools that have been developed, it is challenging for users to choose and compare their performance. Here, we present an overview of the workflow for computational analysis of scRNA-seq data. We detail the steps of a typical scRNA-seq analysis, including experimental design, pre-processing and quality control, feature selection, dimensionality reduction, cell clustering and annotation, and downstream analysis including batch correction, trajectory inference and cell-cell communication. We provide guidelines according to our best practice. This review will be helpful for the experimentalists interested in analyzing their data, and will aid the users seeking to update their analysis pipelines.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

Processing single-cell RNA-seq datasets using SingCellaR

Single-cell RNA sequencing has led to unprecedented levels of data complexity. Although several computational platforms are available, performing data analyses for multiple datasets remains a significant challenge. Here, we provide a comprehensive analytical protocol to interrogate multiple datasets on SingCellaR, an analysis package in R. This tool can be applied to general single-cell transcriptome analyses. We demonstrate steps for data analyses and visualization using bespoke pipelines, in conjunction with existing analysis tools to study human hematopoietic stem and progenitor cells. For complete details on the use and execution of this protocol, please refer to Roy et al. (2021).

scCNC: A method based on Capsule Network for Clustering scRNA-seq Data

MOTIVATION

A large number of studies have shown that clustering is a crucial step in scRNA-seq analysis. Most existing methods are based on unsupervised learning without the prior exploitation of any domain knowledge, which does not utilize available gold-standard labels. When confronted by the high dimensionality and general dropout events of scRNA-seq data, purely unsupervised clustering methods may not produce biologically interpretable clusters, which complicates cell type assignment.

RESULTS

In this paper, we propose a semi-supervised clustering method based on a capsule network named scCNC, that integrates domain knowledge into the clustering step. Significantly, we also propose a Semi-supervised Greedy Iterative Training (SGIT) method used to train the whole network. Experiments on some real scRNA-seq datasets show that scCNC can significantly improve clustering performance and facilitate downstream analyses.

AVAILABILITY

The source code of scCNC is freely available at https://github.com/WHY-17/scCNC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying tumor cells at the single-cell level using machine learning

Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the assignment of cell type or cell state to each sequenced cell-is a challenge, especially identifying tumor cells within single-cell or spatial sequencing experiments. Here, we propose ikarus, a machine learning pipeline aimed at distinguishing tumor cells from normal cells at the single-cell level. We test ikarus on multiple single-cell datasets, showing that it achieves high sensitivity and specificity in multiple experimental contexts.

PRRX1 is a master transcription factor of stromal fibroblasts for myofibroblastic lineage progression

Although stromal fibroblasts play a critical role in cancer progression, their identities remain unclear as they exhibit high heterogeneity and plasticity. Here, a master transcription factor (mTF) constructing core-regulatory circuitry, PRRX1, which determines the fibroblast lineage with a myofibroblastic phenotype, is identified for the fibroblast subgroup. PRRX1 orchestrates the functional drift of fibroblasts into myofibroblastic phenotype via TGF-β signaling by remodeling a super-enhancer landscape. Such reprogrammed fibroblasts have myofibroblastic functions resulting in markedly enhanced tumorigenicity and aggressiveness of cancer. PRRX1 expression in cancer-associated fibroblast (CAF) has an unfavorable prognosis in multiple cancer types. Fibroblast-specific PRRX1 depletion induces long-term and sustained complete remission of chemotherapy-resistant cancer in genetically engineered mice models. This study reveals CAF subpopulations based on super-enhancer profiles including PRRX1. Therefore, mTFs, including PRRX1, provide another opportunity for establishing a hierarchical classification system of fibroblasts and cancer treatment by targeting fibroblasts.

Cancer associated fibroblasts are an important and highly heterogeneous component of the tumor microenvironment. Here the authors identify PRRX1 as a master transcription factor determining a fibroblast lineage with myofibroblastic phenotype, associated with unfavourable prognosis in several cancer types.

scSemiAE: a deep model with semi-supervised learning for single-cell transcriptomics

Background

With the development of modern sequencing technology, hundreds of thousands of single-cell RNA-sequencing (scRNA-seq) profiles allow to explore the heterogeneity in the cell level, but it faces the challenges of high dimensions and high sparsity. Dimensionality reduction is essential for downstream analysis, such as clustering to identify cell subpopulations. Usually, dimensionality reduction follows unsupervised approach.

Results

In this paper, we introduce a semi-supervised dimensionality reduction method named scSemiAE, which is based on an autoencoder model. It transfers the information contained in available datasets with cell subpopulation labels to guide the search of better low-dimensional representations, which can ease further analysis.

Conclusions

Experiments on five public datasets show that, scSemiAE outperforms both unsupervised and semi-supervised baselines whether the transferred information embodied in the number of labeled cells and labeled cell subpopulations is much or less.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04703-0.