Spatial transcriptomics deconvolution at single-cell resolution using Redeconve.

Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution. Here we present Redeconve, an algorithm to deconvolute spatial transcriptomics data at single-cell resolution, enabling interpretation of spatial transcriptomics data with thousands of nuanced cell states. We benchmark Redeconve with the state-of-the-art algorithms on diverse spatial transcriptomics platforms and datasets and demonstrate the superiority of Redeconve in terms of accuracy, resolution, robustness, and speed. Application to a human pancreatic cancer dataset reveals cancer-clone-specific T cell infiltration, and application to lymph node samples identifies differential cytotoxic T cells between IgA+ and IgG+ spots, providing novel insights into tumor immunology and the regulatory mechanisms underlying antibody class switch.

Siglec-9 acts as an immune-checkpoint molecule on macrophages in glioblastoma, restricting T-cell priming and immunotherapy response.

Neoadjuvant immune-checkpoint blockade therapy only benefits a limited fraction of patients with glioblastoma multiforme (GBM). Thus, targeting other immunomodulators on myeloid cells is an attractive therapeutic option. Here, we performed single-cell RNA sequencing and spatial transcriptomics of patients with GBM treated with neoadjuvant anti-PD-1 therapy. We identified unique monocyte-derived tumor-associated macrophage subpopulations with functional plasticity that highly expressed the immunosuppressive SIGLEC9 gene and preferentially accumulated in the nonresponders to anti-PD-1 treatment. Deletion of Siglece (murine homolog) resulted in dramatically restrained tumor development and prolonged survival in mouse models. Mechanistically, targeting Siglece directly activated both CD4+ T cells and CD8+ T cells through antigen presentation, secreted chemokines and co-stimulatory factor interactions. Furthermore, Siglece deletion synergized with anti-PD-1/PD-L1 treatment to improve antitumor efficacy. Our data demonstrated that Siglec-9 is an immune-checkpoint molecule on macrophages that can be targeted to enhance anti-PD-1/PD-L1 therapeutic efficacy for GBM treatment.

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.

Single-cell dissection of remodeled inflammatory ecosystem in primary and metastatic gallbladder carcinoma.

Gallbladder carcinoma (GBC) is the most common biliary tract malignancy with the lowest survival rate, primarily arising from chronic inflammation. To better characterize the progression from inflammation to cancer to metastasis, we performed single-cell RNA sequencing across samples of 6 chronic cholecystitis, 12 treatment-naive GBCs, and 6 matched metastases. Benign epithelial cells from inflamed gallbladders displayed resting, immune-regulating, and gastrointestinal metaplastic phenotypes. A small amount of PLA2G2A+ epithelial cells with copy number variation were identified from a histologically benign sample. We validated significant overexpression of PLA2G2A across in situ GBCs, together with increased proliferation and cancer stemness in PLA2G2A-overexpressing GBC cells, indicating an important role for PLA2G2A during early carcinogenesis. Malignant epithelial cells displayed pervasive cancer hallmarks and cellular plasticity, differentiating into metaplastic, inflammatory, and mesenchymal subtypes with distinct transcriptomic, genomic, and prognostic patterns. Chronic cholecystitis led to an adapted microenvironment characterized by MDSC-like macrophages, CD8+ TRM cells, and CCL2+ immunity-regulating fibroblasts. By contrast, GBC instigated an aggressive and immunosuppressive microenvironment, featured by tumor-associated macrophages, Treg cells, CD8+ TEX cells, and STMN1+ tumor-promoting fibroblasts. Single-cell and bulk RNA-seq profiles consistently showed a more suppressive immune milieu for GBCs with inflammatory epithelial signatures, coupled with strengthened epithelial-immune crosstalk. We further pinpointed a subset of senescence-like fibroblasts (FN1+TGM2+) preferentially enriched in metastatic lesions, which promoted GBC migration and invasion via their secretory phenotype. Collectively, this study provides comprehensive insights into epithelial and microenvironmental reprogramming throughout cholecystitis-propelled carcinogenesis and metastasis, laying a new foundation for the precision therapy of GBC.

Immune phenotypic linkage between colorectal cancer and liver metastasis.

The tumor microenvironment (TME) is connected to immunotherapy responses, but it remains unclear how cancer cells and host tissues differentially influence the immune composition within TME. Here, we performed single-cell analyses for autologous samples from liver metastasized colorectal cancer to disentangle factors shaping TME. By aligning CD45+ cells across different tissues, we classified exhausted CD8+ T cells (Texs) and activated regulatory T cells as M-type, whose phenotypes were associated with the malignancy, while natural killer and mucosal-associated invariant T cells were defined as N-type, whose phenotypes were associated with the niche. T cell receptor sharing between Texs in primary and metastatic tumors implicated the presence of common peripheral non-exhausted precursors. For myeloid cells, a subset of dendritic cells (DC3s) and SPP1+ macrophages were M-type, and the latter were predominant in liver metastasis, indicating its pro-metastasis role. Our analyses bridge immune phenotypes of primary and metastatic tumors, thereby helping to understand the tumor-specific contexture and identify the pro-metastasis components.

Liver Metastasis Modulate Responses of Suppressive Macrophages and Exhausted T Cells to Immunotherapy Revealed by Single Cell Sequencing.

Liver metastasis is associated with immunotherapy resistance, although the underlying mechanisms remain incompletely understood. By applying single cell RNA-sequencing to a concurrent subcutaneous and liver tumor murine model to recapitulate liver metastases, it is identified that subsets within tumor-infiltrating exhausted CD8+ T (Tex) cells and immunosuppressive tumor-associated macrophages (TAMs) display opposite responses to concurrent liver tumors and anti-PD-1 treatment, suggesting a complex immune regulating network. Both angiogenic and interferon-reactive TAMs show increased frequencies in implanted liver tumors, and anti-PD-1 treatment further elevates the frequencies of angiogenic TAMs. Such TAMs frequencies negatively correlate with the proportions of cytotoxic T cell subsets. Further, expression of interferon-stimulated genes in TAMs is dramatically reduced under effective anti-PD-1 treatment, while such tendencies are diminished in mice with implanted liver tumors. Therefore, the study indicates that liver metastases could increase immunosuppressive TAMs frequencies and inhibit Tex responses to PD-1 blockade, resulting in compromised systemic antitumor immunity and limited immunotherapy efficacy.

PEG10 amplification at 7q21.3 potentiates large-cell transformation in cutaneous T-cell lymphoma.

Mycosis fungoides (MF), the most common form of cutaneous T-cell lymphoma, undergo large-cell transformation (LCT) in the late stage, manifesting aggressive behavior, resistance to treatments, and poor prognosis, but the mechanisms involved remain unclear. To identify the molecular driver of LCT, we collected tumor samples from 133 MF patients and performed whole-transcriptome sequencing on 49 advanced-stage MF patients, followed by integrated copy number inference and genomic hybridization. Tumors with LCT showed unique transcriptional programs and enriched expressions of genes at chr7q. Paternally expressed gene 10 (PEG10), an imprinted gene at 7q21.3, was ectopically expressed in malignant T cells from LCT, driven by 7q21.3 amplification. Mechanistically, aberrant PEG10 expression increased cell size, promoted cell proliferation, and conferred treatment resistance by a PEG10/KLF2/NF-κB axis in in vitro and in vivo models. Pharmacologically targeting PEG10 reversed the phenotypes of proliferation and treatment resistance in LCT. Our findings reveal new molecular mechanisms underlying LCT and suggest that PEG10 inhibition may serve as a promising therapeutic approach in late-stage aggressive T-cell lymphoma.

Pan-cancer single-cell landscape of tumor-infiltrating T cells.

T cells play a central role in cancer immunotherapy, but we lack systematic comparison of the heterogeneity and dynamics of tumor-infiltrating T cells across cancer types. We built a single-cell RNA-sequencing pan-cancer atlas of T cells for 316 donors across 21 cancer types and revealed distinct T cell composition patterns. We found multiple state-transition paths in the exhaustion of CD8+ T cells and the preference of those paths among different tumor types. Certain T cell populations showed specific correlation with patient properties such as mutation burden, shedding light on the possible determinants of the tumor microenvironment. T cell compositions within tumors alone could classify cancer patients into groups with clinical trait specificity, providing new insights into T cell immunity and precision immunotherapy targeting T cells.

SARS-CoV-2 exacerbates proinflammatory responses in myeloid cells through C-type lectin receptors and Tweety family member 2.

Despite mounting evidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) engagement with immune cells, most express little, if any, of the canonical receptor of SARS-CoV-2, angiotensin-converting enzyme 2 (ACE2). Here, using a myeloid cell receptor-focused ectopic expression screen, we identified several C-type lectins (DC-SIGN, L-SIGN, LSECtin, ASGR1, and CLEC10A) and Tweety family member 2 (TTYH2) as glycan-dependent binding partners of the SARS-CoV-2 spike. Except for TTYH2, these molecules primarily interacted with spike via regions outside of the receptor-binding domain. Single-cell RNA sequencing analysis of pulmonary cells from individuals with coronavirus disease 2019 (COVID-19) indicated predominant expression of these molecules on myeloid cells. Although these receptors do not support active replication of SARS-CoV-2, their engagement with the virus induced robust proinflammatory responses in myeloid cells that correlated with COVID-19 severity. We also generated a bispecific anti-spike nanobody that not only blocked ACE2-mediated infection but also the myeloid receptor-mediated proinflammatory responses. Our findings suggest that SARS-CoV-2-myeloid receptor interactions promote immune hyperactivation, which represents potential targets for COVID-19 therapy.

A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells.

Tumor-infiltrating myeloid cells (TIMs) are key regulators in tumor progression, but the similarity and distinction of their fundamental properties across different tumors remain elusive. Here, by performing a pan-cancer analysis of single myeloid cells from 210 patients across 15 human cancer types, we identified distinct features of TIMs across cancer types. Mast cells in nasopharyngeal cancer were found to be associated with better prognosis and exhibited an anti-tumor phenotype with a high ratio of TNF+/VEGFA+ cells. Systematic comparison between cDC1- and cDC2-derived LAMP3+ cDCs revealed their differences in transcription factors and external stimulus. Additionally, pro-angiogenic tumor-associated macrophages (TAMs) were characterized with diverse markers across different cancer types, and the composition of TIMs appeared to be associated with certain features of somatic mutations and gene expressions. Our results provide a systematic view of the highly heterogeneous TIMs and suggest future avenues for rational, targeted immunotherapies.

Insights Gained from Single-Cell Analysis of Immune Cells in the Tumor Microenvironment.

Understanding tumor immune microenvironments is critical for identifying immune modifiers of cancer progression and developing cancer immunotherapies. Recent applications of single-cell RNA sequencing (scRNA-seq) in dissecting tumor microenvironments have brought important insights into the biology of tumor-infiltrating immune cells, including their heterogeneity, dynamics, and potential roles in both disease progression and response to immune checkpoint inhibitors and other immunotherapies. This review focuses on the advances in knowledge of tumor immune microenvironments acquired from scRNA-seq studies across multiple types of human tumors, with a particular emphasis on the study of phenotypic plasticity and lineage dynamics of immune cells in the tumor environment. We also discuss several imminent questions emerging from scRNA-seq observations and their potential solutions on the horizon.

Landscape of transcript isoforms in single T cells infiltrating in non-small-cell lung cancer.

Single-cell RNA sequencing (scRNA-seq) has enabled high-resolution characterization of molecular signatures of tumor-infiltrating lymphocytes. However, analyses at the transcript isoform level are rarely reported. As alternative splicing is critical to T-cell differentiation and activation, here, we proposed a computational method named IDEA (Isoform Detection, Enrichment, and functional Annotation) to comprehensively detect and annotate differentially used isoforms across cell subtypes. We applied IDEA on a scRNA-seq data set of 12,346 T cells from non-small-cell lung cancer (NSCLC). We found that most genes tend to dominantly express one isoform in single T cells, enabling typing T cells based on the isotypes, given a gene. Isotype analysis suggested that tumor-infiltrating T cells significantly preferred specific isotypes for 245 genes in CD8+ T cells and 456 genes in CD4+ T cells. Functional annotation suggests that the preferred isoforms involved in coding/noncoding switches, transcription start site changes, gains/losses of domains, and subcellular translocation. Clonal analysis revealed that isoform switching occurred during T-cell activation/differentiation. Our analysis provides precise characterization of the molecular events in tumor-infiltrating T cells and sheds new light on the regulatory mechanisms of tumor-infiltrating T cells.

Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly.

Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic studies by providing unprecedented cellular and molecular throughputs, but spatial information of individual cells is lost during tissue dissociation. While imaging-based technologies such as in situ sequencing show great promise, technical difficulties currently limit their wide usage. Here we hypothesize that cellular spatial organization is inherently encoded by cell identity and can be reconstructed, at least in part, by ligand-receptor interactions, and we present CSOmap, a computational tool to infer cellular interaction de novo from scRNA-seq. We show that CSOmap can successfully recapitulate the spatial organization of multiple organs of human and mouse including tumor microenvironments for multiple cancers in pseudo-space, and reveal molecular determinants of cellular interactions. Further, CSOmap readily simulates perturbation of genes or cell types to gain novel biological insights, especially into how immune cells interact in the tumor microenvironment. CSOmap can be a widely applicable tool to interrogate cellular organizations based on scRNA-seq data for various tissues in diverse systems.

Single-Cell Analyses Inform Mechanisms of Myeloid-Targeted Therapies in Colon Cancer.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for defining cellular diversity in tumors, but its application toward dissecting mechanisms underlying immune-modulating therapies is scarce. We performed scRNA-seq analyses on immune and stromal populations from colorectal cancer patients, identifying specific macrophage and conventional dendritic cell (cDC) subsets as key mediators of cellular cross-talk in the tumor microenvironment. Defining comparable myeloid populations in mouse tumors enabled characterization of their response to myeloid-targeted immunotherapy. Treatment with anti-CSF1R preferentially depleted macrophages with an inflammatory signature but spared macrophage populations that in mouse and human expresses pro-angiogenic/tumorigenic genes. Treatment with a CD40 agonist antibody preferentially activated a cDC population and increased Bhlhe40+ Th1-like cells and CD8+ memory T cells. Our comprehensive analysis of key myeloid subsets in human and mouse identifies critical cellular interactions regulating tumor immunity and defines mechanisms underlying myeloid-targeted immunotherapies currently undergoing clinical testing.

Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma.

The immune microenvironment of hepatocellular carcinoma (HCC) is poorly characterized. Combining two single-cell RNA sequencing technologies, we produced transcriptomes of CD45+ immune cells for HCC patients from five immune-relevant sites: tumor, adjacent liver, hepatic lymph node (LN), blood, and ascites. A cluster of LAMP3+ dendritic cells (DCs) appeared to be the mature form of conventional DCs and possessed the potential to migrate from tumors to LNs. LAMP3+ DCs also expressed diverse immune-relevant ligands and exhibited potential to regulate multiple subtypes of lymphocytes. Of the macrophages in tumors that exhibited distinct transcriptional states, tumor-associated macrophages (TAMs) were associated with poor prognosis, and we established the inflammatory role of SLC40A1 and GPNMB in these cells. Further, myeloid and lymphoid cells in ascites were predominantly linked to tumor and blood origins, respectively. The dynamic properties of diverse CD45+ cell types revealed by this study add new dimensions to the immune landscape of HCC.

Understanding tumor-infiltrating lymphocytes by single cell RNA sequencing.

The clinical success of immune checkpoint blockade provides great hope for curing cancers. However, the patient responses are not even. Precise understanding of tumor immunity is necessary to improving the current cancer immunotherapies and to developing new treatment options. Here we applied full-length single cell RNA-seq (scRNA-seq) to three cancer types and provide a comprehensive single T cell data resource for understanding various characteristics of tumor-infiltrating T cells. We also developed an analytical framework named as STARTRAC to quantitatively characterize the dynamic properties of various T cell subsets including tissue preference, clonal expansion, migration, and state transitions from the scRNA-seq snapshots of tumor immune microenvironments. Conserved and cancer type-specific T cell subsets and developmental patterns were revealed, and detailed molecular portrait of the tumor immunity-relevant T cell clusters were provided, shedding lights into the cellular and molecular mechanisms underlying the composition, heterogeneity, and formation of tumor immune microenvironments. Important genes such as LAYN and IGFLR1 also provided new options for future development of cancer therapeutics.

Identification of transcriptional isoforms associated with survival in cancer patient.

The Cancer Genome Atlas (TCGA) project produced RNA-Seq data for tens of thousands of cancer and non-cancer samples with clinical survival information, providing an unprecedented opportunity for analyzing prognostic genes and their isoforms. In this study, we performed the first large-scale identification of transcriptional isoforms that are specifically associated with patient prognosis, even without gene-level association. These specific isoforms are defined as Transcripts Associated with Patient Prognosis (TAPPs). Although a group of TAPPs are the principal isoforms of their genes with intact functional protein domains, another group of TAPPs lack important protein domains found in their canonical gene isoforms. This dichotomy in the distribution of protein domains may indicate different patterns of TAPPs association with cancer. TAPPs in protein-coding genes, especially those with altered protein domains, are rich in known cancer driver genes. We further identified multiple types of cancer recurrent TAPPs, such as DCAF17-201, providing a new approach for the detection of cancer-associated events. In order to make the wide research community to study prognostic isoforms, we developed a portal named GESUR (http://gesur.cancer-pku.cn/), which illustrates the detailed prognostic characteristics of TAPPs and other isoforms. Overall, our integrated analysis of gene expression and clinical parameters provides a new perspective for understanding the applications of different gene isoforms in tumor progression.

Deep single-cell RNA sequencing data of individual T cells from treatment-naïve colorectal cancer patients.

T cells, as a crucial compartment of the tumour microenvironment, play vital roles in cancer immunotherapy. However, the basic properties of tumour-infiltrating T cells (TILs) such as the functional state, migratory capability and clonal expansion remain elusive. Here, using Smart-seq2 protocol, we have generated a RNA sequencing dataset of 11,138 T cells isolated from peripheral blood, adjacent normal and tumour tissues of 12 colorectal cancer (CRC) patients, including 4 with microsatellite instability (MSI). The dataset contained an expression profile of 10,805 T cells, as well as the full-length T cell receptor (TCR) sequences of 9,878 cells after quality control. To facilitate data mining of our T cell dataset, we developed a web-based application to deliver systematic interrogations and customizable functionalities ( http://crctcell.cancer-pku.cn/ ). Functioning with our dataset, the web tool enables the characterization of TILs based on both transcriptome and assembled TCR sequences at the single cell level, which will help unleash the potential value of our CRC T cell data resource.

Discovering cooperative biomarkers for heterogeneous complex disease diagnoses.

Biomarkers with high reproducibility and accurate prediction performance can contribute to comprehending the underlying pathogenesis of related complex diseases and further facilitate disease diagnosis and therapy. Techniques integrating gene expression profiles and biological networks for the identification of network-based disease biomarkers are receiving increasing interest. The biomarkers for heterogeneous diseases often exhibit strong cooperative effects, which implies that a set of genes may achieve more accurate outcome prediction than any single gene. In this study, we evaluated various biomarker identification methods that consider gene cooperative effects implicitly or explicitly, and proposed the gene cooperation network to explicitly model the cooperative effects of gene combinations. The gene cooperation network-enhanced method, named as MarkRank, achieves superior performance compared with traditional biomarker identification methods in both simulation studies and real data sets. The biomarkers identified by MarkRank not only have a better prediction accuracy but also have stronger topological relationships in the biological network and exhibit high specificity associated with the related diseases. Furthermore, the top genes identified by MarkRank involve crucial biological processes of related diseases and give a good prioritization for known disease genes. In conclusion, MarkRank suggests that explicit modeling of gene cooperative effects can greatly improve biomarker identification for complex diseases, especially for diseases with high heterogeneity.