Applications of Single-Cell Omics in Tumor Immunology

Results from omic approaches in rat or mouse models exposed to inhaled crystalline silica: a systematic review

BACKGROUND

Crystalline silica (cSiO2) is a mineral found in rocks; workers from the construction or denim industries are particularly exposed to cSiO2 through inhalation. cSiO2 inhalation increases the risk of silicosis and systemic autoimmune diseases. Inhaled cSiO2 microparticles can reach the alveoli where they induce inflammation, cell death, auto-immunity and fibrosis but the specific molecular pathways involved in these cSiO2 effects remain unclear. This systematic review aims to provide a comprehensive state of the art on omic approaches and exposure models used to study the effects of inhaled cSiO2 in mice and rats and to highlight key results from omic data in rodents also validated in human.

METHODS

The protocol of systematic review follows PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Eligible articles were identified in PubMed, Embase and Web of Science. The search strategy included original articles published after 1990 and written in English which included mouse or rat models exposed to cSiO2 and utilized omic approaches to identify pathways modulated by cSiO2. Data were extracted and quality assessment was based on the SYRCLE's Risk of Bias tool for animal studies.

RESULTS

Rats and male rodents were the more used models while female rodents and autoimmune prone models were less studied. Exposure of animals were both acute and chronic and the timing of outcome measurement through omics approaches were homogeneously distributed. Transcriptomic techniques were more commonly performed while proteomic, metabolomic and single-cell omic methods were less utilized. Immunity and inflammation were the main domains modified by cSiO2 exposure in lungs of mice and rats. Less than 20% of the results obtained in rodents were finally verified in humans.

CONCLUSION

Omic technics offer new insights on the effects of cSiO2 exposure in mice and rats although the majority of data still need to be validated in humans. Autoimmune prone model should be better characterised and systemic effects of cSiO2 need to be further studied to better understand cSiO2-induced autoimmunity. Single-cell omics should be performed to inform on pathological processes induced by cSiO2 exposure.

Creation of a High-Throughput Microfluidic Platform for Single-Cell Transcriptome Sequencing of Cell-Cell Interactions

Cell-cell interaction is one of the major modalities for transmitting information between cells and activating the effects of functional cells. However, the construction of high-throughput analysis technologies from cell omics focusing on the impact of interactions of functional cells on targets has been relatively unexplored. Here, they propose a droplet-based microfluidic platform for cell-cell interaction sequencing (c-c-seq) and screening in vitro to address this challenge. A class of interacting cells is pre-labeled using cell molecular tags, and additional single-cell sequencing reagents are introduced to quickly form functional droplet mixes. Lastly, gene expression analysis is used to deduce the impact of the interaction, while molecular sequence tracing identifies the type of interaction. Research into the active effect between antigen-presenting cells and T cells, one of the most common cell-to-cell interactions, is crucial for the advancement of cancer therapy, particularly T cell receptor-engineered T cell therapy. As it allows for high throughput, this platform is superior to well plates as a research platform for cell-to-cell interactions. When combined with the next generation of sequencing, the platform may be able to more accurately evaluate interactions between epitopes and receptors and verify their functional relevance.

[Advances in methods and applications of single-cell Hi-C data analysis]

Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.

How single-cell techniques help us look into lung cancer heterogeneity and immunotherapy

Lung cancer patients tend to have strong intratumoral and intertumoral heterogeneity and complex tumor microenvironment, which are major contributors to the efficacy of and drug resistance to immunotherapy. From a new perspective, single-cell techniques offer an innovative way to look at the intricate cellular interactions between tumors and the immune system and help us gain insights into lung cancer and its response to immunotherapy. This article reviews the application of single-cell techniques in lung cancer, with focuses directed on the heterogeneity of lung cancer and the efficacy of immunotherapy. This review provides both theoretical and experimental information for the future development of immunotherapy and personalized treatment for the management of lung cancer.

Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed imaging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lymphonets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1+ PD-1+ progenitor CD8+ T cells involved in responses to immune checkpoint blockade (ICB) therapy. Upon treatment of mice with ICB or an antigen-targeted vaccine, lymphonets retained progenitor and gained cytotoxic CD8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8+ T cell anti-tumor responses.

Applying single cell multi-omic analyses to understand treatment resistance in pediatric high grade glioma

Despite improvements in cancer patient outcomes seen in the past decade, tumor resistance to therapy remains a major impediment to achieving durable clinical responses. Intratumoral heterogeneity related to genetic, epigenetic, transcriptomic, proteomic, and metabolic differences between individual cancer cells has emerged as a driver of therapeutic resistance. This cell to cell heterogeneity can be assessed using single cell profiling technologies that enable the identification of tumor cell clones that exhibit similar defining features like specific mutations or patterns of DNA methylation. Single cell profiling of tumors before and after treatment can generate new insights into the cancer cell characteristics that confer therapeutic resistance by identifying intrinsically resistant sub-populations that survive treatment and by describing new cellular features that emerge post-treatment due to tumor cell evolution. Integrative, single cell analytical approaches have already proven advantageous in studies characterizing treatment-resistant clones in cancers where pre- and post-treatment patient samples are readily available, such as leukemia. In contrast, little is known about other cancer subtypes like pediatric high grade glioma, a class of heterogeneous, malignant brain tumors in children that rapidly develop resistance to multiple therapeutic modalities, including chemotherapy, immunotherapy, and radiation. Leveraging single cell multi-omic technologies to analyze naïve and therapy-resistant glioma may lead to the discovery of novel strategies to overcome treatment resistance in brain tumors with dismal clinical outcomes. In this review, we explore the potential for single cell multi-omic analyses to reveal mechanisms of glioma resistance to therapy and discuss opportunities to apply these approaches to improve long-term therapeutic response in pediatric high grade glioma and other brain tumors with limited treatment options.

Exosomal Non-Coding RNAs: Novel Regulators of Macrophage-Linked Intercellular Communication in Lung Cancer and Inflammatory Lung Diseases

Macrophages are innate immune cells and often classified as M1 macrophages (pro-inflammatory states) and M2 macrophages (anti-inflammatory states). Exosomes are cell-derived nanovesicles that range in diameter from 30 to 150 nm. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), are abundant in exosomes and exosomal ncRNAs influence immune responses. Exosomal ncRNAs control macrophage-linked intercellular communication via their targets or signaling pathways, which can play positive or negative roles in lung cancer and inflammatory lung disorders, including acute lung injury (ALI), asthma, and pulmonary fibrosis. In lung cancer, exosomal ncRNAs mediated intercellular communication between lung tumor cells and tumor-associated macrophages (TAMs), coordinating cancer proliferation, migration, invasion, metastasis, immune evasion, and therapy resistance. In inflammatory lung illnesses, exosomal ncRNAs mediate macrophage activation and inflammation to promote or inhibit lung damage. Furthermore, we also discussed the possible applications of exosomal ncRNA-based therapies for lung disorders.

Single-cell omics: A new direction for functional genetic research in human diseases and animal models

Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.

A roadmap for translational cancer glycoimmunology at single cell resolution

Cancer cells can evade immune responses by exploiting inhibitory immune checkpoints. Immune checkpoint inhibitor (ICI) therapies based on anti-CTLA-4 and anti-PD-1/PD-L1 antibodies have been extensively explored over the recent years to unleash otherwise compromised anti-cancer immune responses. However, it is also well established that immune suppression is a multifactorial process involving an intricate crosstalk between cancer cells and the immune systems. The cancer glycome is emerging as a relevant source of immune checkpoints governing immunosuppressive behaviour in immune cells, paving an avenue for novel immunotherapeutic options. This review addresses the current state-of-the-art concerning the role played by glycans controlling innate and adaptive immune responses, while shedding light on available experimental models for glycoimmunology. We also emphasize the tremendous progress observed in the development of humanized models for immunology, the paramount contribution of advances in high-throughput single-cell analysis in this context, and the importance of including predictive machine learning algorithms in translational research. This may constitute an important roadmap for glycoimmunology, supporting careful adoption of models foreseeing clinical translation of fundamental glycobiology knowledge towards next generation immunotherapies.

The effect of organ-specific tumor microenvironments on response patterns to immunotherapy

Immunotherapy, particularly immune checkpoint inhibitors, have become widely used in various settings across many different cancer types in recent years. Whilst patients are often treated on the basis of the primary cancer type and clinical stage, recent studies have highlighted disparity in response to immune checkpoint inhibitors at different sites of metastasis, and their impact on overall response and survival. Studies exploring the tumor immune microenvironment at different organ sites have provided insights into the immune-related mechanisms behind organ-specific patterns of response to immunotherapy. In this review, we aimed to highlight the key learnings from clinical studies across various cancers including melanoma, lung cancer, renal cell carcinoma, colorectal cancer, breast cancer and others, assessing the association of site of metastasis and response to immune checkpoint inhibitors. We also summarize the key clinical and pre-clinical findings from studies exploring the immune microenvironment of specific sites of metastasis. Ultimately, further characterization of the tumor immune microenvironment at different metastatic sites, and understanding the biological drivers of these differences, may identify organ-specific mechanisms of resistance, which will lead to more personalized treatment approaches for patients with innate or acquired resistance to immunotherapy.

Single-cell sequencing technologies in bladder cancer research: Applications and challenges

Bladder cancer is among the most common malignant tumors with highly heterogeneous molecular characteristics. Despite advancements of the available therapeutic options, several bladder cancer patients exhibit unsatisfactory clinical outcomes. The lack of specific biomarkers for effective targeted therapy or immunotherapy remains a major obstacle in treating bladder cancer. The rapid development of single-cell techniques is transforming our understanding of the intra-tumoral heterogeneity, thereby providing us with a powerful high-throughput sequencing tool that can reveal tumorigenesis, progression, and invasion in bladder tumors. In this review, we summarise and discuss how single-cell sequencing technologies have been applied in bladder cancer research, to advance our collective knowledge on the heterogeneity of bladder tumor cells, as well as to provide new insights into the complex ecosystem of the tumor microenvironment. The application of single-cell approaches also uncovers the therapeutic resistance mechanism in bladder cancer and facilitates the detection of urinary-exfoliated tumor cells. Moreover, benefiting from the powerful technical advantages of single-cell techniques, several key therapeutic targets and prognostic models of bladder cancer have been identified. It is hoped that this paper can provide novel insights into the precision medicine of bladder cancer.

Comprehensive analysis of potential cellular communication networks in advanced osteosarcoma using single-cell RNA sequencing data

Osteosarcoma (OS) is a common bone cancer in children and adolescents, and metastasis and recurrence are the major causes of poor treatment outcomes. A better understanding of the tumor microenvironment is required to develop an effective treatment for OS. In this paper, a single-cell RNA sequencing dataset was taken to a systematic genetic analysis, and potential signaling pathways linked with osteosarcoma development were explored. Our findings revealed 25 clusters across 11 osteosarcoma tissues, with 11 cell types including “Chondroblastic cells”, “Osteoblastic cells”, “Myeloid cells”, “Pericytes”, “Fibroblasts”, “Proliferating osteoblastic cells”, “Osteoclasts”, “TILs”, “Endothelial cells”, “Mesenchymal stem cells”, and “Myoblasts”. The results of Cell communication analysis showed 17 potential cellular communication networks including “COLLAGEN signaling pathway network”, “CD99 signaling pathway network”, “PTN signaling pathway network”, “MIF signaling pathway network”, “SPP1 signaling pathway network”, “FN1 signaling pathway network”, “LAMININ signaling pathway network”, “FGF signaling pathway network”, “VEGF signaling pathway network”, “GALECTIN signaling pathway network”, “PERIOSTIN signaling pathway network”, “VISFATIN signaling pathway network”, “ITGB2 signaling pathway network”, “NOTCH signaling pathway network”, “IGF signaling pathway network”, “VWF signaling pathway network”, “PDGF signaling pathway network”. This research may provide novel insights into the pathophysiology of OS’s molecular processes.

T-Cell Receptor Repertoire Sequencing and Its Applications: Focus on Infectious Diseases and Cancer

The immune system is a dynamic feature of each individual and a footprint of our unique internal and external exposures. Indeed, the type and level of exposure to physical and biological agents shape the development and behavior of this complex and diffuse system. Many pathological conditions depend on how our immune system responds or does not respond to a pathogen or a disease or on how the regulation of immunity is altered by the disease itself. T-cells are important players in adaptive immunity and, together with B-cells, define specificity and monitor the internal and external signals that our organism perceives through its specific receptors, TCRs and BCRs, respectively. Today, high-throughput sequencing (HTS) applied to the TCR repertoire has opened a window of opportunity to disclose T-cell repertoire development and behavior down to the clonal level. Although TCR repertoire sequencing is easily accessible today, it is important to deeply understand the available technologies for choosing the best fit for the specific experimental needs and questions. Here, we provide an updated overview of TCR repertoire sequencing strategies, providers and applications to infectious diseases and cancer to guide researchers’ choice through the multitude of available options. The possibility of extending the TCR repertoire to HLA characterization will be of pivotal importance in the near future to understand how specific HLA genes shape T-cell responses in different pathological contexts and will add a level of comprehension that was unthinkable just a few years ago.

Immune Evasion as the Main Challenge for Immunotherapy of Cancer

Immune evasion is currently considered one of the most prominent hallmarks of cancer [...].

GdClean: removal of Gadolinium contamination in mass cytometry data

MOTIVATION

Mass cytometry (Cytometry by Time-Of-Flight, CyTOF) is a single-cell technology that is able to quantify multiplex biomarker expressions and is commonly used in basic life science and translational research. However, the widely used Gadolinium (Gd)-based contrast agents (GBCAs) in magnetic resonance imaging (MRI) scanning in clinical practice can lead to signal contamination on the Gd channels in the CyTOF analysis. This Gd contamination greatly affects the characterization of the real signal from Gd-isotope-conjugated antibodies, severely impairing the CyTOF data quality and ruining downstream single-cell data interpretation.

RESULTS

We first in-depth characterized the signals of Gd isotopes from a control sample that was not stained with Gd-labeled antibodies but was contaminated by Gd isotopes from GBCAs, and revealed the collinear intensity relationship across Gd contamination signals. We also found that the intensity ratios of detected Gd contamination signals to the reference Gd signal were highly correlated with the natural abundance ratios of corresponding Gd isotopes. We then developed a computational method named by GdClean to remove the Gd contamination signal at the single-cell level in the CyTOF data. We further demonstrated that the GdClean effectively cleaned up the Gd contamination signal while preserving the real Gd-labeled antibodies signal in Gd channels. All of these shed lights on the promising applications of the GdClean method in preprocessing CyTOF datasets for revealing the true single-cell information.

AVAILABILITY AND IMPLEMENTATION

The R package GdClean is available on GitHub at https://github.com/JunweiLiu0208/GdClean.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Application of Single-Cell Multi-Omics in Dissecting Cancer Cell Plasticity and Tumor Heterogeneity

Tumor heterogeneity, a hallmark of cancer, impairs the efficacy of cancer therapy and drives tumor progression. Exploring inter- and intra-tumoral heterogeneity not only provides insights into tumor development and progression, but also guides the design of personalized therapies. Previously, high-throughput sequencing techniques have been used to investigate the heterogeneity of tumor ecosystems. However, they could not provide a high-resolution landscape of cellular components in tumor ecosystem. Recently, advance in single-cell technologies has provided an unprecedented resolution to uncover the intra-tumoral heterogeneity by profiling the transcriptomes, genomes, proteomes and epigenomes of the cellular components and also their spatial distribution, which greatly accelerated the process of basic and translational cancer research. Importantly, it has been demonstrated that some cancer cells are able to transit between different states in order to adapt to the changing tumor microenvironment, which led to increased cellular plasticity and tumor heterogeneity. Understanding the molecular mechanisms driving cancer cell plasticity is critical for developing precision therapies. In this review, we summarize the recent progress in dissecting the cancer cell plasticity and tumor heterogeneity by use of single-cell multi-omics techniques.

The Role of Somatic Mutations on the Immune Response of the Tumor Microenvironment in Prostate Cancer

Immunotherapy has improved patient survival in many types of cancer, but for prostate cancer, initial results with immunotherapy have been disappointing. Prostate cancer is considered an immunologically excluded or cold tumor, unable to generate an effective T-cell response against cancer cells. However, a small but significant percentage of patients do respond to immunotherapy, suggesting that some specific molecular subtypes of this tumor may have a better response to checkpoint inhibitors. Recent findings suggest that, in addition to their function as cancer genes, somatic mutations of PTEN, TP53, RB1, CDK12, and DNA repair, or specific activation of regulatory pathways, such as ETS or MYC, may also facilitate immune evasion of the host response against cancer. This review presents an update of recent discoveries about the role that the common somatic mutations can play in changing the tumor microenvironment and immune response against prostate cancer. We describe how detailed molecular genetic analyses of the tumor microenvironment of prostate cancer using mouse models and human tumors are providing new insights into the cell types and pathways mediating immune responses. These analyses are helping researchers to design drug combinations that are more likely to target the molecular and immunological pathways that underlie treatment failure.