seq-ImmuCC: Cell-Centric View of Tissue Transcriptome Measuring Cellular Compositions of Immune Microenvironment From Mouse RNA-Seq Data

Progress and challenge for computational quantification of tissue immune cells

Tissue immune cells have long been recognized as important regulators for the maintenance of balance in the body system. Quantification of the abundance of different immune cells will provide enhanced understanding of the correlation between immune cells and normal or abnormal situations. Currently, computational methods to predict tissue immune cell compositions from bulk transcriptomes have been largely developed. Therefore, summarizing the advantages and disadvantages is appropriate. In addition, an examination of the challenges and possible solutions for these computational models will assist the development of this field. The common hypothesis of these models is that the expression of signature genes for immune cell types might represent the proportion of immune cells that contribute to the tissue transcriptome. In general, we grouped all reported tools into three groups, including reference-free, reference-based scoring and reference-based deconvolution methods. In this review, a summary of all the currently reported computational immune cell quantification tools and their applications, limitations, and perspectives are presented. Furthermore, some critical problems are found that have limited the performance and application of these models, including inadequate immune cell type, the collinearity problem, the impact of the tissue environment on the immune cell expression level, and the deficiency of standard datasets for model validation. To address these issues, tissue specific training datasets that include all known immune cells, a hierarchical computational framework, and benchmark datasets including both tissue expression profiles and the abundances of all the immune cells are proposed to further promote the development of this field.

The 4-NQO mouse model: An update on a well-established in vivo model of oral carcinogenesis

The early detection and management of oral premalignant lesions (OPMDs) improve their outcomes. Animal models that mimic histological and biological processes of human oral carcinogenesis may help to improve the identification of OPMD at-risk of progression into oral squamous cell carcinoma and to develop preventive strategies for the entire field of cancerization. No animal model is perfectly applicable for investigating human oral carcinogenesis. However, the 4-nitroquinoline 1-oxide (4-NQO) mouse model is well established and mimics several morphological, histological, genomic and molecular features of human oral carcinogenesis. Some of the reasons for the success of this model include its reproducible experimental conditions with limited variation, the possibility of realizing longitudinal studies with invasive intervention or gene manipulation, and sample availability for all stages of oral carcinogenesis, especially premalignant lesions. Moreover, the role of histological and molecular alterations in the field of cancerization (i.e., macroscopically healthy mucosa exposed to a carcinogen) during oral carcinogenesis can be easily explored using this model. In this review, we discuss the advantages and drawbacks of this model for studying human oral carcinogenesis. In summary, the 4-NQO-induced murine oral cancer model is relevant for investigating human oral carcinogenesis, including the immune microenvironment, and for evaluating therapeutic and chemoprevention agents.

EV-origin: Enumerating the tissue-cellular origin of circulating extracellular vesicles using exLR profile

Extracellular vesicles (EVs) are complex ecosystems that can be derived from all body cells and circulated in the body fluids. Characterizing the tissue-cellular source contributing to circulating EVs provides biological information about the cell or tissue of origin and their functional states. However, the relative proportion of tissue-cellular origin of circulating EVs in body fluid has not been thoroughly characterized. Here, we developed an approach for digital EVs quantification, called EV-origin, that enables enumerating of EVs tissue-cellular source contribution from plasma extracellular vesicles long RNA sequencing profiles. EV-origin was constructed by the input matrix of gene expression signatures and robust deconvolution algorithm, collectively used to separate the relative proportions of each tissue or cell type of interest. EV-origin respectively predicted the relative enrichment of seven types of hemopoietic cells and sixteen solid tissue subsets from exLR-seq profile. Using the EV-origin approach, we depicted an integrated landscape of the traceability system of plasma EVs for healthy individuals. We also compared the heterogenous tissue-cellular source components from plasma EVs samples with diverse disease status. Notably, the aberrant liver fraction could reflect the development and progression of hepatic disease. The liver fraction could also serve as a diagnostic indicator and effectively separate HCC patients from normal individuals. The EV-origin provides an approach to decipher the complex heterogeneity of tissue-cellular origin in circulating EVs. Our approach could inform the development of exLR-based applications for liquid biopsy.

The murine Microenvironment Cell Population counter method to estimate abundance of tissue-infiltrating immune and stromal cell populations in murine samples using gene expression

Quantifying tissue-infiltrating immune and stromal cells provides clinically relevant information for various diseases. While numerous methods can quantify immune or stromal cells in human tissue samples from transcriptomic data, few are available for mouse studies. We introduce murine Microenvironment Cell Population counter (mMCP-counter), a method based on highly specific transcriptomic markers that accurately quantify 16 immune and stromal murine cell populations. We validated mMCP-counter with flow cytometry data and showed that mMCP-counter outperforms existing methods. We showed that mMCP-counter scores are predictive of response to immune checkpoint blockade in cancer mouse models and identify early immune impacts of Alzheimer's disease.

Tissue-specific deconvolution of immune cell composition by integrating bulk and single-cell transcriptomes

MOTIVATION

Many methods have been developed to estimate immune cell composition from tissue transcriptomes. One common characteristic of these methods is that they are trained using a set of general immune cell transcriptomes that ignores tissue specificities. However, as immune cells are localized in different tissues, they may have distinct expression profiles. Hence, calculations that use general signature matrices may hinder the deconvolution accuracy.

RESULTS

This study used single cell RNA-sequencing (scRNA-Seq) data from different mouse tissues instead of general signature expression values to generate tissue-specific signature gene matrices that are used as the input of the deconvolution model. First, the transcriptome of immune cells in each tissue was extracted from scRNA-Seq data and used to construct the entire expression matrix of tissue immune cells. Then, after comparing different gene selection strategies, the expressions of 162 seq-ImmuCC derived signature genes in tissue immune cell scRNA-Seq data were regarded as the tissue specific signature matrices. Finally, a modest improvement in performance was observed in multiple tissues that refer to a traditional general signature matrix in the deconvolution model. With the fast accumulation of scRNA-Seq data, the introduction of these data into an estimation of immune cell compositions for different tissues will open a new window for avoiding tissue bias for immune cell expression.

AVAILABILITY AND IMPLEMENTATION

The signature matrices were available at https://github.com/wuaipinglab/ImmuCC/tree/master/tissue_immucc/SignatureMatrix).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cancer cells educate natural killer cells to a metastasis-promoting cell state

Natural killer (NK) cells have potent antitumor and antimetastatic activity. It is incompletely understood how cancer cells escape NK cell surveillance. Using ex vivo and in vivo models of metastasis, we establish that keratin-14+ breast cancer cells are vulnerable to NK cells. We then discovered that exposure to cancer cells causes NK cells to lose their cytotoxic ability and promote metastatic outgrowth. Gene expression comparisons revealed that healthy NK cells have an active NK cell molecular phenotype, whereas tumor-exposed (teNK) cells resemble resting NK cells. Receptor-ligand analysis between teNK cells and tumor cells revealed multiple potential targets. We next showed that treatment with antibodies targeting TIGIT, antibodies targeting KLRG1, or small-molecule inhibitors of DNA methyltransferases (DMNT) each reduced colony formation. Combinations of DNMT inhibitors with anti-TIGIT or anti-KLRG1 antibodies further reduced metastatic potential. We propose that NK-directed therapies targeting these pathways would be effective in the adjuvant setting to prevent metastatic recurrence.

A Spontaneous Aggressive ERα+ Mammary Tumor Model Is Driven by Kras Activation

The NRL-PRL murine model, defined by mammary-selective transgenic rat prolactin ligand rPrl expression, establishes spontaneous ER+ mammary tumors in nulliparous females, mimicking the association between elevated prolactin (PRL) and risk for development of ER+ breast cancer in postmenopausal women. Whole-genome and exome sequencing in a discovery cohort (n = 5) of end-stage tumors revealed canonical activating mutations and copy number amplifications of Kras. The frequent mutations in this pathway were validated in an extension cohort, identifying activating Ras alterations in 79% of tumors (23 of 29). Transcriptome analyses over the course of oncogenesis revealed marked alterations associated with Ras activity in established tumors compared with preneoplastic tissues; in cell-intrinsic processes associated with mitosis, cell adhesion, and invasion; as well as in the surrounding tumor environment. These genomic analyses suggest that PRL induces a selective bottleneck for spontaneous Ras-driven tumors that may model a subset of aggressive clinical ER+ breast cancers.

HIF-1α and HIF-2α differently regulate tumour development and inflammation of clear cell renal cell carcinoma in mice

Mutational inactivation of VHL is the earliest genetic event in the majority of clear cell renal cell carcinomas (ccRCC), leading to accumulation of the HIF-1α and HIF-2α transcription factors. While correlative studies of human ccRCC and functional studies using human ccRCC cell lines have implicated HIF-1α as an inhibitor and HIF-2α as a promoter of aggressive tumour behaviours, their roles in tumour onset have not been functionally addressed. Herein we show using an autochthonous ccRCC model that Hif1a is essential for tumour formation whereas Hif2a deletion has only minor effects on tumour initiation and growth. Both HIF-1α and HIF-2α are required for the clear cell phenotype. Transcriptomic and proteomic analyses reveal that HIF-1α regulates glycolysis while HIF-2α regulates genes associated with lipoprotein metabolism, ribosome biogenesis and E2F and MYC transcriptional activities. HIF-2α-deficient tumours are characterised by increased antigen presentation, interferon signalling and CD8⁺ T cell infiltration and activation. Single copy loss of HIF1A or high levels of HIF2A mRNA expression correlate with altered immune microenvironments in human ccRCC. These studies reveal an oncogenic role of HIF-1α in ccRCC initiation and suggest that alterations in the balance of HIF-1α and HIF-2α activities can affect different aspects of ccRCC biology and disease aggressiveness.

Anti-tumor immune response varies among individuals: A gene expression profiling of mouse melanoma

Melanoma is amongst the most aggressive malignant tumors. The purpose of this study is to detect the tumor microenvironment systematically using multi-omics analyses and to propose strategies for precision medicine. Multiple factors of tumor microenvironment contribute to the drug resistance and immune surveillance failure. Here we analyzed genome mutations and characterized the immune state of tumor microenvironments in mouse melanoma by whole exome sequencing (WES) and RNA sequencing (RNA-Seq) approaches. Somatic mutation analysis revealed 35.1% novel mutations in mouse tumors when compared with B16F10 cell line, provided a basis for multi-site sequencing for accurate neoantigen selection. Mutation cluster, gene expression comparison, and gene ontology (GO) analyses by R packages proved DNA repair damage, inflammation, slower cell division, and metabolic change in tumor microenvironment. Further analyses of T-cell receptor (TCR) sequences, immune signaling pathway activation, tumor infiltrated immune cells and chemokine expression revealed extensive difference of antitumor immune response among individuals. Our study revealed the characteristics of tumor microenvironment with mouse melanoma model, suggested the need of comprehensive genome mutations and personal immune state analyses for cancer precision medicine.

Mitochondrial genetics cooperate with nuclear genetics to selectively alter immune cell development/trafficking

The nuclear genome drives differences in immune cell populations and differentiation potentials, in part regulated by changes in metabolism. Despite this connection, the role of mitochondrial DNA (mtDNA) polymorphisms (SNP) in this process has not been examined. Using mitochondrial nuclear exchange (MNX) mice, we and others have shown that mtDNA strongly influences varying aspects of cell biology and disease. Based upon an established connection between mitochondria and immune cell polarization, we hypothesized that mtDNA SNP alter immune cell development, trafficking, and/or differentiation. Innate and adaptive immune cell populations were isolated and characterizated from the peritoneum and spleen. While most differences between mouse strains are regulated by nuclear DNA (nDNA), there are selective changes that are mediated by mtDNA differences (e.g., macrophage (CD11c) differentiation), These findings highlight how nuclear-mitochondrial crosstalk may alter pathology and physiology via regulation of specific components of the immune system.

The dynamics of the inflammatory response during BBN-induced bladder carcinogenesis in mice

Background

Bladder cancer (BC) is the most common malignant disease of the urinary tract. Recurrent high grade non muscle invasive BC carries a serious risk for progression and subsequent metastases. The most common preclinical mouse model for bladder cancer relies on administration of N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN) to mice. BBN-induced tumors in mice recapitulate the histology of human BC and were characterized with an overexpression of markers typical for basal-like cancer subtype in addition to a high mutational burden with frequent mutations in Trp53, similar to human muscle invasive BC.

Methods

Bladder cancer was induced in C57BL/6J male mice by administering the BBN in the drinking water. A thorough histopathological analysis of bladder specimen during and post BBN treatment was performed at 2, 4, 16, 20 and 25 weeks. RNA sequencing and qPCR was performed to assess the levels of expression of immunologically relevant genes at 2 weeks and 20 weeks during and post BBN treatment.

Results

We characterized the dynamics of the inflammatory response in the BBN-induced BC in mice. The treatment with BBN had gradually induced a robust inflammation in the first 2 weeks of administration, however, the inflammatory response was progressively silenced in the following weeks of the treatment, until the progression of the primary carcinoma. Tumors at 20 weeks were characterized with a marked upregulation of IL18 when compared to premalignant inflammatory response at 2 weeks. In accordance with this, we observed an increase in expression of IFNγ-responsive genes coupled to a pronounced lymphocytic infiltrate during the early stages of malignant transformation in bladder. Similar to human basal-like BC, BBN-induced murine tumors displayed an upregulated expression of immunoinhibitory molecules such as CTLA-4, PD-L1, and IDO1 which can lead to cytotoxic resistance and tumor escape.

Conclusions

Despite the recent advances in bladder cancer therapy which include the use of checkpoint inhibitors, the treatment options for patients with locally advanced and metastatic BC remain limited. BBN-induced BC in mice displays an immunological profile which shares similarities with human MIBC thus representing an optimal model for preclinical studies on immunomodulation in management of BC.

Dietary tryptophan links encephalogenicity of autoreactive T cells with gut microbial ecology

The interaction between the mammalian host and its resident gut microbiota is known to license adaptive immune responses. Nutritional constituents strongly influence composition and functional properties of the intestinal microbial communities. Here, we report that omission of a single essential amino acid - tryptophan – from the diet abrogates CNS autoimmunity in a mouse model of multiple sclerosis. Dietary tryptophan restriction results in impaired encephalitogenic T cell responses and is accompanied by a mild intestinal inflammatory response and a profound phenotypic shift of gut microbiota. Protective effects of dietary tryptophan restriction are abrogated in germ-free mice, but are independent of canonical host sensors of intracellular tryptophan metabolites. We conclude that dietary tryptophan restriction alters metabolic properties of gut microbiota, which in turn have an impact on encephalitogenic T cell responses. This link between gut microbiota, dietary tryptophan and adaptive immunity may help to develop therapeutic strategies for protection from autoimmune neuroinflammation.

Food intake shapes intestinal microbiome composition, which in turn shapes adaptive immune responses. Here the authors show that dietary tryptophan restriction (DTR) protects mice from subsequent autoimmune neuropathology challenge by altering intestinal microbiota, highlighting the potential of diet-regulated microbiota to prevent immune pathology.

Linking Cell Dynamics With Gene Coexpression Networks to Characterize Key Events in Chronic Virus Infections

The host immune response against infection requires the coordinated action of many diverse cell subsets that dynamically adapt to a pathogen threat. Due to the complexity of such a response, most immunological studies have focused on a few genes, proteins, or cell types. With the development of “omic”-technologies and computational analysis methods, attempts to analyze and understand complex system dynamics are now feasible. However, the decomposition of transcriptomic data sets generated from complete organs remains a major challenge. Here, we combined Weighted Gene Coexpression Network Analysis (WGCNA) and Digital Cell Quantifier (DCQ) to analyze time-resolved mouse splenic transcriptomes in acute and chronic Lymphocytic Choriomeningitis Virus (LCMV) infections. This enabled us to generate hypotheses about complex immune functioning after a virus-induced perturbation. This strategy was validated by successfully predicting several known immune phenomena, such as effector cytotoxic T lymphocyte (CTL) expansion and exhaustion. Furthermore, we predicted and subsequently verified experimentally macrophage-CD8 T cell cooperativity and the participation of virus-specific CD8⁺ T cells with an early effector transcriptome profile in the host adaptation to chronic infection. Thus, the linking of gene expression changes with immune cell kinetics provides novel insights into the complex immune processes within infected tissues.