Estimation of immune cell content in tumour tissue using single-cell RNA-seq data

Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease

Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.

Tumour microenvironment characterisation to stratify patients for hyperthermic intraperitoneal chemotherapy in high-grade serous ovarian cancer (OVHIPEC-1)

BACKGROUND

Hyperthermic intraperitoneal chemotherapy (HIPEC) improves survival in patients with Stage III ovarian cancer following interval cytoreductive surgery (CRS). Optimising patient selection is essential to maximise treatment efficacy and avoid overtreatment. This study aimed to identify biomarkers that predict HIPEC benefit by analysing gene signatures and cellular composition of tumours from participants in the OVHIPEC-1 trial.

METHODS

Whole-transcriptome RNA sequencing data were retrieved from high-grade serous ovarian cancer (HGSOC) samples from 147 patients obtained during interval CRS. We performed differential gene expression analysis and applied deconvolution methods to estimate cell-type proportions in bulk mRNA data, validated by histological assessment. We tested the interaction between treatment and potential predictors on progression-free survival using Cox proportional hazards models.

RESULTS

While differential gene expression analysis did not yield any predictive biomarkers, the cellular composition, as characterised by deconvolution, indicated that the absence of macrophages and the presence of B cells in the tumour microenvironment are potential predictors of HIPEC benefit. The histological assessment confirmed the predictive value of macrophage absence.

CONCLUSION

Immune cell composition, in particular macrophages absence, may predict response to HIPEC in HGSOC and these hypothesis-generating findings warrant further investigation.

CLINICAL TRIAL REGISTRATION

NCT00426257.

Integration of single-cell sequencing and bulk RNA-seq to identify and develop a prognostic signature related to colorectal cancer stem cells

The prognosis for patients with colorectal cancer (CRC) remains worse than expected due to metastasis, recurrence, and resistance to chemotherapy. Colorectal cancer stem cells (CRCSCs) play a vital role in tumor metastasis, recurrence, and chemotherapy resistance. However, there are currently no prognostic markers based on CRCSCs-related genes available for clinical use. In this study, single-cell transcriptome sequencing was employed to distinguish cancer stem cells (CSCs) in the CRC microenvironment and analyze their properties at the single-cell level. Subsequently, data from TCGA and GEO databases were utilized to develop a prognostic risk model for CRCSCs-related genes and validate its diagnostic performance. Additionally, functional enrichment, immune response, and chemotherapeutic drug sensitivity of the relevant genes in the risk model were investigated. Lastly, the key gene RPS17 in the risk model was identified as a potential prognostic marker and therapeutic target for further comprehensive studies. Our findings provide new insights into the prognostic treatment of CRC and offer novel perspectives for a systematic and comprehensive understanding of CRC development.

Single-cell RNA-seq analysis reveals the Wnt/Ca2+ signaling pathway with inflammation, apoptosis in nucleus pulposus degeneration

BACKGROUND

Increasing studies have shown degeneration of nucleus pulposus cells (NPCs) as an critical part of the progression of intervertebral disc degeneration (IVDD). However, there are relatively few studies on single-cell transcriptome contrasts in human degenerated NPCs. Moreover, differences in Wnt/Ca2+ signaling in human degenerated nucleus pulposus cells have not been elucidated. The aim of this study is to investigate the differential expression of Wnt/Ca2+ signaling pathway between normal and degenerated nucleus pulposus cells in humans and try to investigate its mechanism.

METHODS

We performed bioinformatics analysis using our previously published findings to construct single cell expression profiles of normal and degenerated nucleus pulposus. Then, in-depth differential analysis was used to characterize the expression of Wnt/Ca2+ signaling pathway between normal and degenerated nucleus pulposus cells in humans.

RESULTS

The obtained cell data were clustered into five different chondrocytes clusters, which chondrocyte 4 and chondrocyte 5 mainly accounted for a high proportion in degenerated nucleus pulposus tissues, but rarely in normal nucleus pulposus tissues. Genes associated within the Wnt/Ca2+ signaling pathway, such as Wnt5B, FZD1, PLC (PLCB1), CaN (PPP3CA) and NAFATC1 are mainly present in chondrocyte 3, chondrocyte 4 and chondrocyte 5 from degenerated nucleus pulposus tissues. In addition, as a receptor that activates Wnt signaling pathway, LRP5 is mainly highly expressed in chondrocyte 5 of degenerated nucleus pulposus cells. Six genes, ANGPTL4, PTGES, IGFBP3, GDF15, TRIB3 and TNFRSF10B, which are associated with apoptosis and inflammatory responses, and are widespread in chondrocyte 4 and chondrocyte 5, may be closely related to degenerative of nucleus pulposus cells.

CONCLUSIONS

Single-cell RNA sequencing revealed differential expression of Wnt/Ca2+ signaling in human normal and degenerated nucleus pulposus cells, and this differential expression may be closely related to the abundance of chondrocyte 4 and chondrocyte 5 in degenerated nucleus pulposus cells. In degenerated nucleus pulposus cells, LRP5 activate Wnt5B, which promotes nucleus pulposus cell apoptosis and inflammatory response by regulating the Wnt/Ca2+ signaling pathway, thereby promoting disc degeneration. ANGPTL4, IGFBP3, PTGES in chondrocyte 4 and TRIB3, GDF15, TNFRSF10B in chondrocyte 5 may play an important role in this process.

Frequencies of 4 tumor-infiltrating lymphocytes potently predict survival in glioblastoma, an immune desert

BACKGROUND

GBM is an aggressive grade 4 primary brain tumor (BT), with a 5%-13% 5-year survival. Most human GBMs manifest as immunologically "cold" tumors or "immune deserts," yet the promoting or suppressive roles of specific lymphocytes within the GBM tumor microenvironment (TME) is of considerable debate.

METHODS

We used meticulous multiparametric flow cytometry (FC) to determine the lymphocytic frequencies in 102 GBMs, lower-grade gliomas, brain metastases, and nontumorous brain specimen. FC-attained frequencies were compared with frequencies estimated by "digital cytometry." The FC-derived data were combined with the patients' demographic, clinical, molecular, histopathological, radiological, and survival data.

RESULTS

Comparison of FC-derived data to CIBERSORT-estimated data revealed the poor capacity of digital cytometry to estimate cell frequencies below 0.2%, the frequency range of most immune cells in BTs. Isocitrate dehydrogenase (IDH) mutation status was found to affect TME composition more than the gliomas' pathological grade. Combining FC and survival data disclosed that unlike other cancer types, the frequency of helper T cells (Th) and cytotoxic T lymphocytes (CTL) correlated negatively with glioma survival. In contrast, the frequencies of γδ-T cells and CD56bright natural killer cells correlated positively with survival. A composite parameter combining the frequencies of these 4 tumoral lymphocytes separated the survival curves of GBM patients with a median difference of 10 months (FC-derived data; P < .0001, discovery cohort), or 4.1 months (CIBERSORT-estimated data; P = .01, validation cohort).

CONCLUSIONS

The frequencies of 4 TME lymphocytes strongly correlate with the survival of patients with GBM, a tumor considered an immune desert.

Computational immunogenomic approaches to predict response to cancer immunotherapies

Cancer immunogenomics is an emerging field that bridges genomics and immunology. The establishment of large-scale genomic collaborative efforts along with the development of new single-cell transcriptomic techniques and multi-omics approaches have enabled characterization of the mutational and transcriptional profiles of many cancer types and helped to identify clinically actionable alterations as well as predictive and prognostic biomarkers. Researchers have developed computational approaches and machine learning algorithms to accurately obtain clinically useful information from genomic and transcriptomic sequencing data from bulk tissue or single cells and explore tumours and their microenvironment. The rapid growth in sequencing and computational approaches has resulted in the unmet need to understand their true potential and limitations in enabling improvements in the management of patients with cancer who are receiving immunotherapies. In this Review, we describe the computational approaches currently available to analyse bulk tissue and single-cell sequencing data from cancer, stromal and immune cells, as well as how best to select the most appropriate tool to address various clinical questions and, ultimately, improve patient outcomes.

Dissecting cellular states of infiltrating microenvironment cells in melanoma by integrating single-cell and bulk transcriptome analysis

BACKGROUND

Cellular states of different immune cells can affect the activity of the whole immune microenvironment.

METHODS

Here, leveraging reference profiles of microenvironment cell states that were constructed based on single-cell RNA-seq data of melanoma, we dissected the composition of microenvironment cell states across 463 skin cutaneous melanoma (SKCM) bulk samples through CIBERSORT-based deconvolution of gene expression profiles and revealed high heterogeneity of their distribution. Correspondence analysis on the estimated cellular fractions of melanoma bulk samples was performed to identify immune phenotypes. Based on the publicly available clinical survival and therapy data, we analyzed the relationship between immune phenotypes and clinical outcomes of melanoma.

RESULTS

By analysis of the relationships among those cell states, we further identified three distinct tumor microenvironment immune phenotypes: "immune hot/active", "immune cold-suppressive" and "immune cold-exhausted". They were characterized by markedly different patterns of cell states: most notably the CD8 T Cytotoxic state, CD8 T Mixed state, B non-regulatory state and cancer-associated fibroblasts (CAFs), depicting distinct types of antitumor immune response (or immune activity). These phenotypes had prognostic significance for progression-free survival and implications in response to immune therapy in an independent cohort of anti-PD1 treated melanoma patients.

CONCLUSIONS

The proposed strategy of leveraging single-cell data to dissect the composition of microenvironment cell states in individual bulk tumors can also extend to other cancer types, and our results highlight the importance of microenvironment cell states for the understanding of tumor immunity.

Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases

Toxicogenomics databases are useful for understanding biological responses in individuals because they include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could extend immune information. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats to retrieve information regarding immune cells from toxicogenomics databases. The rat-specific deconvolution showed high correlations for several types of immune cells between spleen and blood, and between liver treated with toxicants compared with those based on human and mouse data. Additionally, we found 4 clusters of compounds in Open TG-GATEs database based on estimated immune cell trafficking, which are different from those based on transcriptome data itself. The contributions of this work are three-fold. First, we obtained the gene expression profiles of 6 rat immune cells necessary for deconvolution. Second, we clarified the importance of species differences on deconvolution. Third, we retrieved immune cell trafficking from toxicogenomics databases. Accumulated and comparable immune cell profiles of massive data of immune cell trafficking in rats could deepen our understanding of enable us to clarify the relationship between the order and the contribution rate of immune cells, chemokines and cytokines, and pathologies. Ultimately, these findings will lead to the evaluation of organ responses in Adverse Outcome Pathway.

Investigation of LGALS2 expression in the TCGA database reveals its clinical relevance in breast cancer immunotherapy and drug resistance

Breast cancer (BRCA) is known as the leading cause of death in women worldwide and has a poor prognosis. Traditional therapeutic strategies such as surgical resection, radiotherapy and chemotherapy can cause adverse reactions such as drug resistance. Immunotherapy, a new treatment approach with fewer side effects and stronger universality, can prolong the survival of BRCA patients and even achieve clinical cure. However, due to population heterogeneity and other reasons, there are still certain factors that limit the efficacy of immunotherapy. Therefore, the importance of finding new tumor immune biomarker cannot be emphasized enough. Studies have reported that LGALS2 was closely related to immunotherapy efficacy, however, it is unclear whether it can act as an immune checkpoint for BRCA immunotherapy. In the current study, changes in LGALS2 expression were analyzed in public datasets such as TCGA-BRCA. We found that LGALS2 expression was associated with immune infiltration, drug resistance and other characteristics of BRCA. Moreover, high LGALS2 expression was closely related to immunotherapy response, and was associated with methylation modifications and clinical resistance for the first time. These findings may help to elucidate the role of LGALS2 in BRCA for the development and clinical application of future immunotherapy strategies against BRCA.

The mycosis fungoides cutaneous microenvironment shapes dysfunctional cell trafficking, antitumor immunity, matrix interactions, and angiogenesis

Malignant T lymphocyte proliferation in mycosis fungoides (MF) is largely restricted to the skin, implying that malignant cells are dependent on their specific cutaneous tumor microenvironment (TME), including interactions with non-malignant immune and stromal cells, cytokines, and other immunomodulatory factors. To explore these interactions, we performed a comprehensive transcriptome analysis of the TME in advanced-stage MF skin tumors by single-cell RNA sequencing. Our analysis identified cell-type compositions, cellular functions, and cell-to-cell interactions in the MF TME that were distinct from those from healthy skin and benign dermatoses. While patterns of gene expression were common among patient samples, high transcriptional diversity was also observed in immune and stromal cells, with dynamic interactions and crosstalk between these cells and malignant T lymphocytes. This heterogeneity mapped to processes such as cell trafficking, matrix interactions, angiogenesis, immune functions, and metabolism that affect cancer cell growth, migration, and invasion, as well as antitumor immunity. By comprehensively characterizing the transcriptomes of immune and stromal cells within the cutaneous microenvironment of individual MF tumors, we have identified patterns of dysfunction common to all tumors that represent a resource for identifying candidates with therapeutic potential as well as patient-specific heterogeneity that has important implications for personalized disease management.

Integrating spatial transcriptomics data across different conditions, technologies and developmental stages

With the rapid generation of spatial transcriptomics (ST) data, integrative analysis of multiple ST datasets from different conditions, technologies and developmental stages is becoming increasingly important. Here we present a graph attention neural network called STAligner for integrating and aligning ST datasets, enabling spatially aware data integration, simultaneous spatial domain identification and downstream comparative analysis. We apply STAligner to ST datasets of the human cortex slices from different samples, the mouse olfactory bulb slices generated by two profiling technologies, the mouse hippocampus tissue slices under normal and Alzheimer's disease conditions, and the spatiotemporal atlases of mouse organogenesis. STAligner efficiently captures the shared tissue structures across different slices, the disease-related substructures and the dynamical changes during mouse embryonic development. In addition, the shared spatial domain and nearest-neighbor pairs identified by STAligner can be further considered as corresponding pairs to guide the three-dimensional reconstruction of consecutive slices, achieving more accurate local structure-guided registration than the existing method.

Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets

Background

Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches.

Results

We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to train random forest classifiers for immune cell type assignment for single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in random forest-based cell type classification. We further demonstrate how our approach helps to avoid bias in downstream statistical analyses by re-analysis of a published IFN stimulation experiment.

Discussion and conclusion

We demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for significantly more genes compared to previous cell classification algorithms.

Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes

BACKGROUND

RNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, scnRNA-seq for short), can help characterize the composition of tissues and reveal cells that influence key functions in both healthy and disease tissues. However, the use of these technologies is operationally challenging because of high costs and stringent sample-collection requirements. Computational deconvolution methods that infer the composition of bulk-profiled samples using scnRNA-seq-characterized cell types can broaden scnRNA-seq applications, but their effectiveness remains controversial.

RESULTS

We produced the first systematic evaluation of deconvolution methods on datasets with either known or scnRNA-seq-estimated compositions. Our analyses revealed biases that are common to scnRNA-seq 10X Genomics assays and illustrated the importance of accurate and properly controlled data preprocessing and method selection and optimization. Moreover, our results suggested that concurrent RNA-seq and scnRNA-seq profiles can help improve the accuracy of both scnRNA-seq preprocessing and the deconvolution methods that employ them. Indeed, our proposed method, Single-cell RNA Quantity Informed Deconvolution (SQUID), which combines RNA-seq transformation and dampened weighted least-squares deconvolution approaches, consistently outperformed other methods in predicting the composition of cell mixtures and tissue samples.

CONCLUSIONS

We showed that analysis of concurrent RNA-seq and scnRNA-seq profiles with SQUID can produce accurate cell-type abundance estimates and that this accuracy improvement was necessary for identifying outcomes-predictive cancer cell subclones in pediatric acute myeloid leukemia and neuroblastoma datasets. These results suggest that deconvolution accuracy improvements are vital to enabling its applications in the life sciences.

Single-cell transcriptomics reveal a hyperacute cytokine and immune checkpoint axis after cardiac arrest in patients with poor neurological outcome

BACKGROUND

Most patients hospitalized after cardiac arrest (CA) die because of neurological injury. The systemic inflammatory response after CA is associated with neurological injury and mortality but remains poorly defined.

METHODS

We determine the innate immune network induced by clinical CA at single-cell resolution.

FINDINGS

Immune cell states diverge as early as 6 h post-CA between patients with good or poor neurological outcomes 30 days after CA. Nectin-2+ monocyte and Tim-3+ natural killer (NK) cell subpopulations are associated with poor outcomes, and interactome analysis highlights their crosstalk via cytokines and immune checkpoints. Ex vivo studies of peripheral blood cells from CA patients demonstrate that immune checkpoints are a compensatory mechanism against inflammation after CA. Interferon γ (IFNγ)/interleukin-10 (IL-10) induced Nectin-2 on monocytes; in a negative feedback loop, Nectin-2 suppresses IFNγ production by NK cells.

CONCLUSIONS

The initial hours after CA may represent a window for therapeutic intervention in the resolution of inflammation via immune checkpoints.

FUNDING

This work was supported by funding from the American Heart Association, Brigham and Women's Hospital Department of Medicine, the Evergreen Innovation Fund, and the National Institutes of Health.

Autoimmune thyroid disease disrupts immune homeostasis in the endometrium of unexplained infertility women-a single-cell RNA transcriptome study during the implantation window

Objective

Autoimmune thyroid disease (AITD) is known to be associated with unexplained infertility in women. Although the presence of antithyroid antibodies have been speculated to be a marker of an immune imbalance that might lead to implantation failure, its underlying mechanism influencing the endometrial receptivity remains to be elucidated. In this study, we used single-cell RNA sequencing (scRNA-seq) to dissect immune microenvironment in endometrium of AITD patients during window of implantation (WOI).

Methods

We collected CD45⁺ immune cell populations of endometrium samples of unexplained infertile women with AITD (n=3), as well as samples of AITD^- controls (n=3). The cells were then processed with 10X Genomics Chromium for further analysis.

Results

We characterized 28 distinct immune cell subtypes totally, and uncovered differences in the composition and gene expression patterns between AITD patients and controls. The proportions of T CD4⁺, cNK, ILC3, T CD8⁺ GZMK⁺, T CD8⁺ Cytotoxic and ILC3 CD3E ^- cells were increased, and CD366⁺ uNK1 was decreased in AITD⁺ patients. And the abnormal expression of GNLY and chemokines was observed in AITD patients. In addition, uNK and T CD8⁺ Cytotoxic cells showed lower cytotoxicity but activation of immune response. Genes enriched in cell adhesion of ILC3 and Tregs were downregulated, while the number of ILC3 and Tregs were increased.

Conclusion

Immune imbalance exists in endometrium during WOI, which may impact embryo implantation.

Single-Cell Atlas Reveals the Hemocyte Subpopulations and Stress Responses in Asian Giant Softshell Turtle during Hibernation

Hibernation in turtle species is an adaptive survival strategy to colder winter conditions or food restrictions. However, the mechanisms underlying seasonal adaptions remain unclear. In the present study, we collected hemocytes from Pelochelys cantorii and compared the molecular signature of these cells between the active state and hibernation period based on single-cell RNA sequencing (scRNA-seq) analysis. We found six cell types and identified a list of new marker genes for each cell subpopulation. Moreover, several heat shock genes, including the Hsp40 family chaperone gene (DNAJ) and HSP temperature-responsive genes (HSPs), were upregulated during the hibernation period, which predicted these genes may play crucial roles in the stress response during hibernation. Additionally, compared to hemocytes in the active state, several upregulated differentially expressed immune-related genes, such as stat1, traf3, and socs6, were identified in hemocytes during the hibernation period, thus indicating the important immune function of hemocytes. Therefore, our findings provide a unified classification of P. cantorii hemocytes and identify the genes related to the stress response, thereby providing a better understanding of the adaptive mechanisms of hibernation.

Explore the impact of hypoxia-related genes (HRGs) in Cutaneous melanoma

BACKGROUND

Cutaneous melanoma (CM) has an overall poor prognosis due to a high rate of metastasis. This study aimed to explore the role of hypoxia-related genes (HRGs) in CM.

METHODS

We first used on-negative matrix factorization consensus clustering (NMF) to cluster CM samples and preliminarily analyzed the relationship of HRGs to CM prognosis and immune cell infiltration. Subsequently, we identified prognostic-related hub genes by univariate COX regression analysis and the least absolute shrinkage and selection operator (LASSO) and constructed a prognostic model. Finally, we calculated a risk score for patients with CM and investigated the relationship between the risk score and potential surrogate markers of response to immune checkpoint inhibitors (ICIs), such as TMB, IPS values, and TIDE scores.

RESULTS

Through NMF clustering, we identified high expression of HRGs as a risk factor for the prognosis of CM patients, and at the same time, increased expression of HRGs also indicated a poorer immune microenvironment. Subsequently, we identified eight gene signatures (FBP1, NDRG1, GPI, IER3, B4GALNT2, BGN, PKP1, and EDN2) by LASSO regression analysis and constructed a prognostic model.

CONCLUSION

Our study identifies the prognostic significance of hypoxia-related genes in melanoma and shows a novel eight-gene signature to predict the potential efficacy of ICIs.

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data

BACKGROUND

Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data.

METHODS

We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best.

RESULTS

Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis.

CONCLUSIONS

GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.

Gene regulatory network study of rheumatoid arthritis in single-cell chromatin landscapes of peripheral blood mononuclear cells

OBJECTIVES

Assays for transposase-accessible chromatin with single-cell sequencing (scATAC-seq) contribute to the progress in epigenetic studies. The purpose of our project was to discover the transcription factors (TFs) that were involved in the pathogenesis of rheumatoid arthritis (RA) at a single-cell resolution using epigenetic technology.

METHODS

Peripheral blood mononuclear cells of seven RA patients and seven natural controls were extracted nuclei suspensions for library construction. Subsequently, scATAC-seq was performed to generate a high-resolution map of active regulatory DNA for bioinformatics analysis.

RESULTS

We obtained 22 accessible chromatin patterns. Then, 10 key TFs were involved in RA pathogenesis by regulating the activity of mitogen-activated protein kinase. Consequently, two genes (PTPRC and SPAG9) regulated by 10 key TFs were found, which may be associated with RA disease pathogenesis, and these TFs were obviously enriched in RA patients (P < .05, fold change value > 1.2). With further quantitative polymerase chain reaction validation on PTPRC and SPAG9 in monocytes, we found differential expression of these two genes, which were regulated by eight TFs [ZNF384, HNF1B, DMRTA2, MEF2A, NFE2L1, CREB3L4 (var. 2), FOSL2::JUNB (var. 2), and MEF2B], showing highly accessible binding sites in RA patients.

CONCLUSIONS

These findings demonstrate the value of using scATAC-seq to reveal transcriptional regulatory variation in RA-derived peripheral blood mononuclear cells, providing insights into therapy from an epigenetic perspective.

Efficient Generation of Paired Single-Cell Multiomics Profiles by Deep Learning

Recent advances in single-cell sequencing technology have made it possible to measure multiple paired omics simultaneously in a single cell such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) and single-nucleus chromatin accessibility and mRNA expression sequencing (SNARE-seq). However, the widespread application of these single-cell multiomics profiling technologies has been limited by their experimental complexity, noise in nature, and high cost. In addition, single-omics sequencing technologies have generated tremendous and high-quality single-cell datasets but have yet to be fully utilized. Here, single-cell multiomics generation (scMOG), a deep learning-based framework to generate single-cell assay for transposase-accessible chromatin (ATAC) data in silico is developed from experimentally available single-cell RNA-seq measurements and vice versa. The results demonstrate that scMOG can accurately perform cross-omics generation between RNA and ATAC, and generate paired multiomics data with biological meanings when one omics is experimentally unavailable and out of training datasets. The generated ATAC, either alone or in combination with measured RNA, exhibits equivalent or superior performance to that of the experimentally measured counterparts throughout multiple downstream analyses. scMOG is also applied to human lymphoma data, which proves to be more effective in identifying tumor samples than the experimentally measured ATAC data. Finally, the performance of scMOG is investigated in other omics such as proteomics and it still shows robust performance on surface protein generation.

The Application of Single-Cell RNA Sequencing in the Inflammatory Tumor Microenvironment

The initiation and progression of tumors are complex. The cancer evolution-development hypothesis holds that the dysregulation of immune balance is caused by the synergistic effect of immune genetic factors and environmental factors that stimulate and maintain non-resolving inflammation. Throughout the cancer development process, this inflammation creates a microenvironment for the evolution and development of cancer. Research on the inflammatory tumor microenvironment (TME) explains the initiation and progression of cancer and guides anti-cancer immunotherapy. Single-cell RNA sequencing (scRNA-seq) can detect the transcription levels of cells at the single-cell resolution level, reveal the heterogeneity and evolutionary trajectory of infiltrated immune cells and cancer cells, and provide insight into the composition and function of each cell group in the inflammatory TME. This paper summarizes the application of scRNA-seq in inflammatory TME.

Neoepitopes prediction strategies: an integration of cancer genomics and immunoinformatics approaches

A major near-term medical impact of the genomic technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Next-generation sequencing technologies have accelerated the characterization of a tumor, leading to the comprehensive discovery of all the major alterations in a given cancer genome, followed by the translation of this information using computational and immunoinformatics approaches to cancer diagnostics and therapeutic efforts. In the current article, we review various components of cancer immunoinformatics applied to a series of fields of cancer research, including computational tools for cancer mutation detection, cancer mutation and immunological databases, and computational vaccinology.

The Clinical Response of Upadacitinib and Risankizumab Is Associated With Reduced Inflammatory Bowel Disease Anti-TNF-α Inadequate Response Mechanisms

BACKGROUND

Janus kinase (JAK) 1 inhibitor upadacitinib and IL-23 inhibitor risankizumab are efficacious in inflammatory bowel disease (IBD) patients who are antitumor necrosis factor (anti-TNF)-α inadequate responders (TNF-IRs). We aimed to understand the mechanisms mediating the response of upadacitinib and risankizumab.

METHODS

Eight tissue transcriptomic data sets from IBD patients treated with anti-TNF-α therapies along with single-cell RNAseq data from ulcerative colitis were integrated to identify TNF-IR mechanisms. The RNAseq colon tissue data from clinical studies of TNF-IR Crohn's disease patients treated with upadacitinib or risankizumab were used to identify TNF-IR mechanisms that were favorably modified by upadacitinib and risankizumab.

RESULTS

We found 7 TNF-IR upregulated modules related to innate/adaptive immune responses, interferon signaling, and tissue remodeling and 6 TNF-IR upregulated cell types related to inflammatory fibroblasts, postcapillary venules, inflammatory monocytes, macrophages, dendritic cells, and cycling B cells. Upadacitinib was associated with a significant decrease in the expression of most TNF-IR upregulated modules in JAK1 responders (JAK1-R); in contrast, there was no change in these modules among TNF-IR patients treated with a placebo or among JAK1 inadequate responders (JAK1-IR). In addition, 4 of the 6 TNF-IR upregulated cell types were significantly decreased after upadacitinib treatment in JAK1-R but not among subjects treated with a placebo or among JAK1-IR patients. We observed similar findings from colon biopsy samples from TNF-IR patients treated with risankizumab.

CONCLUSIONS

Collectively, these data suggest that upadacitinib and risankizumab affect TNF-IR upregulated mechanisms, which may account for their clinical response among TNF-IR IBD patients.

Single-Cell Sequencing of Malignant Ascites Reveals Transcriptomic Remodeling of the Tumor Microenvironment during the Progression of Epithelial Ovarian Cancer

Epithelial ovarian cancer (EOC) is the main cause of mortality among gynecological malignancies worldwide. Although patients with EOC undergo aggregate treatment, the prognosis is often poor. Peritoneal malignant ascites is a distinguishable clinical feature in EOC patients and plays a pivotal role in tumor progression and recurrence. The mechanisms of the tumor microenvironment (TME) in ascites in the regulation of tumor progression need to be explored. We comprehensively analyzed the transcriptomes of 4680 single cells from five EOC patients (three diagnostic samples and two recurrent samples) derived from Gene Expression Omnibus (GEO) databases. Batch effects between different samples were removed using an unsupervised deep embedding single-cell cluster algorithm. Subcluster analysis identified the different phenotypes of cells. The transition of a malignant cell state was confirmed using pseudotime analysis. The landscape of TME in malignant ascites was profiled during EOC progression. The transformation of epithelial cancer cells into mesenchymal cells was observed to lead to the emergence of related anti-chemotherapy and immune escape phenotypes. We found the activation of multiple biological pathways with the transition of tumor-associated macrophages and fibroblasts, and we identified the infiltration of CD4⁺CD25⁺ T regulatory cells in recurrent samples. The cell adhesion molecules mediated by integrin might be associated with the formation of the tumorsphere. Our study provides novel insights into the remodeling of the TME heterogeneity in malignant ascites during EOC progression, which provides evidence for identifying novel therapeutic targets and promotes the development of ovarian cancer treatment.

Deconvolution analysis of cell-type expression from bulk tissues by integrating with single-cell expression reference

To understand phenotypic variations and key factors which affect disease susceptibility of complex traits, it is important to decipher cell-type tissue compositions. To study cellular compositions of bulk tissue samples, one can evaluate cellular abundances and cell-type-specific gene expression patterns from the tissue transcriptome profiles. We develop both fixed and mixed models to reconstruct cellular expression fractions for bulk-profiled samples by using reference single-cell (sc) RNA-sequencing (RNA-seq) reference data. In benchmark evaluations of estimating cellular expression fractions, the mixed-effect models provide similar results as an elegant machine learning algorithm named cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORTx), which is a well-known and reliable procedure to reconstruct cell-type abundances and cell-type-specific gene expression profiles. In real data analysis, the mixed-effect models outperform or perform similarly as CIBERSORTx. The mixed models perform better than the fixed models in both benchmark evaluations and data analysis. In simulation studies, we show that if the heterogeneity exists in scRNA-seq data, it is better to use mixed models with heterogeneous mean and variance-covariance. As a byproduct, the mixed models provide fractions of covariance between subject-specific gene expression and cell types to measure their correlations. The proposed mixed models provide a complementary tool to dissect bulk tissues using scRNA-seq data.

Establishment of an ovarian cancer omentum metastasis-related prognostic model by integrated analysis of scRNA-seq and bulk RNA-seq

OBJECTIVE

Ovarian cancer has the highest mortality rate among gynecological malignant tumors, and it preferentially metastasizes to omental tissue, leading to intestinal obstruction and death. scRNA-seq is a powerful technique to reveal tumor heterogeneity. Analyzing omentum metastasis of ovarian cancer at the single-cell level may be more conducive to exploring and understanding omentum metastasis and prognosis of ovarian cancer at the cellular function and genetic levels.

METHODS

The omentum metastasis site scRNA-seq data of GSE147082 were acquired from the GEO (Gene Expression Omnibus) database, and single cells were clustered by the Seruat package and annotated by the SingleR package. Cell differentiation trajectories were reconstructed through the monocle package. The ovarian cancer microarray data of GSE132342 were downloaded from GEO and were clustered by using the ConsensusClusterPlus package into omentum metastasis-associated clusters according to the marker genes gained from single-cell differentiation trajectory analysis. The tumor microenvironment (TME) and immune infiltration differences between clusters were analyzed by the estimate and CIBERSORT packages. The expression matrix of genes used to cluster GSE132342 patients was extracted from bulk RNA-seq data of TCGA-OV (The Cancer Genome Atlas ovarian cancer), and least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to establish an omentum metastasis-associated gene (OMAG) signature. The signature was then tested by GSE132342 data. Finally, the clinicopathological characteristics of TCGA-OV were screened by univariate and multivariate Cox regression analysis to draw the nomogram.

RESULTS

A total of 9885 cells from 6 patients were clustered into 18 cell clusters and annotated into 14 cell types. Reconstruction of differentiation trajectories divided the cells into 5 branches, and a total of 781 cell trajectory-related characteristic genes were obtained. A total of 3769 patients in GSE132342 were subtyped into 3 clusters by 74 cell trajectory-related characteristic genes. Kaplan-Meier (K-M) survival analysis showed that the prognosis of cluster 2 was the worst, P < 0.001. The TME analysis showed that the ESTIMATE score and stromal score in cluster 2 were significantly higher than those in the other two clusters, P < 0.001. The immune infiltration analysis showed differences in the fraction of 8 immune cells among the 3 clusters, P < 0.05. The expression data of 74 genes used for GEO clustering were extracted from 379 patients in TCGA-OV, and combined with survival information, 10 candidates for OMAGs were filtered by LASSO. By using multivariate Cox regression, the 6-OMAGs signature was established as RiskScore = 0.307*TIMP3 + 3.516*FBN1-0.109*IGKC + 0.209*RPL21 + 0.870*UCHL1 + 0.365*RARRES1. Taking TCGA-OV as the training set and GSE132342 as the test set, receiver operating characteristic (ROC) curves were drawn to verify the prognostic value of 6-OMAGs. Screened by univariate and multivariate Cox regression analysis, 3 (age, cancer status, primary therapy outcome) of 5 clinicopathological characteristics were used to construct the nomogram combined with risk score.

CONCLUSION

We constructed an ovarian cancer prognostic model related to omentum metastasis composed of 6-OMAGs and 3 clinicopathological features and analyzed the potential mechanism of these 6-OMAGs in ovarian cancer omental metastasis.

Comprehensive analysis of cuproptosis-related genes in immune infiltration and diagnosis in ulcerative colitis

Objectives

Cuproptosis is a recently discovered form of programmed cell death; however, its role in ulcerative colitis (UC) remains a void.

Methods

Three gene expression profiles were acquired from the GEO database. Subsequently, the single sample gene set enrichment analysis (ssGSEA) was performed to identify the immune infiltration characteristics of UC. Correlation analysis between cuproptosis and immune infiltration was further conducted, and the cuproptosis-related genes were applied to construct a UC diagnostic model. Subsequently, analysis results of microarray data were experimentally validated by DSS-induced colitis in mice. Finally, therapeutic agents for the cuproptosis-related genes were screened owing to the gaping field of therapeutic agents on cuproptosis.

Results

Three gene expression profiles with 343 samples (290 UC and 53 healthy samples) were included. Immune infiltration revealed that UC patients had a higher level of DCs, B cells, CD8⁺ T cells, iDCs, Macrophages, neutrophils, pDCs, T helper cells, Tfh, Th1 cells, Th2 cells, TIL and Treg than normal subjects. Moreover, almost all cuproptosis-related genes were significantly negatively associated with immune infiltration in UC patients. The risk prediction model based on cuproptosis-related genes showed an excellent discrimination for UC. Animal experiments revealed significant alterations in genes essential for cuproptosis between DSS-induced colitis mice and healthy controls, providing experimental validation for the analysis results of microarray data. Further analysis revealed that latamoxef, vitinoin, clomipramine, chlorzoxazone, glibenclamide, pyruvic acid, clindamycin, medrysone, caspan, and flavin adenine dinucleotide might be the target agents for cuproptosis-related genes.

Conclusions

In conclusion, cuproptosis was significantly associated with immune infiltration in UC, and the cuproptosis-related genes showed an excellent discrimination for UC.

Immune-related histologic phenotype in pretreatment tumour biopsy predicts the efficacy of neoadjuvant anti-PD-1 treatment in squamous lung cancer

BACKGROUND

Although neoadjuvant anti-PD-1 immunotherapies have shown good efficacy in non-small cell lung cancer (NSCLC) patients, there is still a lack of effective predictive markers. We aimed to develop a pretreatment histologic scoring system to predict the efficacy of neoadjuvant immunotherapy.

METHODS

One hundred forty NSCLC cases were evaluated in this study. Initially, surgical specimens from 31 squamous cell lung cancer patients treated with neoadjuvant anti-PD-1 therapy and their eligible paired pretreatment biopsies were used for pathologic evaluation and developing the pretreatment scoring system, immune-related histologic phenotype assessment criteria (irHPC). Three trained pathologists independently scored the haematoxylin-eosin (HE) slides of the pretreatment tumour biopsies according to irHPC. The follow-up was from 07 March 2018 to 31 December 2021, mainly focusing on disease-free survival (DFS) and overall survival (OS). Second, 109 biopsies of lung squamous cell carcinoma were evaluated to explore the relationship between eosinophils and PD-L1 expression.

RESULTS

Superior 2-year DFS rates and 2-year OS rates were observed in patients who achieved major pathologic response (MPR) (MPR vs. non-MPR: 92.9% vs. 78.6%; 100.0% vs. 93.3%). Whether necrosis was included in the calculation of the per cent of residual viable tumour (%RVT) or not had almost no effect on the consistency of pathologic assessment and the histological response grouping. The interpathologist variability in assessing %RVT with immune-activated phenotype was not statistically significant (P = 0.480). Four immune-related features of pretreatment biopsies were included for calculating the predictive score. The trained pathologist accurately predicted most cases according to irHPC. For interobserver reproducibility using "2 points" as the cutoff, the overall per cent agreement was 77.8%. The reliability between pathologists for a binary tumour evaluation showed "moderate" agreement (κ = 0.54). Patients with scores ≥ 2 points tended to have better 2-year DFS rates and 2-year OS rates than those with scores < 2 points (85.7% vs. 71.4%; 100.0% vs. 87.5%).

CONCLUSIONS

The irHPC scoring system reflecting the preexisting immune response could be used to predict pathologic response to neoadjuvant immunotherapy, possibly further predicting the long-term prognosis, but larger trials are needed for verification.

Characterization of pyruvate metabolism and citric acid cycle patterns predicts response to immunotherapeutic and ferroptosis in gastric cancer

Background

Gastric cancer is one of the most common malignancies of the digestive system with a high lethal rate. Studies have shown that inherited and acquired mutations in pyruvate metabolism and citric acid cycle (P-CA) enzymes are involved in tumorigenesis and tumor development. However, it is unclear how different P-CA patterns affect the tumor microenvironment (TME), which is critical for cancer progression.

Methods

This study mainly concentrated on investigating the role of the P-CA patterns in multicellular immune cell infiltration of GC TME. First, the expression levels of P-CA regulators were profiled in GC samples from The Cancer Genome Atlas and Gene Expression Omnibus cohorts to construct a consensus clustering analysis and identify three distinct P-CA clusters. GSVA was conducted to reveal the different biological processes in three P-CA clusters. Subsequently, 1127 cluster-related differentially expressed genes were identified, and prognostic-related genes were screened using univariate Cox regression analysis. A scoring system was then set up to quantify the P-CA gene signature and further evaluate the response of the patients to the immunotherapy.

Results

We found that GC patients in the high P-CA score group had a higher tumor mutational burden, higher microsatellite instability, and better prognosis. The opposite was observed in the low P-CA score group. Interestingly, we demonstrated P-CA gene cluster could predict the sensitivity to immunotherapy and ferroptosis-induced therapy.

Conclusion

Collectively, the P-CA gene signature in this study exhibits potential roles in the tumor microenvironment and predicts the response to immunotherapeutic. The identification of these P-CA patterns may significantly accelerate the strategic development of immunotherapy for GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02739-z.

Single-cell sequencing reveals effects of chemotherapy on the immune landscape and TCR/BCR clonal expansion in a relapsed ovarian cancer patient

Cancer recurrence and chemoresistance are the leading causes of death in high-grade serous ovarian cancer (HGSOC) patients. However, the unique role of the immune environment in tumor progression for relapsed chemo-resistant patients remains elusive. In single-cell resolution, we characterized a comprehensive multi-dimensional cellular and immunological atlas from tumor, ascites, and peripheral blood of a chemo-resistant patient at different stages of treatment. Our results highlight a role in recurrence and chemoresistance of the immunosuppressive microenvironment in ascites, including MDSC-like myeloid and hypo-metabolic γδT cells, and of peripheral CD8⁺ effector T cells with chemotherapy-induced senescent/exhaustive. Importantly, paired TCR/BCR sequencing demonstrated relative conservation of TCR clonal expansion in hyper-expanded CD8⁺ T cells and extensive BCR clonal expansion without usage bias of V(D)J genes after chemotherapy. Thus, our study suggests strategies for ameliorating chemotherapy-induced immune impairment to improve the clinical outcome of HGSOC.

Combining single-cell RNA sequencing of peripheral blood mononuclear cells and exosomal transcriptome to reveal the cellular and genetic profiles in COPD

Background

It has been a long-held consensus that immune reactions primarily mediate the pathology of chronic obstructive pulmonary disease (COPD), and that exosomes may participate in immune regulation in COPD. However, the relationship between exosomes and peripheral immune status in patients with COPD remains unclear.

Methods

In this study, we sequenced plasma exosomes and performed single-cell RNA sequencing on peripheral blood mononuclear cells (PBMCs) from patients with COPD and healthy controls. Finally, we constructed competing endogenous RNA (ceRNA) and protein–protein interaction (PPI) networks to delineate the interactions between PBMCs and exosomes within COPD.

Results

We identified 135 mRNAs, 132 lncRNAs, and 359 circRNAs from exosomes that were differentially expressed in six patients with COPD compared with four healthy controls. Functional enrichment analyses revealed that many of these differentially expressed RNAs were involved in immune responses including defending viral infection and cytokine–cytokine receptor interaction. We also identified 18 distinct cell clusters of PBMCs in one patient and one control by using an unsupervised cluster analysis called uniform manifold approximation and projection (UMAP). According to resultant cell identification, it was likely that the proportions of monocytes, dendritic cells, and natural killer cells increased in the COPD patient we tested, meanwhile the proportions of B cells, CD4 + T cells, and naïve CD8 + T cells declined. Notably, CD8 + T effector memory CD45RA + (Temra) cell and CD8 + effector memory T (Tem) cell levels were elevated in patient with COPD, which were marked by their lower capacity to differentiate due to their terminal differentiation state and lower reactive capacity to viral pathogens.

Conclusions

We generated exosomal RNA profiling and single-cell transcriptomic profiling of PBMCs in COPD, described possible connection between impaired immune function and COPD development, and finally determined the possible role of exosomes in mediating local and systemic immune reactions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02182-8.

SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition

MOTIVATION

As complex tissues are typically composed of various cell types, deconvolution tools have been developed to computationally infer their cellular composition from bulk RNA sequencing (RNA-seq) data. To comprehensively assess deconvolution performance, gold-standard datasets are indispensable. Gold-standard, experimental techniques like flow cytometry or immunohistochemistry are resource-intensive and cannot be systematically applied to the numerous cell types and tissues profiled with high-throughput transcriptomics. The simulation of 'pseudo-bulk' data, generated by aggregating single-cell RNA-seq expression profiles in pre-defined proportions, offers a scalable and cost-effective alternative. This makes it feasible to create in silico gold standards that allow fine-grained control of cell-type fractions not conceivable in an experimental setup. However, at present, no simulation software for generating pseudo-bulk RNA-seq data exists.

RESULTS

We developed SimBu, an R package capable of simulating pseudo-bulk samples based on various simulation scenarios, designed to test specific features of deconvolution methods. A unique feature of SimBu is the modeling of cell-type-specific mRNA bias using experimentally derived or data-driven scaling factors. Here, we show that SimBu can generate realistic pseudo-bulk data, recapitulating the biological and statistical features of real RNA-seq data. Finally, we illustrate the impact of mRNA bias on the evaluation of deconvolution tools and provide recommendations for the selection of suitable methods for estimating mRNA content. SimBu is a user-friendly and flexible tool for simulating realistic pseudo-bulk RNA-seq datasets serving as in silico gold-standard for assessing cell-type deconvolution methods.

AVAILABILITY AND IMPLEMENTATION

SimBu is freely available at https://github.com/omnideconv/SimBu as an R package under the GPL-3 license.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Onepot-Seq: capturing single-cell transcriptomes simultaneously in a continuous medium via transient localization of mRNA

The development of single-cell RNA-seq has broadened the spectrum for biological research by providing a high-resolution analysis of cellular heterogeneity. However, the requirement for sophisticated devices for the compartmentalization of cells has limited its widespread applicability. Here, we develop Onepot-Seq, a device-free method, that harnesses the transient localization of mRNA after lysis to capture single-cell transcriptomes simultaneously in a continuous fluid medium. In mixed-species experiments, we obtained high-quality single-cell profiles. Further, cell type-specific poly(A)-conjugated antibodies allow Onepot-Seq to effectively capture target cells in complex populations. Chemical perturbations to cells can be profiled by Onepot-Seq at single-cell resolution. Onepot-Seq should allow routine transcriptional profiling at single-cell resolution, accelerating clinical and scientific discoveries in many fields of science.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

Mapping developmental paths of monkey primordial germ-like cells differentiation from pluripotent stem cells by single cell ribonucleic acid sequencing analysis†

The induction of primordial germ-like cells (PGCLCs) from pluripotent stem cells (PSCs) provides a powerful system to study the cellular and molecular mechanisms underlying germline specification, which are difficult to study in vivo. The studies reveal the existence of a species-specific mechanism underlying PGCLCs between humans and mice, highlighting the necessity to study regulatory networks in more species, especially in primates. Harnessing the power of single-cell RNA sequencing (scRNA-seq) analysis, the detailed trajectory of human PGCLCs specification in vitro has been achieved. However, the study of nonhuman primates is still needed. Here, we applied an embryoid body (EB) differentiation system to induce PGCLCs specification from cynomolgus monkey male and female PSCs, and then performed high throughput scRNA-seq analysis of approximately 40 000 PSCs and cells within EBs. We found that EBs provided a niche for PGCLCs differentiation by secreting growth factors critical for PGCLC specification, such as bone morphogenetic protein 2 (BMP2), BMP4, and Wnt Family Member 3. Moreover, the developmental trajectory of PGCLCs was reconstituted, and gene expression dynamics were revealed. Our study outlines the roadmap of PGCLC specification from PSCs and provides insights that will improve the differentiation efficiency of PGCLCs from PSCs.

Comprehensive Analysis of the Immune Cell Infiltration Landscape and Immune-Related Methylation in Retinoblastoma

Retinoblastoma is a common pediatric intraocular cancer, originating from cone precursors. The development of immunotherapies can help eradicate the tumor without vision loss, which would largely improve the quality of life of patients with retinoblastoma. Investigation of the tumor immune microenvironment provides knowledge for developing novel immunotherapies in cancer. However, the immune cell infiltrative landscape of retinoblastoma is unknown. Here, we compared the relative expression of immune gene signatures among 59 patients with retinoblastoma. The patients were divided into two subgroups according to the 28 types of immune cell infiltration (ICI) scores. We found that a subgroup with high ICI scores had increased expression levels of late cone markers, while the other subgroup exhibited larger tumor size and metastasis propensity. Furthermore, hypermethylated genes in the high-ICI subgroup were associated with immune regulation in the tumor microenvironment, suggesting that DNA methylation may play a vital regulatory role in retinoblastoma immunity. Our study provides a comprehensive framework for the systemic analysis of the influences of epigenetic events on the tumor immune microenvironment. We anticipate that our assay can not only provide insights into tumor immune regulation but also open up the perspectives for the identification of novel immunotherapy targets for retinoblastoma.

Three Oxidative Stress-Related Genes That Associate Endometrial Immune Cells Are Considered as Potential Biomarkers for the Prediction of Unexplained Recurrent Implantation Failure

Recurrent implantation failure (RIF) represents a new challenge in the field of assisted reproductive technology (ART). Considering the known effects of immune cell regulation on embryo implantation process, as well as our gene set variation analysis (GSVA) results that suggested the association between RIF and pathways of oxidative stress and immune responses, we hypothesized that oxidative stress- related genes (OSGs) associated with aberrant immunological factor may represent novel biomarkers for unexplained RIF. We therefore screened out the immune cell coexpressed OSGs by performing CIBERSORT, LM22 matrix and Pearson correlation, followed by constructing an OSG signature by least absolute shrinkage and selection operator (LASSO) regression. Three OSGs (AXL, SLC7A11 and UBQLN1) were then identified to establish a RIF risk signature, which showed high ability to discriminating RIF from fertile control. A nomogram was established, with a free online calculator for easier clinical application. Finally, Chilibot, protein-protein interaction analysis and BioGPS were sequentially applied for the investigation of functional relationships of these three genes with RIF and other OSGs, as well as their expression abundance across different human tissues. In conclusion, we identified an OSG signature that are relevant novel markers for the occurrence of unexplained RIF.

Virtual Populations for Quantitative Systems Pharmacology Models

Quantitative systems pharmacology (QSP) places an emphasis on dynamic systems modeling, incorporating considerations from systems biology modeling and pharmacodynamics. The goal of QSP is often to quantitatively predict the effects of clinical therapeutics, their combinations, and their doses on clinical biomarkers and endpoints. In order to achieve this goal, strategies for incorporating clinical data into model calibration are critical. Virtual population (VPop) approaches facilitate model calibration while faced with challenges encountered in QSP model application, including modeling a breadth of clinical therapies, biomarkers, endpoints, utilizing data of varying structure and source, capturing observed clinical variability, and simulating with models that may require more substantial computational time and resources than often found in pharmacometrics applications. VPops are frequently developed in a process that may involve parameterization of isolated pathway models, integration into a larger QSP model, incorporation of clinical data, calibration, and quantitative validation that the model with the accompanying, calibrated VPop is suitable to address the intended question or help with the intended decision. Here, we introduce previous strategies for developing VPops in the context of a variety of therapeutic and safety areas: metabolic disorders, drug-induced liver injury, autoimmune diseases, and cancer. We introduce methodological considerations, prior work for sensitivity analysis and VPop algorithm design, and potential areas for future advancement. Finally, we give a more detailed application example of a VPop calibration algorithm that illustrates recent progress and many of the methodological considerations. In conclusion, although methodologies have varied, VPop strategies have been successfully applied to give valid clinical insights and predictions with the assistance of carefully defined and designed calibration and validation strategies. While a uniform VPop approach for all potential QSP applications may be challenging given the heterogeneity in use considerations, we anticipate continued innovation will help to drive VPop application for more challenging cases of greater scale while developing new rigorous methodologies and metrics.

An unsupervised machine learning approach to evaluate sports facilities condition in primary school

Sports facilities have been acknowledged as one of the crucial environmental factors for children’s physical education, physical fitness, and participation in physical activity. Finding a solution for the effective and objective evaluation of the condition of sports facilities in schools (SSFs) with the responding quantitative magnitude is an uncertain task. This paper describes the utilization of an unsupervised machine learning method to objectively evaluate the condition of sports facilities in primary school (PSSFC). The statistical data of 845 samples with nine PSSFC indicators (indoor and outdoor included) were collected from the Sixth National Sports Facility Census in mainland China (NSFC), an official nationwide quinquennial census. The Fuzzy C-means (FCM) algorithm was applied to cluster the samples in accordance with the similarity of PSSFC. The clustered data were visualized by using t-stochastic neighbor embedding (t-SNE). The statistics results showed that the application of t-SNE and FCM led to the acceptable performance of clustering SSFs data into three types with differences in PSSFC. The effects of school category, location factors, and the interaction on PSSFC were analyzed by two-way analysis of covariance, which indicated that regional PSSFC has geographical and typological characteristics: schools in the suburbs are superior to those in the inner city, schools with more grades of students are configured with better variety and larger size of sports facilities. In conclusion, we have developed a combinatorial machine learning clustering approach that is suitable for objective evaluation on PSSFC and indicates its characteristics.

Data-driven learning how oncogenic gene expression locally alters heterocellular networks

Developing drugs increasingly relies on mechanistic modeling and simulation. Models that capture causal relations among genetic drivers of oncogenesis, functional plasticity, and host immunity complement wet experiments. Unfortunately, formulating such mechanistic cell-level models currently relies on hand curation, which can bias how data is interpreted or the priority of drug targets. In modeling molecular-level networks, rules and algorithms are employed to limit a priori biases in formulating mechanistic models. Here we combine digital cytometry with Bayesian network inference to generate causal models of cell-level networks linking an increase in gene expression associated with oncogenesis with alterations in stromal and immune cell subsets from bulk transcriptomic datasets. We predict how increased Cell Communication Network factor 4, a secreted matricellular protein, alters the tumor microenvironment using data from patients diagnosed with breast cancer and melanoma. Predictions are then tested using two immunocompetent mouse models for melanoma, which provide consistent experimental results.

While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.

Construction and Comprehensive Analysis of ceRNA Networks and Tumor-Infiltrating Immune Cells in Hepatocellular Carcinoma With Vascular Invasion

Background: Hepatocellular carcinoma (HCC) is a common malignant cancer. Metastasis plays a critical role in tumor progression, and vascular invasion is considered one of the most crucial factors for HCC metastasis. However, comprehensive analysis focusing on competitive endogenous RNA (ceRNA) and immune infiltration in the vascular invasion of HCC is lacking.

Methods: The gene expression profiles of 321 samples, including 210 primary HCC cases and 111 HCC cases with vascular invasion, were downloaded from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma project, and used in identifying significant differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs). The RNAs associated with vascular invasion were used in constructing a ceRNA network. A multigene-based risk signature was constructed using the least absolute shrinkage and selection operator algorithm. We detected the fractions of 28 immune cell types in HCC through single-sample gene set enrichment analysis (ssGSEA). Finally, the relationship between the ceRNA network and immune cells was determined through correlation analysis and used in clarifying the potential mechanism involved in vascular invasion.

Results: Overall, 413 DElncRNAs, 27 DEmiRNAs, and 397 DEmRNAs were recognized in HCC. A specific ceRNA network based on the interaction among 3 lncRNA–miRNA pairs and 24 miRNA–mRNA pairs were established. A ceRNA-based prognostic signature was constructed and used in dividing samples into high- and low-risk subgroups. The signature showed significant efficacy; its 3- and 5-year areas under the receiver operating characteristic curves were 0.712 and 0.653, respectively. ceRNA and ssGSEA integration analysis demonstrated that PART1 (p = 0, R = −0.33) and CDK5R2 (p = 0.01, R = −0.15) were negatively correlated to natural killer cells.

Conclusion: This study demonstrated that vascular invasion in HCC might be related to PART1, and its role in regulating CDK5R2 and NK cells. A nomogram was developed to predict the prognosis of patients with HCC and demonstrated the value of the ceRNA network and tumor-infiltrating immune cells value in improving personalized management.

Comprehensive Analysis of Tumor Immune Microenvironment Characteristics for the Prognostic Prediction and Immunotherapy of Oral Squamous Cell Carcinoma

Background: Oral squamous cell carcinoma (OSCC) is the most common cancer of oral and maxillofacial region. A recent clinical research has shown that tumor immune microenvironment (TIME)cells are closely related to immunotherapy sensitivity and OSCC prognosis. Nonetheless, a comprehensive analysis of TIME in OSCC has not been reported.

Methods: Bioinformatics and computational algorithms were employed to determine the significance of TIME cells in 257 OSCC patients. TIME scores were measured by three TIME models, and then used to evaluate the prognosis of OSCC patients.

Results: High TIME score was characterized by better prognosis in OSCC patients less than 60 years old, overexpression of immunotherapy targets (e.g., PD-1 and CLTA-4), and higher T-cell activity to inhibit tumor growth. Besides, poor prognosis was associated with low time score.

Conclusion: TIME score exhibited potential as a prognostic biomarker and an indicator in predict immunotherapeutic outcomes. Through the understanding of TIME model, this study can provide a better scheme for immunotherapy as the effective treatment of OSCC patients in the future.

The immune microenvironment shapes transcriptional and genetic heterogeneity in chronic lymphocytic leukemia

In chronic lymphocytic leukemia (CLL), B-cell receptor signaling, tumor-microenvironment interactions, and somatic mutations drive disease progression. To better understand the intersection between the microenvironment and molecular events in CLL pathogenesis, we integrated bulk transcriptome profiling of paired peripheral blood (PB) and lymph node (LN) samples from 34 patients. Oncogenic processes were upregulated in LN compared with PB and in immunoglobulin heavy-chain variable (IGHV) region unmutated compared with mutated cases. Single-cell RNA sequencing (scRNA-seq) distinguished 3 major cell states: quiescent, activated, and proliferating. The activated subpopulation comprised only 2.2% to 4.3% of the total tumor bulk in LN samples. RNA velocity analysis found that CLL cell fate in LN is unidirectional, starts in the proliferating state, transitions to the activated state, and ends in the quiescent state. A 10-gene signature derived from activated tumor cells was associated with inferior treatment-free survival (TFS) and positively correlated with the proportion of activated CD4+ memory T cells and M2 macrophages in LN. Whole exome sequencing (WES) of paired PB and LN samples showed subclonal expansion in LN in approximately half of the patients. Since mouse models have implicated activation-induced cytidine deaminase in mutagenesis, we compared AICDA expression between cases with and without clonal evolution but did not find a difference. In contrast, the presence of a T-cell inflamed microenvironment in LN was associated with clonal stability. In summary, a distinct minor tumor subpopulation underlies CLL pathogenesis and drives the clinical outcome. Clonal trajectories are shaped by the LN milieu, where T-cell immunity may contribute to suppressing clonal outgrowth. The clinical study is registered at clinicaltrials.gov as NCT00923507.

Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods

The assessment of the cellular heterogeneity and abundance in bulk tissue samples is essential for characterising cellular and organismal states. Computational approaches to estimate cellular abundance from bulk RNA-Seq datasets have variable performances, often requiring benchmarking matrices to select the best performing methods for individual studies. However, such benchmarking investigations are difficult to perform and assess in typical applications because of the absence of gold standard/ground-truth cellular measurements. Here we describe Decosus, an R package that integrates seven methods and signatures for deconvoluting cell types from gene expression profiles (GEP). Benchmark analysis on a range of datasets with ground-truth measurements revealed that our integrated estimates consistently exhibited stable performances across datasets than individual methods and signatures. We further applied Decosus to characterise the immune compartment of skin samples in different settings, confirming the well-established Th1 and Th2 polarisation in psoriasis and atopic dermatitis, respectively. Secondly, we revealed immune system-related UV-induced changes in sun-exposed skin. Furthermore, a significant motivation in the design of Decosus is flexibility and the ability for the user to include new gene signatures, algorithms, and integration methods at run time.

Comprehensive evaluation of deconvolution methods for human brain gene expression

Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing.

Transcriptome deconvolution aims to estimate cellular composition based on gene expression data. Here the authors evaluate deconvolution methods for human brain transcriptome and conclude that partial deconvolution algorithms work best, but that appropriate cell-type signatures are also important.

Differential Survival and Therapy Benefit of Patients with Breast Cancer Are Characterized by Distinct Epithelial and Immune Cell Microenvironments

PURPOSE

Extensive work in preclinical models has shown that microenvironmental cells influence many aspects of cancer cell behavior, including metastatic potential and their sensitivity to therapeutics. In the human setting, this behavior is mainly correlated with the presence of immune cells. Here, in addition to T cells, B cells, macrophages, and mast cells, we identified the relevance of nonimmune cell types for breast cancer survival and therapy benefit, including fibroblasts, myoepithelial cells, muscle cells, endothelial cells, and seven distinct epithelial cell types.

EXPERIMENTAL DESIGN

Using single-cell sequencing data, we generated reference profiles for all these cell types. We used these reference profiles in deconvolution algorithms to optimally detangle the cellular composition of more than 3,500 primary breast tumors of patients that were enrolled in the SCAN-B and MATADOR clinical trials, and for which bulk mRNA sequencing data were available.

RESULTS

This large data set enables us to identify and subsequently validate the cellular composition of microenvironments that distinguish differential survival and treatment benefit for different treatment regimens in patients with primary breast cancer. In addition to immune cells, we have identified that survival and therapy benefit are characterized by various contributions of distinct epithelial cell types.

CONCLUSIONS

From our study, we conclude that differential survival and therapy benefit of patients with breast cancer are characterized by distinct microenvironments that include specific populations of immune and epithelial cells.

FCER1G positively relates to macrophage infiltration in clear cell renal cell carcinoma and contributes to unfavorable prognosis by regulating tumor immunity

Background

Tumor-associated macrophages (TAMs) are closely related to unfavorable prognosis of patients with clear cell renal cell carcinoma (ccRCC). However, the important molecules in the interaction between ccRCC and TAMs are unclear.

Methods

TCGA-KIRC gene expression data of tumor tissues and normal tissues adjacent to tumor were compared to identify differentially expressed genes in ccRCC. TAMs related genes were discovered by analyzing the correlation between these differentially expressed genes and common macrophage biomarkers. Gene set enrichment analysis was performed to predict functions of TAMs related gene. The findings were further validated using RNA sequencing data obtained from the CheckMate 025 study and immunohistochemical analysis of samples from 350 patients with ccRCC. Kaplan–Meier survival curve, Cox regression analysis and Harrell’s concordance index analysis were used to determine the prognostic significance.

Results

In this study, we applied bioinformatic analysis to explore TAMs related differentially expressed genes in ccRCC and identified 5 genes strongly correlated with all selected macrophage biomarkers: STAC3, LGALS9, TREM2, FCER1G, and PILRA. Among them, FCER1G was abundantly expressed in tumor tissues and showed prognostic importance in patients with ccRCC who received treatment with Nivolumab; however, it did not exhibit prognostic value in those treated with Everolimus. We also discovered that high expression levels of FCER1G are related to T cell suppression. Moreover, combination of FCER1G and macrophage biomarker CD68 can improve the prognostic stratification of patients with ccRCC from TCGA-KIRC. Based on the immunohistochemical analysis of samples from patients with ccRCC, we further validated that FCER1G and CD68 are both highly expressed in tumor tissue and correlate with each other. Higher expression of CD68 or FCER1G in ccRCC tissue indicates shorter overall survival and progression-free survival; patients with high expression of both CD68 and FCER1G have the worst outcome. Combining CD68 and FCER1G facilitates the screening of patients with a worse prognosis from the same TNM stage group.

Conclusions

High expression of FCER1G in ccRCC is closely related to TAMs infiltration and suppression of T cell activation and proliferation. Combining the expression levels of FCER1G and macrophage biomarker CD68 may be a promising postoperative prognostic index for patients with ccRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09251-7.

DecOT: Bulk Deconvolution With Optimal Transport Loss Using a Single-Cell Reference

Tissues are constituted of heterogeneous cell types. Although single-cell RNA sequencing has paved the way to a deeper understanding of organismal cellular composition, the high cost and technical noise have prevented its wide application. As an alternative, computational deconvolution of bulk tissues can be a cost-effective solution. In this study, we propose DecOT, a deconvolution method that uses the Wasserstein distance as a loss and applies scRNA-seq data as references to characterize the cell type composition from bulk tissue RNA-seq data. The Wasserstein loss in DecOT is able to utilize additional information from gene space. DecOT also applies an ensemble framework to integrate deconvolution results from multiple individuals’ references to mitigate the individual/batch effect. By benchmarking DecOT with four recently proposed square loss-based methods on pseudo-bulk data from four different single-cell data sets and real pancreatic islet bulk samples, we show that DecOT outperforms other methods and the ensemble framework is robust to the choice of references.