Single-cell transcriptomic landscape reveals the differences in cell differentiation and immune microenvironment of papillary thyroid carcinoma between genders

Spatial and Single-Cell Transcriptomics Unraveled Spatial Evolution of Papillary Thyroid Cancer

Recurrence and metastasis are the major issues for papillary thyroid cancer (PTC). Current morphological and molecular classification systems are not satisfied for PTC diagnosis due to lacking variant-specific morphological criteria and high signal-to-noise in mutation-based diagnosis, respectively. Importantly, intratumor heterogeneity is largely lost in current molecular classification system, which can be resolved by single cell RNA sequencing (scRNA-seq). However, scRNA-seq loses spatial information and morphological features. Herein, scRNA-seq is integrated and spatially-resolved transcriptomics (SRT) to elaborate the mechanisms underlying the spatial heterogeneity, malignancy and metastasis of PTCs by associating transcriptome and local morphology. This results demonstrated that PTC cells evolved with multiple routes, driven by the enhanced aerobic metabolism and the suppressed mRNA translation and protein synthesis and the involvement of cell-cell interaction. Two curated malignant and metastatic footprints can discriminate PTC cells from normal thyrocytes. Ferroptosis resistance contributed to PTC evolution. This results will advance the knowledge of intratumor spatial heterogeneity and evolution of PTCs at spatial and single-cell levels, and propose better diagnostic strategy.

Cell-type deconvolution for bulk RNA-seq data using single-cell reference: a comparative analysis and recommendation guideline

The accurate estimation of cell type proportions in tissues is crucial for various downstream analyses. With the increasing availability of single-cell sequencing data, numerous deconvolution methods that use single-cell RNA sequencing data as a reference have been developed. However, a unified understanding of how these deconvolution approaches perform in practical applications is still lacking. To address this, we systematically assessed the accuracy and robustness of nine deconvolution methods that use single-cell RNA sequencing data as a reference, evaluating them on real bulk data with cell proportions verified through flow cytometry, as well as simulated bulk data generated from five single-cell RNA sequencing datasets. Our study highlights the importance of several factors-including reference dataset construction strategies, dataset size, cell type subdivision, and cell type inconsistency-on the accuracy and robustness of deconvolution results. We also propose a set of recommended guidelines for software users in diverse scenarios.

Tumor microenvironment remodeling after neoadjuvant chemoradiotherapy in local advanced rectal cancer revealed by single-cell RNA sequencing

BACKGROUND

The use of neoadjuvant chemoradiotherapy (neoCRT) followed by surgery has markedly enhanced the quality of survival in patients suffering from local advanced rectal cancer (LARC). Enhancing this treatment requires a deep understanding of its underlying mechanism. The heterogeneous nature of the tumor microenvironment (TME) significantly impacts therapeutic responses, presenting complex therapeutic challenges.

METHODS

In this comprehensive study, we explored the intricate cellular and molecular shifts within the TME of LARC after neoCRT administration. Using single-cell transcriptomic analysis, we meticulously examined 32,417 cells sourced from six samples, each representing different tumor regression grades (TRG: 0 versus 2). This detailed analysis enabled us to characterize the various cell subpopulations, encompassing epithelial cells, lymphocytes, myeloid cells, endothelial cells, and fibroblasts. Additionally, we identified their marker genes for deconvolution calculation in the READ cohort of the TCGA project. And we obtain their marker genes for deconvolution calculation in the READ cohort of the TCGA project.

RESULTS

Through cluster analysis and pathway comparisons of malignant tumor cells, we discerned that samples with poor tumor regression exhibit enhanced metabolic versatility and adaptability, enabling them to counteract the impacts of both radiotherapy and chemotherapy. Interestingly, within the TRG2 cohort, we observed a predominant immunosuppressive state in the TME, characterized by the activation of CD4 + regulatory T cells, maintained CD8 + T cell functionality, and a heightened M1 to M2 macrophage ratio. Moreover, the differing outcomes of neoCRT were reflected in the varying interaction dynamics between macrophages (M1 and M2) and CD4+/CD8 + T cells. Furthermore, our data reveal that neoCRT intricately modulates fibroblasts and endothelial cells, primarily through the extracellular matrix remodeling pathway, which orchestrates tumor angiogenesis. All changes were validated through immunofluorescence staining on intraoperative samples before and after treatment. To summarize, our investigation presents a comprehensive exploration of the cellular and molecular metamorphoses within the TME post-neoCRT.

CONCLUSIONS

By unveiling the sophisticated interaction between the multifaceted cells within the TME and their respective reactions to neoCRT, we establish a robust platform for ensuing future investigations. This study paves the way for novel therapeutic strategies that leverage these insights to bolster the efficacy of neoCRT in managing LARC.

Sex-Based Differences in Thyroid Plasma B Cell Infiltration: Implications for Autoimmune Disease Susceptibility

Thyroid autoimmune diseases, such as Hashimoto thyroiditis and Graves disease, are significantly more prevalent in women than in men, suggesting underlying biological differences in immune system function and regulation between sexes. Plasma B cells are crucial in autoimmunity due to their role in producing antibodies targeting self-antigens, but their presence in the thyroids of women without clinical autoimmune diseases remains largely unexplored. This study investigates the infiltration of plasma B cells in female thyroids specifically excluding those with any clinical signs of autoimmune diseases. Using bulk RNA-seq analysis, we identified significant sex differences in gene expression profiles, particularly in genes associated with plasma B cells. Single-cell RNA-seq and spatial transcriptomic analyses further revealed that the CXCL13-CXCR5 signaling axis plays a pivotal role in recruiting and organizing plasma B cells within the thyroid tissue. These findings suggest that the inherent presence of plasma B cells in the female thyroid, driven by CXCL13, may contribute to the higher risk of developing autoimmune thyroid diseases in women. Our study provides new insights into the immune landscape of the thyroid and underscores the importance of understanding sex-specific differences in immune cell distribution and function.

Impact of Hashimoto's thyroiditis on the tumor microenvironment in papillary thyroid cancer: insights from single-cell analysis

This study investigates the impact of Hashimoto's thyroiditis (HT), an autoimmune disorder, on the papillary thyroid cancer (PTC) microenvironment using a dataset of 140,456 cells from 11 patients. By comparing PTC cases with and without HT, we identify HT-specific cell populations (HASCs) and their role in creating a TSH-suppressive environment via mTE3, nTE0, and nTE2 thyroid cells. These cells facilitate intricate immune-stromal communication through the MIF-(CD74+CXCR4) axis, emphasizing immune regulation in the TSH context. In the realm of personalized medicine, our HASC-focused analysis within the TCGA-THCA dataset validates the utility of HASC profiling for guiding tailored therapies. Moreover, we introduce a novel, objective method to determine K-means clustering coefficients in copy number variation inference from bulk RNA-seq data, mitigating the arbitrariness in conventional coefficient selection. Collectively, our research presents a detailed single-cell atlas illustrating HT-PTC interactions, deepening our understanding of HT's modulatory effects on PTC microenvironments. It contributes to our understanding of autoimmunity-carcinogenesis dynamics and charts a course for discovering new therapeutic targets in PTC, advancing cancer genomics and immunotherapy research.

Single-cell transcriptomics reveals tumor-infiltrating B cell function after neoadjuvant pembrolizumab and chemotherapy in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) is the most pervasive lung cancer subtype. Recent studies have shown that immune checkpoint inhibitors achieved favorable clinical benefits in resectable NSCLC; however, the associated mechanism remains unclear. The role of T cells in antitumor immunity has received considerable attention, while the antitumor effects of tumor-infiltrating B cells (TIBs) in NSCLC remain poorly understood. Here, we conducted a single-cell RNA sequencing analysis of immune cells isolated from 12 patients with stage IIIA NSCLC to investigate B cell subtypes and their functions following neoadjuvant chemoimmunotherapy. We confirmed the simultaneous existence of the 4 B cell subtypes. Among them, memory B cells were found to be associated with a positive therapeutic effect to neoadjuvant chemoimmunotherapy. Furthermore, we found that G protein-coupled receptor 183 was most prevalent in memory B cells and associated with a positive therapeutic response. Multiplex immunofluorescence and flow cytometry experiments in an additional cohort of 22 treatment-naïve and 30 stage IIIA/IIIB NSCLC patients treated with neoadjuvant chemoimmunotherapy verified these findings. Overall, our analysis revealed the functions of TIBs and their potential effect on clinical treatment in NSCLC.

Stem cell index-based RiskScore model for predicting prognosis in thyroid cancer and experimental verification

Objective

An mRNA expression-based stemness index (mRNAsi) has been developed to characterize cancer stemness. However, the predictive value of mRNAsi-based signature in therapeutic resistance and immunotherapy in thyroid cancer (THCA) remains unclarified. This study evaluated and validated the role of mRNAsi in drug sensitivity, its relationship between mRNAsi and THCA clinical features and immunity based on bioinformatics.

Methods

Based on transcriptome data of THCA patients from the Tumor Genome Atlas Project (TCGA) database, and expression data of multifunctional stem cell samples from the Progenitor Cell Biology Consortium (PCBC) databases, mRNAsi was calculated by the " one class logistic regression (OCLR)" method, Molecular subtypes of TCGA-THCA samples were identified with mRNAsi-related genes using ConsensusClusterPlus method. The gene mutation, clinical characteristics, immune characteristics, TIDE and drug sensitivity were compared among molecular subtypes. A prognostic model was designed with Lasso cox method. Modulation of malignant phenotype of THCA cell lines by model characterization genes is validated by CCK-8, flow cytometry. DNA methylation disorder in promoter region was analyzed between risk groups. The model was validated for survival in the internal Test dataset, while TCGA pan-cancer and immunotherapy datasets were further employed to validate the performance of this model.

Results

We obtained a total of 78 stem cell samples, each containing the expression profile of 8087 mRNA genes. Based on mRNAsi, THCA was divided into 3 subtypes. Subtype C2 had the poorest prognosis and highest immune score, while subtype C3 had the best prognosis, lowest mRANsi and highest TIDE score. Patients in subtype C2 showed higher sensitivity to Cisplatin, Erlotinib, Paclitaxel, and Lapatinib. The prognostic signature was generated using 5 mRNAsi-related genes, which could predict prognosis for THCA. qRT-PCR results showed that the expression of 5 genes were various in Hth7 and KTC-1 cells, and inhibition CELSR3 expression increased percentage of apoptosis in Hth7 and KTC-1 cells. mRNAsi related DNA methylation sites were mainly enriched in tumor related pathways. Good performance of this model was validated in Test dataset, pan-cancer and immunotherapy datasets.

Conclusion

This study identified three subtypes for classification and developed a prognostic model with mRNAsi-related genes, which provided great potential for prognosis and immunotherapy prediction.

Clinical prediction models for cervical lymph node metastasis of papillary thyroid carcinoma

PURPOSE

Accurate preoperative diagnosis of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) remains an unsolved problem. This study aimed to construct a nomogram and scoring system for predicting LNM based on the clinical characteristics of patients with PTC.

METHODS

1400 patients with PTC who underwent thyroidectomy and lymph node dissection at the First Affiliated Hospital of Sun Yat-sen University were retrospectively enrolled and randomly divided into training and internal testing sets. Furthermore, 692 patients with PTC from three other medical centers were collected as external testing sets. Least absolute shrinkage and selection operator (LASSO) was used to screen the predictors, and a nomogram was constructed. In addition, a scoring system was constructed using 10-fold cross-validation. The performances of the two models were verified among datasets and compared with preoperative ultrasound (US).

RESULTS

Six independent predictors were included in the multivariate logistic model: age, sex, US diagnosis of LNM, tumor diameter, location, and thyroid peroxidase antibody level. The areas under the receiver operating characteristic curve (AUROC) (95% confidence interval) of this nomogram in the training, internal testing, and three external testing sets were 0.816 (0.791-0.840), 0.782 (0.727-0.837), 0.759 (0.699-0.819), 0.749 (0.667-0.831), and 0.777 (0.726-0.828), respectively. The AUROC of the scoring system were 0.810 (0.785-0.835), 0.772 (0.718-0.826), 0.736 (0.675-0.798), 0.717 (0.635-0.799) and 0.756 (0.704-0.808), respectively. The prediction performances were both significantly superior to those of preoperative US (P < 0.001).

CONCLUSION

The nomogram and scoring system performed well in different datasets and significantly improved the preoperative prediction of LNM than US alone.

Spatial transcriptomics reveals prognosis-associated cellular heterogeneity in the papillary thyroid carcinoma microenvironment

BACKGROUND

Papillary thyroid carcinoma (PTC) is the most common malignant endocrine tumour, and its incidence and prevalence are increasing considerably. Cellular heterogeneity in the tumour microenvironment is important for PTC prognosis. Spatial transcriptomics is a powerful technique for cellular heterogeneity study.

METHODS

In conjunction with a clinical pathologist identification method, spatial transcriptomics was employed to characterise the spatial location and RNA profiles of PTC-associated cells within the tissue sections. The spatial RNA-clinical signature genes for each cell type were extracted and applied to outlining the distribution regions of specific cells on the entire section. The cellular heterogeneity of each cell type was further revealed by ContourPlot analysis, monocle analysis, trajectory analysis, ligand-receptor analysis and Gene Ontology enrichment analysis.

RESULTS

The spatial distribution region of tumour cells, typical and atypical follicular cells (FCs and AFCs) and immune cells were accurately and comprehensively identified in all five PTC tissue sections. AFCs were identified as a transitional state between FCs and tumour cells, exhibiting a higher resemblance to the latter. Three tumour foci were shared among all patients out of the 13 observed. Notably, tumour foci No. 2 displayed elevated expression levels of genes associated with lower relapse-free survival in PTC patients. We discovered key ligand-receptor interactions, including LAMB3-ITGA2, FN1-ITGA3 and FN1-SDC4, involved in the transition of PTC cells from FCs to AFCs and eventually to tumour cells. High expression of these patterns correlated with reduced relapse-free survival. In the tumour immune microenvironment, reduced interaction between myeloid-derived TGFB1 and TGFBR1 in tumour focus No. 2 contributed to tumourigenesis and increased heterogeneity. The spatial RNA-clinical analysis method developed here revealed prognosis-associated cellular heterogeneity in the PTC microenvironment.

CONCLUSIONS

The occurrence of tumour foci No. 2 and three enhanced ligand-receptor interactions in the AFC area/tumour foci reduced the relapse-free survival of PTC patients, potentially leading to improved prognostic strategies and targeted therapies for PTC patients.

Presumed Pathogenic Germ Line and Somatic Variants in African American Thyroid Cancer

Background: African American (AA) thyroid cancer patients have worse prognoses than European Americans (EA), which has been attributed to both health care disparities and possible genetic differences. We investigated the impact of both germ line and somatic variants on clinical outcome in a cohort of AA nonmedullary thyroid cancer (NMTC) patients who had received therapeutic intervention from cancer centers. Methods: Whole-exome sequencing was performed on DNA from available blood/normal tissues (N = 37) and paired tumor samples (N = 32) collected from 37 and 29 AA NMTC patients, respectively. Variants with Combined Annotation Depletion Dependent (CADD) score of ≥20 and VarSome Clinical classification of likely pathogenic or pathogenic were classified as presumed pathogenic germ line or somatic variants (PPGVs/PPSVs). PPGVs/PPSVs in cancer-related genes and PPGVs in cardiovascular risk genes were further investigated, and PPGVs/PPSVs associated with African (AFR) ancestry were identified. Results: Among 17 PPGVs identified in 16 cancer predisposition or known cancer-related genes, only WRN was previously known to associate with NMTC predisposition. Among PPSVs, BRAFV600E was most the prevalent and detected in 12 of the 29 (41%) tumors. Examining PPGVs/PPSVs among three patients who died from NMTC, one patient who died from papillary thyroid carcinoma/anaplastic thyroid carcinoma (PTC/ATC) led us to speculate that the PPGV ERCC4R799W may have increased the risk of PPSV TP53R273H acquisition. Among PPGVs identified in 18 cardiovascular risk genes, PPGVs in SC5NA, GYG1, CBS, CFTR, and SI are known to have causal and pathogenic implications in cardiovascular disease. Conclusion: In this cohort, most AA-NMTC patients exhibit favorable outcomes after therapeutic intervention given at cancer centers, suggesting that health care disparity is the major contributor for worse prognoses among AA-NMTC patients. Nevertheless, the clinical impact of PPGVs that might facilitate the acquisition of TP53 tumor mutations, and/or PPGVs that predispose individuals to adverse cardiovascular events, which could be exacerbated by therapy-induced cardiotoxicity, needs to be further explored. Integrated analysis of PPGV/PPSV profiles among NMTC patients with different stages of disease may help to identify NMTC patients who require close monitoring or proactive intervention.

Prediction Model of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma

BACKGROUND

The objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma (PTC).

METHODS

A retrospective study was conducted on 212 patients with PTC who underwent initial surgical treatment from August 2022 to April 2023 in 2 hospitals. Data were randomly split into 7:3 training-validation sets. Logistic regression was used for feature selection and predictive model creation. Model performance was assessed using receiver operating characteristic (ROC) and calibration curves. Clinical utility was determined using decision curves.

RESULTS

Among the 212 patients with PTC, 104 cases (49.1%) exhibited cervical lymph node metastasis, while 108 cases (50.9%) did not. Multivariate logistic regression analysis revealed that age (OR = 0.95), FT3 (OR = 0.41), tumor maximum diameter ≥0.9 cm (OR = 1.85), intratumoral microcalcifications (OR = 1.84), and suspicious lymph node on ultrasound (OR = 2.96) were independent risk factors for lymph node metastasis in PTC patients (P < 0.05). The constructed model for predicting the risk of cervical lymph node metastasis demonstrated a training set ROC curve area under the curve (AUC) of 0.742 (95% CI: 0.664 - 0.821), with a cut-off value of 0.615, specificity of 87.8%, and sensitivity of 51.4%. The validation set exhibited an AUC of 0.648 (95% CI: 0.501 - 0.788), with a cut-off value of 0.644, specificity of 91.2%, and sensitivity of 43.3%. Including the BRAF V600 E mutation did not improve the model's diagnostic performance significantly. Decision curve analysis indicated clinical feasibility of the model.

CONCLUSION

The predictive model developed in this study effectively predicts lymph node metastasis risk in PTC patients by incorporating ultrasound features, demographic characteristics, and serum parameters. However, including the BRAF V600 E mutation does not significantly improve the model's diagnostic performance.

Single-cell RNA-seq reveals MIF-(CD74 + CXCR4) dependent inhibition of macrophages in metastatic papillary thyroid carcinoma

BACKGROUND

The mechanism promoting papillary thyroid carcinoma (PTC) metastasis remains unclear. We aimed to investigate the potential metastatic mechanisms at a single-cell resolution.

METHODS

We performed single-cell RNA-seq (scRNA-seq) profiling of thyroid tumour (TT), adjacent normal thyroid (NT) and lymph node metastasized tumour (LN) from a young female with PTC. Validation of our results was conducted in 31 tumours with metastasis and 30 without metastasis.

RESULTS

ScRNA-seq analysis generated data on 38,215 genes and 0.14 billion transcripts from 28,839 cells, classified into 18 clusters, each annotated to represent 10 cell types. PTC cells were found to originate from epithelial cells. Epithelial cells and macrophages emerged as the strongest signal emitters and receivers, respectively. After reclustering epithelial cells and macrophages, our analysis, incorporating gene set variation analysis (GSVA), SCENIC analysis, and pseudotime trajectory analysis, indicated that subcluster 0 of epithelial cells (EP_0) showed a more malignant phenotype, and subclusters 3 and 4 of macrophages (M_3 and M_4) demonstrated heightened activity. Further analysis suggested that EP_0 may suppress the activity of M_3 and M_4 via MIF - (CD74 + CXCR4) in the MIF pathway. After analysing the expression of the 4 genes in the MIF pathway in both the TCGA cohort and our cohort (n = 61), CD74 was identified as significantly overexpressed in PTC tumours particularly those with lymph node metastasis.

CONCLUSION

Our study revealed that PTC may facilitate lymph node metastasis by inhibiting macrophages via MIF signalling. It is suggested that malignant PTC cells may suppress the immune activity of macrophages by consistently releasing signals to them via MIF-(CD74 + CXCR4).

Exploring the advances of single-cell RNA sequencing in thyroid cancer: a narrative review

Thyroid cancer, a prevalent form of endocrine malignancy, has witnessed a substantial increase in occurrence in recent decades. To gain a comprehensive understanding of thyroid cancer at the single-cell level, this narrative review evaluates the applications of single-cell RNA sequencing (scRNA-seq) in thyroid cancer research. ScRNA-seq has revolutionised the identification and characterisation of distinct cell subpopulations, cell-to-cell communications, and receptor interactions, revealing unprecedented heterogeneity and shedding light on novel biomarkers for therapeutic discovery. These findings aid in the construction of predictive models on disease prognosis and therapeutic efficacy. Altogether, scRNA-seq has deepened our understanding of the tumour microenvironment immunologic insights, informing future studies in the development of effective personalised treatment for patients. Challenges and limitations of scRNA-seq, such as technical biases, financial barriers, and ethical concerns, are discussed. Advancements in computational methods, the advent of artificial intelligence (AI), machine learning (ML), and deep learning (DL), and the importance of single-cell data sharing and collaborative efforts are highlighted. Future directions of scRNA-seq in thyroid cancer research include investigating intra-tumoral heterogeneity, integrating with other omics technologies, exploring the non-coding RNA landscape, and studying rare subtypes. Overall, scRNA-seq has transformed thyroid cancer research and holds immense potential for advancing personalised therapies and improving patient outcomes. Efforts to make this technology more accessible and cost-effective will be crucial to ensuring its widespread utilisation in healthcare.

Transcription factor E4F1 dictates spermatogonial stem cell fate decisions by regulating mitochondrial functions and cell cycle progression

BACKGROUND

Spermatogonial stem cells (SSCs) provide a foundation for robust and continual spermatogenesis in mammals. SSCs self-renew to maintain a functional stem cell pool and differentiate to supply committed progenitors. Metabolism acts as a crucial determinant of stem cell fates; however, factors linking metabolic programs to SSC development and maintenance are poorly understood.

RESULTS

We analyzed the chromatin accessibility of undifferentiated spermatogonia at the single-cell level and identified 37 positive TF regulators that may have potential roles in dictating SSC fates. The transcription factor E4F1 is expressed in spermatogonia, and its conditional deletion in mouse germ cells results in progressive loss of the entire undifferentiated spermatogonial pool. Single-cell RNA-seq analysis of control and E4f1-deficient spermatogonia revealed that E4F1 acts as a key regulator of mitochondrial function. E4F1 binds to promotors of genes that encode components of the mitochondrial respiratory chain, including Ndufs5, Cox7a2, Cox6c, and Dnajc19. Loss of E4f1 function caused abnormal mitochondrial morphology and defects in fatty acid metabolism; as a result, undifferentiated spermatogonia were gradually lost due to cell cycle arrest and elevated apoptosis. Deletion of p53 in E4f1-deficient germ cells only temporarily prevented spermatogonial loss but did not rescue the defects in SSC maintenance.

CONCLUSIONS

Emerging evidence indicates that metabolic signals dictate stem cell fate decisions. In this study, we identified a list of transcription regulators that have potential roles in the fate transitions of undifferentiated spermatogonia in mice. Functional experiments demonstrated that the E4F1-mediated transcription program is a crucial regulator of metabolism and SSC fate decisions in mammals.

Single cell transcriptomic analysis of Graves' disease thyroid glands reveals the broad immunoregulatory potential of thyroid follicular and stromal cells and implies a major re-interpretation of the role of aberrant HLA class II expression in autoimmunity

The study of the immune response in thyroid autoimmunity has been mostly focused on the autoantibodies and lymphocytes, but there are indications that intrinsic features of thyroid tissue cells may play a role in disrupting tolerance that needs further investigation. The overexpression of HLA and adhesion molecules by thyroid follicular cells (TFC) and our recent demonstration that PD-L1 is also moderately expressed by TFCs in autoimmune thyroid indicates that TFCs they may activate but also inhibit the autoimmune response. Intriguingly, we have recently found that in vitro cultured TFCs are able to suppress the proliferation of autologous lymphocyte T in a contact-dependent manner which is independent of the PD-1/PD-L1 signaling pathway. To get a more comprehensive picture of TFC activating and inhibitory molecules/pathways driving the autoimmune response in the thyroid glands, preparations of TFCs and stromal cells from five Graves' disease (GD) and four control thyroid glands were compared by scRNA-seq. The results confirmed the previously described interferon type I and type II signatures in GD TFCs and showed unequivocally that they express the full array of genes that intervene in the processing and presentation of endogenous and exogeneous antigens. GD TFCs lack however expression of costimulatory molecules CD80 and CD86 required for priming T cells. A moderate overexpression of CD40 by TFCs was confirmed. GD Fibroblasts showed widespread upregulation of cytokine genes. The results from this first single transcriptomic profiling of TFC and thyroid stromal cells provides a more granular view of the events occurring in GD. The new data point at an important contribution of stromal cells and prompt a major re-interpretation of the role of MHC over-expression by TFC, from deleterious to protective. Most importantly this re-interpretation could also apply to other tissues, like pancreatic beta cells, where MHC over-expression has been detected in diabetic pancreas.

Integrating spatial and single-nucleus transcriptomic data elucidates microglial-specific responses in female cynomolgus macaques with depressive-like behaviors

Major depressive disorder represents a serious public health challenge worldwide; however, the underlying cellular and molecular mechanisms are mostly unknown. Here, we profile the dorsolateral prefrontal cortex of female cynomolgus macaques with social stress-associated depressive-like behaviors using single-nucleus RNA-sequencing and spatial transcriptomics. We find gene expression changes associated with depressive-like behaviors mostly in microglia, and we report a pro-inflammatory microglia subpopulation enriched in the depressive-like condition. Single-nucleus RNA-sequencing data result in the identification of six enriched gene modules associated with depressive-like behaviors, and these modules are further resolved by spatial transcriptomics. Gene modules associated with huddle and sit alone behaviors are expressed in neurons and oligodendrocytes of the superficial cortical layer, while gene modules associated with locomotion and amicable behaviors are enriched in microglia and astrocytes in mid-to-deep cortical layers. The depressive-like behavior associated microglia subpopulation is enriched in deep cortical layers. In summary, our findings show cell-type and cortical layer-specific gene expression changes and identify one microglia subpopulation associated with depressive-like behaviors in female non-human primates.

Regulatory role of Chitinase 3-like 1 gene in papillary thyroid carcinoma proved by integration analyses of single-cell sequencing with cohort and experimental validations

Papillary thyroid carcinoma (PTC) is one of the most common thyroid carcinomas. The gross extrathyroidal extension and extensive metastases of PTC lead to high rates of recurrence and poor clinical outcomes. However, the mechanisms underlying PTC development are poorly understood. In this study, using single-cell RNA sequencing, the transcriptome profiles of two PTC patients were addressed, including PTC1 with low malignancy and good prognosis and PTC2 with high malignancy and poor prognosis. We found that epithelial subcluster Epi02 was the most associated with the malignant development of PTC cells, with which the fold change of Chitinase 3-like 1 (CHI3L1) is on the top of the differentially expressed genes between PTC1 and PTC2 (P < 0.001). However CHI3L1 is rarely investigated in PTC as far. We then studied its role in PTC with a series of experiments. Firstly, qRT-PCR analysis of 14 PTC patients showed that the expression of CHI3L1 was positively correlated with malignancy. In addition, overexpression or silencing of CHI3L1 in TPC-1 cells, a PTC cell line, cultured in vitro showed that the proliferation, invasion, and metastasis of the cells were promoted or alleviated by CHI3L1. Further, immunohistochemistry analysis of 110 PTC cases revealed a significant relationship between CHI3L1 protein expression and PTC progression, especially the T (P < 0.001), N (P < 0.001), M stages (P = 0.007) and gross ETE (P < 0.001). Together, our results prove that CHI3L1 is a positive regulator of malignant development of PTC, and it promotes proliferation, invasion, and metastasis of PTC cells. Our study improves understanding of the molecular mechanisms underlying the progression of PTC and provides new insights for the clinical diagnosis and treatment of PTC.

Understanding tumour endothelial cell heterogeneity and function from single-cell omics

Anti-angiogenic therapies (AATs) are used to treat different types of cancers. However, their success is limited owing to insufficient efficacy and resistance. Recently, single-cell omics studies of tumour endothelial cells (TECs) have provided new mechanistic insight. Here, we overview the heterogeneity of human TECs of all tumour types studied to date, at the single-cell level. Notably, most human tumour types contain varying numbers but only a small population of angiogenic TECs, the presumed targets of AATs, possibly contributing to the limited efficacy of and resistance to AATs. In general, TECs are heterogeneous within and across all tumour types, but comparing TEC phenotypes across tumours is currently challenging, owing to the lack of a uniform nomenclature for endothelial cells and consistent single-cell analysis protocols, urgently raising the need for a more consistent approach. Nonetheless, across most tumour types, universal TEC markers (ACKR1, PLVAP and IGFBP3) can be identified. Besides angiogenesis, biological processes such as immunomodulation and extracellular matrix organization are among the most commonly predicted enriched signatures of TECs across different tumour types. Although angiogenesis and extracellular matrix targets have been considered for AAT (without the hoped success), the immunomodulatory properties of TECs have not been fully considered as a novel anticancer therapeutic approach. Therefore, we also discuss progress, limitations, solutions and novel targets for AAT development.

siVAE: interpretable deep generative models for single-cell transcriptomes

Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.

The Immune Landscape of Papillary Thyroid Cancer in the Context of Autoimmune Thyroiditis

Simple Summary

The association between papillary thyroid cancer and Hashimoto’s thyroiditis went through a long-standing human debate recently elucidated by the establishment of a novel mouse model. Papillary thyroid carcinoma is an excellent model for studying the tumor immune microenvironment because it is naturally accompanied by immune cells, making it a good candidate for the treatment with immune checkpoint inhibitors.

Abstract

Papillary thyroid cancer (PTC) often co-occurs with Hashimoto’s thyroiditis, an association that has long been reported in clinical studies, remaining controversial. Experimental evidence has recently shown that pre-existing thyroiditis has a beneficial effect on PTC growth and progression by a distinctive expansion of effector memory CD8 T cells. Although the link between inflammation and PTC might involve different components of the immune system, a deep characterization of them which includes T cells, B cells and tertiary lymphoid structures, Mye-loid cells, Neutrophils, NK cells and dendritic cells will be desirable. The present review article considers the role of the adaptive and innate immune response surrounding PTC in the context of Hashimoto’s thyroiditis. This review will focus on the current knowledge by in vivo and in vitro studies specifically performed on animals’ models; thyroid cancer cells and human samples including (i) the dual role of tumor-infiltrating lymphocytes; (ii) the emerging role of B cells and tertiary lymphoid structures; (iii) the role of myeloid cells, dendritic cells, and natural killer cells; (iv) the current knowledge of the molecular biomarkers implicated in the complex link between thyroiditis and PTC and the potential implication of cancer immunotherapy in PTC patients in the context of thyroiditis.

Receptor–ligand pair typing and prognostic risk model for papillary thyroid carcinoma based on single-cell sequencing

The papillary thyroid carcinoma (PTC) microenvironment consists of various cancer and surrounding cells, and the communication between them is mainly performed through ligand–receptor (LR) interactions. Single-cell RNA sequencing (scRNA-seq) has been performed to investigate the role of intercellular communication networks in tumor progression. In addition, scRNA-seq can accurately identify the characteristics of immune cell subsets, which is of great significance for predicting the efficacy of immunotherapy. In this study, the cell–cell communication network was analyzed through LR pairs, and a new PTC molecular phenotype was developed based on LR pairs. Furthermore, a risk model was established to predict patient response to PD-1 blockade immunotherapy. The scRNA-seq dataset was obtained from GSE184362, and the bulk tumor RNA-seq dataset was obtained from The Cancer Genome Atlas. CellPhoneDB was used for cellular communication analysis. LR pair correlations were calculated and used to identify molecular subtypes, and the least absolute shrinkage and selection operator (Lasso) Cox regression was used to develop a risk model based on LR pairs. The IMvigor210 and GSE78220 cohorts were used as external validations for the LR.score to predict responses to PD-L1 blockade therapy. A total of 149 LR pairs with significant expression and prognostic correlation were included, and three PTC molecular subtypes were obtained from those with significant prognostic differences. Then, five LR pairs were selected to construct the risk scoring model, a reliable and independent prognostic factor in the training set, test set, and whole dataset. Furthermore, two external validation sets confirmed the predictive efficacy of the LR.score for response to PD-1 blockade therapy.

Single-Cell Transcriptome Analysis Reveals Inter-Tumor Heterogeneity in Bilateral Papillary Thyroid Carcinoma

BACKGROUND

The tumor microenvironment (TME) plays a pivotal role in cancer progression in papillary thyroid carcinoma (PTC), yet the composition and the phenotype of cells within the TME in bilateral PTC are poorly understood.

METHODS

We performed unbiased transcriptome-wide single-cell RNA sequencing (scRNA-seq) analysis on 29,561 cells from 3 pairs of bilateral PTC and 1 non-tumor thyroid sample. The results of the analysis were validated by a large-scale bulk transcriptomic dataset deposited in The Cancer Genome Atlas (TCGA) database.

RESULTS

Our integrative analysis of thyroid follicular cells revealed 42 signaling pathways enriched in malignant follicular cells, including cytokine-cytokine receptor interaction, PI3K/Akt signaling pathway, mitogen-activated protein kinase (MAPK) signaling pathway, and tumor necrosis factor (TNF) signaling pathway. A 6-gene signature (CXCL3, CXCL1, IL1A, CCL5, TNFRSF12A, and IL18) in the cytokine-cytokine receptor interaction pathway was constructed to predict the prognosis of patients with PTC, with high risk scores being associated with decreased overall survival [hazard ratio (HR) = 3.863, 95% CI = 2.233-6.682, p < 0.001]. Gene set variation analysis (GSVA) indicated that the pathways enriched in bilateral PTC were significantly different, indicating great heterogeneity in bilateral PTC, even with the same BRAF V600E mutation. Comprehensive analysis of T cells revealed that the proportion of CD8+ tissue-resident memory T cells expressing IFNG decreased in tumor samples with advanced N stage. Within the myeloid compartment, the ratio of suppressive M2-like to pro-inflammatory M1-like macrophages increased with advanced disease stage, which was confirmed in the bulk dataset using transcriptomic profiles. In addition, we also identified numerous biologically critical interactions among myeloid cells, T cells, and follicular cells, which were related to T-cell recruitment, M2-like macrophage polarization, malignant follicular cell progression, and T-cell inhibitory signaling.

CONCLUSION

Our integrative analyses revealed great inter-tumor heterogeneity within the TME in bilateral PTC, which will offer assistance for precise diagnosis and treatment.

Thyroid cancer under the scope of emerging technologies

The vast majority of thyroid cancers originate from follicular cells. We outline outstanding issues at each step along the path of cancer patient care, from prevention to post-treatment follow-up and highlight how emerging technologies will help address them in the coming years. Three directions will dominate the coming technological landscape. Genomics will reveal tumoral evolutionary history and shed light on how these cancers arise from the normal epithelium and the genomics alteration driving their progression. Transcriptomics will gain cellular and spatial resolution providing a full account of intra-tumor heterogeneity and opening a window on the microenvironment supporting thyroid tumor growth. Artificial intelligence will set morphological analysis on an objective quantitative ground laying the foundations of a systematic thyroid tumor classification system. It will also integrate into unified representations the molecular and morphological perspectives on thyroid cancer.

Papillary Thyroid Carcinoma Landscape and Its Immunological Link With Hashimoto Thyroiditis at Single-Cell Resolution

The tumor microenvironment heterogeneity of papillary thyroid cancer (PTC) is poorly characterized. The relationship between PTC and Hashimoto thyroiditis (HT) is also in doubt. Here, we used single-cell RNA sequencing to map the transcriptome landscape of PTC from eight PTC patients, of which three were concurrent with HT. Predicted copy number variation in epithelial cells and mesenchymal cells revealed the distinct molecular signatures of carcinoma cells. Carcinoma cells demonstrated intertumoral heterogeneity based on BRAF V600E mutation or lymph node metastasis, and some altered genes were identified to be correlated with disease-free survival in The Cancer Genome Atlas datasets. In addition, transcription factor regulons of follicular epithelial cells unveil the different transcription activation state in PTC patients with or without concurrent HT. The immune cells in tumors exhibited distinct transcriptional states, and the presence of tumor-infiltrating B lymphocytes was predominantly linked to concurrent HT origin. Trajectory analysis of B cells and plasma cells suggested their migration potential from HT adjacent tissues to tumor tissues. Furthermore, we revealed diverse ligand–receptor pairs between non-immune cells, infiltrating myeloid cells, and lymphocytes. Our results provided a single-cell landscape of human PTC. These data would deepen the understanding of PTC, as well as the immunological link between PTC and HT.

Single-cell transcriptomic analysis of the tumor ecosystems underlying initiation and progression of papillary thyroid carcinoma

The tumor ecosystem of papillary thyroid carcinoma (PTC) is poorly characterized. Using single-cell RNA sequencing, we profile transcriptomes of 158,577 cells from 11 patients’ paratumors, localized/advanced tumors, initially-treated/recurrent lymph nodes and radioactive iodine (RAI)-refractory distant metastases, covering comprehensive clinical courses of PTC. Our data identifies a “cancer-primed” premalignant thyrocyte population with normal morphology but altered transcriptomes. Along the developmental trajectory, we also discover three phenotypes of malignant thyrocytes (follicular-like, partial-epithelial-mesenchymal-transition-like, dedifferentiation-like), whose composition shapes bulk molecular subtypes, tumor characteristics and RAI responses. Furthermore, we uncover a distinct BRAF-like-B subtype with predominant dedifferentiation-like thyrocytes, enriched cancer-associated fibroblasts, worse prognosis and promising prospect of immunotherapy. Moreover, potential vascular-immune crosstalk in PTC provides theoretical basis for combined anti-angiogenic and immunotherapy. Together, our findings provide insight into the PTC ecosystem that suggests potential prognostic and therapeutic implications.

The characterisation of the papillary thyroid carcinoma (PTC) tumour microenvironment remains crucial. Here, the authors perform single-cell RNA sequencing in 11 patients and identify potential opportunities for the use of immunotherapy and its combination with anti-angiogenic therapy in PTC.

PPARγ Targets-Derived Diagnostic and Prognostic Index for Papillary Thyroid Cancer

Simple Summary

Through targeted next-generation sequencing of thyroid cancer-related genes in monozygotic twins with papillary thyroid cancer (PTC), we identified common variants of the gene encoding peroxisome proliferator activated receptor gamma (PPARG). Notably, the expression levels of PPARγ target genes were frequently deregulated in PTC compared to benign tissues and were closely associated with disease-specific survival (DSS) outcomes in a TCGA-PTC cohort. Machine learning-powered personalized scoring index comprising 10 PPARγ targets, termed as PPARGi, achieved a near-perfect accuracy in distinguishing cancers from benign tissues, and further identified a small subpopulation of patients at high-risk across different profiling platforms.

Abstract

In most cases, papillary thyroid cancer (PTC) is highly curable and associated with an excellent prognosis. Yet, there are several clinicopathological features that lead to a poor prognosis, underscoring the need for a better genomic strategy to refine prognostication and patient management. We hypothesized that PPARγ targets could be potential markers for better diagnosis and prognosis due to the variants found in PPARG in three pairs of monozygotic twins with PTC. Here, we developed a 10-gene personalized prognostic index, designated PPARGi, based on gene expression of 10 PPARγ targets. Through scRNA-seq data analysis of PTC tissues derived from patients, we found that PPARGi genes were predominantly expressed in macrophages and epithelial cells. Machine learning algorithms showed a near-perfect performance of PPARGi in deciding the presence of the disease and in selecting a small subset of patients with poor disease-specific survival in TCGA-THCA and newly developed merged microarray data (MMD) consisting exclusively of thyroid cancers and normal tissues.