Single-cell Deconvolution of a Specific Malignant Cell Population as a Poor Prognostic Biomarker in Low-risk Clear Cell Renal Cell Carcinoma Patients

Systematic inference of super-resolution cell spatial profiles from histology images

Inferring cell spatial profiles from histology images is critical for cancer diagnosis and treatment in clinical settings. In this study, we report a weakly-supervised deep-learning method, HistoCell, to directly infer super-resolution cell spatial profiles consisting of cell types, cell states and their spatial network from histology images at the single-nucleus-level. Benchmark analysis demonstrates that HistoCell robustly achieves state-of-the-art performance in terms of cell type/states prediction solely from histology images across multiple cancer tissues. HistoCell can significantly enhance the deconvolution accuracy for the spatial transcriptomics data and enable accurate annotation of subtle cancer tissue architectures. Moreover, HistoCell is applied to de novo discovery of clinically relevant spatial organization indicators, including prognosis and drug response biomarkers, across diverse cancer types. HistoCell also enable image-based screening of cell populations that drives phenotype of interest, and is applied to discover the cell population and corresponding spatial organization indicators associated with gastric malignant transformation risk. Overall, HistoCell emerges as a powerful and versatile tool for cancer studies in histology image-only cohorts.

Single-cell transcriptomic atlas of the chicken cecum reveals cellular responses and state shifts during Eimeria tenella infection

Eimeria tenella (E. tenella) infection is a major cause of coccidiosis in chickens, leading to significant economic losses in the poultry industry due to its impact on the cecum. This study presents a comprehensive single-cell atlas of the chicken cecal epithelium by generating 7,394 cells using 10X Genomics single-cell RNA sequencing (scRNA-seq). We identified 13 distinct cell types, including key immune and epithelial populations, and characterized their gene expression profiles and cell-cell communication networks. Integration of this single-cell data with bulk RNA-seq data from E. tenella-infected chickens revealed significant alterations in cell type composition and state, particularly a marked decrease in APOB+ enterocytes and an increase in cycling T cells during infection. Trajectory analysis of APOB+ enterocytes uncovered shifts toward cellular states associated with cell death and a reduction in those linked to mitochondrial and cytoplasmic protection when infected with E. tenella. These findings highlight the substantial impact of E. tenella on epithelial integrity and immune responses, emphasizing the parasite's role in disrupting nutrient absorption and energy metabolism. Our single-cell atlas serves as a critical resource for understanding the cellular architecture of the chicken cecum and provides a valuable framework for future investigations into cecal diseases and metabolic functions, with potential applications in enhancing poultry health and productivity.

Case report: Tongue metastasis as an initial sign of clear cell renal cell carcinoma and its prognosis

Clear cell renal cell carcinoma (ccRCC) is the most prevalent and lethal subtype of renal cell carcinoma (RCC), characterized by a poor prognosis and a high likelihood of distant metastasis. Nonetheless, metastasis of ccRCC to the tongue remains rare. Diagnosing and planning treatment for patients who initially present with tongue metastasis can be particularly challenging, as few cases have been reported in the literature. We present a case of a 62-year-old man who presented with a painful lump on the right anterior border of his tongue. Histological examination revealed lobulated and nested epithelial cell clusters with moderate dysplasia and frequent mitotic figures within the lamina propria. Immunohistochemistry showed positivity for vimentin, CD10, PAX-8, and epithelial membrane antigen (EMA), but negativity for PAX-2, calponin, S-100 protein, periodic acid-Schiff with diastase (PAS-D), P63, P40, and CK7, confirming the diagnosis of ccRCC metastasis to the tongue. After comprehensive evaluation and multidisciplinary team consultation, the patient underwent cytoreductive nephrectomy (CN), metastasectomy, and targeted therapy. According to the Response Evaluation Criteria in Solid Tumors (RECIST) Version 1.1, the patient maintained stable disease (SD) during systemic treatment. Unfortunately, treatment was discontinued due to adverse drug reactions, and the patient was transitioned to palliative care. His disease progressed to progressive disease (PD), and he ultimately succumbed to systemic infection, with a progression-free survival (PFS) of approximately 15 months. This case highlights the urgent need for improved therapeutic strategies to manage symptoms and prolong survival in patients with this rare metastatic presentation.

Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma

The connection between metabolic reprogramming and tumor progression has been demonstrated in an increasing number of researches. However, further research is required to identify how metabolic reprogramming affects interpatient heterogeneity and prognosis in clear cell renal cell carcinoma (ccRCC). In this work, single-cell RNA sequencing (scRNA-seq) based deconvolution was utilized to create a malignant cell hierarchy with metabolic differences and to investigate the relationship between metabolic biomarkers and prognosis. Simultaneously, we created a machine learning-based approach for creating metabolism-related prognostic signature (MRPS). Gamma-glutamyltransferase 6 (GGT6) was further explored for deep biological insights through in vitro experiments. Compared to 51 published signatures and conventional clinical features, MRPS showed substantially higher accuracy. Meanwhile, high MRPS-risk samples demonstrated an immunosuppressive phenotype with more infiltrations of regulatory T cell (Treg) and tumour-associated macrophage (TAM). Following the administration of immune checkpoint inhibitors (ICIs), MRPS showed consistent and strong performance and was an independent risk factor for overall survival. GGT6, an essential metabolic indicator and component of MRPS, has been proven to support proliferation and invasion in ccRCC. MRPS has the potential to be a highly effective tool in improving the clinical results of patients with ccRCC.

SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multicell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell-type-specific spatial organization. To this end, we developed Spatial Deconvolution Explorer (SpatialDeX), a regression model-based method for estimating cell-type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring single-cell RNA-seq references. Significance: The development of a reference-free method for deconvolving the identity of cells in spatial transcriptomics datasets enables exploration of tumor architecture to gain deeper insights into the dynamics of the tumor microenvironment.

Single-nucleus sequencing unveils heterogeneity in renal cell carcinomas microenvironment: Insights into pathogenic origins and treatment-responsive cellular subgroups

BACKGROUND

Different individuals with renal cell carcinoma (RCC) exhibit substantial heterogeneity in histomorphology, genetic alterations in the proteome, immune cell infiltration patterns, and clinical behavior.

OBJECTIVES

This study aims to use single-nucleus sequencing on ten samples (four normal, three clear cell renal cell carcinoma (ccRCC), and three chromophobe renal cell carcinoma (chRCC)) to uncover pathogenic origins and prognostic characteristics in patients with RCC.

METHODS

By using two algorithms, inferCNV and k-means, the study explores malignant cells and compares them with the normal group to reveal their origins. Furthermore, we explore the pathogenic factors at the gene level through Summary-data-based Mendelian Randomization and co-localization methods. Based on the relevant malignant markers, a total of 212 machine-learning combinations were compared to develop a prognostic signature with high precision and stability. Finally, the study correlates with clinical data to investigate which cell subtypes may impact patients' prognosis.

RESULTS & CONCLUSION

Two main origin tumor cells were identified: Proximal tubule cell B and Intercalated cell type A, which were highly differentiated in epithelial cells, and three gene loci were determined as potential pathogenic genes. The best malignant signature among the 212 prognostic models demonstrated high predictive power in ccRCC: (AUC: 0.920 (1-year), 0.920 (3-year) and 0.930 (5-year) in the training dataset; 0.756 (1-year), 0.828 (3-year), and 0.832 (5-year) in the testing dataset. In addition, we confirmed that LYVE1+ tissue-resident macrophage and TOX+ CD8 significantly impact the prognosis of ccRCC patients, while monocytes play a crucial role in the prognosis of chRCC patients.

Single-cell deconvolution algorithms analysis unveils autocrine IL11-mediated resistance to docetaxel in prostate cancer via activation of the JAK1/STAT4 pathway

BACKGROUND

Docetaxel resistance represents a significant obstacle in the treatment of prostate cancer. The intricate interplay between cytokine signalling pathways and transcriptional control mechanisms in cancer cells contributes to chemotherapeutic resistance, yet the underlying molecular determinants remain only partially understood. This study elucidated a novel resistance mechanism mediated by the autocrine interaction of interleukin-11 (IL-11) and its receptor interleukin-11 receptor subunit alpha(IL-11RA), culminating in activation of the JAK1/STAT4 signalling axis and subsequent transcriptional upregulation of the oncogene c-MYC.

METHODS

Single-cell secretion profiling of prostate cancer organoid was analyzed to determine cytokine production profiles associated with docetaxel resistance.Analysis of the expression pattern of downstream receptor IL-11RA and enrichment of signal pathway to clarify the potential autocrine mechanism of IL-11.Next, chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) was performed to detect the nuclear localization and DNA-binding patterns of phosphorylated STAT4 (pSTAT4). Coimmunoprecipitation and reporter assays were utilized to assess interaction between pSTAT4 and the cotranscription factor CREB-binding protein (CBP) as well as their role in c-MYC transcriptional activity.

RESULTS

Autocrine secretion of IL-11 was markedly increased in docetaxel-resistant prostate cancer cells. IL-11 stimulation resulted in robust activation of JAK1/STAT4 signalling. Upon activation, pSTAT4 translocated to the nucleus and associated with CBP at the c-MYC promoter region, amplifying its transcriptional activity. Inhibition of the IL-11/IL-11RA interaction or disruption of the JAK1/STAT4 pathway significantly reduced pSTAT4 nuclear entry and its binding to CBP, leading to downregulation of c-MYC expression and restoration of docetaxel sensitivity.

CONCLUSION

Our findings identify an autocrine loop of IL-11/IL-11RA that confers docetaxel resistance through the JAK1/STAT4 pathway. The pSTAT4-CBP interaction serves as a critical enhancer of c-MYC transcriptional activity in prostate cancer cells. Targeting this signalling axis presents a potential therapeutic strategy to overcome docetaxel resistance in advanced prostate cancer.

Metabolic heterogeneity in clear cell renal cell carcinoma revealed by single-cell RNA sequencing and spatial transcriptomics

BACKGROUND

Clear cell renal cell carcinoma is a prototypical tumor characterized by metabolic reprogramming, which extends beyond tumor cells to encompass diverse cell types within the tumor microenvironment. Nonetheless, current research on metabolic reprogramming in renal cell carcinoma mostly focuses on either tumor cells alone or conducts analyses of all cells within the tumor microenvironment as a mixture, thereby failing to precisely identify metabolic changes in different cell types within the tumor microenvironment.

METHODS

Gathering 9 major single-cell RNA sequencing databases of clear cell renal cell carcinoma, encompassing 195 samples. Spatial transcriptomics data were selected to conduct metabolic activity analysis with spatial localization. Developing scMet program to convert RNA-seq data into scRNA-seq data for downstream analysis.

RESULTS

Diverse cellular entities within the tumor microenvironment exhibit distinct infiltration preferences across varying histological grades and tissue origins. Higher-grade tumors manifest pronounced immunosuppressive traits. The identification of tumor cells in the RNA splicing state reveals an association between the enrichment of this particular cellular population and an unfavorable prognostic outcome. The energy metabolism of CD8+ T cells is pivotal not only for their cytotoxic effector functions but also as a marker of impending cellular exhaustion. Sphingolipid metabolism evinces a correlation with diverse macrophage-specific traits, particularly M2 polarization. The tumor epicenter is characterized by heightened metabolic activity, prominently marked by elevated tricarboxylic acid cycle and glycolysis while the pericapsular milieu showcases a conspicuous enrichment of attributes associated with vasculogenesis, inflammatory responses, and epithelial-mesenchymal transition. The scMet facilitates the transformation of RNA sequencing datasets sourced from TCGA into scRNA sequencing data, maintaining a substantial degree of correlation.

CONCLUSIONS

The tumor microenvironment of clear cell renal cell carcinoma demonstrates significant metabolic heterogeneity across various cell types and spatial dimensions. scMet exhibits a notable capability to transform RNA sequencing data into scRNA sequencing data with a high degree of correlation.