Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer

Comparison of Histologic Parameters and Predictive Gene Signatures in Clear Cell Renal Cell Carcinoma Response to Systemic Therapy

Growing experience has correlated the histomorphological characteristics of clear cell renal cell carcinoma (ccRCC), ranging from cytoplasmic features to architectural patterns and tumor immune microenvironment, with clinical outcomes. However, further assessment is needed to determine which of these histologic parameters best correlate with outcomes of interest, especially response to tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). Herein, we evaluated four histologic parameters: (i) World Health Organization (WHO)/International Society of Urological Pathology (ISUP) grade; (ii) clear and eosinophilic cytological phenotypes; (iii) immunophenotypes; and (iv) vascularity-based architectural classification, using hematoxylin and eosin-stained whole slide images for The Cancer Genome Atlas (TCGA) ccRCC cohort (n = 433). We then correlated these parameters with gene expression signatures associated with TKI and ICI response. Multivariate analysis found that the cytological phenotype and vascularity-based architectural classification were independently associated with an angiogenesis-related gene signature (both p < 0.05). Conversely, WHO/ISUP grade and immunophenotype were independently associated with effector T-cell and immune checkpoint gene signatures (both p < 0.05). In conclusion, histologic parameters, including cytological features, architectural patterns, and tumor immune microenvironment, are associated with gene signatures related to therapy response, with different parameters informative for TKIs versus ICIs. These findings may help guide prospective validation studies.

Multiomics in Renal Cell Carcinoma: Current Landscape and Future Directions for Precision Medicine

PURPOSE OF REVIEW

Renal cell carcinoma (RCC) is a prevalent and increasingly diagnosed malignancy associated with high mortality and recurrence rates. Traditional diagnostic and therapeutic approaches have limitations due to the disease's molecular heterogeneity. This review aims to explore how the integration of omics sciences-genomics, transcriptomics, proteomics, and metabolomics-can enhance the diagnosis, prognosis, and treatment of RCC.

RECENT FINDINGS

Genomic analyses have uncovered critical mutations, including VHL, PBRM1, and BAP1, which support improved risk stratification and the development of targeted therapies. Transcriptomic and spatial transcriptomic studies have provided deeper insights into RCC heterogeneity and tumor microenvironment dynamics. Proteomic investigations have revealed potential biomarkers, while metabolomic approaches have highlighted RCC-specific metabolic shifts. Despite these advancements, several challenges persist, including intratumoral heterogeneity, difficulties in multi-omics data integration, and the limited clinical validation of biomarkers. Omics-driven approaches hold significant promise for advancing precision medicine in RCC. These technologies can facilitate earlier diagnosis, guide individualized therapies, and enhance prognostic evaluations. Future research must focus on validating multi-omic biomarkers and leveraging artificial intelligence to manage complex datasets, thereby supporting more informed clinical decision-making and personalized treatment strategies.

Integrative Analysis of Immune- and Metabolism-Related Genes Identifies Robust Prognostic Signature and PYCR1 as a Carcinogenic Regulator in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is distinguished by metabolic irregularities and unique immunological profiles. Nevertheless, the comprehensive examination of immune and metabolic attributes within the tumor microenvironment of ccRCC remains inadequately elucidated. In this study, we identified two distinct molecular subtypes (C1 and C2) of ccRCC using the non-negative matrix factorization (NMF) algorithm. Utilizing univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we developed a prognostic signature comprising eight immune- and metabolism-related genes (IMRGs) associated with the tumor microenvironment. The validation of this signature was performed using both testing and entire datasets. A nomogram was developed using IMRGs prognostic signature and various clinical parameters, including age and TNM stage. We also performed the in vitro experiments to validate the carcinogenic role of PYCR1 in ccRCC cells. Subtype C1 exhibited a more favorable prognosis and higher levels of immune cell infiltration compared to subtype C2. The AUCs of the nomogram at 1-, 3-, and 5-year intervals (AUC = 0.874, 0.820, and 0.794) were slightly higher than those of the IMRGs signature alone (AUC = 0.773, 0.755, and 0.764). The association between risk score and immune checkpoint expressions, immunophenoscore (IPS), and microsatellite instability (MSI) collectively predicted treatment efficacy accurately. Additionally, in vitro experiments confirmed the involvement of PYCR1 in promoting the aggressive behaviors of ccRCC cells, as evidenced by reduced proliferation, invasion, and enhanced apoptosis upon PYCR1 knockdown. In conclusion, the IMRGs signature shows promise in predicting prognostic risk, assessing the effectiveness of immunotherapy, and tailoring treatment for ccRCC patients.

Phenotypic heterogeneity and plasticity in colorectal cancer metastasis

Phenotypic heterogeneity and plasticity in colorectal cancer (CRC) has a crucial role in tumor progression, metastasis, and therapy resistance. However, the regulatory factors and the extrinsic signals driving phenotypic heterogeneity remain unknown. Using a combination of single-cell multiomics and spatial transcriptomics data from primary and metastatic CRC patients, we reveal cancer cell states with regenerative and inflammatory phenotypes that closely resemble metastasis-initiating cells in mouse models. We identify an intermediate population with a hybrid regenerative and stem phenotype. We reveal the transcription factors AP-1 and nuclear factor κB (NF-κB) as their key regulators and show localization of these states in an immunosuppressive niche both at the invasive edge in primary CRC and in liver metastasis. We uncover ligand-receptor interactions predicted to activate the regenerative and inflammatory phenotype in cancer cells. Together, our findings reveal regulatory and signaling factors that mediate distinct cancer cell states and can serve as potential targets to impair metastasis.

Circulating immune cells exhibit distinct traits linked to metastatic burden in breast cancer

BACKGROUND

Circulating immune cells play a crucial role in the anti-tumour immune response, yet the systemic immune system in metastatic breast cancers is not fully characterised. Investigating the cellular and molecular changes in peripheral blood mononuclear cells (PBMCs) from breast cancer patients could elucidate the role of circulating immune cells in metastasis and aid in identifying biomarkers for disease burden and progression.

METHODS

In this study, we characterised the systemic immune landscape associated with varying levels of metastatic burden by analysing the single-cell transcriptomes of PBMCs from breast cancer patients and healthy controls. Our research focused on identifying changes in immune cell composition, transcriptional programs, and immune-cell communication networks linked to metastatic burden. Additionally, we compared these PBMC features onto a single-cell atlas of primary breast tumours to study corresponding traits in tumour-infiltrating immune cells.

RESULTS

In metastatic breast cancer, PBMCs exhibit a significant downregulation of the adaptive immune system and a decreased number and activity of unconventional T cells, such as γδ T cells. Additionally, metastatic burden is associated with impaired cell communication pathways involved in immunomodulatory functions. We also identified a gene signature derived from myeloid cells shared between tumour immune infiltrates and circulating immune cells in breast cancer patients.

CONCLUSIONS

Our study provides a comprehensive single-cell molecular profile of the peripheral immune system in breast cancer, offering a valuable resource for understanding metastatic disease in terms of tumour burden. By identifying immune traits linked to metastasis, we have unveiled potential new biomarkers of metastatic disease.

Single-Cell RNA Sequencing Analysis Reveals the Role of Macrophage-Mediated CD44-AKT-CCL2 Pathways in Renal Tubule Injury during Calcium Oxalate Crystal Formation

Oxalate-induced crystalline kidney injury is a common form of crystal nephropathy. The accumulation of calcium oxalate (CaOx) crystal could lead to renal epithelium injury and inflammation. The underlying cellular events in kidney after CaOx crystal formation are largely unknown. This study was aimed to gain a better understanding of mouse kidney function in the development of renal CaOx formation. The study utilized a mouse CaOx model to analyze the cellular response at 5 time points using single-cell RNA sequencing and investigate the interaction of different cells during renal CaOx crystal formation. Additionally, the study investigated the communication between these cells and macrophages, as well as the role of chemokines in recruiting infiltrating macrophages. RNA velocity analysis uncovered an alternative differentiation pathway for injured and S1 proximal tubule cells, which mainly communicate with macrophages through the SPP1-CD44 pair, along with the expression of proinflammatory factors and stone matrix genes during renal CaOx crystal formation. Furthermore, resident Fn1 macrophages were found to express chemokines, such as CCL2, which recruited infiltrating macrophages. The CCL2 secretion was mediated by the CD44-AKT pathway. Blocking CCL2 decreased the expression of injury markers in the kidney, including CLU, LCN2, and KIM-1, and inhibited CaOx crystal deposition. The study identified potential cell types and target genes involved in renal tubule injury in oxalate-related crystal nephropathy. The findings shed light on the cellular processes that contribute to the formation and damage caused by CaOx crystals within the kidney, which could lead to the development of potential cell types and target genes for treating this condition.

Integration analysis of PLAUR as a sunitinib resistance and macrophage related biomarker in ccRCC, an in silicon and experimental study

Sunitinib remains the preferred systemic treatment option for specific patients with advanced RCC who are ineligible for immune therapy. However, it's essential to recognize that Sunitinib fails to elicit a favourable response in all patients. Moreover, most patients eventually develop resistance to Sunitinib. Therefore, identifying new targets associated with Sunitinib resistance is crucial. Utilizing multiple datasets from public cohorts, we conducted an exhaustive analysis and identified a total of 8 microRNAs and 112 mRNAs displaying significant expression differences between Sunitinib responsive and resistant groups. A particular set of six genes, specifically NIPSNAP1, STK40, SDC4, NEU1, TBC1D9, and PLAUR, were identified as highly significant via WGCNA. To delve deeper into the resistance mechanisms, we performed additional investigations using cell, molecular, and flow cytometry tests. These studies confirmed PLAUR's pivotal role in fostering Sunitinib resistance, both in vitro and in vivo. Our findings suggest that PLAUR could be a promising therapeutic target across various cancer types. In conclusion, this investigation not only uncovers vital genes and microRNAs associated with Sunitinib resistance in RCC but also introduces PLAUR as a prospective therapeutic target for diverse cancers. The outcomes contribute to advancing personalized healthcare and developing superior therapeutic strategies.

Somatic mutations acquired during life: state of the art and implications for the kidney

As a consequence of continuous interaction with mutagens, the genome sequence accumulates changes, which are referred to as somatic mutations. Somatic variants acquired by normal cells during a lifetime are difficult to detect with common sequencing methods. This review provides a basic description of currently available technologies for somatic mutation detection and summarizes the studies that have explored somatic mutation in the kidneys. Given the role of somatic mutations in the formation of kidney cysts, genomic analyses can be used to investigate mechanisms that influence disease progression in inherited cystic kidney disorders. Moreover, genomic analyses are an important method to explore the evolution from a normal cell to cancer, providing insights into mechanisms of tumor initiation. Somatic mutation data can be used to discover endogenous and exogenous mutagens that harness the kidneys, including tobacco and aristolochic acid. In addition, genomic analyses have highlighted a link between kidney damage and mutation. This information is going to be key for understanding lifestyle factors that influence kidney cancer risk, overall impacting clinical decisions and public health strategies.

IL-1β+ macrophages and the control of pathogenic inflammation in cancer

While highlighting the complexity and heterogeneity of tumor immune microenvironments, the application of single-cell analyses in human cancers has identified recurrent subsets of tumor-associated macrophages (TAMs). Among these, interleukin (IL)-1β+ TAMs - cells with high levels of expression of inflammatory response and tissue repair genes, but with limited capacity to stimulate cytotoxic immunity - are emerging as key drivers of pathogenic inflammation in cancer. In this review we discuss recent literature defining the phenotypical, molecular, and functional properties of IL-1β+ TAMs, as well as their temporal dynamics and spatial organization. Elucidating the biology of these cells across tumor initiation, progression, metastasis, and therapy could inform the design and interpretation of clinical trials targeting IL-1β and/or other inflammatory factors in cancer immunotherapy.

The Hypoxia-Associated High-Risk Cell Subpopulation Distinctly Enhances the Progression of Glioma

Less-aggressive lower-grade gliomas (LGGs) frequently transform into glioblastoma (GBM). Most previous studies of gliomas have not focused on LGG-original high-risk subpopulations, which may be one of the most critical hallmarks of glioma progression. In this study, LGG samples are collected to perform single-cell sequencing (scRNA-seq) and identify a unique cell subpopulation marked by CDC20, KIF20A and PTTG1, correlating with poor survival in multiple cohorts. Importantly, the CDC20+KIF20A+PTTG1+ cell subpopulation is strongly associated with transforming LGG to GBM according to scRNA-seq and multiplexed immunofluorescence staining assays. In vitro, ex vivo and in vivo investigations further hint that this cell subpopulation is critical to the proliferation and growth of gliomas, and is associated with the hypoxia core activation. Pharmaceutically and therapeutically, the inhibition of this cell subpopulation showed significant anti-tumor effects and effective enhancement of the Temozolomide treatment efficiency. These findings provide insights into the therapeutic strategies of glioma progression, highlighting promising ways to avoid early-stage gliomas developing into advanced gliomas.

Deciphering of intra-tumoural heterogeneity and the interplay between metastasis-associated meta-program and myofibroblasts in gastric cancer

BACKGROUND

Gastric cancer (GC) exhibits high heterogeneity that relies on the oncogenic properties of cancer cells and multicellular interactions in the tumour microenvironment. However, the heterogeneity of GC and their molecular characteristics are still largely unexplored.

METHODS

We employed single-cell and spatial transcriptomics to comprehensively map the intra-tumoural heterogeneity within GC. Additionally, in vitro experiments, clinical sample analyses, and patient-derived organoid models (PDOs) were conducted to validate the key interaction patterns between tumor cells and stromal cells.

RESULTS

Seven robust meta-programs (MP1-MP7) in GC were defined with distinct biological significance and spatial distributions. MP3 and MP4 were intimately associated with distinct CD8 T cells skewed toward a cytotoxic or exhaustion state, while MP7, characterised by the highest degree of malignancy, harboured an immune lockdown microenvironment around it and spatially associated with myofibroblasts (myCAFs). Notably, we clarified the interplay between the MP7 and myCAFs, where MP7 induces the chemotactic migration of fibroblasts and promoting their transformation into myCAFs via GDF15/TGFBR2, and in turn, myCAFs-derived RSPO3 up-regulates EGR1 to promote the transformation to MP7 in GC cells and human PDOs. Ultimately, the accumulation of myCAFs around MP7 led to fewer infiltration of CD8 T cells, resulting an immune-deprived microenvironment and the diminished efficacy of immunotherapy. Additionally, based on the gene expression signatures of MP7 GC cells, we predicted specific drugs and verified more potent inhibitory effects of Taselisib and Lapatinib for MP7 GC cells than conventional drugs at the same concentration.

CONCLUSION

Taken together, these results deepened the understanding of GC heterogeneity and paved the way for novel therapeutic strategies by targeting MP7 GC cells and their interaction loop with myCAFs in GC treatment.

KEY POINTS

Seven robust meta-programs (MP1-MP7) were identified in gastric cancer. MP7 was strongly correlated with cancer metastasis and poor survival of gastric cancer patients. MP7 promoted fibroblast transformation into myCAFs via GDF15/TGFBR2, creating an immune lockdown microenvironment. MyCAFs induced MP7 transformation via the RSPO3/EGR1 pathway, promoting gastric cancer cell migration. Taselisib and Lapatinib were potent inhibitors of MP7 GC cells.

Angiogenic and immune predictors of neoadjuvant axitinib response in renal cell carcinoma with venous tumour thrombus

Venous tumour thrombus (VTT), where the primary tumour invades the renal vein and inferior vena cava, affects 10-15% of renal cell carcinoma (RCC) patients. Curative surgery for VTT is high-risk, but neoadjuvant therapy may improve outcomes. The NAXIVA trial demonstrated a 35% VTT response rate after 8 weeks of neoadjuvant axitinib, a VEGFR-directed therapy. However, understanding non-response is critical for better treatment. Here we show that response to axitinib in this setting is characterised by a distinct and predictable set of features. We conduct a multiparametric investigation of samples collected during NAXIVA using digital pathology, flow cytometry, plasma cytokine profiling and RNA sequencing. Responders have higher baseline microvessel density and increased induction of VEGF-A and PlGF during treatment. A multi-modal machine learning model integrating features predict response with an AUC of 0.868, improving to 0.945 when using features from week 3. Key predictive features include plasma CCL17 and IL-12. These findings may guide future treatment strategies for VTT, improving the clinical management of this challenging scenario.

The role of adherent-to-suspension transition factors in clear cell renal cell carcinoma progression: a comprehensive analysis

Recent studies have identified a biological process called adherent-to-suspension transition (AST) as a key factor in promoting metastasis. The involvement of AST in the progression of clear cell renal cell carcinoma (ccRCC) remains largely unexplored. We comprehensively investigated the overall landscape of all 20 AST factors at the pan-cancer level. The risk stratification method and the prognostic model based on AST factors were established with consensus clustering analysis and LASSO regression algorithm. The potential molecular mechanism of AST was further investigated by multi-omics analysis and cell experiments. We found that AST factors play distinct roles in different cancer types. Consequently, we utilized AST factors to develop a risk stratification method and a prognostic model, which can effectively guide the treatment of ccRCC patients. We proposed that the promotion of AST is facilitated by SPIB through the SAA1-AKT pathway, enhancing the likelihood of ccRCC metastasis, and conducted rigorous analyses using multi-omics data and cellular experiments. Our study presents a novel risk stratification method and prognostic model for ccRCC. And we identified the SPIB-SAA1-AKT pathway as one of the potential mechanisms by which AST factors promote ccRCC metastasis.

PDT-regulated immune gene prognostic model reveals tumor microenvironment in colorectal cancer liver metastases

Liver metastasis is the most common site of metastasis in colorectal cancer, and the prognosis of colorectal cancer patients with liver metastasis is extremely poor. Revealing the key genes of CLM and implementing targeted interventions is of great significance for colorectal cancer patients. By using the weighted gene co-expression network analysis (WGCNA) algorithm, key gene modules related to metastasis in colorectal cancer were identified. Subsequently, immune-regulating and prognostic-influencing key gene sets were identified from these modules to construct a prognostic model related to colorectal cancer metastasis. Genetic background differences underlying this model were analyzed using colorectal cancer methylation and mutation data, followed by Gene Ontology (GO) analysis and Gene Set Enrichment Analysis (GSEA) analysis of the relevant biological processes associated with the model. The value of predicting tumor drug response through the model was assessed using drug half maximal inhibitory concentration (IC50) data from colorectal cancer cell lines. Subsequently, utilizing single-cell sequencing data about liver metastasis, the colorectal cancer immune microenvironment reflected in the predictive model was analyzed, and a key gene set of the model was identified. Lastly, experimental validation was conducted to investigate the regulatory effects of photodynamic therapy (PDT) on the key genes of the model, and the cytotoxic effect of PDT on colorectal cancer was confirmed. An immune-related gene prognostic model regulating CLM was constructed, consisting of HSPA1A, ULBP2, RBP7, OXT, SLC11A1, INHBB, and ICOS. This model can predict the clinical response of colorectal cancer patients to Oxaliplatin, Cisplatin, Irinotecan, and 5-Fluorouracil. Single-cell sequencing results demonstrate that the model is associated with an immunosuppressive microenvironment in CLM. The higher the model's riskscore, the weaker the MHC-I, MHC-II, and various tumor immune signaling pathway networks in the colorectal cancer microenvironment. Causal analysis reveals that SLC11A1, ICOS, and HSPA1A play key roles in this model. PDT can kill colorectal cancer cells, inhibit colorectal cancer cell metastasis, significantly influence the expression of genes such as SLC11A1, ICOS, and HSPA1A in these processes, and suppress the infiltration of macrophages in the colorectal microenvironment, inhibiting the immune escape process of PD-1/PD-L1. A prognostic model based on immunity regulated by PDT has been established for assessing the prognosis of CLM patients, as well as clinical responses to chemotherapy drugs and immunotherapy.

Intratumoral mycobiome heterogeneity influences the tumor microenvironment and immunotherapy outcomes in renal cell carcinoma

The intratumoral mycobiome plays a crucial role in the tumor microenvironment, but its impact on renal cell carcinoma (RCC) remains unclear. We collected and quantitatively profiled the intratumoral mycobiome data from 1044 patients with RCC across four international cohorts, of which 466 patients received immunotherapy. Patients were stratified into mycobiota ecology-depauperate and mycobiota ecology-flourishing (MEF) groups based on fungal abundance. The MEF group had worse prognosis, higher fungal diversity, down-regulated lipid catabolism, and exhausted CD8+ T cells. We developed the intratumoral mycobiota signature and intratumoral mycobiota-related genes expression signature, which robustly predicted prognosis and immunotherapy outcomes in RCC and other cancers. Aspergillus tanneri was identified as a potential key fungal species influencing RCC prognosis. Our findings suggest that the intratumoral mycobiome suppresses lipid catabolism and induces T cell exhaustion in RCC.

Novel insights into kidney disease: the scRNA-seq and spatial transcriptomics approaches: a literature review

Over the past decade, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) have revolutionized biomedical research, particularly in understanding cellular heterogeneity in kidney diseases. This review summarizes the application and development of scRNA-seq combined with ST in the context of kidney disease. By dissecting cellular heterogeneity at an unprecedented resolution, these advanced techniques have identified novel cell subpopulations and their dynamic interactions within the renal microenvironment. The integration of scRNA-seq with ST has been instrumental in elucidating the cellular and molecular mechanisms underlying kidney development, homeostasis, and disease progression. This approach has not only identified key cellular players in renal pathophysiology but also revealed the spatial organization of cells within the kidney, which is crucial for understanding their functional specialization. This paper highlights the transformative impact of these techniques on renal research that have paved the way for targeted therapeutic interventions and personalized medicine in the management of kidney disease.

Functional analysis of the effects of propofol on tamoxifen‑resistant breast cancer cells: Insights into transcriptional regulation

Although 70% of patients with estrogen receptor-positive breast cancer benefit from tamoxifen (TAM) therapy, the development of resistance to TAM leads to high rates of metastasis and a poor prognosis. Propofol, a commonly used anesthetic, can inhibit the occurrence and progression of breast cancer. In the present study, the effects of propofol on TAM-resistant (TR) breast cancer cells were evaluated. MCF7-TR cells were treated with or without propofol. Subsequently, cell cycle progression and the induction of apoptosis were detected by flow cytometry, whereas cell proliferation was assessed using Cell Counting Kit-8 and colony formation assays. Furthermore, the potential transcriptional regulatory effects of propofol on MCF7-TR cells were investigated using RNA sequencing. The results indicated that propofol significantly promoted cell cycle arrest, induced apoptosis, and inhibited proliferation and colony formation in MCF7-TR cells. Furthermore, transcriptome sequencing analysis revealed 1,065 differentially expressed genes between propofol-treated MCF7-TR and untreated MCF7-TR cells. Gene Ontology annotation enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis and Gene Set Enrichment Analysis indicated that propofol affected the expression levels of genes located on the 'plasma membrane' and 'cell periphery', while mainly regulating signals involved in cancer biology, immune response and metabolic pathways. These results identified the potential effects of propofol on TR breast cancer cells and provided a theoretical basis for clinical treatment, particularly for individuals with TAM resistance.

scPANDA: PAN-Blood Data Annotator with a 10-Million Single-Cell Atlas

OBJECTIVES

Recent advancements in single-cell RNA sequencing (scRNA-seq) have revolutionized the study of cellular heterogeneity, particularly within the hematological system. However, accurately annotating cell types remains challenging due to the complexity of immune cells. To address this challenge, we develop a PAN-blood single-cell Data Annotator (scPANDA), which leverages a comprehensive 10-million-cell atlas to provide precise cell type annotation.

METHODS

The atlas, constructed from data collected in 16 studies, incorporated rigorous quality control, preprocessing, and integration steps to ensure a high-quality reference for annotation. scPANDA utilizes a three-layer inference approach, progressively refining cell types from broad compartments to specific clusters. Iterative clustering and harmonization processes were employed to maintain cell type purity throughout the analysis. Furthermore, the performance of scPANDA was evaluated in three external datasets.

RESULTS

The atlas was structured hierarchically, consisting of 16 compartments, 54 classes, 4,‍460 low-level clusters (pd_cc_cl_tfs), and 611 high-level clusters (pmid_cts). Robust performance of the tool was demonstrated in annotating diverse immune scRNA-seq datasets, analyzing immune-tumor coexisting clusters in renal cell carcinoma, and identifying conserved cell clusters across species.

CONCLUSIONS

scPANDA exemplifies effective reference mapping with a large-scale atlas, enhancing the accuracy and reliability of blood cell type identification.

Deciphering the preeclampsia-specific immune microenvironment and the role of pro-inflammatory macrophages at the maternal-fetal interface

Preeclampsia (PE), a major cause of maternal and perinatal mortality with highly heterogeneous causes and symptoms, is usually complicated by gestational diabetes mellitus (GDM). However, a comprehensive understanding of the immune microenvironment in the placenta of PE and the differences between PE and GDM is still lacking. In this study, cytometry by time of flight indicated that the frequencies of memory-like Th17 cells (CD45RA-CCR7+IL-17A+CD4+), memory-like CD8+ T cells (CD38+CXCR3-CCR7+Helios-CD127-CD8+) and pro-inflam Macs (CD206-CD163-CD38midCD107alowCD86midHLA-DRmidCD14+) were increased, while the frequencies of anti-inflam Macs (CD206+CD163-CD86midCD33+HLA-DR+CD14+) and granulocyte myeloid-derived suppressor cells (gMDSCs, CD11b+CD15hiHLA-DRlow) were decreased in the placenta of PE compared with that of normal pregnancy (NP), but not in that of GDM or GDM&PE. The pro-inflam Macs were positively correlated with memory-like Th17 cells and memory-like CD8+ T cells but negatively correlated with gMDSCs. Single-cell RNA sequencing revealed that transferring the F4/80+CD206- pro-inflam Macs with a Folr2+Ccl7+Ccl8+C1qa+C1qb+C1qc+ phenotype from the uterus of PE mice to normal pregnant mice induced the production of memory-like IL-17a+Rora+Il1r1+TNF+Cxcr6+S100a4+CD44+ Th17 cells via IGF1-IGF1R, which contributed to the development and recurrence of PE. Pro-inflam Macs also induced the production of memory-like CD8+ T cells but inhibited the production of Ly6g+S100a8+S100a9+Retnlg+Wfdc21+ gMDSCs at the maternal-fetal interface, leading to PE-like symptoms in mice. In conclusion, this study revealed the PE-specific immune cell network, which was regulated by pro-inflam Macs, providing new ideas about the pathogenesis of PE.

scMalignantFinder distinguishes malignant cells in single-cell and spatial transcriptomics by leveraging cancer signatures

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing tumor heterogeneity, yet accurately identifying malignant cells remains challenging. Here, we propose scMalignantFinder, a machine learning tool specifically designed to distinguish malignant cells from their normal counterparts using a data- and knowledge-driven strategy. To develop the tool, multiple cancer datasets were collected, and the initially annotated malignant cells were calibrated using nine carefully curated pan-cancer gene signatures, resulting in over 400,000 single-cell transcriptomes for training. The union of differentially expressed genes across datasets was taken as the features for model construction to comprehensively capture tumor transcriptional diversity. scMalignantFinder outperformed existing automated methods across two gold-standard and eleven patient-derived scRNA-seq datasets. The capability to predict malignancy probability empowers scMalignantFinder to capture dynamic characteristics during tumor progression. Furthermore, scMalignantFinder holds the potential to annotate malignant regions in tumor spatial transcriptomics. Overall, we provide an efficient tool for detecting heterogeneous malignant cell populations.

Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

The tumor microenvironment (TME) plays a crucial role in orchestrating tumor cell behavior and cancer progression. Recent advances in spatial profiling technologies have uncovered novel spatial signatures, including univariate distribution patterns, bivariate spatial relationships, and higher-order structures. These signatures have the potential to revolutionize tumor mechanism and treatment. In this review, we summarize the current state of spatial signature research, highlighting computational methods to uncover spatially relevant biological significance. We discuss the impact of these advances on fundamental cancer biology and translational research, address current challenges and future research directions.

Deciphering ovarian cancer heterogeneity through spatial transcriptomics, single-cell profiling, and copy number variations

High-grade serous ovarian carcinoma (HGSOC) poses a formidable clinical challenge due to multidrug resistance (MDR) caused by tumor heterogeneity. To elucidate the intricate mechanisms underlying HGSOC heterogeneity, we conducted a comprehensive analysis of five single-cell transcriptomes and eight spatial transcriptomes derived from eight HGSOC patients. This study provides a comprehensive view of tumor heterogeneity across the spectrum of gene expression, copy number variation (CNV), and single-cell profiles. Our CNV analysis revealed intratumor heterogeneity by identifying distinct tumor clones, illuminating their evolutionary trajectories and spatial relationships. We further explored the homogeneity and heterogeneity of CNV across tumors to pinpoint the origin of heterogeneity. At the cellular level, single-cell RNA sequencing (scRNA seq) analysis identified three meta-programs that delineate the functional profile of tumor cells. The communication networks between tumor cell clusters exhibited unique patterns associated with the meta-programs governing these clusters. Notably, the ligand-receptor pair MDK - NCL emerged as a highly enriched interaction in tumor cell communication. To probe the functional significance of this interaction, we induced NCL overexpression in the SOVK3 cell line and observed enhanced tumor cell proliferation. These findings indicate that the MDK - NCL interaction plays a crucial role in promoting HGSOC tumor growth and may represent a promising therapeutic target. In conclusion, this study comprehensively unravels the multifaceted nature of HGSOC heterogeneity, providing potential therapeutic strategies for this challenging malignancy.

A novel stemness-related lncRNA signature predicts prognosis, immune infiltration and drug sensitivity of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is a prevalent urogenital malignancy characterized by heterogeneous patterns. Stemness is a pivotal factor in tumor progression, recurrence, and metastasis. Nevertheless, the impact of stemness-related long non-coding RNAs (SRlncRNAs) on the prognosis of ccRCC remains elusive. In this study, we aimed to delve into the SRlncRNAs of ccRCC and develop a signature for risk stratification and prognosis prediction.

METHOD

Gene-expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We calculated RNA stemness scores (RNAss) for the samples to evaluate their stemness. SRlncRNAs and stemness-related mRNAs (SRmRNAs) in ccRCC were identified through weighted correlation network analysis (WGCNA), which employed sophisticated statistical methodologies to identify interconnected modules of related genes. Enrichment analysis was performed to explore the potential functions of SRmRNAs. Multiple machine learning algorithms were employed to construct a prognostic signature. Samples from TCGA-KIRC and GSE29609 cohorts were designated as the training and validation cohorts, respectively. Based on their risk scores, samples were stratified into low- and high-risk groups. Prognosis analysis, immune infiltration assessment, drug sensitivity prediction, mutation landscape, and gene set enrichment analysis (GSEA) were conducted to investigate the distinct characteristics of the low- and high-risk groups. Additionally, a web-based calculator was developed to facilitate clinical application. Expression and effects of SRlncRNAs in ccRCC were further corroborated through the utilization of single-cell RNA-seq (scRNA-seq), as well as in vitro and in vivo experiments.

RESULTS

SRlncRNAs and SRmRNAs were identified based on RNAss and WGCNA. The least absolute shrinkage and selection operator (LASSO) in combination with multivariate Cox regression was selected as the optimal approach. Six SRlncRNAs were used to construct the prognostic signature. Samples in the low- and high-risk groups exhibited distinct characteristics in terms of prognosis, GSEA pathways, immune infiltration profiles, drug sensitivity, and mutation status. A nomogram and a web-based calculator were developed to facilitate the clinical application of the model. ScRNA-seq and RT-qPCR demonstrated the differential expression of SRlncRNAs between ccRCC tumors and normal tissues. In vitro and in vivo experiments demonstrated that downregulation of EMX2OS and LINC00944 affected the proliferation, migration, invasion, apoptosis, and metastasis of ccRCC cells.

CONCLUSION

We uncovered the crucial associations between SRlncRNAs and the prognosis of ccRCC. By leveraging these findings, we developed a novel SRlncRNA-related signature and a user-friendly web calculator. This signature holds great potential in facilitating risk stratification and guiding tailored treatment strategies for ccRCC patients. Both in vitro and in vivo experiments confirmed the role of SRlncRNAs in the progression of ccRCC.

The spatial landscape of cancer hallmarks reveals patterns of tumor ecological dynamics and drug sensitivity

Tumors are complex ecosystems of interacting cell types. The concept of cancer hallmarks distills this complexity into underlying principles that govern tumor growth. Here, we explore the spatial distribution of cancer hallmarks across 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. We show that hallmark activity is spatially organized, with the cancer compartment contributing to the activity of seven out of 13 hallmarks, while the tumor microenvironment (TME) contributes to the activity of the rest. Additionally, we discover that genomic distance between tumor subclones correlates with differences in hallmark activity, even leading to clone-hallmark specialization. Finally, we demonstrate interdependent relationships between hallmarks at the junctions of TME and cancer compartments and how they relate to sensitivity to different neoadjuvant treatments in 33 bladder cancer patients from the DUTRENEO trial. In conclusion, our findings may improve our understanding of tumor ecology and help identify new drug biomarkers.

STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics

One of the major challenges in spatial transcriptomics is to detect spatially variable genes (SVGs), whose expression patterns are non-random across tissue locations. Many SVGs correlate with cell type compositions, introducing the concept of cell type-specific SVGs (ctSVGs). Existing ctSVG detection methods treat cell type-specific spatial effects as fixed effects, leading to tissue spatial rotation-dependent results. Moreover, SVGs may exhibit random spatial patterns within cell types, meaning an SVG is not always a ctSVG, and vice versa, further complicating detection. We propose STANCE, a unified statistical model for both SVGs and ctSVGs detection under a linear mixed-effect model framework that integrates gene expression, spatial location, and cell type composition information. STANCE ensures tissue rotation-invariant results, with a two-stage approach: initial SVG/ctSVG detection followed by ctSVG-specific testing. We demonstrate its performance through extensive simulations and analyses of public datasets. Downstream analyses reveal STANCE's potential in spatial transcriptomics analysis.

Copper Drives Remodeling of Metabolic State and Progression of Clear Cell Renal Cell Carcinoma

SIGNIFICANCE

The work establishes a requirement for glucose-dependent coordination between energy production and redox homeostasis, which is fundamental for the survival of cancer cells that accumulate Cu and contributes to tumor growth.

Instance-level semantic segmentation of nuclei based on multimodal structure encoding

BACKGROUND

Accurate segmentation and classification of cell nuclei are crucial for histopathological image analysis. However, existing deep neural network-based methods often struggle to capture complex morphological features and global spatial distributions of cell nuclei due to their reliance on local receptive fields.

METHODS

This study proposes a graph neural structure encoding framework based on a vision-language model. The framework incorporates: (1) A multi-scale feature fusion and knowledge distillation module utilizing the Contrastive Language-Image Pre-training (CLIP) model's image encoder; (2) A method to transform morphological features of cells into textual descriptions for semantic representation; and (3) A graph neural network approach to learn spatial relationships and contextual information between cell nuclei.

RESULTS

Experimental results demonstrate that the proposed method significantly improves the accuracy of cell nucleus segmentation and classification compared to existing approaches. The framework effectively captures complex nuclear structures and global distribution features, leading to enhanced performance in histopathological image analysis.

CONCLUSIONS

By deeply mining the morphological features of cell nuclei and their spatial topological relationships, our graph neural structure encoding framework achieves high-precision nuclear segmentation and classification. This approach shows significant potential for enhancing histopathological image analysis, potentially leading to more accurate diagnoses and improved understanding of cellular structures in pathological tissues.

Single-Cell and Spatial Transcriptomics Delineate the Microstructure and Immune Landscape of Intrahepatic Cholangiocarcinoma in the Leading-Edge Area

Intrahepatic cholangiocarcinoma (ICC) tumor cells and their interactions with the immune microenvironment, particularly at the leading-edge area, have been underexplored. This study employs single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) analysis on samples from the tumor core, adjacent non-tumorous tissue, and the leading-edge area of nine ICC patients. These findings indicate that tumor cells at the leading-edge area demonstrate enhanced proliferation and are tightly associated with the stroma, including endothelial cells and POSTN+ FAP+ fibroblasts. Notably, CD8+ T cells in this region exhibit a naive phenotype with low cytotoxicity and signs of exhaustion, likely due to compromised antigen presentation by antigen-presenting cells (APCs). The predominant CD8+ T cell subset, mucosal-associated invariant T (MAIT) cells, recruits SPP1+ macrophages within the stroma. This interaction, along with the presence of POSTN+ cancer-associated fibroblasts (CAFs) and endothelial cells, forms a unique "triad structure" that fosters tumor growth and ICC progression. The research highlights the intricate characteristics and interactions of ICC tumor cells in the leading-edge area, offering insights into potential therapeutic targets for intervention.

Conserved patterns of transcriptional dysregulation, heterogeneity, and cell states in clear cell kidney cancer

Clear cell kidney cancers are characterized both by conserved oncogenic driver events and by marked intratumor genetic and phenotypic heterogeneity, which help drive tumor progression, metastasis, and resistance to therapy. How these are reflected in transcriptional programs within the cancer and stromal cell components remains an important question with the potential to drive novel therapeutic approaches to treating cancer. To better understand these programs, we perform single-cell transcriptomics on 75 multi-regional biopsies from kidney tumors and normal kidney. We identify conserved patterns of transcriptional dysregulation and their upstream regulators within the tumor and associated vasculature. We describe recurrent subclonal transcriptional consequences of Chr14q loss linked to metastatic potential. We identify prognostically significant conserved patterns of intratumor transcriptional heterogeneity. These reflect co-existing cell states found in both cancer cells and normal kidney cells, indicating that rather than arising from genetic heterogeneity they are a consequence of lineage plasticity.

Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics

An essential task in spatial transcriptomics is identifying spatially variable genes (SVGs). Here, we present Celina, a statistical method for systematically detecting cell type-specific SVGs (ct-SVGs)-a subset of SVGs exhibiting distinct spatial expression patterns within specific cell types. Celina utilizes a spatially varying coefficient model to accurately capture each gene's spatial expression pattern in relation to the distribution of cell types across tissue locations, ensuring effective type I error control and high power. Celina proves powerful compared to existing methods in single-cell resolution spatial transcriptomics and stands as the only effective solution for spot-resolution spatial transcriptomics. Applied to five real datasets, Celina uncovers ct-SVGs associated with tumor progression and patient survival in lung cancer, identifies metagenes with unique spatial patterns linked to cell proliferation and immune response in kidney cancer, and detects genes preferentially expressed near amyloid-β plaques in an Alzheimer's model.

Lessons learned from spatial transcriptomic analyses in clear-cell renal cell carcinoma

Spatial transcriptomics has emerged as a powerful tool for discerning the heterogeneity of the tumour microenvironment across various cancers, including renal cell carcinoma (RCC). Spatial transcriptomics-based studies conducted in clear-cell RCC (the only RCC subtype studied using this technique to date) have given insights into spatial interactions within this disease. These insights include the role of epithelial-to-mesenchymal transitioning, revealing proximity-dependent interactions between tumour cells, fibroblasts, interleukin-2-expressing macrophages and hyalinized regions. Investigations into metabolic programmes have shown high transcriptional heterogeneity within tumours, with a tendency of increased metabolic activity towards the tumour centre. T cell infiltration has been shown to be independent of neoantigen burden, although T cell activity correlates with both metabolic states and various transcripts expressed by tumour cells, fibroblasts and monocytes. The role of tertiary lymphoid structures in both plasma cell maturation and their infiltration of the tumour has been shown through tracks of fibroblasts. Collectively, these findings indicate the potential of spatial transcriptomics to reveal predictive spatial features, supporting its promise in the development of biomarkers for clear-cell RCC management.

Single-cell transcriptomics reveals the heterogeneity and function of mast cells in human ccRCC

Introduction

The role of mast cells (MCs) in clear cell renal carcinoma (ccRCC) is unclear, and comprehensive single-cell studies of ccRCC MCs have not yet been performed.

Methods

To investigate the heterogeneity and effects of MCs in ccRCC, we studied single-cell transcriptomes from four ccRCC patients, integrating both single-cell sequencing and bulk tissue sequencing data from online sequencing databases, followed by validation via spatial transcriptomics and multiplex immunohistochemistry (mIHC).

Results

We identified four MC signature genes (TPSB2, TPSAB1, CPA3, and HPGDS). MC density was significantly greater in ccRCC tissues than in normal tissues, but MC activation characteristics were not significantly different between ccRCC and normal tissues. Activated and resting MCs were defined as having high and low expression of MC receptors and mediators, respectively, whereas proliferating MCs had high expression of proliferation-related genes. The overall percentage of activated MCs in ccRCC tissues did not change significantly but shifted toward a more activated subpopulation (VEGFA+ MCs), with a concomitant decrease in proliferative MCs (TNF+ MCs) and resting MCs. An analysis of the ratio of TNF+/VEGFA+ MCs in tumors revealed that MCs exerted antitumor effects on ccRCC. However, VEGFA+MC was produced in large quantities in ccRCC tissues and promoted tumor angiogenesis compared with adjacent normal tissues, which aroused our concern. In addition, MC signature genes were associated with a better prognosis in the KIRC patient cohort in the TCGA database, which is consistent with our findings. Furthermore, the highest level of IL1B expression was observed in macrophages in ccRCC samples, and spatial transcriptome analysis revealed the colocalization of VEGFA+ MCs with IL1B+ macrophages at the tumor-normal interface.

Discussion

In conclusion, this study revealed increased MC density in ccRCC. Although the proportion of activated MCs was not significantly altered in ccRCC tissues compared with normal tissues, this finding highlights a shift in the MC phenotype from CTSGhighMCs to more activated VEGFA+MCs, providing a potential therapeutic target for inhibiting ccRCC progression.

Spatial and single-cell transcriptomics reveal cellular heterogeneity and a novel cancer-promoting Treg cell subset in human clear-cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common histologic type of RCC. However, the spatial and functional heterogeneity of immunosuppressive cells and the mechanisms by which their interactions promote immunosuppression in the ccRCC have not been thoroughly investigated.

METHODS

To further investigate the cellular and regional heterogeneity of ccRCC, we analyzed single-cell and spatial transcriptome RNA sequencing data from four patients, which were obtained from samples from multiple regions, including the tumor core, tumor-normal interface, and distal normal tissue. On the basis, the findings were investigated in vitro using tissue and blood samples from 15 patients with ccRCC and validated in the broader samples on tissue microarrays.

RESULTS

In this study, we revealed previously unreported subsets of both stromal and immune cells, as well as mapped their spatial location at finer resolution. In addition, we validated the clusters of tumor cells after removing batch effects according to six characterized gene sets, including epithelial-mesenchymal transitionhigh clusters, metastatic clusters and proximal tubulehigh clusters. Importantly, we identified a special regulatory T (Treg) cell subpopulation that has the molecular characteristics of terminal effector Treg cells but expresses multiple cytokines, such as interleukin (IL)-1β and IL-18. This group of Treg cells has stronger immunosuppressive function and was associated with a worse prognosis in ccRCC cohorts. They were colocalized with MRC1 + FOLR2 + tumor-associated macrophages (TAMs) at the tumor-normal interface to form a positive feedback loop, maintaining a synergistic procarcinogenic effect. In addition, we traced the origin of IL-1β+ Treg cells and revealed that IL-18 can induce the expression of IL-1β in Treg cells via the ERK/NF-κB pathway.

CONCLUSIONS

We demonstrated a novel cancer-promoting Treg cell subset and its interactions with MRC1 + FOLR2 +TAMs, which provides new insight into Treg cell heterogeneity and potential therapeutic targets for ccRCC.

The Dual Role of Cellular Senescence in Macrophages: Unveiling the Hidden Driver of Age-Related Inflammation in Kidney Disease

Aging is a complex biological process that involves the gradual decline of cellular, tissue, and organ functions. In kidney, aging manifests as tubular atrophy, glomerulosclerosis, and progressive renal function decline. The critical role of senescence-associated macrophage in diseases, particularly kidney diseases, is increasingly recognized. During this process, macrophages exhibit a range of pro-damage response to senescent tissues and cells, while the aging of macrophages themselves also significantly influences disease progression, creating a bidirectional regulatory role between aging and macrophages. To explore this bidirectional mechanism, this review will elucidate the origin, characteristic, phenotype, and function of macrophages in response to the senescence-associated secretory phenotype (SASP), extracellular vesicles from senescent cells, and the senescence cell-engulfment suppression (SCES), particularly in the context of kidney disease. Additionally, it will discuss the characteristics of senescent macrophage, such as common markers, and changes in autophagy, metabolism, gene regulation, phagocytosis, antigen presentation, and exosome secretion, along with their physiological and pathological impacts on renal tissue cells. Furthermore, exploring therapies and drugs that modulate the function of senescent macrophages or eliminate senescent cells may help slow the progression of kidney aging and damage.

Clinical Implications of Basic Research: Exploring the Transformative Potential of Spatial 'Omics in Uro-oncology

New spatial molecular technologies are poised to transform our understanding and treatment of urological cancers. By mapping the spatial molecular architecture of tumours, these platforms uncover the complex heterogeneity within and around individual malignancies, offering novel insights into disease development, progression, diagnosis, and treatment. They enable tracking of clonal phylogenetics in situ and immune-cell interactions in the tumour microenvironment. A whole transcriptome/genome/proteome-level spatial analysis is hypothesis generating, particularly in the areas of risk stratification and precision medicine. Current challenges include reagent costs, harmonisation of protocols, and computational demands. Nonetheless, the evolving landscape of the technology and evolving machine learning applications have the potential to overcome these barriers, pushing towards a future of personalised cancer therapy, leveraging detailed spatial cellular and molecular data. PATIENT SUMMARY: Tumours are complex and contain many different components. Although we have been able to observe some of these differences visually under the microscope, until recently, we have not been able to observe the genetic changes that underpin cancer development. Scientists are now able to explore molecular/genetic differences using approaches such as "spatial transcriptomics" and "spatial proteomics", which allow them to see genetic and cellular variation across a region of normal and cancerous tissue without destroying the tissue architecture. Currently, these technologies are limited by high associated costs, and a need for powerful and complex computational analysis workflows. Future advancements and results through these new technologies may assist patients and their doctors as they make decisions about treating their cancer.

Increased spatial coupling of integrin and collagen IV in the immunoresistant clear-cell renal-cell carcinoma tumor microenvironment

BACKGROUND

Immunotherapy has improved survival for patients with advanced clear cell renal cell carcinoma (ccRCC), but resistance to therapy develops in most patients. We use cellular-resolution spatial transcriptomics in patients with immunotherapy naïve and exposed primary ccRCC tumors to better understand immunotherapy resistance.

RESULTS

Spatial molecular imaging of tumor and adjacent stroma samples from 21 tumors suggests that viable tumors following immunotherapy harbor more stromal CD8 + T cells and neutrophils than immunotherapy naïve tumors. YES1 is significantly upregulated in immunotherapy exposed tumor cells. Spatial GSEA shows that the epithelial-mesenchymal transition pathway is spatially enriched and the associated ligand-receptor transcript pair COL4A1-ITGAV has significantly higher autocorrelation in the stroma after exposure to immunotherapy. More integrin αV + cells are observed in immunotherapy exposed stroma on multiplex immunofluorescence validation. Compared to other cancers in TCGA, ccRCC tumors have the highest expression of both COL4A1 and ITGAV. Assessing bulk RNA expression and proteomic correlates in CPTAC databases reveals that collagen IV protein is more abundant in advanced stages of disease.

CONCLUSIONS

Spatial transcriptomics of samples of 3 patient cohorts with cRCC tumors indicates that COL4A1 and ITGAV are more autocorrelated in immunotherapy-exposed stroma compared to immunotherapy-naïve tumors, with high expression among fibroblasts, tumor cells, and endothelium. Further research is needed to understand changes in the ccRCC tumor immune microenvironment and explore potential therapeutic role of integrin after immunotherapy treatment.

Investigating intra-tumoural heterogeneity and microenvironment diversity in primary cardiac angiosarcoma through single-cell RNA sequencing

BACKGROUND

Primary cardiac angiosarcoma (PCAS) is a rare and aggressive heart tumour with limited treatment options and a poor prognosis. Understanding cellular heterogeneity and tumour microenvironment (TME) is crucial for the development of effective therapies. Here, we investigated the intratumoural heterogeneity and TME diversity of PCAS using single-cell RNA sequencing (scRNA-seq).

METHODS

We performed scRNA-seq analysis on tumour samples from four patients with PCAS, supplemented with multicolour immunohistochemistry for identification. We used scRNA-seq data from five normal cardiac tissue samples downloaded from public databases for comparative analyses. Bioinformatic analyses, including Cell Ranger, Seurat, Monocle2, hdWGCNA, SCENIC and NicheNet, were utilized to identify distinct cell populations, transcriptional patterns, and co-regulating gene modules.

RESULTS

Our analysis revealed significant intratumoural heterogeneity in PCAS driven by diverse biological processes such as protein synthesis, degradation, and RIG-I signalling inhibition. The SCENIC analysis identified three primary transcription factors' clusters (CEBPB, MYC and TAL1). T-cell subset analysis showed exhausted antigen-specific T-cells, complicating the efficacy of immune checkpoint blockade. Furthermore, we observed suppressive macrophages (SPP1+ and OLR1+) and reduced mitochondrial gene MT-RNR2 (MTRNR2L12) expression in TME-infiltrating cells, indicating impaired mitochondrial function.

CONCLUSION

This study elucidates the complex cellular landscape and immune microenvironment of PCAS, highlighting potential molecular targets for the development of novel therapies. These findings underscore the importance of a multifaceted therapeutic approach for addressing the challenges posed by PCAS's heterogeneity and immune evasion.

KEY POINTS

Insights into the heterogeneity and transcriptional patterns of sarcoma cells may explain the challenges in treating primary cardiac angiosarcoma (PCAS) using the current therapeutic modalities. Characterization of the immune microenvironment revealed significant immunosuppression mediated by specific myeloid cell populations (SPP1+ and OLR1+ macrophages). Identification of mitochondrial dysfunction in immune cells within the PCAS microenvironment, particularly the notable downregulation of the MTRNR2L12 protein, suggests a new avenue for therapeutic targeting.

The implications for urological malignancies of non-coding RNAs in the the tumor microenvironment

Urological malignancies are a major global health issue because of their complexity and the wide range of ways they affect patients. There's a growing need for in-depth research into these cancers, especially at the molecular level. Recent studies have highlighted the importance of non-coding RNAs (ncRNAs) – these don't code for proteins but are crucial in controlling genes – and the tumor microenvironment (TME), which is no longer seen as just a background factor but as an active player in cancer progression. Understanding how ncRNAs and the TME interact is key for finding new ways to diagnose and predict outcomes in urological cancers, and for developing new treatments. This article reviews the basic features of ncRNAs and goes into detail about their various roles in the TME, focusing specifically on how different ncRNAs function and act in urological malignancies.

Understanding the biological processes of kidney carcinogenesis: an integrative multi-omics approach

Biological mechanisms related to cancer development can leave distinct molecular fingerprints in tumours. By leveraging multi-omics and epidemiological information, we can unveil relationships between carcinogenesis processes that would otherwise remain hidden. Our integrative analysis of DNA methylome, transcriptome, and somatic mutation profiles of kidney tumours linked ageing, epithelial-mesenchymal transition (EMT), and xenobiotic metabolism to kidney carcinogenesis. Ageing process was represented by associations with cellular mitotic clocks such as epiTOC2, SBS1, telomere length, and PBRM1 and SETD2 mutations, which ticked faster as tumours progressed. We identified a relationship between BAP1 driver mutations and the epigenetic upregulation of EMT genes (IL20RB and WT1), correlating with increased tumour immune infiltration, advanced stage, and poorer patient survival. We also observed an interaction between epigenetic silencing of the xenobiotic metabolism gene GSTP1 and tobacco use, suggesting a link to genotoxic effects and impaired xenobiotic metabolism. Our pan-cancer analysis showed these relationships in other tumour types. Our study enhances the understanding of kidney carcinogenesis and its relation to risk factors and progression, with implications for other tumour types.

The advance of single cell transcriptome to study kidney immune cells in diabetic kidney disease

Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and a primary cause of end-stage renal disease (ESRD). Increasing studies suggest that immune cells are involved in regulating renal inflammation, which contributes to the progression of DKD. Compared with conventional methods, single-cell sequencing technology is more developed technique that has advantages in resolving cellular heterogeneity, parallel multi-omics studies, and discovering new cell types. ScRNA-seq helps researchers to analyze specifically gene expressions, signaling pathways, intercellular communication as well as their regulations in various immune cells of kidney biopsy and urine samples. It is still challenging to investigate the function of each cell type in the pathophysiology of kidney due to its complex and heterogeneous structure and function. Here, we discuss the application of single-cell transcriptomics in the field of DKD and highlight several recent studies that explore the important role of immune cells including macrophage, T cells, B cells etc. in DKD through scRNA-seq analyses. Through combing the researches of scRNA-seq on immune cells in DKD, this review provides novel perspectives on the pathogenesis and immune therapeutic strategy for DKD.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

Novel mechanisms of intestinal flora regulation in high-altitude hypoxia

Background

This study investigates the molecular mechanisms behind firmicutes-mediated macrophage (Mψ) polarization and glycolytic metabolic reprogramming through HIF-1α in response to intrinsic mucosal barrier injury induced by high-altitude hypoxia.

Methods

Establishing a hypoxia mouse model of high altitude, we utilized single-cell transcriptome sequencing to identify key cell types involved in regulating intestinal mucosal barrier damage caused by high-altitude hypoxia. Through proteomic analysis of colonic tissue Mψ and metabolomic analysis of Mψ metabolites, we determined crucial proteins and metabolic pathways influencing intestinal mucosal barrier damage induced by high-altitude hypoxia. Mechanistic validation was conducted using RAW264.7 Mψ in vitro by assessing cell viability with CCK-8 assay following treatment with different metabolites. The hypoxia mouse model was further validated in vivo by transplanting gut microbiota of Firmicutes. Histological examinations through H&E staining assessed colonic cell morphology and structure, while the FITC-dextran assay evaluated intestinal tissue permeability. Hypoxia probe signal intensity in mouse colonic tissue was assessed via metronidazole staining. Various experimental techniques, including flow cytometry, immunofluorescence, ELISA, Western blot, and RT-qPCR, were employed to study the impact of HIF-1α/glycolysis pathway and different gut microbiota metabolites on Mψ polarization.

Results

Bioinformatics analysis revealed that single-cell transcriptomics identified Mψ as a key cell type, with their polarization pattern playing a crucial role in the intestinal mucosal barrier damage induced by high-altitude hypoxia. Proteomics combined with metabolomics analysis indicated that HIF-1α and the glycolytic pathway are pivotal proteins and signaling pathways in the intestinal mucosal barrier damage caused by high-altitude hypoxia. In vitro cell experiments demonstrated that activation of the glycolytic pathway by HIF-1α led to a significant upregulation of mRNA levels of IL-1β, IL-6, and TNFα while downregulating mRNA levels of IL-10 and TGFβ, thereby promoting M1 Mψ activation and inhibiting M2 Mψ polarization. Further mechanistic validation experiments revealed that the metabolite butyric acid from Firmicutes bacteria significantly downregulated the protein expression of HIF-1α, GCK, PFK, PKM, and LDH, thus inhibiting the HIF-1α/glycolytic pathway that suppresses M1 Mψ and activates M2 Mψ, consequently alleviating the hypoxic symptoms in RAW264.7 cells. Subsequent animal experiments confirmed that Firmicutes bacteria inhibited the HIF-1α/glycolytic pathway to modulate Mψ polarization, thereby mitigating intestinal mucosal barrier damage in high-altitude hypoxic mice.

Conclusion

The study reveals that firmicutes, through the inhibition of the HIF-1α/glycolysis pathway, mitigate Mψ polarization, thereby alleviating intrinsic mucosal barrier injury in high-altitude hypoxia.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Applications of single-cell analysis in immunotherapy for lung cancer: Current progress, new challenges and expectations

BACKGROUND

Lung cancer is a prevalent form of cancer worldwide, presenting a substantial risk to human well-being. Lung cancer is classified into two main types: non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). The advancement of tumor immunotherapy, specifically immune checkpoint inhibitors and adaptive T-cell therapy, has encountered substantial obstacles due to the rapid progression of SCLC and the metastasis, recurrence, and drug resistance of NSCLC. These challenges are believed to stem from the tumor heterogeneity of lung cancer within the tumor microenvironment.

AIM OF REVIEW

This review aims to comprehensively explore recent strides in single-cell analysis, a robust sequencing technology, concerning its application in the realm of tumor immunotherapy for lung cancer. It has been effectively integrated with transcriptomics, epigenomics, genomics, and proteomics for various applications. Specifically, these techniques have proven valuable in mapping the transcriptional activity of tumor-infiltrating lymphocytes in patients with NSCLC, identifying circulating tumor cells, and elucidating the heterogeneity of the tumor microenvironment.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review emphasizes the paramount significance of single-cell analysis in mapping the immune cells within NSCLC patients, unveiling circulating tumor cells, and elucidating the tumor microenvironment heterogeneity. Notably, these advancements highlight the potential of single-cell analysis to revolutionize lung cancer immunotherapy by characterizing immune cell fates, improving therapeutic strategies, and identifying promising targets or prognostic biomarkers. It is potential to unravel the complexities within the tumor microenvironment and enhance treatment strategies marks a significant step towards more effective therapies and improved patient outcomes.

Single-cell and spatial omics unravel the spatiotemporal biology of tumour border invasion and haematogenous metastasis

Solid tumours exhibit a well-defined architecture, comprising a differentiated core and a dynamic border that interfaces with the surrounding tissue. This border, characterised by distinct cellular morphology and molecular composition, serves as a critical determinant of the tumour's invasive behaviour. Notably, the invasive border of the primary tumour represents the principal site for intravasation of metastatic cells. These cells, known as circulating tumour cells (CTCs), function as 'seeds' for distant dissemination and display remarkable heterogeneity. Advancements in spatial sequencing technology are progressively unveiling the spatial biological features of tumours. However, systematic investigations specifically targeting the characteristics of the tumour border remain scarce. In this comprehensive review, we illuminate key biological insights along the tumour body-border-haematogenous metastasis axis over the past five years. We delineate the distinctive landscape of tumour invasion boundaries and delve into the intricate heterogeneity and phenotype of CTCs, which orchestrate haematogenous metastasis. These insights have the potential to explain the basis of tumour invasion and distant metastasis, offering new perspectives for the development of more complex and precise clinical interventions and treatments.

Immune Phenotype-Genotype Associations in Primary Clear Cell Renal Cell Carcinoma and Matched Metastatic Tissue

Adjuvant immunotherapy has been recently recommended for patients with metastatic clear cell renal cell carcinoma (ccRCC), but there are no tissue biomarkers to predict treatment response in ccRCC. Potential predictive biomarkers are mainly assessed in primary tumor tissue, whereas metastases (METs) remain understudied. To explore potential differences between genomic alterations and immune phenotypes in primary tumors and their matched METs, we analyzed primary tumors (PTs) of 47 ccRCC patients and their matched distant METs by comprehensive targeted parallel sequencing, whole-genome copy number variation analysis, determination of microsatellite instability, and tumor mutational burden. We quantified the spatial distribution of tumor-infiltrating CD8+ T cells and coexpression of the T-cell-exhaustion marker thymocyte selection-associated high mobility group box (TOX) by digital immunoprofiling and quantified tertiary lymphoid structures. Most METs were pathologically "cold." Inflamed, pathologically "hot" PTs were associated with decreased disease-free survival, worst for patients with high levels of CD8+TOX+ T cells. Interestingly, inflamed METs showed a relative increase in exhausted CD8+TOX+ T cells and increased accumulative size of tertiary lymphoid structures compared with PTs. Integrative analysis of molecular and immune phenotypes revealed BAP1 and CDKN2A/B deficiency to be associated with an inflamed immune phenotype. Our results highlight the distinct spatial distribution and differentiation of CD8+ T cells at metastatic sites, and the association of an inflamed microenvironment with specific genomic alterations.

Multifocal, multiphenotypic tumours arising from an MTOR mutation acquired in early embryogenesis

Embryogenesis is a vulnerable time. Mutations in developmental cells can result in the wide dissemination of cells predisposed to disease within mature organs. We characterised the evolutionary history of four synchronous renal tumours from a 14-year-old girl using whole genome sequencing alongside single cell and bulk transcriptomic sequencing. Phylogenetic reconstruction timed the origin of all tumours to a multipotent embryonic cell committed to the right kidney, around 4 weeks post-conception. Biochemical and structural analysis of their shared MTOR mutation, absent from normal tissues, demonstrates enhanced protein flexibility, enabling a FAT domain hinge to dramatically increase activity of mTORC1 and mTORC2. Developmental mutations, not usually detected in traditional genetic screening, have vital clinical importance in guiding prognosis, targeted treatment, and family screening decisions for paediatric tumours.

Deciphering breast cancer dynamics: insights from single-cell and spatial profiling in the multi-omics era

As one of the most common tumors in women, the pathogenesis and tumor heterogeneity of breast cancer have long been the focal point of research, with the emergence of tumor metastasis and drug resistance posing persistent clinical challenges. The emergence of single-cell sequencing (SCS) technology has introduced novel approaches for gaining comprehensive insights into the biological behavior of malignant tumors. SCS is a high-throughput technology that has rapidly developed in the past decade, providing high-throughput molecular insights at the individual cell level. Furthermore, the advent of multitemporal point sampling and spatial omics also greatly enhances our understanding of cellular dynamics at both temporal and spatial levels. The paper provides a comprehensive overview of the historical development of SCS, and highlights the most recent advancements in utilizing SCS and spatial omics for breast cancer research. The findings from these studies will serve as valuable references for future advancements in basic research, clinical diagnosis, and treatment of breast cancer.

VHL synthetic lethality screens uncover CBF-β as a negative regulator of STING

Clear cell renal cell carcinoma (ccRCC) represents the most common form of kidney cancer and is typified by biallelic inactivation of the von Hippel-Lindau (VHL) tumour suppressor gene. Here, we undertake genome-wide CRISPR/Cas9 screening to reveal synthetic lethal interactors of VHL, and uncover that loss of Core Binding Factor β (CBF-β) causes cell death in VHL-null ccRCC cell lines and impairs tumour establishment and growth in vivo. This synthetic relationship is independent of the elevated activity of hypoxia inducible factors (HIFs) in VHL-null cells, but does involve the RUNX transcription factors that are known binding partners of CBF-β. Mechanistically, CBF-β loss leads to upregulation of type I interferon signalling, and we uncover a direct inhibitory role for CBF-β at the STING locus controlling Interferon Stimulated Gene expression. Targeting CBF-β in kidney cancer both selectively induces tumour cell lethality and promotes activation of type I interferon signalling.