Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors

VSIG4 induces the immunosuppressive microenvironment by promoting the infiltration of M2 macrophage and Tregs in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and has a poor prognosis. Despite the impressive advancements in treating ccRCC using immune checkpoint (IC) blockade, such as PD-1/PD-L1 inhibitors, a considerable number of ccRCC patients experience adaptive resistance. Therefore, exploring new targetable ICs will provide additional treatment options for ccRCC patients. We comprehensively analyzed multi-omics data and performed functional experiments, such as pathologic review, bulk transcriptome data, single-cell sequencing data, Western blotting, immunohistochemistry and in vitro/in vivo experiments, to explore novel immunotherapeutic targets in ccRCC. It was found that immune-related genes VSIG4, SAA1, CD7, FOXP3, IL21, TNFSF13B, BATF, CD72, MZB1, LTB, CCL25 and KLRK1 were significantly upregulated in ccRCC (Student's t test and p-value < 0.05; 36 normal and 267 ccRCC tissues in raining cohort; 36 normal and 266 ccRCC tissues in validation cohort) and correlated with the poor prognosis of ccRCC patients (Wald test and p-value < 0.05 in univariate cox analysis; log-rank test and p-value < 0.05 in Kaplan-Meier method; 267 patients in training cohort and 266 in validation cohort). In particular, we found the novel IC target VSIG4 was specifically expressed in inhibitory immune cells M2-biased tumor-associated macrophages (TAMs), conventional dendritic cell 2 (cDC2) cells, and cycling myeloid cells in ccRCC microenvironment. Moreover, VSIG4 showed a closely relation with resistance of Ipilimumab/Nivolumab immunotherapy in ccRCC. Furthermore, VSIG4 promoted the infiltration of M2 macrophages, Tregs, and cDC2 in ccRCC tissues. VSIG4+ TAMs and VSIG4+ cDC2s may be a kind of immune cell subtypes related to immunosuppression. VSIG4 may play similar roles with other IC ligands, as it is highly expressed on the surface of antigen-presenting cells and ccRCC cells to inhibit T cells activity and facilitate immune escape. Targeting IC gene VSIG4 may provide a novel immunotherapeutic strategy to ccRCC patients with resistance to existing targeted therapy options.

Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development

The mammalian kidney maintains fluid homeostasis through diverse epithelial cell types generated from nephron and ureteric progenitor cells. To extend a developmental understanding of the kidney's epithelial networks, we compared chromatin organization (single-nuclear assay for transposase-accessible chromatin sequencing [ATAC-seq]; 112,864 nuclei) and gene expression (single-cell/nuclear RNA sequencing [RNA-seq]; 109,477 cells/nuclei) in the developing human (10.6-17.6 weeks; n = 10) and mouse (post-natal day [P]0; n = 10) kidney, supplementing analysis with published mouse datasets from earlier stages. Single-cell/nuclear datasets were analyzed at a species level, and then nephron and ureteric cellular lineages were extracted and integrated into a common, cross-species, multimodal dataset. Comparative computational analyses identified conserved and divergent features of chromatin organization and linked gene activity, identifying species-specific and cell-type-specific regulatory programs. In situ validation of human-enriched gene activity points to human-specific signaling interactions in kidney development. Further, human-specific enhancer regions were linked to kidney diseases through genome-wide association studies (GWASs), highlighting the potential for clinical insight from developmental modeling.

Developmental Dysregulation of Childhood Cancer

Most childhood cancers possess distinct clinicopathological profiles from those seen in adulthood, reflecting their divergent mechanisms of carcinogenesis. Rather than depending on the decades-long, stepwise accumulation of changes within a mature cell that defines adult carcinomas, many pediatric malignancies emerge rapidly as the consequence of random errors during development. These errors-whether they be genetic, epigenetic, or microenvironmental-characteristically block maturation, resulting in phenotypically primitive neoplasms. Only an event that falls within a narrow set of spatiotemporal parameters will forge a malignant clone; if it occurs too soon then the event might be lethal, or negatively selected against, while if it is too late or in an incorrectly primed precursor cell then the necessary intracellular conditions for transformation will not be met. The precise characterization of these changes, through the study of normal tissues and tumors from patients and model systems, will be essential if we are to develop new strategies to diagnose, treat, and perhaps even prevent childhood cancer.

Optimized protocol for the multiomics processing of cryopreserved human kidney tissue

Biobanking of tissue from clinically obtained kidney biopsies for later analysis with multiomic approaches, such as single-cell technologies, proteomics, metabolomics, and the different types of imaging, is an inevitable step to overcome the need of disease model systems and toward translational medicine. Hence, collection protocols that ensure integration into daily clinical routines by the usage of preservation media that do not require liquid nitrogen but instantly preserve kidney tissue for both clinical and scientific analyses are necessary. Thus, we modified a robust single-nucleus dissociation protocol for kidney tissue stored snap-frozen or in the preservation media RNAlater and CellCover. Using at first porcine kidney tissue as a surrogate for human kidney tissue, we conducted single-nucleus RNA sequencing with the widely recognized Chromium 10X Genomics platform. The resulting datasets from each storage condition were analyzed to identify any potential variations in transcriptomic profiles. Furthermore, we assessed the suitability of the preservation media for additional analysis techniques such as proteomics, metabolomics, and the preservation of tissue architecture for histopathological examination including immunofluorescence staining. In this study, we show that in daily clinical routines, the preservation medium RNAlater facilitates the collection of highly preserved human kidney biopsies and enables further analysis with cutting-edge techniques like single-nucleus RNA sequencing, proteomics, and histopathological evaluation. Only metabolome analysis is currently restricted to snap-frozen tissue. This work will contribute to build tissue biobanks with well-defined cohorts of the respective kidney disease that can be deeply molecularly characterized, opening up new horizons for the identification of unique cells, pathways and biomarkers for the prevention, early identification, and targeted therapy of kidney diseases.NEW & NOTEWORTHY In this study, we addressed challenges in integrating clinically obtained kidney biopsies into everyday clinical routines. Using porcine kidneys, we evaluated preservation media (RNAlater and CellCover) versus snap freezing for multi-omics processing. Our analyses highlighted RNAlater's suitability for single-nucleus RNA sequencing, proteome analysis and histopathological evaluation. Only metabolomics are currently restricted to snap-frozen biopsies. Our research established a cryopreservation protocol that facilitates tissue biobanking for advancing precision medicine in nephrology.

Single-nucleus multiomics unravels the genetic mechanisms underlying musk secretion in Chinese forest musk deer (Moschus berezovskii)

Musk secreted by the musk glands in male forest musk deer (FMD; Moschus berezovskii) is highly valued for its pharmaceutical and perfumery applications. However, the regulatory mechanisms underlying musk secretion are not well understood. This study aimed to investigate the genes and transcription factors involved in musk secretion across different periods and ages. We analyzed the musk glands of adult male FMD during the non-secretory and secretory periods, as well as juvenile and adult male FMD during the secretory period, using single-cell multiome ATAC+gene expression technique. Our analysis identified 13 cell types, including acinar cells of Types 1 and 2. Chromatin accessibility analysis and gene expression data confirmed that the genes Map3k2, Hsd17b12, and Jun are critical for musk secretion. Additionally, EHF, NR4A2, and FOXO1 proteins play crucial regulatory roles. Weighted gene co-expression network analysis (WGCNA) highlighted the importance of GnRH signaling pathway in musk secretion. Gene set enrichment analysis (GSEA) showed that the steroid hormone biosynthesis pathway is notably enriched in acinar cells. Furthermore, intercellular communication appears to influence both the initiation and maintenance of musk secretion. These findings provide valuable insights into the molecular pathways of musk secretion in FMD, offering potential avenues for increasing musk production and developing treatment for inflammation and tumors.

Toward a CRISPR-based mouse model of Vhl-deficient clear cell kidney cancer: Initial experience and lessons learned

CRISPR is revolutionizing the ability to do somatic gene editing in mice for the purpose of creating new cancer models. Inactivation of the VHL tumor suppressor gene is the signature initiating event in the most common form of kidney cancer, clear cell renal cell carcinoma (ccRCC). Such tumors are usually driven by the excessive HIF2 activity that arises when the VHL gene product, pVHL, is defective. Given the pressing need for a robust immunocompetent mouse model of human ccRCC, we directly injected adenovirus-associated viruses (AAVs) encoding sgRNAs against VHL and other known/suspected ccRCC tumor suppressor genes into the kidneys of C57BL/6 mice under conditions where Cas9 was under the control of one of two different kidney-specific promoters (Cdh16 or Pax8) to induce kidney tumors. An AAV targeting Vhl, Pbrm1, Keap1, and Tsc1 reproducibly caused macroscopic ccRCCs that partially resembled human ccRCC tumors with respect to transcriptome and cell of origin and responded to a ccRCC standard-of-care agent, axitinib. Unfortunately, these tumors, like those produced by earlier genetically engineered mouse ccRCCs, are HIF2 independent.

Lipid imaging mass spectrometry: Towards a new molecular histology

Lipid research is attracting greater attention, as these molecules are key components to understand cell metabolism and the connection between genotype and phenotype. The study of lipids has also been fueled by the development of new and powerful technologies, able to identify an increasing number of species in a single run and at decreasing concentrations. One of such key developments has been the image techniques that enable the visualization of lipid distribution over a tissue with cell resolution. Thanks to the spatial information reported by such techniques, it is possible to associate a lipidome trait to individual cells, in fixed metabolic stages, which greatly facilitates understanding the metabolic changes associated to diverse pathological conditions, such as cancer. Furthermore, the image of lipids is becoming a kind of new molecular histology that has great chances to make an impact in the diagnostic units of the hospitals. Here, we examine the current state of the technology and analyze what the next steps to bring it into the diagnosis units should be. To illustrate the potential and challenges of this technology, we present a case study on clear cell renal cell carcinoma, a good model for analyzing malignant tumors due to their significant cellular and molecular heterogeneity.

Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations

Elucidating the molecular dependencies behind the cancer-type specificity of driver mutations may reveal new therapeutic opportunities. We hypothesized that developmental programs would impact the transduction of oncogenic signaling activated by a driver mutation and shape its cancer-type specificity. Therefore, we designed a computational analysis framework by combining single-cell gene expression profiles during fetal organ development, latent factor discovery, and information theory-based differential network analysis to systematically identify transcription factors that selectively respond to driver mutations under the influence of organ-specific developmental programs. After applying this approach to VHL mutations, which are highly specific to clear cell renal cell carcinoma (ccRCC), we revealed important regulators downstream of VHL mutations in ccRCC and used their activities to cluster patients with ccRCC into three subtypes. This classification revealed a more significant difference in prognosis than the previous mRNA profile-based method and was validated in an independent cohort. Moreover, we found that EP300, a key epigenetic factor maintaining the regulatory network of the subtype with the worst prognosis, can be targeted by a small inhibitor, suggesting a potential treatment option for a subset of patients with ccRCC. This work demonstrated an intimate relationship between organ development and oncogenesis from the perspective of systems biology, and the method can be generalized to study the influence of other biological processes on cancer driver mutations.

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.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

Single-cell RNA sequencing revealed the roles of macromolecule epidermal growth factor receptor (EGFR) in the hybrid sterility of hermaphroditic Argopecten scallops

The macromolecule epidermal growth factor receptor (EGFR) is a transmembrane glycoprotein that belongs to the protein kinase superfamily, which plays versatile functions in cell proliferation, development and fertility regulation. Almost all F1 hybrids obtained from the hermaphroditic bay scallops and Peruvian scallops exhibit infertility, and the genetic mechanism remains unclear. In this study, the comprehensive scRNA-seq was first conducted in the gonads of hybrid scallops, deducing the developmental sequence of germ cells and identifying the critical regulators in hybrid sterility: epidermal growth factor receptor. During the development from oogenesis phase germ cells to oocytes, the expression of the EGFR gene gradually decreased in sterile hybrids but increased in fertile hybrids. The significantly lower EGFR expression and ATP content, but higher ROS production rate was detected in the gonad of sterile hybrids than that in fertile hybrids, which might cause slow development of oocytes, stagnation of cell cycle, insufficient energy supply, high level of apoptosis and final sterility. Specific knock-down of EGFR gene led to decreased ATP content, increased ROS production rate, and inhibited oocyte maturation and gonadal development. These findings provide new insights into the roles of EGFR in hybrid infertility of bivalves and the healthy development of scallop breeding.

scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks

Single-cell ribonucleic acid sequencing (scRNA-seq) technology can be used to perform high-resolution analysis of the transcriptomes of individual cells. Therefore, its application has gained popularity for accurately analyzing the ever-increasing content of heterogeneous single-cell datasets. Central to interpreting scRNA-seq data is the clustering of cells to decipher transcriptomic diversity and infer cell behavior patterns. However, its complexity necessitates the application of advanced methodologies capable of resolving the inherent heterogeneity and limited gene expression characteristics of single-cell data. Herein, we introduce a novel deep learning-based algorithm for single-cell clustering, designated scDFN, which can significantly enhance the clustering of scRNA-seq data through a fusion network strategy. The scDFN algorithm applies a dual mechanism involving an autoencoder to extract attribute information and an improved graph autoencoder to capture topological nuances, integrated via a cross-network information fusion mechanism complemented by a triple self-supervision strategy. This fusion is optimized through a holistic consideration of four distinct loss functions. A comparative analysis with five leading scRNA-seq clustering methodologies across multiple datasets revealed the superiority of scDFN, as determined by better the Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI) metrics. Additionally, scDFN demonstrated robust multi-cluster dataset performance and exceptional resilience to batch effects. Ablation studies highlighted the key roles of the autoencoder and the improved graph autoencoder components, along with the critical contribution of the four joint loss functions to the overall efficacy of the algorithm. Through these advancements, scDFN set a new benchmark in single-cell clustering and can be used as an effective tool for the nuanced analysis of single-cell transcriptomics.

Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering

Clustering plays a crucial role in analyzing scRNA-seq data and has been widely used in studying cellular distribution over the past few years. However, the high dimensionality and complexity of scRNA-seq data pose significant challenges to achieving accurate clustering from a singular perspective. To address these challenges, we propose a novel approach, called multi-level multi-view network based on structural consistency contrastive learning (scMMN), for scRNA-seq data clustering. Firstly, the proposed method constructs shallow views through the $k$-nearest neighbor ($k$NN) and diffusion mapping (DM) algorithms, and then deep views are generated by utilizing the graph Laplacian filters. These deep multi-view data serve as the input for representation learning. To improve the clustering performance of scRNA-seq data, contrastive learning is introduced to enhance the discrimination ability of our network. Specifically, we construct a group contrastive loss for representation features and a structural consistency contrastive loss for structural relationships. Extensive experiments on eight real scRNA-seq datasets show that the proposed method outperforms other state-of-the-art methods in scRNA-seq data clustering tasks. Our source code has already been available at https://github.com/szq0816/scMMN.

Single-cell RNA sequencing data analysis utilizing multi-type graph neural networks

Single-cell RNA sequencing (scRNA-seq) is the sequencing technology of a single cell whose expression reflects the overall characteristics of the individual cell, facilitating the research of problems at the cellular level. However, the problems of scRNA-seq such as dimensionality reduction processing of massive data, technical noise in data, and visualization of single-cell type clustering cause great difficulties for analyzing and processing scRNA-seq data. In this paper, we propose a new single-cell data analysis model using denoising autoencoder and multi-type graph neural networks (scDMG), which learns cell-cell topology information and latent representation of scRNA-seq data. scDMG introduces the zero-inflated negative binomial (ZINB) model into a denoising autoencoder (DAE) to perform dimensionality reduction and denoising on the raw data. scDMG integrates multiple-type graph neural networks as the encoder to further train the preprocessed data, which better deals with various types of scRNA-seq datasets, resolves dropout events in scRNA-seq data, and enables preliminary classification of scRNA-seq data. By employing TSNE and PCA algorithms for the trained data and invoking Louvain algorithm, scDMG has better dimensionality reduction and clustering optimization. Compared with other mainstream scRNA-seq clustering algorithms, scDMG outperforms other state-of-the-art methods in various clustering performance metrics and shows better scalability, shorter runtime, and great clustering results.

scVSC: Deep Variational Subspace Clustering for Single-Cell Transcriptome Data

Single-cell RNA sequencing (scRNA-seq) is a potent advancement for analyzing gene expression at the individual cell level, allowing for the identification of cellular heterogeneity and subpopulations. However, it suffers from technical limitations that result in sparse and heterogeneous data. Here, we propose scVSC, an unsupervised clustering algorithm built on deep representation neural networks. The method incorporates the variational inference into the subspace model, which imposes regularization constraints on the latent space and further prevents overfitting. In a series of experiments across multiple datasets, scVSC outperforms existing state-of-the-art unsupervised and semi-supervised clustering tools regarding clustering accuracy and running efficiency. Moreover, the study indicates that scVSC could visually reveal the state of trajectory differentiation, accurately identify differentially expressed genes, and further discover biologically critical pathways.

Single-nucleus transcriptomics and chromatin accessibility analysis of musk gland development in Chinese forest musk deer (Moschus berezovskii)

Musk secreted by male forest musk deer (Moschus berezovskii) musk glands is an invaluable component of medicine and perfume. Musk secretion depends on musk gland maturation; however, the mechanism of its development remains elusive. Herein, using single cell multiome ATAC + gene expression coupled with several bioinformatic analyses, a dynamic transcriptional cell atlas of musk gland development was revealed, and key genes and transcription factors affecting its development were determined. Twelve cell types, including two different types of acinar cells (Clusters 0 and 10) were identified. Single-nucleus RNA and single-nucleus ATAC sequencing analyses revealed that seven core target genes associated with musk secretion (Hsd17b2, Acacb, Lss, Vapa, Aldh16a1, Aldh7a1, and Sqle) were regulated by 12 core transcription factors (FOXO1, CUX2, RORA, RUNX1, KLF6, MGA, NFIC, FOXO3, ETV5, NR3C1, HSF4, and MITF) during the development of Cluster 0 acinar cells. Kyoto Encyclopedia of Genes and Genomes enrichment showed significant changes in the pathways associated with musk secretion during acinar cell development. Gene set variation analysis also revealed that certain pathways associated with musk secretion were enriched in 6-year-old acinar cells. A gene co-expression network was constructed during acinar cell development to provide a precise understanding of the connections between transcription factors, genes, and pathways. Finally, intercellular communication analysis showed that intercellular communication is involved in musk gland development. This study provides crucial insights into the changes and key factors underlying musk gland development, which serve as valuable resources for studying musk secretion mechanisms and promoting the protection of this endangered species.

Precise detection of cell-type-specific domains in spatial transcriptomics

Cell-type-specific domains are the anatomical domains in spatially resolved transcriptome (SRT) tissues where particular cell types are enriched coincidentally. It is challenging to use existing computational methods to detect specific domains with low-proportion cell types, which are partly overlapped with or even inside other cell-type-specific domains. Here, we propose De-spot, which synthesizes segmentation and deconvolution as an ensemble to generate cell-type patterns, detect low-proportion cell-type-specific domains, and display these domains intuitively. Experimental evaluation showed that De-spot enabled us to discover the co-localizations between cancer-associated fibroblasts and immune-related cells that indicate potential tumor microenvironment (TME) domains in given slices, which were obscured by previous computational methods. We further elucidated the identified domains and found that Srgn may be a critical TME marker in SRT slices. By deciphering T cell-specific domains in breast cancer tissues, De-spot also revealed that the proportions of exhausted T cells were significantly increased in invasive vs. ductal carcinoma.

Oncofetal TRIM71 drives liver cancer carcinogenesis through remodeling CEBPA-mediated serine/glycine metabolism

Rationale: Tumor cells remodel transcriptome to construct an ecosystem with stemness features, which maintains tumor growth and highly malignant characteristics. However, the core regulatory factors involved in this process still need to be further discovered. Methods: Single cell RNA-sequncing (scRNA-seq) and bulk RNA-sequencing profiles derived from fetal liver, normal liver, liver tumors, and their adjacent samples were collected to analyze the ecosystem of liver cancer. Mouse models were established to identify molecular functions of oncofetal-related oncogenes using hydrodynamic tail vein injection. Results: We found that liver cancer rebuilt oncofetal ecosystem to maintain malignant features. Interestingly, we identified a group of RNA-binding proteins (RBPs) that were highly overexpressed with oncofetal features. Among them, TRIM71 was specifically expressed in liver cancers and was associated with poor outcomes. TRIM71 drove the carcinogenesis of hepatocellular carcinoma (HCC), and knockdown of TRIM71 significantly abolished liver cancer cell proliferation. Mechanistically, TRIM71 formed a protein complex with IGF2BP1, bound to and stabilized the mRNA of CEBPA in an m6A-dependent manner, enhance the serine/glycine metabolic pathway, and ultimately promoted liver cancer progression. Furthermore, we identified that all-trans-retinoic acid (ATRA) combined with e1A binding protein p300 (EP300) inhibitor A-485 repressed TRIM71, attenuated glycine/serine metabolism, and inhibited liver cancer cell proliferation with high TRIM71 levels. Conclusions: We demonstrated the oncofetal status in liver cancer and highlighted the crucial role of TRIM71 and provided potential therapeutic strategies and liver cancer-specific biomarker for liver cancer patients.

Single-nucleus and spatial transcriptome reveal adrenal homeostasis in normal and tumoural adrenal glands

The human adrenal gland is a complex endocrine tissue. Studies on adrenal renewal have been limited to animal models or human foetuses. Enhancing our understanding of adult human adrenal homeostasis is crucial for gaining insights into the pathogenesis of adrenal diseases, such as adrenocortical tumours. Here, we present a comprehensive cellular genomics analysis of the adult human normal adrenal gland, combining single-nuclei RNA sequencing and spatial transcriptome data to reconstruct adrenal gland homeostasis. As expected, we identified primary cells of the various zones of the adrenal cortex and medulla, but we also uncovered additional cell types. They constitute the adrenal microenvironment, including immune cells, mostly composed of a large population of M2 macrophages, and new cell populations, including different subpopulations of vascular-endothelial cells and cortical-neuroendocrine cells. Utilizing spatial transcriptome and pseudotime trajectory analysis, we support evidence of the centripetal dynamics of adrenocortical cell maintenance and the essential role played by Wnt/β-catenin, sonic hedgehog, and fibroblast growth factor pathways in the adult adrenocortical homeostasis. Furthermore, we compared single-nuclei transcriptional profiles obtained from six healthy adrenal glands and twelve adrenocortical adenomas. This analysis unveiled a notable heterogeneity in cell populations within the adenoma samples. In addition, we identified six distinct adenoma-specific clusters, each with varying distributions based on steroid profiles and tumour mutational status. Overall, our results provide novel insights into adrenal homeostasis and molecular mechanisms potentially underlying early adrenocortical tumorigenesis and/or autonomous steroid secretion. Our cell atlas represents a powerful resource to investigate other adrenal-related pathologies.

Natural Hydrogels Support Kidney Organoid Generation and Promote In Vitro Angiogenesis

To date, strategies aiming to modulate cell to extracellular matrix (ECM) interactions during organoid derivation remain largely unexplored. Here renal decellularized ECM (dECM) hydrogels are fabricated from porcine and human renal cortex as biomaterials to enrich cell-to-ECM crosstalk during the onset of kidney organoid differentiation from human pluripotent stem cells (hPSCs). Renal dECM-derived hydrogels are used in combination with hPSC-derived renal progenitor cells to define new approaches for 2D and 3D kidney organoid differentiation, demonstrating that in the presence of these biomaterials the resulting kidney organoids exhibit renal differentiation features and the formation of an endogenous vascular component. Based on these observations, a new method to produce kidney organoids with vascular-like structures is achieved through the assembly of hPSC-derived endothelial-like organoids with kidney organoids in 3D. Major readouts of kidney differentiation and renal cell morphology are assessed exploiting these culture platforms as new models of nephrogenesis. Overall, this work shows that exploiting cell-to-ECM interactions during the onset of kidney differentiation from hPSCs facilitates and optimizes current approaches for kidney organoid derivation thereby increasing the utility of these unique cell culture platforms for personalized medicine.

Varied cellular abnormalities in thin vs. normal endometrium in recurrent implantation failure by single-cell transcriptomics

BACKGROUND

Reduced endometrium thickness and receptivity are two important reasons for recurrent implantation failure (RIF). In order to elucidate differences between these two types of endometrial defects in terms of molecular signatures, cellular interactions, and structural changes, we systematically investigated the single-cell transcriptomic atlas across three distinct groups: RIF patients with thin endometrium (≤ 6 mm, TE-RIF), RIF patients with normal endometrium thickness (≥ 8 mm, NE-RIF), and fertile individuals (Control).

METHODS

The late proliferative and mid-secretory phases of the endometrium were collected from three individuals in the TE-RIF group, two in the NE-RIF group, and three in the control group. The study employed a combination of advanced techniques. Single-cell RNA sequencing (scRNA-seq) was utilized to capture comprehensive transcriptomic profiles at the single-cell level, providing insights into gene expression patterns within specific cell types. Scanning and transmission electron microscopy were employed to visualize ultrastructural details of the endometrial tissue, while hematoxylin and eosin staining facilitated the examination of tissue morphology and cellular composition. Immunohistochemistry techniques were also applied to detect and localize specific protein markers relevant to endometrial receptivity and function.

RESULTS

Through comparative analysis of differentially expressed genes among these groups and KEGG pathway analysis, the TE-RIF group exhibited notable dysregulations in the TNF and MAPK signaling pathways, which are pivotal in stromal cell growth and endometrial receptivity. Conversely, in the NE-RIF group, disturbances in energy metabolism emerged as a primary contributor to reduced endometrial receptivity. Additionally, using CellPhoneDB for intercellular communication analysis revealed aberrant interactions between epithelial and stromal cells, impacting endometrial receptivity specifically in the TE-RIF group.

CONCLUSION

Overall, our findings provide valuable insights into the heterogeneous molecular pathways and cellular interactions associated with RIF in different endometrial conditions. These insights may pave the way for targeted therapeutic interventions aimed at improving endometrial receptivity and enhancing reproductive outcomes in patients undergoing ART. Further research is warranted to validate these findings and translate them into clinical applications for personalized fertility treatments.

TRIAL REGISTRATION

Not applicable.

DEPDC1 as a metabolic target regulates glycolysis in renal cell carcinoma through AKT/mTOR/HIF1α pathway

Renal cell carcinoma (RCC) is considered a "metabolic disease" characterized by elevated glycolysis in patients with advanced RCC. Tyrosine kinase inhibitor (TKI) therapy is currently an important treatment option for advanced RCC, but drug resistance may develop in some patients. Combining TKI with targeted metabolic therapy may provide a more effective approach for patients with advanced RCC. An analysis of 14 RCC patients (including three needle biopsy samples with TKI resistance) revealed by sing-cell RNA sequencing (scRNA-seq) that glycolysis played a crucial role in poor prognosis and drug resistance in RCC. TCGA-KIRC and glycolysis gene set analysis identified DEPDC1 as a target associated with malignant progression and drug resistance in KIRC. Subsequent experiments demonstrated that DEPDC1 promoted malignant progression and glycolysis of RCC, and knockdown DEPDC1 could reverse TKI resistance in RCC cell lines. Bulk RNA sequencing (RNA-seq) and non-targeted metabolomics sequencing suggested that DEPDC1 may regulate RCC glycolysis via AKT/mTOR/HIF1α pathway, a finding supported by protein-level analysis. Clinical tissue samples from 98 RCC patients demonstrated that DEPDC1 was associated with poor prognosis and predicted RCC metastasis. In conclusion, this multi-omics analysis suggests that DEPDC1 could serve as a novel target for TKI combined with targeted metabolic therapy in advanced RCC patients with TKI resistance.

Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression

Kidneys are intricate three-dimensional structures in the body, yet the spatial and molecular principles of kidney health and disease remain inadequately understood. We generated high-quality datasets for 81 samples, including single-cell, single-nuclear, spot-level (Visium) and single-cell resolution (CosMx) spatial-RNA expression and single-nuclear open chromatin, capturing cells from healthy, diabetic and hypertensive diseased human kidneys. Combining these data, we identify cell types and map them to their locations within the tissue. Unbiased deconvolution of the spatial data identifies the following four distinct microenvironments: glomerular, immune, tubule and fibrotic. We describe the complex organization of microenvironments in health and disease and find that the fibrotic microenvironment is able to molecularly classify human kidneys and offers an improved prognosis compared to traditional histopathology. We provide a comprehensive spatially resolved molecular roadmap of the human kidney and the fibrotic process, demonstrating the clinical utility of spatial transcriptomics.

Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity

Studying human fetal lungs can inform how developmental defects and disease states alter the function of the lungs. Here, we sequenced >150,000 single cells from 19 healthy human pseudoglandular fetal lung tissues ranging between gestational weeks 10-19. We capture dynamic developmental trajectories from progenitor cells that express abundant levels of the cystic fibrosis conductance transmembrane regulator (CFTR). These cells give rise to multiple specialized epithelial cell types. Combined with spatial transcriptomics, we show temporal regulation of key signalling pathways that may drive the temporal and spatial emergence of specialized epithelial cells including ciliated and pulmonary neuroendocrine cells. Finally, we show that human pluripotent stem cell-derived fetal lung models contain CFTR-expressing progenitor cells that capture similar lineage developmental trajectories as identified in the native tissue. Overall, this study provides a comprehensive single-cell atlas of the developing human lung, outlining the temporal and spatial complexities of cell lineage development and benchmarks fetal lung cultures from human pluripotent stem cell differentiations to similar developmental window.

Single-cell sequencing, spatial transcriptome ad periodontitis: Rethink pathogenesis and classification

OBJECTIVE

This narrative review illuminates on the application of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) in periodontitis and highlights the probability of relating cell population and gene signatures to the pathogenesis of the disease for a better diagnosis.

METHODS

An electronic search of the literature in the PubMed database for the keywords, "single cell sequencing" OR "spatial transcriptomics" and "periodontitis" OR "gingiva" OR "oral mucosa" yielded 486 research articles and reviews. After filtering duplicates and careful curation, 22 papers conducted in humans were retained.

RESULTS

The molecular mechanisms underlying periodontitis are complex and involve the interaction of multiple cells and various gene expressions. Most residing cells in periodontal tissues participate in maintaining homeostasis and health, while in addition to infiltrating immune cells contribute to the fight against the bacterial insult.

CONCLUSION

scRNA-seq and ST have provided new insights into the cellular and molecular changes associated with periodontitis for a better diagnosis and clinical outcome. New functions of cells and genes are revealed with these techniques; however, no cells or gene signatures are attributed to periodontitis so far.

Crosstalk among proximal tubular cells, macrophages, and fibroblasts in acute kidney injury: single-cell profiling from the perspective of ferroptosis

The link between ferroptosis, a form of cell death mediated by iron and acute kidney injury (AKI) is recently gaining widespread attention. However, the mechanism of the crosstalk between cells in the pathogenesis and progression of acute kidney injury remains unexplored. In our research, we performed a non-negative matrix decomposition (NMF) algorithm on acute kidney injury single-cell RNA sequencing data based specifically focusing in ferroptosis-associated genes. Through a combination with pseudo-time analysis, cell-cell interaction analysis and SCENIC analysis, we discovered that proximal tubular cells, macrophages, and fibroblasts all showed associations with ferroptosis in different pathways and at various time. This involvement influenced cellular functions, enhancing cellular communication and activating multiple transcription factors. In addition, analyzing bulk expression profiles and marker genes of newly defined ferroptosis subtypes of cells, we have identified crucial cell subtypes, including Egr1 + PTC-C1, Jun + PTC-C3, Cxcl2 + Mac-C1 and Egr1 + Fib-C1. All these subtypes which were found in AKI mice kidneys and played significantly distinct roles from those of normal mice. Moreover, we verified the differential expression of Egr1, Jun, and Cxcl2 in the IRI mouse model and acute kidney injury human samples. Finally, our research presented a novel analysis of the crosstalk of proximal tubular cells, macrophages and fibroblasts in acute kidney injury targeting ferroptosis, therefore, contributing to better understanding the acute kidney injury pathogenesis, self-repairment and acute kidney injury-chronic kidney disease (AKI-CKD) progression.

The tumor microenvironment of VETC+ hepatocellular carcinoma is enriched of immunosuppressive TAMs spatially close to endothelial cells

BACKGROUND AND AIM

VETC (vessel that encapsulate tumor cluster) is a peculiar vascular phenotype observed in hepatocellular carcinoma (HCC), associated with distant metastases and poor outcome. VETC has been linked to the Tie2/Ang2 axis and is characterized by lymphocytes poor (cold) tumor microenvironment (TME). In this setting the role of Tumor Associated Macrophages (TAMs) has never been explored. Aim of the study is to investigate the presence and features of TAMs in VETC+ HCC and the possible interplay between TAMs and endothelial cells (ECs).

METHODS

The series under study included 42 HCC. Once separated according to the VETC phenotype (21 VETC+; 21 VETC-) we stained consecutive slides with immunohistochemistry for CD68, CD163 and Tie2. Slides were then scanned and QuPath used to quantify morphological features.

RESULTS

VETC+ cases were significantly (p < 0.001) enriched with large, lipid rich CD163+ TAMs (M2 oriented) that were spatially close to ECs; HCC cells significantly (p: 0.002) overexpressed Tie2 with a polarization toward ECs.

CONCLUSIONS

The pro-metastatic attitude of VETC is sustained by a strict morphological relationship between immunosuppressive M2-TAMs, ECs and Tie2-expressing HCC cells.

Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer, accounting for over 75% of cases. The asymptomatic nature of the disease contributes to late-stage diagnoses and poor survival. Highly vascularized and immune infiltrated microenvironment are prominent features of ccRCC, yet the interplay between vasculature and immune cells, disease progression and response to therapy remains poorly understood. Using droplet-based single-cell RNA sequencing we profile 50,236 transcriptomes from paired tumor and healthy adjacent kidney tissues. Our analysis reveals significant heterogeneity and inter-patient variability of the tumor microenvironment. Notably, we discover a previously uncharacterized vasculature subpopulation associated with epithelial-mesenchymal transition. The cell-cell communication analysis reveals multiple modes of immunosuppressive interactions within the tumor microenvironment, including clinically relevant interactions between tumor vasculature and stromal cells with immune cells. The upregulation of the genes involved in these interactions is associated with worse survival in the TCGA KIRC cohort. Our findings demonstrate the role of tumor vasculature and stromal cell populations in shaping the ccRCC microenvironment and uncover a subpopulation of cells within the tumor vasculature that is associated with an angiogenic phenotype.

Protective role for kidney TREM2high macrophages in obesity- and diabetes-induced kidney injury

Diabetic kidney disease (DKD), the most common cause of kidney failure, is a frequent complication of diabetes and obesity, and yet to date, treatments to halt its progression are lacking. We analyze kidney single-cell transcriptomic profiles from DKD patients and two DKD mouse models at multiple time points along disease progression-high-fat diet (HFD)-fed mice aged to 90-100 weeks and BTBR ob/ob mice (a genetic model)-and report an expanding population of macrophages with high expression of triggering receptor expressed on myeloid cells 2 (TREM2) in HFD-fed mice. TREM2high macrophages are enriched in obese and diabetic patients, in contrast to hypertensive patients or healthy controls in an independent validation cohort. Trem2 knockout mice on an HFD have worsening kidney filter damage and increased tubular epithelial cell injury, all signs of worsening DKD. Together, our studies suggest that strategies to enhance kidney TREM2high macrophages may provide therapeutic benefits for DKD.

Distinctive multicellular immunosuppressive hubs confer different intervention strategies for left- and right-sided colon cancers

Primary colon cancers arising from the left and right sides exhibit distinct clinical and molecular characteristics. Sidedness-associated heterogeneity relies intricately on the oncogenic properties of cancer cells and multicellular interactions in tumor microenvironments. Here, combining transcriptomic profiling of 426,863 single cells from 105 colon cancer patients and validation with spatial transcriptomics and large-scale histological analysis, we capture common transcriptional heterogeneity patterns between left- and right-sided malignant epithelia through delineating two side-specific expression meta-programs. The proliferation stemness meta-program is notably enriched in left-sided malignant epithelia that colocalize with Mph-PLTP cells, activated regulatory T cells (Tregs), and exhausted CD8-LAYN cells, constituting the glucose metabolism reprogramming niche. The immune secretory (IS) meta-program exhibits specific enrichment in right-sided malignant epithelia, especially in smoking patients with right-sided colon cancer. The IShigh malignant epithelia spatially localize in hypoxic regions and facilitate immune evasion through attenuating Mph-SPP1 cell antigen presentation and recruiting innate-like cytotoxicity-reduced CD8-CD161 cells.

Oncogenic Cell Tagging and Single-Cell Transcriptomics Reveal Cell Type-Specific and Time-Resolved Responses to Vhl Inactivation in the Kidney

Defining the initial events in oncogenesis and the cellular responses they entrain, even in advance of morphologic abnormality, is a fundamental challenge in understanding cancer initiation. As a paradigm to address this, we longitudinally studied the changes induced by loss of the tumor suppressor gene von Hippel Lindau (VHL), which ultimately drives clear cell renal cell carcinoma. Vhl inactivation was directly coupled to expression of a tdTomato reporter within a single allele, allowing accurate visualization of affected cells in their native context and retrieval from the kidney for single-cell RNA sequencing. This strategy uncovered cell type-specific responses to Vhl inactivation, defined a proximal tubular cell class with oncogenic potential, and revealed longer term adaptive changes in the renal epithelium and the interstitium. Oncogenic cell tagging also revealed markedly heterogeneous cellular effects including time-limited proliferation and elimination of specific cell types. Overall, this study reports an experimental strategy for understanding oncogenic processes in which cells bearing genetic alterations can be generated in their native context, marked, and analyzed over time. The observed effects of loss of Vhl in kidney cells provide insights into VHL tumor suppressor action and development of renal cell carcinoma.

SIGNIFICANCE

Single-cell analysis of heterogeneous and dynamic responses to Vhl inactivation in the kidney suggests that early events shape the cell type specificity of oncogenesis, providing a focus for mechanistic understanding and therapeutic targeting.

Developmental origins shape the paediatric cancer genome

In the past two decades, technological advances have brought unprecedented insights into the paediatric cancer genome revealing characteristics distinct from those of adult cancer. Originating from developing tissues, paediatric cancers generally have low mutation burden and are driven by variants that disrupt the transcriptional activity, chromatin state, non-coding cis-regulatory regions and other biological functions. Within each tumour, there are multiple populations of cells with varying states, and the lineages of some can be tracked to their fetal origins. Genome-wide genetic screening has identified vulnerabilities associated with both the cell of origin and transcription deregulation in paediatric cancer, which have become a valuable resource for designing new therapeutic approaches including those for small molecules, immunotherapy and targeted protein degradation. In this Review, we present recent findings on these facets of paediatric cancer from a pan-cancer perspective and provide an outlook on future investigations.

Single-cell lineage tracing approaches to track kidney cell development and maintenance

The kidney is a complex organ consisting of various cell types. Previous studies have aimed to elucidate the cellular relationships among these cell types in developing and mature kidneys using Cre-loxP-based lineage tracing. However, this methodology falls short of fully capturing the heterogeneous nature of the kidney, making it less than ideal for comprehensively tracing cellular progression during kidney development and maintenance. Recent technological advancements in single-cell genomics have revolutionized lineage tracing methods. Single-cell lineage tracing enables the simultaneous tracing of multiple cell types within complex tissues and their transcriptomic profiles, thereby allowing the reconstruction of their lineage tree with cell state information. Although single-cell lineage tracing has been successfully applied to investigate cellular hierarchies in various organs and tissues, its application in kidney research is currently lacking. This review comprehensively consolidates the single-cell lineage tracing methods, divided into 4 categories (clustered regularly interspaced short palindromic repeat [CRISPR]/CRISPR-associated protein 9 [Cas9]-based, transposon-based, Polylox-based, and native barcoding methods), and outlines their technical advantages and disadvantages. Furthermore, we propose potential future research topics in kidney research that could benefit from single-cell lineage tracing and suggest suitable technical strategies to apply to these topics.

scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback-Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods.

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

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

Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

Single-Cell multiomics reveals ENL mutation perturbs kidney developmental trajectory by rewiring gene regulatory landscape

Cell differentiation during organogenesis relies on precise epigenetic and transcriptional control. Disruptions to this regulation can result in developmental abnormalities and malignancies, yet the underlying mechanisms are not well understood. Wilms tumors, a type of embryonal tumor closely linked to disrupted organogenesis, harbor mutations in epigenetic regulators in 30-50% of cases. However, the role of these regulators in kidney development and pathogenesis remains unexplored. By integrating mouse modeling, histological characterizations, and single-cell transcriptomics and chromatin accessibility profiling, we show that a Wilms tumor-associated mutation in the chromatin reader protein ENL disrupts kidney development trajectory by rewiring the gene regulatory landscape. Specifically, the mutant ENL promotes the commitment of nephron progenitors while simultaneously restricting their differentiation by dysregulating key transcription factor regulons, particularly the HOX clusters. It also induces the emergence of abnormal progenitor cells that lose their chromatin identity associated with kidney specification. Furthermore, the mutant ENL might modulate stroma-nephron interactions via paracrine Wnt signaling. These multifaceted effects caused by the mutation result in severe developmental defects in the kidney and early postnatal mortality in mice. Notably, transient inhibition of the histone acetylation binding activity of mutant ENL with a small molecule displaces transcriptional condensates formed by mutant ENL from target genes, abolishes its gene activation function, and restores developmental defects in mice. This work provides new insights into how mutations in epigenetic regulators can alter the gene regulatory landscape to disrupt kidney developmental programs at single-cell resolution in vivo . It also offers a proof-of-concept for the use of epigenetics-targeted agents to rectify developmental defects.

Molecular mechanisms and clinical relevance of endothelial cell cross-talk in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer in adults and stands out as one of the most vascularized and immune-infiltrated solid tumors. Overproduction of vascular endothelial growth factor A promotes uncontrolled growth of abnormal vessels and immunosuppression, and the tumor microenvironment (TME) has a prominent role in disease progression, drug targeting and drug response, and for patient outcome.

Methods

Studies of experimental models, large-scale omics approaches, and patient prognosis and therapy prediction, using gene expression signatures and tissue biomarker analysis, have been reviewed for enhanced understanding of the endothelium in ccRCC and the interplay with the surrounding TME.

Results

Preclinical and clinical studies have discovered molecular mechanisms of endothelial cross-talk of relevance for disease progression, patient prognosis, and therapy prediction. There is, however, a lack of representative ccRCC experimental models. Omics approaches have identified clinically relevant subsets of angiogenic and immune-infiltrated tumors with distinct molecular signatures and distinct endothelial cell and immune cell populations in patients.

Conclusions

Recent genetically engineered ccRCC mouse models together with emerging evidence from single cell RNA sequencing data open up for future validation studies, including multiplex imaging of ccRCC patient cohorts. These studies are of importance for therapy benefit and personalized treatment of ccRCC patients.

scTPC: a novel semisupervised deep clustering model for scRNA-seq data

MOTIVATION

Continuous advancements in single-cell RNA sequencing (scRNA-seq) technology have enabled researchers to further explore the study of cell heterogeneity, trajectory inference, identification of rare cell types, and neurology. Accurate scRNA-seq data clustering is crucial in single-cell sequencing data analysis. However, the high dimensionality, sparsity, and presence of "false" zero values in the data can pose challenges to clustering. Furthermore, current unsupervised clustering algorithms have not effectively leveraged prior biological knowledge, making cell clustering even more challenging.

RESULTS

This study investigates a semisupervised clustering model called scTPC, which integrates the triplet constraint, pairwise constraint, and cross-entropy constraint based on deep learning. Specifically, the model begins by pretraining a denoising autoencoder based on a zero-inflated negative binomial distribution. Deep clustering is then performed in the learned latent feature space using triplet constraints and pairwise constraints generated from partial labeled cells. Finally, to address imbalanced cell-type datasets, a weighted cross-entropy loss is introduced to optimize the model. A series of experimental results on 10 real scRNA-seq datasets and five simulated datasets demonstrate that scTPC achieves accurate clustering with a well-designed framework.

AVAILABILITY AND IMPLEMENTATION

scTPC is a Python-based algorithm, and the code is available from https://github.com/LF-Yang/Code or https://zenodo.org/records/10951780.

Multidimensional Transcriptomics Unveils RNF34 as a Prognostic Biomarker and Potential Indicator of Chemotherapy Sensitivity in Wilms' Tumour

Nephroblastoma, colloquially known as Wilms' tumour (WT), is the predominant malignant renal neoplasm arising in the paediatric population. Modern therapeutic approaches for WT incorporate a synergistic combination of surgical intervention, radiotherapy, and chemotherapy, which substantially ameliorate the overall patient survival rate. Despite this, the optimal sequence of chemotherapy and surgical intervention remains a matter of contention, with each strategy presenting its own strengths and weaknesses that could influence clinical decision-making. To make some headway on this clinical dilemma, we deployed a multidimensional transcriptomics integration approach by analysing bulk RNA sequencing data with 136 samples, as well as single-nucleus RNA sequencing (snRNA-seq) and paired spatial transcriptome sequencing (stRNA) data from 32 WT specimens. Our findings identified a distinct elevation of RNF34 expression within WT samples, which correlated with unfavourable prognostic outcomes. Leveraging the Genomics of Drug Sensitivity in Cancer (GDSC), we simultaneously revealed that patients with high expression of RNF34 have higher sensitivity to commonly used chemotherapy drugs for WT. Furthermore, our analysis of snRNA and stRNA data unveiled a reduced proportion of RNF34 expression in neoplastic cells after chemotherapy. Moreover, stRNA data delineated a significant association between a higher proportion of RNF34 expression in cancer cells and adverse features such as anaplastic histology and tumour recurrence. Intriguingly, we also observed a close association between elevated RNF34 expression and a characteristic exhausted tumour immune microenvironment. Collectively, our findings underscore the pivotal role of RNF34 in the prognostic prediction potential and treatment sensitivity of WT. This comprehensive analysis can potentially inform and refine clinical decision-making for WT patients and guide future studies towards the development of optimized, rational therapeutic strategies.

Single-cell RNA sequencing reveals myeloid and T cell co-stimulation mediated by IL-7 anti-cancer immunotherapy

BACKGROUND

Immune checkpoint inhibitors unleash inhibitory signals on T cells conferred by tumors and surrounding stromal cells. Despite the clinical efficacy of checkpoint inhibitors, the lack of target expression and persistence of immunosuppressive cells limit the pervasive effectiveness of the therapy. These limitations may be overcome by alternative approaches that co-stimulate T cells and the immune microenvironment.

METHODS

We analyzed single-cell RNA sequencing data from multiple human cancers and a mouse tumor transplant model to discover the pleiotropic expression of the Interleukin 7 (IL-7) receptor on T cells, macrophages, and dendritic cells.

RESULTS

Our experiment on the mouse model demonstrated that recombinant IL-7 therapy induces tumor regression, expansion of effector CD8 T cells, and pro-inflammatory activation of macrophages. Moreover, spatial transcriptomic data support immunostimulatory interactions between macrophages and T cells.

CONCLUSION

These results indicate that IL-7 therapy induces anti-tumor immunity by activating T cells and pro-inflammatory myeloid cells, which may have diverse therapeutic applicability.

Quantification of CD3, FoxP3, and granzyme B immunostaining in canine renal cell carcinoma

Tumor-infiltrating lymphocyte (TIL) density plays an important role in anti-tumor immunity and is associated with patient outcome in various human and canine malignancies. As a first assessment of the immune landscape of the tumor microenvironment in canine renal cell carcinoma (RCC), we retrospectively analyzed clinical data and quantified CD3, FoxP3, and granzyme B immunostaining in formalin-fixed paraffin-embedded tumor samples from 16 dogs diagnosed with renal cell carcinoma treated with ureteronephrectomy. Cell density was low for all markers evaluated. Increased numbers of intratumoral FoxP3 labelled (+) cells, as well as decreased granzyme B+: FoxP3+ TIL ratio, were associated with poor patient outcomes. Our initial study of canine RCC reveals that these tumors are immunologically cold and Tregs may play an important role in immune evasion.

A novel pyroptosis-related gene signature exhibits distinct immune cells infiltration landscape in Wilms' tumor

BACKGROUND

Wilms' tumor (WT) is the most common renal tumor in childhood. Pyroptosis, a type of inflammation-characterized and immune-related programmed cell death, has been extensively studied in multiple tumors. In the current study, we aim to construct a pyroptosis-related gene signature for predicting the prognosis of Wilms' tumor.

METHODS

We acquired RNA-seq data from TARGET kidney tumor projects for constructing a gene signature, and snRNA-seq data from GEO database for validating signature-constructing genes. Pyroptosis-related genes (PRGs) were collected from three online databases. We constructed the gene signature by Lasso Cox regression and then established a nomogram. Underlying mechanisms by which gene signature is related to overall survival states of patients were explored by immune cell infiltration analysis, differential expression analysis, and functional enrichment analysis.

RESULTS

A pyroptosis-related gene signature was constructed with 14 PRGs, which has a moderate to high predicting capacity with 1-, 3-, and 5-year area under the curve (AUC) values of 0.78, 0.80, and 0.83, respectively. A prognosis-predicting nomogram was established by gender, stage, and risk score. Tumor-infiltrating immune cells were quantified by seven algorithms, and the expression of CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages are positively or negatively correlated with risk score. Two single nuclear RNA-seq samples of different histology were harnessed for validation. The distribution of signature genes was identified in various cell types.

CONCLUSIONS

We have established a pyroptosis-related 14-gene signature in WT. Moreover, the inherent roles of immune cells (CD8( +) T cells, B cells, Th2 cells, dendritic cells, and type 2 macrophages), functions of differentially expressed genes (tissue/organ development and intercellular communication), and status of signaling pathways (proteoglycans in cancer, signaling pathways regulating pluripotent of stem cells, and Wnt signaling pathway) have been elucidated, which might be employed as therapeutic targets in the future.

Renal Endothelial Single-Cell Transcriptomics Reveals Spatiotemporal Regulation and Divergent Roles of Differential Gene Transcription and Alternative Splicing in Murine Diabetic Nephropathy

Endothelial cell (EC) injury is a crucial contributor to the progression of diabetic kidney disease (DKD), but the specific EC populations and mechanisms involved remain elusive. Kidney ECs (n = 5464) were collected at three timepoints from diabetic BTBRob/ob mice and non-diabetic littermates. Their heterogeneity, transcriptional changes, and alternative splicing during DKD progression were mapped using SmartSeq2 single-cell RNA sequencing (scRNAseq) and elucidated through pathway, network, and gene ontology enrichment analyses. We identified 13 distinct transcriptional EC phenotypes corresponding to different kidney vessel subtypes, confirmed through in situ hybridization and immunofluorescence. EC subtypes along nephrons displayed extensive zonation related to their functions. Differential gene expression analyses in peritubular and glomerular ECs in DKD underlined the regulation of DKD-relevant pathways including EIF2 signaling, oxidative phosphorylation, and IGF1 signaling. Importantly, this revealed the differential alteration of these pathways between the two EC subtypes and changes during disease progression. Furthermore, glomerular and peritubular ECs also displayed aberrant and dynamic alterations in alternative splicing (AS), which is strongly associated with DNA repair. Strikingly, genes displaying differential transcription or alternative splicing participate in divergent biological processes. Our study reveals the spatiotemporal regulation of gene transcription and AS linked to DKD progression, providing insight into pathomechanisms and clues to novel therapeutic targets for DKD treatment.

Single-Cell RNA Sequencing in Organ and Cell Transplantation

Single-cell RNA sequencing is a high-throughput novel method that provides transcriptional profiling of individual cells within biological samples. This method typically uses microfluidics systems to uncover the complex intercellular communication networks and biological pathways buried within highly heterogeneous cell populations in tissues. One important application of this technology sits in the fields of organ and stem cell transplantation, where complications such as graft rejection and other post-transplantation life-threatening issues may occur. In this review, we first focus on research in which single-cell RNA sequencing is used to study the transcriptional profile of transplanted tissues. This technology enables the analysis of the donor and recipient cells and identifies cell types and states associated with transplant complications and pathologies. We also review the use of single-cell RNA sequencing in stem cell implantation. This method enables studying the heterogeneity of normal and pathological stem cells and the heterogeneity in cell populations. With their remarkably rapid pace, the single-cell RNA sequencing methodologies will potentially result in breakthroughs in clinical transplantation in the coming years.

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC's protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.

Bioinformatics in urology - molecular characterization of pathophysiology and response to treatment

The application of bioinformatics has revolutionized the practice of medicine in the past 20 years. From early studies that uncovered subtypes of cancer to broad efforts spearheaded by the Cancer Genome Atlas initiative, the use of bioinformatics strategies to analyse high-dimensional data has provided unprecedented insights into the molecular basis of disease. In addition to the identification of disease subtypes - which enables risk stratification - informatics analysis has facilitated the identification of novel risk factors and drivers of disease, biomarkers of progression and treatment response, as well as possibilities for drug repurposing or repositioning; moreover, bioinformatics has guided research towards precision and personalized medicine. Implementation of specific computational approaches such as artificial intelligence, machine learning and molecular subtyping has yet to become widespread in urology clinical practice for reasons of cost, disruption of clinical workflow and need for prospective validation of informatics approaches in independent patient cohorts. Solving these challenges might accelerate routine integration of bioinformatics into clinical settings.

Applicability of Spatial Technology in Cancer Research

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Cell Heterogeneity Analysis Revealed the Key Role of Fibroblasts in the Magnum Regression of Ducks

Duck egg production, like that of laying hens, follows a typical low-peak-low cycle, reflecting the dynamics of the reproductive system. Post-peak, some ducks undergo a cessation of egg laying, indicative of a regression process in the oviduct. Notably, the magnum, being the longest segment of the oviduct, plays a crucial role in protein secretion. Despite its significance, few studies have investigated the molecular mechanisms underlying oviduct regression in ducks that have ceased laying eggs. In this study, we conducted single-cell transcriptome sequencing on the magnum tissue of Shaoxing ducks at 467 days of age, utilizing the 10× Genomics platform. This approach allowed us to generate a detailed magnum transcriptome map of both egg-laying and ceased-laying ducks. We collected transcriptome data from 13,708 individual cells, which were then subjected to computational analysis, resulting in the identification of 27 distinct cell clusters. Marker genes were subsequently employed to categorize these clusters into specific cell types. Our analysis revealed notable heterogeneity in magnum cells between the egg-laying and ceased-laying ducks, primarily characterized by variations in cells involved in protein secretion and extracellular matrix (ECM)-producing fibroblasts. Specifically, cells engaged in protein secretion were predominantly observed in the egg-laying ducks, indicative of their role in functional albumen deposition within the magnum, a phenomenon not observed in the ceased-laying ducks. Moreover, the proportion of THY1+ cells within the ECM-producing fibroblasts was found to be significantly higher in the egg-laying ducks (59%) compared to the ceased-laying ducks (24%). Similarly, TIMP4+ fibroblasts constituted a greater proportion of the ECM-producing fibroblasts in the egg-laying ducks (83%) compared to the ceased-laying ducks (58%). These findings suggest a potential correlation between the expression of THY1 and TIMP4 in ECM-producing fibroblasts and oviduct activity during functional reproduction. Our study provides valuable single-cell insights that warrant further investigation into the biological implications of fibroblast subsets in the degeneration of the reproductive tract. Moreover, these insights hold promise for enhancing the production efficiency of laying ducks.