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

Gene expressions associated with longer lifespan and aging exhibit similarity in mammals

Although molecular features underlying aging and species maximum lifespan (MLS) have been comprehensively studied by transcriptome analyses, the actual impact of transcriptome on aging and MLS remains elusive. Here, we found that transcriptional signatures that are associated with mammalian MLS exhibited significant similarity to those of aging. Moreover, transcriptional signatures of longer MLS and aging both exhibited significant similarity to that of longer-lived mouse strains, suggesting that gene expression patterns associated with species MLS contribute to extended lifespan even within a species and that aging-related gene expression changes overall represent adaptations that extend lifespan rather than deterioration. Finally, we found evidence of co-evolution of MLS and promoter sequences of MLS-associated genes, highlighting the evolutionary contribution of specific transcription factor binding motifs such as that of E2F1 in shaping MLS-associated gene expression signature. Our results highlight the importance of focusing on adaptive aspects of aging transcriptome and demonstrate that cross-species genomics can be a powerful approach for understanding adaptive aging transcriptome.

Single-Cell RNA Sequencing for Studying Human Cancers

Since the first publication a decade ago describing the use of single-cell RNA sequencing (scRNA-seq) in the context of cancer, over 200 datasets and thousands of scRNA-seq studies have been published in cancer biology. scRNA-seq technologies have been applied across dozens of cancer types and a diverse array of study designs to improve our understanding of tumor biology, the tumor microenvironment, and therapeutic responses, and scRNA-seq is on the verge of being used to improve decision-making in the clinic. Computational methodologies and analytical pipelines are key in facilitating scRNA-seq research. Numerous computational methods utilizing the most advanced tools in data science have been developed to extract meaningful insights. Here, we review the advancements in cancer biology gained by scRNA-seq and discuss the computational challenges of the technology that are specific to cancer research.

Restoring gluconeogenesis by TEF inhibited proliferation and promoted apoptosis and immune surveillance in kidney renal clear cell carcinoma

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is the major histological subtype of kidney tumor which covers approximately 80% of the cases. Although various therapies have been developed, the clinical outcome remains unsatisfactory. Metabolic dysregulation is a key feature of KIRC, which impacts progression and prognosis of the disease. Therefore, understanding of the metabolic changes in KIRC is of great significance in improving the treatment outcomes.

METHODS

The glycolysis/gluconeogenesis genes were analyzed in the KIRC transcriptome from the Cancer Genome Atlas (TCGA) by the different expression genes (DEGs) test and survival analysis. The gluconeogenesis-related miRNAs were identified by ImmuLncRNA. The expression levels of indicated genes and miRNAs were validated in KIRC tumor and adjunct tissues by QPCR. The effects of miR-4477b and PCK1 on cell proliferation and apoptosis were examined using the cell viability assay, cell apoptosis assay, and clone information. The interaction of miR-4477b with TEF was tested by the luciferase report gene assay. The different gluconeogenesis statuses of tumor cells and related signatures were investigated by single-cell RNA sequencing (scRNA-seq) analysis.

RESULTS

The 11 gluconeogenesis genes were found to be suppressed in KIRC (referring as PGNGs), and the less suppression of PGNGs indicated better survival outcomes. Among the 11 PGNGs, we validated four rate-limiting enzyme genes in clinical tumor patients. Moreover, restoring gluconeogenesis by overexpressing PCK1 or TEF through miR-4477b inhibition significantly inhibited tumor cell proliferation, colony formation, and induced cell apoptosis in vitro. Independent single-cell RNA sequencing (scRNA-seq) data analysis revealed that the tumor cells had high levels of PGNG expression (PGNG + tumor cells) represented a phenotype of early stage of neoplasia and prompted immune surveillance.

CONCLUSIONS

Our study suggests that the deficiency of gluconeogenesis is a key metabolic feature of KIRC, and restoring gluconeogenesis could effectively inhibit the proliferation and progression of KIRC cells.

Targeting intratumor heterogeneity suppresses colorectal cancer chemoresistance and metastasis

Intratumor heterogeneity (ITH) is a barrier to effective therapy. However, it is largely unknown how ITH is established at the onset of tumor progression, such as in colorectal cancer (CRC). Here, we integrate single-cell RNA-seq and functional validation to show that asymmetric division of CRC stem-like cells (CCSC) is critical for early ITH establishment. We find that CCSC-derived xenografts contain seven cell subtypes, including CCSCs, that dynamically change during CRC xenograft progression. Furthermore, three of the subtypes are generated by asymmetric division of CCSCs. They are functionally distinct and appear at the early stage of xenografts. In particular, we identify a chemoresistant and an invasive subtype, and investigate the regulators that control their generation. Finally, we show that targeting the regulators influences cell subtype composition and CRC progression. Our findings demonstrate that asymmetric division of CCSCs contributes to the early establishment of ITH. Targeting asymmetric division may alter ITH and benefit CRC therapy.

Biomarkers for patients with Wilms tumor: a review

Wilms tumor, originating from aberrant fetal nephrogenesis, is the most common renal malignancy in childhood. The overall survival of children is approximately 90%. Although existing risk-stratification systems are helpful in identifying patients with poor prognosis, the recurrence rate of Wilms tumors remains as high as 15%. To resolve this clinical problem, diverse studies on the occurrence and progression of the disease have been conducted, and the results are encouraging. A series of molecular biomarkers have been identified with further studies on the mechanism of tumorigenesis. Some of these show prognostic value and have been introduced into clinical practice. Identification of these biomarkers can supplement the existing risk-stratification systems. In the future, more biomarkers will be discovered, and more studies are required to validate their roles in improving the detection rate of occurrence or recurrence of Wilms tumor and to enhance clinical outcomes.

Mapping the tumor microenvironment in clear cell renal carcinoma by single-cell transcriptome analysis

Introduction: Clear cell renal cell carcinoma (ccRCC) is associated with unfavorable clinical outcomes. To identify viable therapeutic targets, a comprehensive understanding of intratumoral heterogeneity is crucial. In this study, we conducted bioinformatic analysis to scrutinize single-cell RNA sequencing data of ccRCC tumor and para-tumor samples, aiming to elucidate the intratumoral heterogeneity in the ccRCC tumor microenvironment (TME). Methods: A total of 51,780 single cells from seven ccRCC tumors and five para-tumor samples were identified and grouped into 11 cell lineages using bioinformatic analysis. These lineages included tumor cells, myeloid cells, T-cells, fibroblasts, and endothelial cells, indicating a high degree of heterogeneity in the TME. Copy number variation (CNV) analysis was performed to compare CNV frequencies between tumor and normal cells. The myeloid cell population was further re-clustered into three major subgroups: monocytes, macrophages, and dendritic cells. Differential expression analysis, gene ontology, and gene set enrichment analysis were employed to assess inter-cluster and intra-cluster functional heterogeneity within the ccRCC TME. Results: Our findings revealed that immune cells in the TME predominantly adopted an inflammatory suppression state, promoting tumor cell growth and immune evasion. Additionally, tumor cells exhibited higher CNV frequencies compared to normal cells. The myeloid cell subgroups demonstrated distinct functional properties, with monocytes, macrophages, and dendritic cells displaying diverse roles in the TME. Certain immune cells exhibited pro-tumor and immunosuppressive effects, while others demonstrated antitumor and immunostimulatory properties. Conclusion: This study contributes to the understanding of intratumoral heterogeneity in the ccRCC TME and provides potential therapeutic targets for ccRCC treatment. The findings emphasize the importance of considering the diverse functional roles of immune cells in the TME for effective therapeutic interventions.

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

Objective

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

Methods

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

Results

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

Conclusion

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

The tumor suppressor CREBBP and the oncogene MYCN cooperate to induce malignant brain tumors in mice

The tumor suppressor and chromatin modifier cAMP response element-binding protein binding protein (CREBBP) and v-myc avian myelocytomatosis viral oncogene neuroblastoma derived homolog (MYCN), a member of the MYC oncogene family, are critically involved in brain development. Both genes are frequently mutated in the same tumor entities, including high-grade glioma and medulloblastoma. Therefore, we hypothesized that alterations in both genes cooperate to induce brain tumor formation. For further investigation, hGFAP-cre::Crebbp^Fl/Fl::lsl-MYCN mice were generated, which combine Crebbp deletion with overexpression of MYCN in neural stem cells (NSCs). Within eight months, these animals developed aggressive forebrain tumors. The first tumors were detectable in the olfactory bulbs of seven-day-old mice. This location raises the possibility that presumptive founder cells are derived from the ventricular-subventricular zone (V-SVZ). To examine the cellular biology of these tumors, single-cell RNA sequencing was performed, which revealed high intratumoral heterogeneity. Data comparison with reference CNS cell types indicated the highest similarity of tumor cells with transit-amplifying NSCs or activated NSCs of the V-SVZ. Consequently, we analyzed V-SVZ NSCs of our mouse model aiming to confirm that the tumors originate from this stem cell niche. Mutant V-SVZ NSCs showed significantly increased cell viability and proliferation as well as reduced glial and neural differentiation in vitro compared to control cells. In summary, we demonstrate the oncogenic potential of a combined loss of function of CREBBP and overexpression of MYCN in this cell population. hGFAP-cre::Crebbp^Fl/Fl::lsl-MYCN mice thus provide a valuable tool to study tumor-driving mechanisms in a key neural stem/ progenitor cell niche.

The Utility of Spatial Transcriptomics for Solid Organ Transplantation

Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing-based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.

Transcriptional signatures associated with persisting CD19 CAR-T cells in children with leukemia

In the context of relapsed and refractory childhood pre-B cell acute lymphoblastic leukemia (R/R B-ALL), CD19-targeting chimeric antigen receptor (CAR)-T cells often induce durable remissions, which requires the persistence of CAR-T cells. In this study, we systematically analyzed CD19 CAR-T cells of 10 children with R/R B-ALL enrolled in the CARPALL trial via high-throughput single-cell gene expression and T cell receptor sequencing of infusion products and serial blood and bone marrow samples up to 5 years after infusion. We show that long-lived CAR-T cells developed a CD4/CD8 double-negative phenotype with an exhausted-like memory state and distinct transcriptional signature. This persistence signature was dominant among circulating CAR-T cells in all children with a long-lived treatment response for which sequencing data were sufficient (4/4, 100%). The signature was also present across T cell subsets and clonotypes, indicating that persisting CAR-T cells converge transcriptionally. This persistence signature was also detected in two adult patients with chronic lymphocytic leukemia with decade-long remissions who received a different CD19 CAR-T cell product. Examination of single T cell transcriptomes from a wide range of healthy and diseased tissues across children and adults indicated that the persistence signature may be specific to long-lived CAR-T cells. These findings raise the possibility that a universal transcriptional signature of clinically effective, persistent CD19 CAR-T cells exists.

Serum extracellular vesicles derived hsa-miR-320d as an indicator for progression of clear cell renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is a prevalent malignancy with a rising incidence in developing countries. Clear cell renal cell carcinoma (ccRCC) constitutes 70% of RCC cases and is prone to metastasis and recurrence, yet lacks a liquid biomarker for surveillance. Extracellular vesicles (EVs) have shown promise as biomarkers in various malignancies. In this study, we investigated the potential of serum EV-derived miRNAs as a biomarker for ccRCC metastasis and recurrence.

MATERIALS AND METHODS

Patients diagnosed with ccRCC between 2017 and 2020 were recruited in this study. In the discovery phase, high throughput small RNA sequencing was used to analyze RNA extracted from serum EVs derived from localized ccRCC (LccRCC) and advanced ccRCC (AccRCC). In the validation phase, qPCR was employed for quantitative detection of candidate biomarkers. Migration and invasion assays were performed on ccRCC cell line OSRC2.

RESULTS

Serum EVs derived hsa-miR-320d was significantly up-regulated in patients with AccRCC than in patients with LccRCC (p < 0.01). In addition, Serum EVs derived hsa-miR-320d was also significantly up-regulated in patients who experienced recurrence or metastasis (p < 0.01). Besides, hsa-miR-320d enhances the pro-metastatic phenotype of ccRCC cells in vitro.

CONCLUSIONS

Serum EVs derived hsa-miR-320d as a liquid biomarker exhibits significant potential for identifying the recurrence or metastasis of ccRCC, as well as hsa-miR-320d promotes ccRCC cells migration and invasion.

Single-cell and spatial transcriptomics reveal 5-methylcytosine RNA methylation regulators immunologically reprograms tumor microenvironment characterizations, immunotherapy response and precision treatment of clear cell renal cell carcinoma

Clear cell Renal Cell Carcinoma (ccRCC) is a highly heterogeneous disease, making it challenging to predict prognosis and therapy efficacy. In this study, we aimed to explore the role of 5-methylcytosine (m5C) RNA modification in ccRCC and its potential as a predictor for therapy response and overall survival (OS). We established a novel 5-methylcytosine RNA modification-related gene index (M5CRMRGI) and studied its effect on the tumor microenvironment (TME) using single-cell sequencing data for in-depth analysis, and verified it using spatial sequencing data. Our results showed that M5CRMRGI is an independent predictor of OS in multiple datasets and exhibited outstanding performance in predicting the OS of ccRCC. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in TME were observed between high- and low-M5CRMRGI groups. Single-cell/spatial transcriptomics revealed that M5CRMRGI could reprogram the distribution of tumor-infiltrating immune cells. Moreover, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade therapy of the high-risk group. We also predicted six potential drugs binding to the core target of the M5CRMRGI signature via molecular docking. Real-world treatment cohort data proved once again that high-risk patients were appropriate for immune checkpoint blockade therapy, while low-risk patients were appropriate for Everolimus. Our study shows that the m5C modification landscape plays a role in TME distribution. The proposed M5CRMRGI-guided strategy for predicting survival and immunotherapy efficacy, we reported here, might also be applied to more cancers other than ccRCC.

Schizophrenia-associated NRXN1 deletions induce developmental-timing- and cell-type-specific vulnerabilities in human brain organoids

De novo mutations and copy number deletions in NRXN1 (2p16.3) pose a significant risk for schizophrenia (SCZ). It is unclear how NRXN1 deletions impact cortical development in a cell type-specific manner and disease background modulates these phenotypes. Here, we leveraged human pluripotent stem cell-derived forebrain organoid models carrying NRXN1 heterozygous deletions in isogenic and SCZ patient genetic backgrounds and conducted single-cell transcriptomic analysis over the course of brain organoid development from 3 weeks to 3.5 months. Intriguingly, while both deletions similarly impacted molecular pathways associated with ubiquitin-proteasome system, alternative splicing, and synaptic signaling in maturing glutamatergic and GABAergic neurons, SCZ-NRXN1 deletions specifically perturbed developmental trajectories of early neural progenitors and accumulated disease-specific transcriptomic signatures. Using calcium imaging, we found that both deletions led to long-lasting changes in spontaneous and synchronous neuronal networks, implicating synaptic dysfunction. Our study reveals developmental-timing- and cell-type-dependent actions of NRXN1 deletions in unique genetic contexts.

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

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

OCT4 induces long-lived dedifferentiated kidney progenitors poised to redifferentiate in 3D kidney spheroids

Upscaling of kidney epithelial cells is crucial for renal regenerative medicine. Nonetheless, the adult kidney lacks a distinct stem cell hierarchy, limiting the ability to long-term propagate clonal populations of primary cells that retain renal identity. Toward this goal, we tested the paradigm of shifting the balance between differentiation and stemness in the kidney by introducing a single pluripotency factor, OCT4. Here we show that ectopic expression of OCT4 in human adult kidney epithelial cells (hKEpC) induces the cells to dedifferentiate, stably proliferate, and clonally emerge over many generations. Control hKEpC dedifferentiate, assume fibroblastic morphology, and completely lose clonogenic capacity. Analysis of gene expression and histone methylation patterns revealed that OCT4 represses the HNF1B gene module, which is critical for kidney epithelial differentiation, and concomitantly activates stemness-related pathways. OCT4-hKEpC can be long-term expanded in the dedifferentiated state that is primed for renal differentiation. Thus, when expanded OCT4-hKEpC are grown as kidney spheroids (OCT4-kSPH), they reactivate the HNF1B gene signature, redifferentiate, and efficiently generate renal structures in vivo. Hence, changes occurring in the cellular state of hKEpC following OCT4 induction, long-term propagation, and 3D aggregation afford rapid scale-up technology of primary renal tissue-forming cells.

Meta-analysis of single-cell and single-nucleus transcriptomics reveals kidney cell type consensus signatures

While the amount of studies involving single-cell or single-nucleus RNA-sequencing technologies grows exponentially within the biomedical research area, the kidney field requires reference transcriptomic signatures to allocate each cluster its matching cell type. The present meta-analysis of 39 previously published datasets, from 7 independent studies, involving healthy human adult kidney samples, offers a set of 24 distinct consensus kidney cell type signatures. The use of these signatures may help to assure the reliability of cell type identification in future studies involving single-cell and single-nucleus transcriptomics while improving the reproducibility in cell type allocation.

Single-cell transcriptomics uncovers cellular architecture and developmental trajectories in hepatoblastoma

BACKGROUND AND AIMS

Hepatoblastoma (HB) is the predominant type of childhood liver cancer. Treatment options for the clinically advanced HB remain limited. We aimed to dissect the cellular and molecular basis underlying HB oncogenesis and heterogeneity at the single-cell level, which could facilitate a better understanding of HB at both the biological and clinical levels.

APPROACH AND RESULTS

Single-cell transcriptome profiling of tumor and paired distal liver tissue samples from five patients with HB was performed. Deconvolution analysis was used for integrating the single-cell transcriptomic profiles with the bulk transcriptomes of our HB cohort of post-neoadjuvant chemotherapy tumor samples. A single-cell transcriptomic landscape of early human liver parenchymal development was established for exploring the cellular root and hierarchy of HB oncogenesis. As a result, seven distinct tumor cell subpopulations were annotated, and an effective HB subtyping method was established based on their compositions. A HB tumor cell hierarchy was further revealed to not only fit with the classical cancer stem cell (CSC) model but also mirror the early human liver parenchymal development. Moreover, FACT inhibition, which could disrupt the oncogenic positive feedback loop between MYC and SSRP1 in HB, was identified as a promising epigenetic-targeted therapeutic strategy against the CSC-like HB1-Pro-like1 subpopulation and its related high-risk "Pro-like1" subtype of HB.

CONCLUSIONS

Our findings illustrate the cellular architecture and developmental trajectories of HB via integrative bulk and single-cell transcriptome analyses, thus establishing a resourceful framework for the development of targeted diagnostics and therapeutics in the future.

Single-cell technologies in multiple myeloma: new insights into disease pathogenesis and translational implications

Multiple myeloma (MM) is a hematological malignancy characterized by clonal proliferation of plasma cells. Although therapeutic advances have been made to improve clinical outcomes and to prolong patients' survival in the past two decades, MM remains largely incurable. Single-cell sequencing (SCS) is a powerful method to dissect the cellular and molecular landscape at single-cell resolution, instead of providing averaged results. The application of single-cell technologies promises to address outstanding questions in myeloma biology and has revolutionized our understanding of the inter- and intra-tumor heterogeneity, tumor microenvironment, and mechanisms of therapeutic resistance in MM. In this review, we summarize the recently developed SCS methodologies and latest MM research progress achieved by single-cell profiling, including information regarding the cancer and immune cell landscapes, tumor heterogeneities, underlying mechanisms and biomarkers associated with therapeutic response and resistance. We also discuss future directions of applying transformative SCS approaches with contribution to clinical translation.

SMARCB1 regulates a TFCP2L1-MYC transcriptional switch promoting renal medullary carcinoma transformation and ferroptosis resistance

Renal medullary carcinoma (RMC) is an aggressive tumour driven by bi-allelic loss of SMARCB1 and tightly associated with sickle cell trait. However, the cell-of-origin and oncogenic mechanism remain poorly understood. Using single-cell sequencing of human RMC, we defined transformation of thick ascending limb (TAL) cells into an epithelial-mesenchymal gradient of RMC cells associated with loss of renal epithelial transcription factors TFCP2L1, HOXB9 and MITF and gain of MYC and NFE2L2-associated oncogenic and ferroptosis resistance programs. We describe the molecular basis for this transcriptional switch that is reversed by SMARCB1 re-expression repressing the oncogenic and ferroptosis resistance programs leading to ferroptotic cell death. Ferroptosis resistance links TAL cell survival with the high extracellular medullar iron concentrations associated with sickle cell trait, an environment propitious to the mutagenic events associated with RMC development. This unique environment may explain why RMC is the only SMARCB1-deficient tumour arising from epithelial cells, differentiating RMC from rhabdoid tumours arising from neural crest cells.

scSemiAAE: a semi-supervised clustering model for single-cell RNA-seq data

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical to transcriptome research as it allows for further identification and discovery of new cell types. Unsupervised clustering cannot integrate prior knowledge where relevant information is widely available. Purely unsupervised clustering algorithms may not yield biologically interpretable clusters when confronted with the high dimensionality of scRNA-seq data and frequent dropout events, which makes identification of cell types more challenging.

RESULTS

We propose scSemiAAE, a semi-supervised clustering model for scRNA sequence analysis using deep generative neural networks. Specifically, scSemiAAE carefully designs a ZINB adversarial autoencoder-based architecture that inherently integrates adversarial training and semi-supervised modules in the latent space. In a series of experiments on scRNA-seq datasets spanning thousands to tens of thousands of cells, scSemiAAE can significantly improve clustering performance compared to dozens of unsupervised and semi-supervised algorithms, promoting clustering and interpretability of downstream analyses.

CONCLUSION

scSemiAAE is a Python-based algorithm implemented on the VSCode platform that provides efficient visualization, clustering, and cell type assignment for scRNA-seq data. The tool is available from https://github.com/WHang98/scSemiAAE .

ScRNA-seq revealed an immunosuppression state and tumor microenvironment heterogeneity related to lymph node metastasis in prostate cancer

BACKGROUND

Metastasis is a crucial aspect of disease progression leading to death in patients with prostate cancer (PCa). However, its mechanism remains unclear. We aimed to explore the mechanism of lymph node metastasis (LNM) by analyzing the heterogeneity of tumor microenvironment (TME) in PCa using scRNA-seq.

METHODS

A total of 32,766 cells were obtained from four PCa tissue samples for scRNA-seq, annotated, and grouped. InferCNV, GSVA, DEG functional enrichment analysis, trajectory analysis, intercellular network evaluation, and transcription factor analysis were carried out for each cell subgroup. Furthermore, validation experiments targeting luminal cell subgroups and CXCR4 + fibroblast subgroup were performed.

RESULTS

The results showed that only EEF2 + and FOLH1 + luminal subgroups were present in LNM, and they appeared at the initial stage of luminal cell differentiation, which were comfirmed by verification experiments. The MYC pathway was enriched in the EEF2 + and FOLH1 + luminal subgroups, and MYC was associated with PCa LNM. Moreover, MYC did not only promote the progression of PCa, but also led to immunosuppression in TME by regulating PDL1 and CD47. The proportion of CD8 + T cells in TME and among NK cells and monocytes was lower in LNM than in the primary lesion, while the opposite was true for Th and Treg cells. Furthermore, these immune cells in TME underwent transcriptional reprogramming, including CD8 + T subgroups of CCR7 + and IL7R+, as well as M2-like monocyte subgroups expressing tumor-associated signature genes, like CCR7, SGKI, and RPL31. Furthermore, STEAP4+, ADGRF5 + and CXCR4+, and SRGNC + fibroblast subgroups were closely related to tumor progression, tumor metabolism, and immunosuppression, indicating their contributions in PCa metastasis. Meanwhile, The presence of CXCR4 + Fibroblasts in PCa was confirmed by polychromatic immunofluorescence.

CONCLUSIONS

The significant heterogeneity of luminal, immune, and interstitial cells in PCa LNM may not only directly contribute to tumor progression, but also indirectly result in TME immunosuppression, which may be the cause of metastasis in PCa and in which MYC played an role.

Single-Cell RNA-Seq Analysis Reveals Ferroptosis in the Tumor Microenvironment of Clear Cell Renal Cell Carcinoma

In this study, we investigated the role of ferroptosis in the tumor microenvironment (TME) of clear cell renal cell carcinoma (ccRCC), the leading cause of renal cancer-related death. We analyzed single-cell data from seven ccRCC cases to determine cell types most correlated with ferroptosis and performed pseudotime analysis on three myeloid subtypes. We identified 16 immune-related ferroptosis genes (IRFGs) by analyzing differentially expressed genes between cell subgroups and between high and low immune infiltration groups in the TCGA-KIRC dataset and the FerrDb V2 database. Using univariate and multivariate Cox regression, we identified two independent prognostic genes, AMN and PDK4, and constructed an IRFG score model immune-related ferroptosis genes risk score (IRFGRs) to evaluate its prognostic value in ccRCC. The IRFGRs demonstrated excellent and stable performance for predicting ccRCC patient survival in both the TCGA training set and the ArrayExpress validation set, with an AUC range of 0.690-0.754, outperforming other commonly used clinicopathological indicators. Our findings enhance the understanding of TME infiltration with ferroptosis and identify immune-mediated ferroptosis genes associated with prognosis in ccRCC.

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

Mammalian kidneys maintain fluid homeostasis through the cellular activity of nephrons and the conjoined collecting system. Each epithelial network originates from distinct progenitor cell populations that reciprocally interact during development. To extend our understanding of human and mouse kidney development, we profiled chromatin organization (ATAC-seq) and gene expression (RNA-seq) in developing human and mouse kidneys. Data were analyzed at a species level and then integrated into a common, cross-species multimodal data set. Comparative analysis of cell types and developmental trajectories identified conserved and divergent features of chromatin organization and linked gene activity, revealing species- and cell-type specific regulatory programs. Identification of human-specific enhancer regions linked through GWAS studies to kidney disease highlights the potential of developmental modeling to provide clinical insight.

Single-Cell Analysis in Immuno-Oncology

The complexity of the cellular and non-cellular milieu surrounding human tumors plays a decisive role in the course and outcome of disease. The high variability in the distribution of the immune and non-immune compartments within the tumor microenvironments (TME) among different patients governs the mode of their response or resistance to current immunotherapeutic approaches. Through deciphering this diversity, one can tailor patients' management to meet an individual's needs. Single-cell (sc) omics technologies have given a great boost towards this direction. This review gathers recent data about how multi-omics profiling, including the utilization of single-cell RNA sequencing (scRNA-seq), assay for transposase-accessible chromatin with sequencing (scATAC-seq), T-cell receptor sequencing (scTCR-seq), mass, tissue-based, or microfluidics cytometry, and related bioinformatics tools, contributes to the high-throughput assessment of a large number of analytes at single-cell resolution. Unravelling the exact TCR clonotype of the infiltrating T cells or pinpointing the classical or novel immune checkpoints across various cell subsets of the TME provide a boost to our comprehension of adaptive immune responses, their antigen specificity and dynamics, and grant suggestions for possible therapeutic targets. Future steps are expected to merge high-dimensional data with tissue localization data, which can serve the investigation of novel multi-modal biomarkers for the selection and/or monitoring of the optimal treatment from the current anti-cancer immunotherapeutic armamentarium.

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

BACKGROUND

Intratumor heterogeneity (ITH) is a key feature in clear cell renal cell carcinomas (ccRCCs) that impacts outcomes such as aggressiveness, response to treatments, or recurrence. In particular, it may explain tumor relapse after surgery in clinically low-risk patients who did not benefit from adjuvant therapy. Recently, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to unravel expression ITH (eITH) and might enable better assessment of clinical outcomes in ccRCC.

OBJECTIVE

To explore eITH in ccRCC with a focus on malignant cells (MCs) and assess its relevance to improve prognosis for low-risk patients.

DESIGN, SETTING, AND PARTICIPANTS

We performed scRNA-seq on tumor samples from five untreated ccRCC patients ranging from pT1a to pT3b. Data were complemented with a published dataset composed of pairs of matched normal and ccRCC samples.

INTERVENTION

Radical or partial nephrectomy on untreated ccRCC patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Viability and cell type proportions were determined by flow cytometry. Following scRNA-seq, a functional analysis was performed and tumor progression trajectories were inferred. A deconvolution approach was applied on an external cohort, and Kaplan-Meier survival curves were estimated with respect to the prevalence of malignant clusters.

RESULTS AND LIMITATIONS

We analyzed 54 812 cells and identified 35 cell subpopulations. The eITH analysis revealed that each tumor contained various degrees of clonal diversity. The transcriptomic signatures of MCs in one particularly heterogeneous sample were used to design a deconvolution-based strategy that allowed the risk stratification of 310 low-risk ccRCC patients.

CONCLUSIONS

We described eITH in ccRCCs, and used this information to establish significant cell population-based prognostic signatures and better discriminate ccRCC patients. This approach has the potential to improve the stratification of clinically low-risk patients and their therapeutic management.

PATIENT SUMMARY

We sequenced the RNA content of individual cell subpopulations composed of clear cell renal cell carcinomas and identified specific malignant cells the genetic information of which can be used to predict tumor progression.

The transcriptional and regulatory identity of erythropoietin producing cells

Erythropoietin (Epo) is the master regulator of erythropoiesis and oxygen homeostasis. Despite its physiological importance, the molecular and genomic contexts of the cells responsible for renal Epo production remain unclear, limiting more-effective therapies for anemia. Here, we performed single-cell RNA and transposase-accessible chromatin (ATAC) sequencing of an Epo reporter mouse to molecularly identify Epo-producing cells under hypoxic conditions. Our data indicate that a distinct population of kidney stroma, which we term Norn cells, is the major source of endocrine Epo production in mice. We use these datasets to identify the markers, signaling pathways and transcriptional circuits characteristic of Norn cells. Using single-cell RNA sequencing and RNA in situ hybridization in human kidney tissues, we further provide evidence that this cell population is conserved in humans. These preliminary findings open new avenues to functionally dissect EPO gene regulation in health and disease and may serve as groundwork to improve erythropoiesis-stimulating therapies.

Isthmin-1 (Ism1) modulates renal branching morphogenesis and mesenchyme condensation during early kidney development

The outgrowth of epithelial bud followed by reiterated bifurcations during renal development is driven by the ligand-receptor interactions between the epithelium and the surrounding mesenchyme. Here, by exploring ligand-receptor interactions in E10.5 and E11.5 kidneys by single cell RNA-seq, we find that Isthmin1 (Ism1), a secreted protein, resembles Gdnf expression and modulates kidney branching morphogenesis. Mice deficient for Ism1 exhibit defective ureteric bud bifurcation and impaired metanephric mesenchyme condensation in E11.5 embryos, attributable to the compromised Gdnf/Ret signaling, ultimately leading to renal agenesis and hypoplasia/dysplasia. By HRP-induced proximity labelling, we further identify integrin α8β1 as a receptor of Ism1 in E11.5 kidney and demonstrate that Ism1 promoted cell-cell adhesion through interacting with Integrin α8β1, the receptor whose activation is responsible for Gdnf expression and mesenchyme condensation. Taken together, our work reveals Ism1 as a critical regulator of cell-cell interaction that modulates Gdnf/Ret signaling during early kidney development.

MicroRNA-142-3p promotes renal cell carcinoma progression by targeting RhoBTB3 to regulate HIF-1 signaling and GGT/GSH pathways

MicroRNAs play a critical regulatory role in different cancers, but their functions in renal cell carcinoma (RCC) have not been elucidated. Reportedly, miR-142-3p is involved in the tumorigenesis and the development of RCC in vitro and is clinically correlated with the poor prognosis of RCC patients. However, the molecular target of miR-142-3p and the underlying mechanism are unclear. In this study, we found that miR-142-3p was upregulated in RCC tumor tissues and downregulated in exosomes compared to normal tissues. The expression of miR-142-3p was inversely associated with the survival of patients with kidney renal clear cell carcinoma (KIRC). RhoBTB3 was reduced in RCC, and miR-142-3p plays an inverse function with RhoBTB3 in KIRC. The direct interaction between RhoBTB3 and miR-142-3p was demonstrated by a dual luciferase reporter assay. miR-142-3p promoted metastasis in the xenograft model, and the suppression of miR-142-3p upregulated RhoBTB3 protein expression and inhibited the mRNAs and proteins of HIF1A, VEGFA, and GGT1. Also, the miR-142-3p overexpression upregulated the mRNA of HIF1A, VEGFA, and GGT1. In conclusion, miR-142-3p functions as an oncogene in RCC, especially in KIRC, by targeting RhoBTB3 to regulate HIF-1 signaling and GGT/GSH pathways, which needs further exploration.

Uncovering a novel role of focal adhesion and interferon-gamma in cellular rejection of kidney allografts at single cell resolution

Background

Kidney transplant recipients are currently treated with nonspecific immunosuppressants that cause severe systemic side effects. Current immunosuppressants were developed based on their effect on T-cell activation rather than the underlying mechanisms driving alloimmune responses. Thus, understanding the role of the intragraft microenvironment will help us identify more directed therapies with lower side effects.

Methods

To understand the role of the alloimmune response and the intragraft microenvironment in cellular rejection progression, we conducted a Single nucleus RNA sequencing (snRNA-seq) on one human non-rejecting kidney allograft sample, one borderline sample, and T-cell mediated rejection (TCMR) sample (Banff IIa). We studied the differential gene expression and enriched pathways in different conditions, in addition to ligand-receptor (L-R) interactions.

Results

Pathway analysis of T-cells in borderline sample showed enrichment for allograft rejection pathway, suggesting that the borderline sample reflects an early rejection. Hence, this allows for studying the early stages of cellular rejection. Moreover, we showed that focal adhesion (FA), IFNg pathways, and endomucin (EMCN) were significantly upregulated in endothelial cell clusters (ECs) of borderline compared to ECs TCMR. Furthermore, we found that pericytes in TCMR seem to favor endothelial permeability compared to borderline. Similarly, T-cells interaction with ECs in borderline differs from TCMR by involving DAMPS-TLRs interactions.

Conclusion

Our data revealed novel roles of T-cells, ECs, and pericytes in cellular rejection progression, providing new clues on the pathophysiology of allograft rejection.

Epigenetic and transcriptomic characterization reveals progression markers and essential pathways in clear cell renal cell carcinoma

Identifying tumor-cell-specific markers and elucidating their epigenetic regulation and spatial heterogeneity provides mechanistic insights into cancer etiology. Here, we perform snRNA-seq and snATAC-seq in 34 and 28 human clear cell renal cell carcinoma (ccRCC) specimens, respectively, with matched bulk proteogenomics data. By identifying 20 tumor-specific markers through a multi-omics tiered approach, we reveal an association between higher ceruloplasmin (CP) expression and reduced survival. CP knockdown, combined with spatial transcriptomics, suggests a role for CP in regulating hyalinized stroma and tumor-stroma interactions in ccRCC. Intratumoral heterogeneity analysis portrays tumor cell-intrinsic inflammation and epithelial-mesenchymal transition (EMT) as two distinguishing features of tumor subpopulations. Finally, BAP1 mutations are associated with widespread reduction of chromatin accessibility, while PBRM1 mutations generally increase accessibility, with the former affecting five times more accessible peaks than the latter. These integrated analyses reveal the cellular architecture of ccRCC, providing insights into key markers and pathways in ccRCC tumorigenesis.

Multi-modal quantification of pathway activity with MAYA

Signaling pathways can be activated through various cascades of genes depending on cell identity and biological context. Single-cell atlases now provide the opportunity to inspect such complexity in health and disease. Yet, existing reference tools for pathway scoring resume activity of each pathway to one unique common metric across cell types. Here, we present MAYA, a computational method that enables the automatic detection and scoring of the diverse modes of activation of biological pathways across cell populations. MAYA improves the granularity of pathway analysis by detecting subgroups of genes within reference pathways, each characteristic of a cell population and how it activates a pathway. Using multiple single-cell datasets, we demonstrate the biological relevance of identified modes of activation, the robustness of MAYA to noisy pathway lists and batch effect. MAYA can also predict cell types starting from lists of reference markers in a cluster-free manner. Finally, we show that MAYA reveals common modes of pathway activation in tumor cells across patients, opening the perspective to discover shared therapeutic vulnerabilities.

Pan-cancer classification of single cells in the tumour microenvironment

Single-cell RNA sequencing can reveal valuable insights into cellular heterogeneity within tumour microenvironments (TMEs), paving the way for a deep understanding of cellular mechanisms contributing to cancer. However, high heterogeneity among the same cancer types and low transcriptomic variation in immune cell subsets present challenges for accurate, high-resolution confirmation of cells' identities. Here we present scATOMIC; a modular annotation tool for malignant and non-malignant cells. We trained scATOMIC on >300,000 cancer, immune, and stromal cells defining a pan-cancer reference across 19 common cancers and employ a hierarchical approach, outperforming current classification methods. We extensively confirm scATOMIC's accuracy on 225 tumour biopsies encompassing >350,000 cancer and a variety of TME cells. Lastly, we demonstrate scATOMIC's practical significance to accurately subset breast cancers into clinically relevant subtypes and predict tumours' primary origin across metastatic cancers. Our approach represents a broadly applicable strategy to analyse multicellular cancer TMEs.

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

A single-cell transcriptome atlas profiles early organogenesis in human embryos

The early window of human embryogenesis is largely a black box for developmental biologists. Here we probed the cellular diversity of 4-6 week human embryos when essentially all organs are just laid out. On the basis of over 180,000 single-cell transcriptomes, we generated a comprehensive atlas of 313 clusters in 18 developmental systems, which were annotated with a collection of ontology and markers from 157 publications. Together with spatial transcriptome on embryonic sections, we characterized the molecule and spatial architecture of previously unappreciated cell types. Combined with data from other vertebrates, the rich information shed light on spatial patterning of axes, systemic temporal regulation of developmental progression and potential human-specific regulation. Our study provides a compendium of early progenitor cells of human organs, which can serve as the root of lineage analysis in organogenesis.

Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5

Sepsis is a disease with a very high mortality rate, mainly involving an immune-dysregulated response due to bacterial infection. Most studies are currently limited to the whole blood transcriptome level; however, at the single cell level, there is still a great deal unknown about specific cell subsets and disease markers. We obtained 29 peripheral blood single-cell sequencing data, including 66,283 cells from 10 confirmed samples of sepsis infection and 19 healthy samples. Cells related to the sepsis phenotype were identified and characterized by the "scissor" method. The regulatory relationships of sepsis-related phenotype cells in the cellular communication network were clarified using the "cell chat" method. The least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) were used to identify sepsis signature genes of diagnostic value. External validation was performed using multiple datasets from the GEO database (GSE28750, GSE185263, GSE57065) and 40 clinical samples. Bayesian algorithm was used to calculate the regulatory network of LILRA5 co-expressed genes. The stability of atenolol-targeting LILRA5 was determined by molecular docking techniques. Ultimately, action trajectory and survival analyses demonstrate the effectiveness of atenolol-targeted LILRA5 in treating patients with sepsis. We successfully identified 1215 healthy phenotypic cells and 462 sepsis phenotypic cells. We focused on 447 monocytes of the sepsis phenotype. Among the cellular communications, there were a large number of differences between these cells and other immune cells showing a significant inflammatory phenotype compared to the healthy phenotypic cells. Together, the three machine learning algorithms identified the LILRA5 marker gene in sepsis patients, and validation results from multiple external datasets as well as real-world clinical samples demonstrated the robust diagnostic performance of LILRA5. The AUC values of LILRA5 in the external datasets GSE28750, GSE185263, and GSE57065 could reach 0.875, 0.940, and 0.980, in that order. Bayesian networks identified a large number of unknown regulatory relationships for LILRA5 co-expression. Molecular docking results demonstrated the possibility of atenolol targeting LILRA5 for the treatment of sepsis. Behavioral trajectory analysis and survival analysis demonstrate that atenolol has a desirable therapeutic effect. LILRA5 is a marker gene in sepsis patients, and atenolol can stably target LILRA5.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Integrative Single-Cell Analysis Reveals Transcriptional and Epigenetic Regulatory Features of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) frequently features a high level of tumor heterogeneity. Elucidating the chromatin landscape of ccRCC at the single-cell level could provide a deeper understanding of the functional states and regulatory dynamics underlying the disease. Here, we performed single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) on 19 ccRCC samples, and whole-exome sequencing was used to understand the heterogeneity between individuals. Single-cell transcriptome and chromatin accessibility maps of ccRCC were constructed to reveal the regulatory characteristics of different tumor cell subtypes in ccRCC. Two long noncoding RNAs (RP11-661C8.2 and CTB-164N12.1) were identified that promoted the invasion and migration of ccRCC, which was validated with in vitro experiments. Taken together, this study comprehensively characterized the gene expression and DNA regulation landscape of ccRCC, which could provide new insights into the biology and treatment of ccRCC.

SIGNIFICANCE

A comprehensive analysis of gene expression and DNA regulation in ccRCC using scATAC-seq and scRNA-seq reveals the DNA regulatory programs of ccRCC at the single-cell level.

Cancer stem cells: Recent insights and therapies

Tumors are intricate ecosystems containing malignant components that generate adaptive and evolutionarily driven abnormal tissues. Through self-renewal and differentiation, cancers are reconstructed by a dynamic subset of stem-like cells that enforce tumor heterogeneity and remodel the tumor microenvironment (TME). Through recent technology advances, we are now better equipped to investigate the fundamental role of cancer stem cells (CSCs) in cancer biology. In this review, we discuss the latest insights into characteristics, markers and mechanism of CSCs and describe the crosstalk between CSCs and other cells in TME. Additionally, we explore the performance of single-cell sequencing and spatial transcriptome analysis in CSCs studies and summarize the therapeutic strategies to eliminate CSCs, which could broaden the understanding of CSCs and exploit for therapeutic benefit.

scGCL: an imputation method for scRNA-seq data based on graph contrastive learning

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) is widely used to reveal cellular heterogeneity, complex disease mechanisms and cell differentiation processes. Due to high sparsity and complex gene expression patterns, scRNA-seq data present a large number of dropout events, affecting downstream tasks such as cell clustering and pseudo-time analysis. Restoring the expression levels of genes is essential for reducing technical noise and facilitating downstream analysis. However, existing scRNA-seq data imputation methods ignore the topological structure information of scRNA-seq data and cannot comprehensively utilize the relationships between cells.

RESULTS

Here, we propose a single-cell Graph Contrastive Learning method for scRNA-seq data imputation, named scGCL, which integrates graph contrastive learning and Zero-inflated Negative Binomial (ZINB) distribution to estimate dropout values. scGCL summarizes global and local semantic information through contrastive learning and selects positive samples to enhance the representation of target nodes. To capture the global probability distribution, scGCL introduces an autoencoder based on the ZINB distribution, which reconstructs the scRNA-seq data based on the prior distribution. Through extensive experiments, we verify that scGCL outperforms existing state-of-the-art imputation methods in clustering performance and gene imputation on 14 scRNA-seq datasets. Further, we find that scGCL can enhance the expression patterns of specific genes in Alzheimer's disease datasets.

AVAILABILITY AND IMPLEMENTATION

The code and data of scGCL are available on Github: https://github.com/zehaoxiong123/scGCL.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Development and validation of a prognostic model based on a single-cell RNA-seq in Wilms tumor in children

To analyze the heterogeneity between different cell types in pediatric Wilms tumor (WT) tissue, and identify the differentially expressed genes (DEGs) of malignant tumor cells, thereby establishing a prognostic model. The single-cell sequencing data of pediatric WT tissues were downloaded from the public database. Data filtration and normalization, principal component analysis, and T-distributed stochastic neighbor embedding cluster analysis were performed using the Seurat package of R language. Cells were divided into different clusters, malignant tumor cells were extracted, and DEGs were obtained. Then, the pseudo-time trajectory analysis was performed. Prognostic biomarkers were determined by univariate and multivariate COX regression analyses and LASSO regression analysis. Kaplan-Meier survival analysis and receiver operator characteristic curve analysis were performed. Combined with the prognostic biomarkers and clinical characteristics, a nomogram was generated to predict WT prognosis. The prognostic power was validated in the external datasets. Cells in the WT tissue were divided into 10 clusters. Three prognostic biomarkers that affected the survival time of patients were screened from 215 DEGs in malignant tumor cells, and a nomogram was constructed using the three genes and clinical characteristics. The area under the curve (AUC) values of 3- and 5-year disease-free survival were 0.756 and 0.734, respectively. In the external validation dataset, the AUC value of this nomogram model was 0.826. Based on the single-cell RNA-seq, we recognized cell clusters in the WT tissue of children, identified prognostic biomarkers in malignant tumor cells, and established a comprehensive prognostic model. Our findings might provide new ideas and methods for the diagnosis and treatment of WT.

Vascular endothelial cell development and diversity

Vascular endothelial cells form the inner layer of blood vessels where they have a key role in the development and maintenance of the functional circulatory system and provide paracrine support to surrounding non-vascular cells. Technical advances in the past 5 years in single-cell genomics and in in vivo genetic labelling have facilitated greater insights into endothelial cell development, plasticity and heterogeneity. These advances have also contributed to a new understanding of the timing of endothelial cell subtype differentiation and its relationship to the cell cycle. Identification of novel tissue-specific gene expression patterns in endothelial cells has led to the discovery of crucial signalling pathways and new interactions with other cell types that have key roles in both tissue maintenance and disease pathology. In this Review, we describe the latest findings in vascular endothelial cell development and diversity, which are often supported by large-scale, single-cell studies, and discuss the implications of these findings for vascular medicine. In addition, we highlight how techniques such as single-cell multimodal omics, which have become increasingly sophisticated over the past 2 years, are being utilized to study normal vascular physiology as well as functional perturbations in disease.

Effects of donor source on transcriptomic profiles of human kidney tissue

Normal human tissue is a critical reference control in biomedical research. However, the type of tissue donor can significantly affect the underlying biology of the samples. We investigated the impact of tissue donor source type by performing transcriptomic analysis on healthy kidney tissue from three donor source types: cadavers, organ donors, and normal-adjacent tissue from surgical resections of clear cell renal cell carcinomas, and we compared the gene expression profiles to those of clear cell renal cell carcinoma samples. Comparisons among the normal samples revealed general similarity, with notable differences in gene expression pathways involving immune system and inflammatory processes, response to extracellular stimuli, ion transport, and metabolism. When compared to tumors, the transcriptomic profiles of the normal adjacent tissue were highly similar to the profiles from cadaveric and organ donor tissue samples, arguing against the presence of a field cancerization effect in clear cell renal cell carcinoma. We conclude that all three normal source types are suitable for reference kidney control samples, but important differences must be noted for particular research areas and tissue banking strategies.

Fumarate induces vesicular release of mtDNA to drive innate immunity

Mutations in fumarate hydratase (FH) cause hereditary leiomyomatosis and renal cell carcinoma1. Loss of FH in the kidney elicits several oncogenic signalling cascades through the accumulation of the oncometabolite fumarate2. However, although the long-term consequences of FH loss have been described, the acute response has not so far been investigated. Here we generated an inducible mouse model to study the chronology of FH loss in the kidney. We show that loss of FH leads to early alterations of mitochondrial morphology and the release of mitochondrial DNA (mtDNA) into the cytosol, where it triggers the activation of the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING)-TANK-binding kinase 1 (TBK1) pathway and stimulates an inflammatory response that is also partially dependent on retinoic-acid-inducible gene I (RIG-I). Mechanistically, we show that this phenotype is mediated by fumarate and occurs selectively through mitochondrial-derived vesicles in a manner that depends on sorting nexin 9 (SNX9). These results reveal that increased levels of intracellular fumarate induce a remodelling of the mitochondrial network and the generation of mitochondrial-derived vesicles, which allows the release of mtDNAin the cytosol and subsequent activation of the innate immune response.

Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating CD8+ T cells

Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying the heterogeneous functional and dysfunctional states of human tumor-infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify gene-regulatory landscapes in a wide breadth of functional and dysfunctional CD8⁺ TIL states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor-entity-matching samples and prioritize cell-state-specific genes by super-enhancer analysis. Besides revealing entity-specific chromatin remodeling in exhausted TILs, our analyses identify a common chromatin trajectory to TIL dysfunction and determine key enhancers, transcriptional regulators, and deregulated genes involved in this process. Finally, we validate enhancer regulation at immunotherapeutically relevant loci by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study provides a framework for understanding and manipulating cell-state-specific gene-regulatory cues from human tumor-infiltrating lymphocytes.

Cell-cell communications shape tumor microenvironment and predict clinical outcomes in clear cell renal carcinoma

BACKGROUND

Cell-cell communications of various cell populations within tumor microenvironment play an essential role in primary tumor growth, metastasis evolution, and immune escape. Nevertheless, comprehensive investigation of cell-cell communications in the ccRCC (Clear cell renal carcinoma) microenvironment and how this interplay affects prognosis still remains limited.

METHODS

Intercellular communications were characterized by single-cell data. Firstly, we employed "CellChat" package to characterize intercellular communications across all types of cells in microenvironment in VHL mutated and non-mutated samples from 8 patients, respectively. And pseudotime trajectory analyses were performed with monocle analyses. Finally clinical prognosis and immunotherapy efficacy with different landscapes of intercellular interplay are evaluated by TCGA-KIRC and immunotherapy cohort.

RESULTS

Firstly, the VHL phenotype may be related to the intercellular communication landscape. And trajectory analysis reveals the potential relationship of cell-cell communication molecules with T cells and Myeloid cells differentiation. Furthermore, those molecules also correlate with the infiltration of T cells and Myeloid cells. A tumor cluster with highly expressed ligands was defined by quantitative analysis and transcription factor enrichment analysis, which was identified to be pivotal for intercellular communications in tumor microenvironment. Finally, bulk data indicates bulk that different clusters with different intercellular communications have significant predictive value for prognosis and distinguished immunotherapy efficiency.

CONCLUSIONS

The intercellular communication landscapes of VHL wild and VHL mutant ccRCC vary. Intercellular communications within the tumor microenvironment also influence T cell and myeloid cell development and infiltration, as well as predict clinical prognosis and immunotherapy efficacy in ccRCC.

GPX8 regulates clear cell renal cell carcinoma tumorigenesis through promoting lipogenesis by NNMT

BACKGROUND

Clear cell renal cell carcinoma (ccRCC), with its hallmark phenotype of high cytosolic lipid content, is considered a metabolic cancer. Despite the implication of this lipid-rich phenotype in ccRCC tumorigenesis, the roles and regulators of de novo lipid synthesis (DNL) in ccRCC remain largely unexplained.

METHODS

Our bioinformatic screening focused on ccRCC-lipid phenotypes identified glutathione peroxidase 8 (GPX8), as a clinically relevant upstream regulator of DNL. GPX8 genetic silencing was performed with CRISPR-Cas9 or shRNA in ccRCC cell lines to dissect its roles. Untargeted metabolomics, RNA-seq analyses, and other biochemical assays (e.g., lipid droplets staining, fatty acid uptake, cell proliferation, xenograft, etc.) were carried out to investigate the GPX8's involvement in lipid metabolism and tumorigenesis in ccRCC. The lipid metabolic function of GPX8 and its downstream were also measured by isotope-tracing-based DNL flux measurement.

RESULTS

GPX8 knockout or downregulation substantially reduced lipid droplet levels (independent of lipid uptake), fatty acid de novo synthesis, triglyceride esterification in vitro, and tumor growth in vivo. The downstream regulator was identified as nicotinamide N-methyltransferase (NNMT): its knockdown phenocopied, and its expression rescued, GPX8 silencing both in vitro and in vivo. Mechanically, GPX8 regulated NNMT via IL6-STAT3 signaling, and blocking this axis suppressed ccRCC survival by activating AMPK. Notably, neither the GPX8-NNMT axis nor the DNL flux was affected by the von Hippel Lindau (VHL) status, the conventional regulator of ccRCC high lipid content.

CONCLUSIONS

Taken together, our findings unravel the roles of the VHL-independent GPX8-NNMT axis in ccRCC lipid metabolism as related to the phenotypes and growth of ccRCC, which may be targeted for therapeutic purposes.

The clinical utility of integrative genomics in childhood cancer extends beyond targetable mutations

We conducted integrative somatic-germline analyses by deeply sequencing 864 cancer-associated genes, complete genomes and transcriptomes for 300 mostly previously treated children and adolescents/young adults with cancer of poor prognosis or with rare tumors enrolled in the SickKids Cancer Sequencing (KiCS) program. Clinically actionable variants were identified in 56% of patients. Improved diagnostic accuracy led to modified management in a subset. Therapeutically targetable variants (54% of patients) were of unanticipated timing and type, with over 20% derived from the germline. Corroborating mutational signatures (SBS3/BRCAness) in patients with germline homologous recombination defects demonstrates the potential utility of PARP inhibitors. Mutational burden was significantly elevated in 9% of patients. Sequential sampling identified changes in therapeutically targetable drivers in over one-third of patients, suggesting benefit from rebiopsy for genomic analysis at the time of relapse. Comprehensive cancer genomic profiling is useful at multiple points in the care trajectory for children and adolescents/young adults with cancer, supporting its integration into early clinical management.

Single-nucleus RNA and ATAC sequencing uncovers the molecular and cellular characteristics in the musk gland of Chinese forest musk deer (Moschus berezovskii)

The Chinese forest musk deer (FMD; Moschus berezovskii) is an endangered artiodactyl mammal. Musk secreted by the musk gland of male has extremely high economic and medicinal value. However, the molecular and cellular characteristics of the musk gland have not been studied. Here, we investigated the diversity and transcriptional composition of musk gland cell types and the effect of cell type-specific chromatin accessibility on gene expression using single-nucleus RNA sequencing (snRNA-seq) and single-nucleus ATAC sequencing (snATAC-seq) association analysis. Based on uniform manifold approximation and projection (UMAP) analysis, we identified 13 cell types from the musk gland, which included two different acinar cells (cluster 0 and cluster 10). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that many pathways related to musk secretion were enriched in acinar cells. Our analysis also revealed acinar cell core transcription factors and core target genes, and further constructed acinar cell-specific regulatory networks. In cluster 0, 11 core target genes (Nedd4l, Adcy9, Akr1c1, Vapb, Me1, Acsl1, Acss3, Srd5a1, Scnn1a, Acadm, and Nceh1) possibly related to musk secretion were regulated by 24 core transcription factors (SP3, NFIC, NR6A1, EHF, RUNX1, TFAP2A, RREB1, GRHL2, NFIB, ELF1, MAX, KLF5, REL, HES1, POU2F3, TFDP1, NR2C1, ATF7, MEIS1, NR4A2, NFIA, PBX1, ZNF652, and NFKB1). In cluster 10, four core target genes (Akr1c1, Pcca, Atp1b1, and Sgk1) possibly related to musk secretion were regulated by 10 core transcription factors (BARX2, EHF, PBX1, RUNX1, NFIB, FOXP1, KLF3, KLF6, ETV6, and NR3C2). Moreover, the credibility of snRNA-seq and snATAC-seq data was verified by fluorescence in situ hybridization and immunohistochemistry. Finally, cell communication analysis demonstrated that the two types of acinar cells mainly have communications in musk secretion-related processes. In conclusion, we provided important insights and invaluable resources for the molecular and cellular characteristics of the musk gland, which will lay a foundation for the study of musk secretion mechanism in the future.

Decision level integration of unimodal and multimodal single cell data with scTriangulate

Decisively delineating cell identities from uni- and multimodal single-cell datasets is complicated by diverse modalities, clustering methods, and reference atlases. We describe scTriangulate, a computational framework to mix-and-match multiple clustering results, modalities, associated algorithms, and resolutions to achieve an optimal solution. Rather than ensemble approaches which select the "consensus", scTriangulate picks the most stable solution through coalitional iteration. When evaluated on diverse multimodal technologies, scTriangulate outperforms alternative approaches to identify high-confidence cell-populations and modality-specific subtypes. Unlike existing integration strategies that rely on modality-specific joint embedding or geometric graphs, scTriangulate makes no assumption about the distributions of raw underlying values. As a result, this approach can solve unprecedented integration challenges, including the ability to automate reference cell-atlas construction, resolve clonal architecture within molecularly defined cell-populations and subdivide clusters to discover splicing-defined disease subtypes. scTriangulate is a flexible strategy for unified integration of single-cell or multimodal clustering solutions, from nearly unlimited sources.

Hallmarks of crustacean immune hemocytes at single-cell resolution

In invertebrates, hemocytes are the key factors in innate immunity. However, the types of invertebrate immune hemocytes are unclassified due to the limitation of morphological classification. To determine the immune hemocytes of crustaceans, the heterogeneity of hemocytes of shrimp Marsupenaeus japonicus and crayfish Procambarus clarkii, two representative crustacean species, were characterized in this study. The results of single-cell RNA sequencing indicated that shrimp and crayfish contained 11 and 12 types of hemocytes, respectively. Each of different types of hemocytes specifically expressed the potential marker genes. Based on the responses of shrimp and crayfish to the infection of white spot syndrome virus (WSSV) and the challenge of lipopolysaccharide (LPS), four types of immune hemocytes of crustaceans were classified, including semi-granular hemocytes involved in antimicrobial peptide production, granular hemocytes responsible for the production of antimicrobial peptides, hemocytes related to cell proliferation and hemocytes in immunity-activated state. Therefore, our study provided the first classification of crustacean hemocytes as well as of immune hemocytes of crustaceans at the single-cell resolution, which would be helpful to understand the innate immunity of invertebrates.