An entropy-based metric for assessing the purity of single cell populations

Large-scale bulk and single-cell RNA sequencing combined with machine learning reveals glioblastoma-associated neutrophil heterogeneity and establishes a VEGFA+ neutrophil prognostic model

BACKGROUND

Neutrophils play a key role in the tumor microenvironment (TME); however, their functions in glioblastoma (GBM) are overlooked and insufficiently studied. A detailed analysis of GBM-associated neutrophil (GBMAN) subpopulations may offer new insights and opportunities for GBM immunotherapy.

METHODS

We analyzed single-cell RNA sequencing (scRNA-seq) data from 127 isocitrate dehydrogenase (IDH) wild-type GBM samples to characterize the GBMAN subgroups, emphasizing developmental trajectories, cellular communication, and transcriptional networks. We implemented 117 machine learning combinations to develop a novel risk model and compared its performance to existing glioma models. Furthermore, we assessed the biological and molecular features of the GBMAN subgroups in patients.

RESULTS

From integrated large-scale scRNA-seq data (498,747 cells), we identified 5,032 neutrophils and classified them into four distinct subtypes. VEGFA+GBMAN exhibited reduced inflammatory response characteristics and a tendency to interact with stromal cells. Furthermore, these subpopulations exhibited significant differences in transcriptional regulation. We also developed a risk model termed the "VEGFA+neutrophil-related signature" (VNRS) using machine learning methods. The VNRS model showed higher accuracy than previously published risk models and was an independent prognostic factor. Additionally, we observed significant differences in immunotherapy responses, TME interactions, and chemotherapy efficacy between high-risk and low-risk VNRS score groups.

CONCLUSION

Our study highlights the critical role of neutrophils in the TME of GBM, allowing for a better understanding of the composition and characteristics of GBMAN. The developed VNRS model serves as an effective tool for evaluating the risk and guiding clinical treatment strategies for GBM.

CLINICAL TRIAL NUMBER

Not applicable.

Comparative analysis of nuclei isolation methods for brain single-nucleus RNA sequencing

Single-nucleus RNA sequencing (snRNA-seq) enables resolving cellular heterogeneity in complex tissues. snRNA-seq overcomes limitations of traditional single-cell RNA-seq by using nuclei instead of cells, allowing to utilize frozen tissues and difficult-to-isolate cell types. Although various nuclei isolation methods have been developed, systematic evaluations of their effects on nuclear integrity and subsequent data quality remain lacking, a critical gap with profound implications for the rigor and reproducibility. To address this, we compared three mechanistically distinct nuclei isolation strategies with brain tissues: a sucrose gradient centrifugation-based method, a spin column-based method, and a machine-assisted platform. All methods successfully captured diverse cell types but revealed considerable protocol-dependent differences in cell type proportions, transcriptional homogeneity, and the preservation of cell-type-specific and cell-state-specific markers. Moreover, isolation workflows differentially influenced contamination levels from ambient, mitochondrial, and ribosomal RNAs. Our findings establish nuclei isolation methodology as a critical experimental variable shaping snRNA-seq data quality and biological interpretation.

MOTIVATION

Single-nucleus RNA sequencing (snRNA-seq) has become an essential tool for transcriptomic analysis of complex tissues. However, the quality and efficiency of data generation depend heavily on the method used for nuclear isolation. The existing isolation techniques vary in their ability to preserve nuclear integrity, minimize ambient RNA contamination, and optimize recovery rates. Despite these differences in quality, a systematic comparison of these methods, specifically for brain tissue, is lacking. This gap poses a challenge for researchers in choosing the most suitable approach for their particular experimental requirements. To address this critical issue, our study directly compared three nuclei isolation methods and evaluated their performance in terms of yield, purity, and downstream sequencing quality. By providing a comprehensive assessment, we aim to guide researchers in selecting the most appropriate isolation protocol for their snRNA-seq experiments, ensuring optimal results and advancing the study of complex brain tissues at the single-nucleus level.

Single-cell RNA sequencing revealed changes in the tumor microenvironment induced by radiotherapy for cervical cancer and the molecular mechanism of mast cells in immunosuppression

Radiotherapy (RT) is an important treatment for cervical cancer (CC), effectively controlling tumor growth and improving survival rates. However, radiotherapy-induced cell heterogeneity and its underlying mechanisms remain unclear, which may potentially impact treatment efficacy. This study aims to investigate tumor microenvironment changes following radiotherapy for CC, hoping to provide evidence to improve the therapeutic effects of radiotherapy. For the first time, we applied single-cell RNA sequencing (scRNA-seq) to analyze tissue samples from three CC patients pre- and post-radiotherapy. We obtained gene expression data from 52,506 cells to identify the cellular changes and molecular mechanisms induced by radiotherapy. Radiotherapy significantly alters cellular composition and gene expression within the tumor microenvironment (TME), notably upregulating mast cell expression. Mast cells are involved in multiple cell axes in the CC ecosystem after radiotherapy, and play a pivotal role in tumor immunosuppression and matrix remodeling. scRNA-seq revealed gene expression variations among cell types after radiotherapy, underscoring the importance of specific cell types in modulating the TME post-treatment. This study revealed the molecular mechanism of radiotherapy for CC and the role of mast cells, providing a foundation for optimizing the personalized treatment of CC.

Single-cell transcriptomic analysis of chondrocytes in cartilage and pathogenesis of osteoarthritis

Chondrocyte is considered the only cell type in cartilage. However, the cell heterogeneity of chondrocytes in human articular cartilage is still not well defined, which hinders our understanding of the pathogenesis of osteoarthritis (OA). Here, we constructed a single-cell transcriptomic atlas of chondrocytes in healthy cartilage and identified nine chondrocyte subsets including homeostatic chondrocytes, proliferate fibrochondrocytes, and hypertrophic chondrocytes (HTC). Interestingly, we identified two distinct HTC subpopulations, among which HTC-1 specifically expressed genes associated with apoptosis and programmed cell death. We identified two main trajectories of chondrocytes, one of which differentiates into fibrochondrocytes, while the other terminates in apoptosis. Comparison of chondrocyte subsets between healthy and OA cartilage showed that proliferate fibrochondrocytes and HTC-1 expanded in OA patients, whereas homeostatic chondrocytes decreased. Interestingly, we discovered an OA-specific proliferate fibrochondrocyte subset that may contribute to the development of OA via inflammation. In summary, this study significantly enhanced our understanding of cell heterogeneity of chondrocytes in articular cartilage and provides insight into the pathogenesis of OA.

Exploring the heterogeneous targets of metabolic aging at single-cell resolution

Our limited understanding of metabolic aging poses major challenges to comprehending the diverse cellular alterations that contribute to age-related decline, and to devising targeted interventions. This review provides insights into the heterogeneous nature of cellular metabolism during aging and its response to interventions, with a specific focus on cellular heterogeneity and its implications. By synthesizing recent findings using single-cell approaches, we explored the vulnerabilities of distinct cell types and key metabolic pathways. Delving into the cell type-specific alterations underlying the efficacy of systemic interventions, we also discuss the complexity of integrating single-cell data and advocate for leveraging computational tools and artificial intelligence to harness the full potential of these data, develop effective strategies against metabolic aging, and promote healthy aging.

Epithelial and immune transcriptomic characteristics and possible regulatory mechanisms in asthma exacerbation: insights from integrated studies

Background

Asthma exacerbation significantly contribute to disease mortality and result in heightened health care expenditures. This study was aimed at gaining important new insights into the heterogeneity of epithelial and immune cells and elucidating key regulatory genes involved in the pathogenesis of asthma exacerbation.

Methods

Functional enrichment, pseudotime, metabolism and cell-cell communication analyses of epithelial cells and immune cells in single-cell RNA sequencing (scRNA-seq) dataset were applied. Immune infiltration analysis was performed in bulk RNA sequencing (bulk RNA-seq) dataset. Key regulatory genes were obtained by taking the intersection of the differentially expressed genes (DEGs) between control and asthma group in epithelial cells, immune cells and bulk RNA-seq data. Asthma animal and in vitro cell line models were established to verify the key regulatory genes expression by employing quantitative reverse transcription polymerase chain reaction (qRT-PCR).

Results

ScRNA-seq analysis identified 7 epithelial subpopulations and 14 distinct immune cell types based on gene expression profiles. Further analysis demonstrated that these cells manifested high heterogeneity at the levels of functional variations, dynamics, communication patterns and metabolic changes. Notably, TMPRSS11A, TUBA1A, SCEL, ICAM4, TMPRSS11B, IGFBP2, CLC, NFAM1 and F13A1 were identified as key regulatory genes of asthma. The results of the qRT-PCR demonstrated that the 9 key regulatory genes were involved in asthma.

Conclusions

We systematically explored epithelial and immune characteristics in asthma exacerbation and identified 9 key regulatory genes underlying asthma occurrence and progression, which may be valuable for providing new insights into the cellular and molecular mechanisms driving asthma exacerbations.

Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses

Double dipping is a well-known pitfall in single-cell and spatial transcriptomics data analysis: after a clustering algorithm finds clusters as putative cell types or spatial domains, statistical tests are applied to the same data to identify differentially expressed (DE) genes as potential cell-type or spatial-domain markers. Because the genes that contribute to clustering are inherently likely to be identified as DE genes, double dipping can result in false-positive cell-type or spatial-domain markers, especially when clusters are spurious, leading to ambiguously defined cell types or spatial domains. To address this challenge, we propose ClusterDE, a statistical method designed to identify post-clustering DE genes as reliable markers of cell types and spatial domains, while controlling the false discovery rate (FDR) regardless of clustering quality. The core of ClusterDE involves generating synthetic null data as an in silico negative control that contains only one cell type or spatial domain, allowing for the detection and removal of spurious discoveries caused by double dipping. We demonstrate that ClusterDE controls the FDR and identifies canonical cell-type and spatial-domain markers as top DE genes, distinguishing them from housekeeping genes. ClusterDE's ability to discover reliable markers, or the absence of such markers, can be used to determine whether two ambiguous clusters should be merged. Additionally, ClusterDE is compatible with state-of-the-art analysis pipelines like Seurat and Scanpy.

MPCD Index for Hepatocellular Carcinoma Patients Based on Mitochondrial Function and Cell Death Patterns

Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer with a poor prognosis. During the development of cancer cells, mitochondria influence various cell death patterns by regulating metabolic pathways such as oxidative phosphorylation. However, the relationship between mitochondrial function and cell death patterns in HCC remains unclear. In this study, we used a comprehensive machine learning framework to construct a mitochondrial functional activity-associated programmed cell death index (MPCDI) based on scRNA-seq and RNA-seq data from TCGA, GEO, and ICGC datasets. The index signature was used to classify HCC patients, and studied the multi-omics features, immune microenvironment, and drug sensitivity of the subtypes. Finally, we constructed the MPCDI signature consisting of four genes (S100A9, FYN, LGALS3, and HMOX1), which was one of the independent risk factors for the prognosis of HCC patients. The HCC patients were divided into high- and low-MPCDI groups, and the immune status was different between the two groups. Patients with a high MPCDI had higher TIDE scores and poorer responses to immunotherapy, suggesting that high-MPCDI patients might not be suitable for immunotherapy. By analyzing the drug sensitivity data of CTRP, GDSC, and PRISM databases, it was found that staurosporine has potential therapeutic significance for patients with a high MPCDI. In summary, based on the characteristics of mitochondria function and PCD patterns, we used single-cell and transcriptome data to identify four genes and construct the MPCDI signature, which provided new perspectives and directions for the clinical diagnosis and personalized treatment of HCC patients.

Zi Shen Wan Fang repaired blood-brain barrier integrity in diabetic cognitive impairment mice via preventing cerebrovascular cells senescence

BACKGROUND

Blood-brain barrier (BBB) integrity disruption is a key pathological link of diabetes-induced cognitive impairment (DCI), but the detailed mechanism of how the diabetic environment induces BBB integrity disruption is not fully understood. Our previous study found that Zi Shen Wan Fang (ZSWF), an optimized prescription consisting of Anemarrhenae Rhizoma (Anemarrhena asphodeloides Bge.), Phellodendri Chinensis Cortex (Phellodendron chinense Schneid.) and Cistanches Herba (Cistanche deserticola Y.C.Ma) has excellent efficacy in alleviating DCI, however, whether its mechanism is related to repairing BBB integrity remains unclear. This study aims to reveal the mechanism of BBB integrity destruction in DCI mice, and to elucidate the mechanism by which ZSWF repairs BBB integrity and improves cognitive function in DCI mice.

METHODS

Diabetic mouse model was established by feeding a 60% high-fat diet combined with a single intraperitoneal injection of 120 mg/kg streptozotocin (STZ). DCI mice were screened with morris water maze (MWM) after 8 weeks of sustained hyperglycemic stimulation. ZSWF was administered daily at doses of 9.36 and 18.72 g/kg for 8 weeks. Cognitive function was evaluated using MWM, blood-brain-barrier (BBB) integrity was tested using immunostaining and western blot, the underlying mechanisms were explored using single-cell RNA sequencing (scRNA-seq), validation experiments were performed with immunofluorescence analysis, and the potential active ingredients of ZSWF against cerebrovascular senescence were predicted using molecular docking. Moreover, cerebral microvascular endothelial cells were cultured, and the effects of mangiferin on the expression of p21 and Vcam1 were investigated by immunofluorescence staining and RT-qPCR.

RESULTS

ZSWF treatment significantly ameliorated cognitive function and repaired BBB integrity in DCI mice. Using scRNA-seq, we identified 14 brain cell types. In BBB constituent cells (endothelial cells and pericytes), we found that Cdkn1a and senescence-associated secretory phenotype (SASP) genes were significantly overexpressed in DCI mice, while ZSWF intervention significantly inhibited the expression of Cdkn1a and SASP genes in cerebrovascular cells of DCI mice. Moreover, we also found that the communication between brain endothelial cells and pericytes was decreased in DCI mice, while ZSWF significantly increased the communication between them, especially the expression of PDGFRβ in pericytes. Molecular docking results showed that mangiferin, the blood component of ZSWF, had a stronger affinity with the upstream proteins of p21. In vitro experiments showed that high glucose significantly increased the expression of p21 and Vcam1 in bEnd.3 cells, while mangiferin significantly inhibited the expression of p21 and Vcam1 induced by high glucose.

CONCLUSION

Our study reveals that ZSWF can ameliorate cognitive function in DCI mice by repairing BBB integrity, and the specific mechanism of which may be related to preventing cerebrovascular cells senescence, and mangiferin is its key active ingredient.

BrainCellR: A precise cell type nomenclature pipeline for comparative analysis across brain single-cell datasets

Single-cell studies in neuroscience require precise cell type classification and consistent nomenclature that allows for meaningful comparisons across diverse datasets. Current approaches often lack the ability to identify fine-grained cell types and establish standardized annotations at the cluster level, hindering comprehensive understanding of the brain's cellular composition. To facilitate data integration across multiple models and datasets, we designed BrainCellR. This pipeline provides researchers with a powerful and user-friendly tool for efficient cell type classification and nomination from single-cell transcriptomic data. While initially focused on brain studies, BrainCellR is applicable to other tissues with complex cellular compositions. BrainCellR goes beyond conventional classification approaches by incorporating a standardized nomenclature system for cell types at the cluster level. This feature enables consistent and comparable annotations across different studies, promoting data integration and providing deeper insights into the complex cellular landscape of the brain. All documents for BrainCellR, including source code, user manual and tutorials, are freely available at https://github.com/WangLab-SINH/BrainCellR.

Functional profiling of murine glioma models highlights targetable immune evasion phenotypes

Cancer-intrinsic immune evasion mechanisms and pleiotropy are a barrier to cancer immunotherapy. This is apparent in certain highly fatal cancers, including high-grade gliomas and glioblastomas (GBM). In this study, we evaluated two murine syngeneic glioma models (GL261 and CT2A) as preclinical models for human GBM using functional genetic screens, single-cell transcriptomics and machine learning approaches. Through CRISPR genome-wide co-culture killing screens with various immune cells (cytotoxic T cells, natural killer cells, and macrophages), we identified three key cancer-intrinsic evasion mechanisms: NFκB signaling, autophagy/endosome machinery, and chromatin remodeling. Additional fitness screens identified dependencies in murine gliomas that partially recapitulated those seen in human GBM (e.g., UFMylation). Our single-cell analyses showed that different glioma models exhibited distinct immune infiltration patterns and recapitulated key immune gene programs observed in human GBM, including hypoxia, interferon, and TNF signaling. Moreover, in vivo orthotopic tumor engraftment was associated with phenotypic shifts and changes in proliferative capacity, with murine tumors recapitulating the intratumoral heterogeneity observed in human GBM, exhibiting propensities for developmental- and mesenchymal-like phenotypes. Notably, we observed common transcription factors and cofactors shared with human GBM, including developmental (Nfia and Tcf4), mesenchymal (Prrx1 and Wwtr1), as well as cycling-associated genes (Bub3, Cenpa, Bard1, Brca1, and Mis18bp1). Perturbation of these genes led to reciprocal phenotypic shifts suggesting intrinsic feedback mechanisms that balance in vivo cellular states. Finally, we used a machine-learning approach to identify two distinct immune evasion gene programs, one of which represents a clinically-relevant phenotype and delineates a subpopulation of stem-like glioma cells that predict response to immune checkpoint inhibition in human patients. This comprehensive characterization helps bridge the gap between murine glioma models and human GBM, providing valuable insights for future therapeutic development.

A novel computational model ITHCS for enhanced prognostic risk stratification in ESCC by correcting for intratumor heterogeneity

Intratumor heterogeneity significantly challenges the accuracy of existing prognostic models for esophageal squamous cell carcinoma (ESCC) by introducing biases related to the varied genetic and molecular landscapes within tumors. Traditional models, relying on single-sample, single-region bulk RNA sequencing, fall short of capturing the complexity of intratumor heterogeneity. To fill this gap, we developed a computational model for intratumor heterogeneity corrected signature (ITHCS) by employing both multiregion bulk and single-cell RNA sequencing to pinpoint genes that exhibit consistent expression patterns across different tumor regions but vary significantly among patients. Utilizing these genes, we applied multiple machine-learning algorithms for sophisticated feature selection and model construction. The ITHCS model significantly outperforms existing prognostic indicators in accuracy and generalizability, markedly reducing sampling biases caused by intratumor heterogeneity. This improvement is especially notable in the prognostic assessment of early-stage ESCC patients, where the model exhibits exceptional predictive power. Additionally, we found that the risk score based on ITHCS may be associated with epithelial-mesenchymal transition characteristics, indicating that high-risk patients may exhibit a diminished efficacy to immunotherapy.

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types

BACKGROUND

Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited.

METHODS

We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties.

FINDINGS

Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells.

INTERPRETATION

This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism.

FUNDING

National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Decoding the multiple functions of ZBP1 in the mechanism of sepsis-induced acute lung injury

Sepsis-induced acute lung injury (ALI), characterized by severe hypoxemia and pulmonary leakage, remains a leading cause of mortality in intensive care units. The exacerbation of ALI during sepsis is largely attributed to uncontrolled inflammatory responses and endothelial dysfunction. Emerging evidence suggests an important role of Z-DNA binding protein 1 (ZBP1) as a sensor in innate immune to drive inflammatory signaling and cell death during infections. However, the role of ZBP1 in sepsis-induced ALI has yet to be defined. We utilized ZBP1 knockout mice and combined single-cell RNA sequencing with experimental validation to investigate ZBP1's roles in the regulation of macrophages and lung endothelial cells during sepsis. We demonstrate that in sepsis, ZBP1 deficiency in macrophages reduces mitochondrial damage and inhibits glycolysis, thereby altering the metabolic status of macrophages. Consequently, this metabolic shift leads to a reduction in the differentiation of macrophages into pro-inflammatory states and decreases macrophage pyroptosis triggered by activation of the NLRP3 inflammasome. These changes significantly weaken the inflammatory signaling pathways between macrophages and endothelial cells and alleviate endothelial dysfunction and cellular damage. These findings reveal important roles for ZBP1 in mediating multiple pathological processes involved in sepsis-induced ALI by modulating the functional states of macrophages and endothelial cells, thereby highlighting its potential as a promising therapeutic target.

Lineage tracing of stem cell-derived dopamine grafts in a Parkinson's model reveals shared origin of all graft-derived cells

Stem cell therapies for Parkinson's disease are at an exciting time of development, and several clinical trials have recently been initiated. Human pluripotent stem cells are differentiated into transplantable dopamine (DA) progenitors which are proliferative at the time of grafting and undergo terminal differentiation and maturation in vivo. While the progenitors are homogeneous at the time of transplantation, they give rise to heterogeneous grafts composed not only of therapeutic DA neurons but also of other mature cell types. The mechanisms for graft diversification are unclear. We used single-nucleus RNA-seq and ATAC-seq to profile DA progenitors before transplantation combined with molecular barcode-based tracing to determine origin and shared lineages of the mature cell types in the grafts. Our data demonstrate that astrocytes, vascular leptomeningeal cells, and DA neurons are the main component of the DAergic grafts, originating from a common progenitor that is tripotent at the time of transplantation.

Cross-tissue human fibroblast atlas reveals myofibroblast subtypes with distinct roles in immune modulation

Fibroblasts, known for their functional diversity, play crucial roles in inflammation and cancer. In this study, we conduct comprehensive single-cell RNA sequencing analyses on fibroblast cells from 517 human samples, spanning 11 tissue types and diverse pathological states. We identify distinct fibroblast subpopulations with universal and tissue-specific characteristics. Pathological conditions lead to significant shifts in fibroblast compositions, including the expansion of immune-modulating fibroblasts during inflammation and tissue-remodeling myofibroblasts in cancer. Within the myofibroblast category, we identify four transcriptionally distinct subpopulations originating from different developmental origins, with LRRC15+ myofibroblasts displaying terminally differentiated features. Both LRRC15+ and MMP1+ myofibroblasts demonstrate pro-tumor potential that contribute to the immune-excluded and immune-suppressive tumor microenvironments (TMEs), whereas PI16+ fibroblasts show potential anti-tumor functions in adjacent non-cancerous regions. Fibroblast-subtype compositions define patient subtypes with distinct clinical outcomes. This study advances our understanding of fibroblast biology and suggests potential therapeutic strategies for targeting specific fibroblast subsets in cancer treatment.

A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection

Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.

Tumour evolution and microenvironment interactions in 2D and 3D space

To study the spatial interactions among cancer and non-cancer cells1, we here examined a cohort of 131 tumour sections from 78 cases across 6 cancer types by Visium spatial transcriptomics (ST). This was combined with 48 matched single-nucleus RNA sequencing samples and 22 matched co-detection by indexing (CODEX) samples. To describe tumour structures and habitats, we defined 'tumour microregions' as spatially distinct cancer cell clusters separated by stromal components. They varied in size and density among cancer types, with the largest microregions observed in metastatic samples. We further grouped microregions with shared genetic alterations into 'spatial subclones'. Thirty five tumour sections exhibited subclonal structures. Spatial subclones with distinct copy number variations and mutations displayed differential oncogenic activities. We identified increased metabolic activity at the centre and increased antigen presentation along the leading edges of microregions. We also observed variable T cell infiltrations within microregions and macrophages predominantly residing at tumour boundaries. We reconstructed 3D tumour structures by co-registering 48 serial ST sections from 16 samples, which provided insights into the spatial organization and heterogeneity of tumours. Additionally, using an unsupervised deep-learning algorithm and integrating ST and CODEX data, we identified both immune hot and cold neighbourhoods and enhanced immune exhaustion markers surrounding the 3D subclones. These findings contribute to the understanding of spatial tumour evolution through interactions with the local microenvironment in 2D and 3D space, providing valuable insights into tumour biology.

Single-cell characterization of differentiation trajectories and drug resistance features in gastric cancer with peritoneal metastasis

BACKGROUND

Gastric cancer patients with peritoneal metastasis (GCPM) experience a rapidly deteriorating clinical trajectory characterized by therapeutic resistance and dismal survival, particularly following the development of malignant ascites. However, the intricate dynamics within the peritoneal microenvironment (PME) during the treatment process remain largely unknown.

METHODS

Matched samples from primary tumours (PT), peritoneal metastases (PM), and paired pre-treatment and post-chemo/immunotherapy (anti-PD-1/PD-L1) progression malignant ascites samples, were collected from 48 patients. These samples were subjected to single-cell RNA sequencing (n = 30), multiplex immunofluorescence (n = 30), and spatial transcriptomics (n = 3). Furthermore, post hoc analyses of a phase 1 clinical trial (n = 20, NCT03710265) and an in-house immunotherapy cohort (n = 499) were conducted to validate the findings.

RESULTS

Tracing the evolutionary trajectory of epithelial cells unveiled the terminally differentially MUC1+ cancer cells with a high epithelial-to-mesenchymal transition potential, and they demonstrated spatial proximity with fibroblasts and endothelial cells, correlating with poor prognosis. A significant expansion of macrophage infiltrates, which exhibited the highest proangiogenic activity, was observed in the ascites compared with PT and PM. Besides, higher C1Q+ macrophage infiltrates correlated with significantly lower GZMA+ T-lymphocyte infiltrates in therapeutic failure cases, potentially mediated by the LGALS9-CD45 and SPP1-CD44 ligand-receptor interactions. In the chemoresistant group, intimate interactions between C1Q+ macrophages and fibroblasts through the complement activation pathway were found. In the group demonstrating immunoresistance, heightened TGF-β production activity was detected in MUC1+ cancer cells, and they were skewed to interplay with C1Q+ macrophages through the GDF15-TGF-βR2 axis. Ultimately, post hoc analyses indicated that co-targeting TGF-β and PDL1 pathways may confer superior clinical benefits than sole anti-PD-1/PD-L1 therapy for patients presenting with GCPM at the time of diagnosis.

CONCLUSIONS

Our findings elucidated the cellular differentiation trajectories and crucial drug resistance features within PME, facilitating the exploration of effective targets for GCPM treatment.

HIGHLIGHTS

MUC1+ cancer cells with a high epithelial-to-mesenchymal transition potential and exhibiting spatial proximity to fibroblasts and endothelial cells constitute the driving force of gastric cancer peritoneal metastasis (GCPM). Higher C1Q+ macrophage infiltrates correlated with significantly lower GZMA+ T-lymphocyte infiltrates within the peritoneal microenvironment in therapeutic failure cases. Co-targeting TGF-β and PDL1 pathways may confer superior clinical benefits than sole anti-PD-1/PD-L1 therapy for patients presenting with GCPM at diagnosis.

Trajectory-centric framework TrajAtlas reveals multi-scale differentiation heterogeneity among cells, genes, and gene modules in osteogenesis

Osteoblasts, the key cells responsible for bone formation and the maintenance of skeletal integrity, originate from a diverse array of progenitor cells. However, the mechanisms underlying osteoblast differentiation from these multiple osteoprogenitors remain poorly understood. To address this knowledge gap, we developed a comprehensive framework to investigate osteoblast differentiation at multiple scales, encompassing cells, genes, and gene modules. We constructed a reference atlas focused on differentiation, which incorporates various osteoprogenitors and provides a seven-level cellular taxonomy. To reconstruct the differentiation process, we developed a model that identifies the transcription factors and pathways involved in differentiation from different osteoprogenitors. Acknowledging that covariates such as age and tissue type can influence differentiation, we created an algorithm to detect differentially expressed genes throughout the differentiation process. Additionally, we implemented methods to identify conserved pseudotemporal gene modules across multiple samples. Overall, our framework systematically addresses the heterogeneity observed during osteoblast differentiation from diverse sources, offering novel insights into the complexities of bone formation and serving as a valuable resource for understanding osteogenesis.

Clustering scRNA-seq data with the cross-view collaborative information fusion strategy

Single-cell RNA sequencing (scRNA-seq) technology has revolutionized biological research by enabling high-throughput, cellular-resolution gene expression profiling. A critical step in scRNA-seq data analysis is cell clustering, which supports downstream analyses. However, the high-dimensional and sparse nature of scRNA-seq data poses significant challenges to existing clustering methods. Furthermore, integrating gene expression information with potential cell structure data remains largely unexplored. Here, we present scCFIB, a novel information bottleneck (IB)-based clustering algorithm that leverages the power of IB for efficient processing of high-dimensional sparse data and incorporates a cross-view fusion strategy to achieve robust cell clustering. scCFIB constructs a multi-feature space by establishing two distinct views from the original features. We then formulate the cell clustering problem as a target loss function within the IB framework, employing a collaborative information fusion strategy. To further optimize scCFIB's performance, we introduce a novel sequential optimization approach through an iterative process. Benchmarking against established methods on diverse scRNA-seq datasets demonstrates that scCFIB achieves superior performance in scRNA-seq data clustering tasks. Availability: the source code is publicly available on GitHub: https://github.com/weixiaojiao/scCFIB.

Transcriptome sequencing reveals inflammation and macrophage heterogeneity in subacromial bursa from degenerative shoulder disorders

PURPOSE

We aimed to investigate the transcriptomic alterations that occur in the subacromial bursa (SAB) following degenerative or traumatic shoulder diseases.

MATERIALS AND METHODS

RNA sequencing was employed to evaluate the transcriptomic alterations of the SAB in individuals afflicted with degenerative rotator cuff tear (RCT), traumatic RCT and proximal humerus fracture (PHF). To gain insights into the biological significance of differentially expressed genes (DEGs), we conducted an enrichment analysis utilizing Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We further utilized single-cell RNA sequencing datasets of SAB from a recently published study to explore the associated cellular dynamics and alterations.

RESULTS

We detected 1,790 up-regulated and 1,964 down-regulated DEGs between degenerative RCT and PHF, 2,085 up-regulated and 1,919 down-regulated DEGs between degenerative RCT and traumatic RCT, and 20 up-regulated and 12 down-regulated DEGs between traumatic RCT and PHF. Given the similar expression pattern between traumatic RCT and PHF, they were integrated as the traumatic group. In comparison with the traumatic group, 1,983 up-regulated and 2,205 down-regulated DEGs were detected in degenerative SAB. Enrichment analysis of up-regulated DEGs uncovered an elevated inflammatory and immunologic responses in degenerative SAB. Single-cell transcriptomic analysis revealed macrophage represented the immune cell with the most DEGs between the degenerative and traumatic RCT.

CONCLUSION

Our results revealed that the SAB in degenerative RCT exhibited a different transcriptional signature compared to that in traumatic RCT, and enrichment analysis showed immunologic and inflammatory activations. Macrophages may play a fundamental role in this process.

Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution

Cancer is a highly heterogeneous disease, where phenotypically distinct subpopulations coexist and can be primed to different fates. Both genetic and epigenetic factors may drive cancer evolution, however little is known about whether and how such a process is pre-encoded in cancer clones. Using single-cell multi-omic lineage tracing and phenotypic assays, we investigate the predictive features of either tumour initiation or drug tolerance within the same cancer population. Clones primed to tumour initiation in vivo display two distinct transcriptional states at baseline. Remarkably, these states share a distinctive DNA accessibility profile, highlighting an epigenetic basis for tumour initiation. The drug tolerant niche is also largely pre-encoded, but only partially overlaps the tumour-initiating one and evolves following two genetically and transcriptionally distinct trajectories. Our study highlights coexisting genetic, epigenetic and transcriptional determinants of cancer evolution, unravelling the molecular complexity of pre-encoded tumour phenotypes.

Multi-omics analysis reveals the landscape of tumor microenvironments in left-sided and right-sided colon cancer

Background

Distinct clinical features and molecular characteristics of left-sided colon cancer (LCC) and right-sided colon cancer (RCC) suggest significant variations in their tumor microenvironments (TME). These differences can impact the efficacy of immunotherapy, making it essential to investigate and understand these disparities.

Methods

We conducted a multi-omics analysis, including bulk RNA sequencing (bulk RNA-seq), single-cell RNA sequencing (scRNA-seq), and whole-exome sequencing (WES), to investigate the constituents and characteristic differences of the tumor microenvironment (TME) in left-sided colon cancer (LCC) and right-sided colon cancer (RCC).

Result

Deconvolution algorithms revealed significant differences in infiltrated immune cells between left-sided colon cancer (LCC) and right-sided colon cancer (RCC), including dendritic cells, neutrophils, natural killer (NK) cells, CD4 and CD8 T cells, and M1 macrophages (P < 0.05). Notably, whole-exome sequencing (WES) data analysis showed a significantly higher mutation frequency in RCC compared to LCC (82,187/162 versus 18,726/115, P < 0.01). Single-cell analysis identified predominant tumor cell subclusters in RCC characterized by heightened proliferative potential and increased expression of major histocompatibility complex class I molecules. However, the main CD8 + T cell subpopulations in RCC exhibited a highly differentiated state, marked by T cell exhaustion and recent activation, defined as tumor-specific cytotoxic T lymphocytes (CTLs). Immunofluorescence and flow cytometry results confirmed this trend. Additionally, intercellular communication analysis demonstrated a greater quantity and intensity of interactions between tumor-specific CTLs and tumor cells in RCC.

Conclusion

RCC patients with an abundance of tumor-specific cytotoxic T lymphocytes (CTLs) and increased immunogenicity of tumor cells in the TME may be better candidates for immune checkpoint inhibitor therapy.

Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes

Characterizing the compositional and phenotypic characteristics of tumor-infiltrating B cells (TIBs) is important for advancing our understanding of their role in cancer development. Here, we establish a comprehensive resource of human B cells by integrating single-cell RNA sequencing data of B cells from 649 patients across 19 major cancer types. We demonstrate substantial heterogeneity in their total abundance and subtype composition and observe immunoglobulin G (IgG)-skewness of antibody-secreting cell isotypes. Moreover, we identify stress-response memory B cells and tumor-associated atypical B cells (TAABs), two tumor-enriched subpopulations with prognostic potential, shared in a pan-cancer manner. In particular, TAABs, characterized by a high clonal expansion level and proliferative capacity as well as by close interactions with activated CD4 T cells in tumors, are predictive of immunotherapy response. Our integrative resource depicts distinct clinically relevant TIB subsets, laying a foundation for further exploration of functional commonality and diversity of B cells in cancer.

ClusterMatch aligns single-cell RNA-sequencing data at the multi-scale cluster level via stable matching

MOTIVATION

Unsupervised clustering of single-cell RNA sequencing (scRNA-seq) data holds the promise of characterizing known and novel cell type in various biological and clinical contexts. However, intrinsic multi-scale clustering resolutions poses challenges to deal with multiple sources of variability in the high-dimensional and noisy data.

RESULTS

We present ClusterMatch, a stable match optimization model to align scRNA-seq data at the cluster level. In one hand, ClusterMatch leverages the mutual correspondence by canonical correlation analysis and multi-scale Louvain clustering algorithms to identify cluster with optimized resolutions. In the other hand, it utilizes stable matching framework to align scRNA-seq data in the latent space while maintaining interpretability with overlapped marker gene set. Through extensive experiments, we demonstrate the efficacy of ClusterMatch in data integration, cell type annotation, and cross-species/timepoint alignment scenarios. Our results show ClusterMatch's ability to utilize both global and local information of scRNA-seq data, sets the appropriate resolution of multi-scale clustering, and offers interpretability by utilizing marker genes.

AVAILABILITY AND IMPLEMENTATION

The code of ClusterMatch software is freely available at https://github.com/AMSSwanglab/ClusterMatch.

Immunosuppression and phenotypic plasticity in an atlas of human hepatocholangiocarcinoma

Background

Hepatocholangiocarcinoma (H-ChC) has the clinicopathological features of both hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) and is a more aggressive subtype of primary hepatic carcinoma than HCC or iCCA.

Methods

We sequenced 91,112 single-cell transcriptomes from 16 human samples to elucidate the molecular mechanisms underlying the coexistence of HCC and iCCA components in H-ChC.

Results

We observed two molecular subtypes of H-ChC at the whole-transcriptome level (CHP and CIP), where a metabolically active tumour cell subpopulation enriched in CHP was characterized by a cellular pre-differentiation property. To define the heterogeneity of tumours and their associated microenvironments, we observe greater tumour diversity in H-ChC than HCC and iCCA. H-ChC exhibits weaker immune cell infiltration and greater CD8+ exhausted T cell (Tex) dysfunction than HCC and iCCA. Then we defined two broad cell states of 6,852 CD8+ Tex cells: GZMK+ CD8+ Tex cells and terminal CD8+ Tex cells. GZMK+ CD8+ Tex cells exhibited higher infiltration of after treatment in H-ChC, the effector scores and expression of the immune checkpoints of them greatly increased after immunotherapy, which indicated that H-ChC might be more sensitive than HCC or iCCA to immunotherapy.

Conclusions

In this paper, H-ChC was explored, hoping to contribute to the study of mixed tumours in other cancers.

Single-cell resolution characterization of myeloid-derived cell states with implication in cancer outcome

Tumor-associated myeloid-derived cells (MDCs) significantly impact cancer prognosis and treatment responses due to their remarkable plasticity and tumorigenic behaviors. Here, we integrate single-cell RNA-sequencing data from different cancer types, identifying 29 MDC subpopulations within the tumor microenvironment. Our analysis reveals abnormally expanded MDC subpopulations across various tumors and distinguishes cell states that have often been grouped together, such as TREM2+ and FOLR2+ subpopulations. Using deconvolution approaches, we identify five subpopulations as independent prognostic markers, including states co-expressing TREM2 and PD-1, and FOLR2 and PDL-2. Additionally, TREM2 alone does not reliably predict cancer prognosis, as other TREM2+ macrophages show varied associations with prognosis depending on local cues. Validation in independent cohorts confirms that FOLR2-expressing macrophages correlate with poor clinical outcomes in ovarian and triple-negative breast cancers. This comprehensive MDC atlas offers valuable insights and a foundation for futher analyses, advancing strategies for treating solid cancers.

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines.

RESULTS

We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation.

CONCLUSIONS

No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.

Phenotypic and spatial heterogeneity of brain myeloid cells after stroke is associated with cell ontogeny, tissue damage, and brain connectivity

Acute stroke triggers extensive changes to myeloid immune cell populations in the brain that may be targets for limiting brain damage and enhancing repair. Immunomodulatory approaches will be most effective with precise manipulation of discrete myeloid cell phenotypes in time and space. Here, we investigate how stroke alters mononuclear myeloid cell composition and phenotypes at single-cell resolution and key spatial patterns. Our results show that multiple reactive microglial states and monocyte-derived populations contribute to an extensive myeloid cell repertoire in post-stroke brains. We identify important overlaps and distinctions among different cell types/states that involve ontogeny- and spatial-related properties. Notably, brain connectivity with infarcted tissue underpins the pattern of local and remote altered cell accumulation and reactivity. Our discoveries suggest a global but anatomically governed brain myeloid cell response to stroke that comprises diverse phenotypes arising through intrinsic cell ontogeny factors interacting with exposure to spatially organized brain damage and neuro-axonal cues.

scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data

In droplet-based single-cell and single-nucleus RNA-seq assays, systematic contamination of ambient RNA molecules biases the quantification of gene expression levels. Existing methods correct the contamination for all genes globally. However, there lacks specific evaluation of correction efficacy for varying contamination levels. Here, we show that DecontX and CellBender under-correct highly contaminating genes, while SoupX and scAR over-correct lowly/non-contaminating genes. Here, we develop scCDC as the first method to detect the contamination-causing genes and only correct expression levels of these genes, some of which are cell-type markers. Compared with existing decontamination methods, scCDC excels in decontaminating highly contaminating genes while avoiding over-correction of other genes.

A comprehensive single-cell breast tumor atlas defines epithelial and immune heterogeneity and interactions predicting anti-PD-1 therapy response

We present an integrated single-cell RNA sequencing atlas of the primary breast tumor microenvironment (TME) containing 236,363 cells from 119 biopsy samples across eight datasets. In this study, we leverage this resource for multiple analyses of immune and cancer epithelial cell heterogeneity. We define natural killer (NK) cell heterogeneity through six subsets in the breast TME. Because NK cell heterogeneity correlates with epithelial cell heterogeneity, we characterize epithelial cells at the level of single-gene expression, molecular subtype, and 10 categories reflecting intratumoral transcriptional heterogeneity. We develop InteractPrint, which considers how cancer epithelial cell heterogeneity influences cancer-immune interactions. We use T cell InteractPrint to predict response to immune checkpoint inhibition (ICI) in two breast cancer clinical trials testing neoadjuvant anti-PD-1 therapy. T cell InteractPrint was predictive of response in both trials versus PD-L1 (AUC = 0.82, 0.83 vs. 0.50, 0.72). This resource enables additional high-resolution investigations of the breast TME.

Single-cell analyses reveal evolution mimicry during the specification of breast cancer subtype

Background: The stem or progenitor antecedents confer developmental plasticity and unique cell identities to cancer cells via genetic and epigenetic programs. A comprehensive characterization and mapping of the cell-of-origin of breast cancer using novel technologies to unveil novel subtype-specific therapeutic targets is still absent. Methods: We integrated 195,144 high-quality cells from normal breast tissues and 406,501 high-quality cells from primary breast cancer samples to create a large-scale single-cell atlas of human normal and cancerous breasts. Potential heterogeneous origin of malignant cells was explored by contrasting cancer cells against reference normal epithelial cells. Multi-omics analyses and both in vitro and in vivo experiments were performed to screen and validate potential subtype-specific treatment targets. Novel biomarkers of identified immune and stromal cell subpopulations were validated by immunohistochemistry in our cohort. Results: Tumor stratification based on cancer cell-of-origin patterns correlated with clinical outcomes, genomic aberrations and diverse microenvironment constitutions. We found that the luminal progenitor (LP) subtype was robustly associated with poor prognosis, genomic instability and dysfunctional immune microenvironment. However, the LP subtype patients were sensitive to neoadjuvant chemotherapy (NAC), PARP inhibitors (PARPi) and immunotherapy. The LP subtype-specific target PLK1 was investigated by both in vitro and in vivo experiments. Besides, large-scale single-cell profiling of breast cancer inspired us to identify a range of clinically relevant immune and stromal cell subpopulations, including subsets of innate lymphoid cells (ILCs), macrophages and endothelial cells. Conclusion: The present single-cell study revealed the cellular repertoire and cell-of-origin patterns of breast cancer. Combining single-cell and bulk transcriptome data, we elucidated the evolution mimicry from normal to malignant subtypes and expounded the LP subtype with vital clinical implications. Novel immune and stromal cell subpopulations of breast cancer identified in our study could be potential therapeutic targets. Taken together, Our findings lay the foundation for the precise prognostic and therapeutic stratification of breast cancer.

A blueprint for tumor-infiltrating B cells across human cancers

B lymphocytes are essential mediators of humoral immunity and play multiple roles in human cancer. To decode the functions of tumor-infiltrating B cells, we generated a B cell blueprint encompassing single-cell transcriptome, B cell-receptor repertoire, and chromatin accessibility data across 20 different cancer types (477 samples, 269 patients). B cells harbored extraordinary heterogeneity and comprised 15 subsets, which could be grouped into two independent developmental paths (extrafollicular versus germinal center). Tumor types grouped into the extrafollicular pathway were linked with worse clinical outcomes and resistance to immunotherapy. The dysfunctional extrafollicular program was associated with glutamine-derived metabolites through epigenetic-metabolic cross-talk, which promoted a T cell-driven immunosuppressive program. These data suggest an intratumor B cell balance between extrafollicular and germinal-center responses and suggest that humoral immunity could possibly be harnessed for B cell-targeting immunotherapy.

High-Throughput Host-Microbe Single-Cell RNA Sequencing Reveals Ferroptosis-Associated Heterogeneity during Acinetobacter baumannii Infection

Interactions between host and bacterial cells are integral to human physiology. The complexity of host-microbe interactions extends to different cell types, spatial aspects, and phenotypic heterogeneity, requiring high-resolution approaches to capture their full complexity. The latest breakthroughs in single-cell RNA sequencing (scRNA-seq) have opened up a new era of studies in host-pathogen interactions. Here, we first report a high-throughput cross-species dual scRNA-seq technology by using random primers to simultaneously capture both eukaryotic and bacterial RNAs (scRandom-seq). Using reference cells, scRandom-seq can detect individual eukaryotic and bacterial cells with high throughput and high specificity. Acinetobacter baumannii (A.b) is a highly opportunistic and nosocomial pathogen that displays resistance to many antibiotics, posing a significant threat to human health, calling for discoveries and treatment. In the A.b infection model, scRandom-seq witnessed polarization of THP-1 derived-macrophages and the intracellular A.b-induced ferroptosis-stress in host cells. The inhibition of ferroptosis by Ferrostatin-1 (Fer-1) resulted in the improvement of cell vitality and resistance to A.b infection, indicating the potential to resist related infections. scRandom-seq provides a high-throughput cross-species dual single-cell RNA profiling tool that will facilitate future discoveries in unraveling the complex interactions of host-microbe interactions in infection systems and tumor micro-environments.

Multi-Omics Profiling Reveals Phenotypic and Functional Heterogeneity of Neutrophils in COVID-19

Accumulating evidence has revealed unexpected phenotypic heterogeneity and diverse functions of neutrophils in several diseases. Coronavirus disease (COVID-19) can alter the leukocyte phenotype based on disease severity, including neutrophil activation in severe cases. However, the plasticity of neutrophil phenotypes and their relative impact on COVID-19 pathogenesis has not been well addressed. This study aimed to identify and validate the heterogeneity of neutrophils in COVID-19 and evaluate the functions of each subpopulation. We analyzed public single-cell RNA-seq, bulk RNA-seq, and proteome data from healthy donors and patients with COVID-19 to investigate neutrophil subpopulations and their response to disease pathogenesis. We identified eight neutrophil subtypes: pro-neutrophil, pre-neutrophil, immature neutrophil, and five mature neutrophil subpopulations. The subtypes exhibited distinct features, including diverse activation signatures and multiple enriched pathways. The pro-neutrophil subtype was associated with severe and fatal disease, while the pre-neutrophil subtype was particularly abundant in mild/moderate disease. One of the mature neutrophil subtypes showed consistently large fractions in patients with different disease severity. Bulk RNA-seq dataset analyses using a cellular deconvolution approach validated the relative abundances of neutrophil subtypes and the expansion of pro-neutrophils in severe COVID-19 patients. Cell-cell communication analysis revealed representative ligand-receptor interactions among the identified neutrophil subtypes. Further investigation into transcription factors and differential protein abundance revealed the regulatory network differences between healthy donors and patients with severe COVID-19. Overall, we demonstrated the complex interactions among heterogeneous neutrophil subtypes and other blood cell types during COVID-19 disease. Our work has great value in terms of both clinical and public health as it furthers our understanding of the phenotypic and functional heterogeneity of neutrophils and other cell populations in multiple diseases.

Fallopian tube single cell analysis reveals myeloid cell alterations in high-grade serous ovarian cancer

Most high-grade serous ovarian cancers (HGSCs) likely initiate from fallopian tube (FT) epithelia. While epithelial subtypes have been characterized using single-cell RNA-sequencing (scRNA-Seq), heterogeneity of other compartments and their involvement in tumor progression are poorly defined. Integrated analysis of human FT scRNA-Seq and HGSC-related tissues, including tumors, revealed greater immune and stromal transcriptional diversity than previously reported. We identified abundant monocytes in FTs across two independent cohorts. The ratio of macrophages to monocytes is similar between benign FTs, ovaries, and adjacent normal tissues but significantly greater in tumors. FT-defined monocyte and macrophage signatures, cell-cell communication, and gene set enrichment analyses identified monocyte- and macrophage-specific interactions and functional pathways in paired tumors and adjacent normal tissues. Further reanalysis of HGSC scRNA-Seq identified monocyte and macrophage subsets associated with neoadjuvant chemotherapy. Taken together, our work provides data that an altered FT myeloid cell composition could inform the discovery of early detection markers for HGSC.

An atlas of epithelial cell states and plasticity in lung adenocarcinoma

Understanding the cellular processes that underlie early lung adenocarcinoma (LUAD) development is needed to devise intervention strategies1. Here we studied 246,102 single epithelial cells from 16 early-stage LUADs and 47 matched normal lung samples. Epithelial cells comprised diverse normal and cancer cell states, and diversity among cancer cells was strongly linked to LUAD-specific oncogenic drivers. KRAS mutant cancer cells showed distinct transcriptional features, reduced differentiation and low levels of aneuploidy. Non-malignant areas surrounding human LUAD samples were enriched with alveolar intermediate cells that displayed elevated KRT8 expression (termed KRT8+ alveolar intermediate cells (KACs) here), reduced differentiation, increased plasticity and driver KRAS mutations. Expression profiles of KACs were enriched in lung precancer cells and in LUAD cells and signified poor survival. In mice exposed to tobacco carcinogen, KACs emerged before lung tumours and persisted for months after cessation of carcinogen exposure. Moreover, they acquired Kras mutations and conveyed sensitivity to targeted KRAS inhibition in KAC-enriched organoids derived from alveolar type 2 (AT2) cells. Last, lineage-labelling of AT2 cells or KRT8+ cells following carcinogen exposure showed that KACs are possible intermediates in AT2-to-tumour cell transformation. This study provides new insights into epithelial cell states at the root of LUAD development, and such states could harbour potential targets for prevention or intervention.

Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters

Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell's 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

FABP6 serves as a new therapeutic target in esophageal tumor

BACKGROUND

Esophageal cancer is one of the most common malignant tumors with high incidence and mortality rates. Despite the continuous development of treatment options, the prognosis for esophageal cancer patients remains poor. Therefore, there is an urgent need for new diagnostic and therapeutic targets in clinical practice to improve the survival of patients with esophageal cancer.

METHODS

In this study, we conducted a comprehensive scRNA-seq analysis of the tumor microenvironment in primary esophageal tumors to elucidate cell composition and heterogeneity. Using Seurat, we identified eight clusters, encompassing non-immune cells (fibroblasts, myofibroblasts, endothelial cells, and epithelial cells) and immunocytes (myeloid-derived cells, T cells, B cells, and plasma cells). Compared to normal tissues, tumors exhibited an increased proportion of epithelial cells and alterations in immune cell infiltration. Analysis of epithelial cells revealed a cluster (cluster 0) with a high differentiation score and early distribution, suggesting its importance as a precursor cell.

RESULTS

Cluster 0 was characterized by high expression of FABP6, indicating a potential role in fatty acid metabolism and tumor growth. T cell analysis revealed shifts in the balance between Treg and CD8+ effector T cells in tumor tissues. Cellular communication analysis identified increased interactions between FABP6+ tumor cells and T cells, with the involvement of the MIF-related pathway and the CD74-CD44 interaction. This study provides insights into the cellular landscape and immune interactions within esophageal tumors, contributing to a better understanding of tumor heterogeneity and potential therapeutic targets.

Compartments in medulloblastoma with extensive nodularity are connected through differentiation along the granular precursor lineage

Medulloblastomas with extensive nodularity are cerebellar tumors characterized by two distinct compartments and variable disease progression. The mechanisms governing the balance between proliferation and differentiation in MBEN remain poorly understood. Here, we employ a multi-modal single cell transcriptome analysis to dissect this process. In the internodular compartment, we identify proliferating cerebellar granular neuronal precursor-like malignant cells, along with stromal, vascular, and immune cells. In contrast, the nodular compartment comprises postmitotic, neuronally differentiated malignant cells. Both compartments are connected through an intermediate cell stage resembling actively migrating CGNPs. Notably, we also discover astrocytic-like malignant cells, found in proximity to migrating and differentiated cells at the transition zone between the two compartments. Our study sheds light on the spatial tissue organization and its link to the developmental trajectory, resulting in a more benign tumor phenotype. This integrative approach holds promise to explore intercompartmental interactions in other cancers with varying histology.

A second-generation M1-polarized CAR macrophage with antitumor efficacy

Chimeric antigen receptor (CAR) T cell therapies have successfully treated hematological malignancies. Macrophages have also gained attention as an immunotherapy owing to their immunomodulatory capacity and ability to infiltrate solid tumors and phagocytize tumor cells. The first-generation CD3ζ-based CAR-macrophages could phagocytose tumor cells in an antigen-dependent manner. Here we engineered induced pluripotent stem cell-derived macrophages (iMACs) with toll-like receptor 4 intracellular toll/IL-1R (TIR) domain-containing CARs resulting in a markedly enhanced antitumor effect over first-generation CAR-macrophages. Moreover, the design of a tandem CD3ζ-TIR dual signaling CAR endows iMACs with both target engulfment capacity and antigen-dependent M1 polarization and M2 resistance in a nuclear factor kappa B (NF-κB)-dependent manner, as well as the capacity to modulate the tumor microenvironment. We also outline a mechanism of tumor cell elimination by CAR-induced efferocytosis against tumor cell apoptotic bodies. Taken together, we provide a second-generation CAR-iMAC with an ability for orthogonal phagocytosis and polarization and superior antitumor functions in treating solid tumors relative to first-generation CAR-macrophages.

Identification of CGNL1 as a diagnostic marker in fibroblasts of diabetic foot ulcers: Insights from single cell RNA sequencing and bulk sequencing data

OBJECTIVES

This study aimed to explore the unique transcriptional feature of fibroblasts subtypes and the role of ferroptosis in diabetic foot ulcers (DFUs).

METHODS

The GEO (Gene Expression Omnibus) was searched to obtain the DFUs single-cell and transcriptional datasets. After identifying cell types by classic marker genes, the integrated single-cell dataset was used to run trajectory inference, RNA velocity, and ligand-receptor interaction analysis. Next, bulk RNA-seq datasets of DFUs were analyzed to the key ferroptosis genes.

RESULTS

Here, we profile 83529 single transcriptomes from the foot samples utilizing single-cell sequencing (scRNA-seq) data of DFU from GEO database and identified 12 cell types, with fibroblasts exhibiting elevated levels of ferroptosis activity and substantial cellular heterogeneity. Our results defined six main fibroblast subsets that showed mesenchymal, secretory-reticular, secretory-papillary, pro-inflammatory, myogenesis, and healing-enriched functional annotations. Trajectory inference and cell-cell communication analysis revealed two major cell fates with subpopulations of fibroblasts and altered ligand-receptor interactions. Bulk RNA sequencing data identified CGNL1 as a distinctive diagnostic signature in fibroblasts. Notably, CGNL1 positively correlated with pro-inflammatory fibroblasts.

CONCLUSIONS

Overall, our analysis delineated the heterogeneity present in cell populations of DFUs, showing distinct fibroblast subtypes characterized by their own unique transcriptional features and enrichment functions. Our study will help us better understand DFUs pathogenesis and identifies CGNL1 as a potential target for DFUs therapies.

Single-cell RNA sequencing unveils Lrg1's role in cerebral ischemia‒reperfusion injury by modulating various cells

BACKGROUND AND PURPOSE

Cerebral ischemia‒reperfusion injury causes significant harm to human health and is a major contributor to stroke-related deaths worldwide. Current treatments are limited, and new, more effective prevention and treatment strategies that target multiple cell components are urgently needed. Leucine-rich alpha-2 glycoprotein 1 (Lrg1) appears to be associated with the progression of cerebral ischemia‒reperfusion injury, but the exact mechanism of it is unknown.

METHODS

Wild-type (WT) and Lrg1 knockout (Lrg1-/-) mice were used to investigate the role of Lrg1 after cerebral ischemia‒reperfusion injury. The effects of Lrg1 knockout on brain infarct volume, blood‒brain barrier permeability, and neurological score (based on 2,3,5-triphenyl tetrazolium chloride, evans blue dye, hematoxylin, and eosin staining) were assessed. Single-cell RNA sequencing (scRNA-seq), immunofluorescence, and microvascular albumin leakage tests were utilized to investigate alterations in various cell components in brain tissue after Lrg1 knockout.

RESULTS

Lrg1 expression was increased in various cell types of brain tissue after cerebral ischemia‒reperfusion injury. Lrg1 knockout reduced cerebral edema and infarct size and improved neurological function after cerebral ischemia‒reperfusion injury. Single-cell RNA sequencing analysis of WT and Lrg1-/- mouse brain tissues after cerebral ischemia‒reperfusion injury revealed that Lrg1 knockout enhances blood‒brain barrier (BBB) by upregulating claudin 11, integrin β5, protocadherin 9, and annexin A2. Lrg1 knockout also promoted an anti-inflammatory and tissue-repairing phenotype in microglia and macrophages while reducing neuron and oligodendrocyte cell death.

CONCLUSIONS

Our results has shown that Lrg1 mediates numerous pathological processes involved in cerebral ischemia‒reperfusion injury by altering the functional states of various cell types, thereby rendering it a promising therapeutic target for cerebral ischemia‒reperfusion injury.

Cofea: correlation-based feature selection for single-cell chromatin accessibility data

Single-cell chromatin accessibility sequencing (scCAS) technologies have enabled characterizing the epigenomic heterogeneity of individual cells. However, the identification of features of scCAS data that are relevant to underlying biological processes remains a significant gap. Here, we introduce a novel method Cofea, to fill this gap. Through comprehensive experiments on 5 simulated and 54 real datasets, Cofea demonstrates its superiority in capturing cellular heterogeneity and facilitating downstream analysis. Applying this method to identification of cell type-specific peaks and candidate enhancers, as well as pathway enrichment analysis and partitioned heritability analysis, we illustrate the potential of Cofea to uncover functional biological process.

Cross-disease characterization of fibroblast heterogeneities and their pathogenic roles in skin inflammation

Fibroblasts are critical pro-inflammatory regulators in chronic inflammatory and fibrotic skin diseases. However, fibroblast heterogeneity and the absence of a unified cross-disease taxonomy have hindered our understanding of the shared/distinct pathways in non-communicable skin inflammation. By integrating 10× single-cell data from 75 skin samples, we constructed a single-cell atlas across inflammatory and fibrotic skin diseases and identified 9 distinct subsets of skin fibroblasts. We found a shared subset of CCL19+ fibroblasts across these diseases, potentially attracting and educating immune cells. Moreover, COL6A5+ fibroblasts were a distinct subset implicated in the initiation and relapse of psoriasis, which tended to differentiate into CXCL1+ fibroblasts, inducing neutrophil chemotaxis and infiltration; while CXCL1+ fibroblasts exhibited a more heterogeneous response to certain inflammatory conditions. Differentiation trajectory and regulatory factors of these fibroblast subsets were also revealed. Therefore, our study presents a comprehensive atlas of skin fibroblasts and highlights pathogenic fibroblast subsets in skin disorders.

Decoding Human Biology and Disease Using Single-cell Omics Technologies

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

scDEED: a statistical method for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters

Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-SNE and UMAP are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embedding might not reliably inform the similarities among cell clusters. Motivated by this challenge, we developed a statistical method, scDEED, for detecting dubious cell embeddings output by any 2D-embedding method. By calculating a reliability score for every cell embedding, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. Applied to multiple scRNA-seq datasets, scDEED demonstrates its effectiveness for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.

A pan-cancer single-cell panorama of human natural killer cells

Natural killer (NK) cells play indispensable roles in innate immune responses against tumor progression. To depict their phenotypic and functional diversities in the tumor microenvironment, we perform integrative single-cell RNA sequencing analyses on NK cells from 716 patients with cancer, covering 24 cancer types. We observed heterogeneity in NK cell composition in a tumor-type-specific manner. Notably, we have identified a group of tumor-associated NK cells that are enriched in tumors, show impaired anti-tumor functions, and are associated with unfavorable prognosis and resistance to immunotherapy. Specific myeloid cell subpopulations, in particular LAMP3+ dendritic cells, appear to mediate the regulation of NK cell anti-tumor immunity. Our study provides insights into NK-cell-based cancer immunity and highlights potential clinical utilities of NK cell subsets as therapeutic targets.

Single-cell RNA sequencing reveals cellular and molecular heterogeneity in fibrocartilaginous enthesis formation

The attachment site of the rotator cuff (RC) is a classic fibrocartilaginous enthesis, which is the junction between bone and tendon with typical characteristics of a fibrocartilage transition zone. Enthesis development has historically been studied with lineage tracing of individual genes selected a priori, which does not allow for the determination of single-cell landscapes yielding mature cell types and tissues. Here, in together with open-source GSE182997 datasets (three samples) provided by Fang et al., we applied Single-cell RNA sequencing (scRNA-seq) to delineate the comprehensive postnatal RC enthesis growth and the temporal atlas from as early as postnatal day 1 up to postnatal week 8. And, we furtherly performed single-cell spatial transcriptomic sequencing on postnatal day 1 mouse enthesis, in order to deconvolute bone-tendon junction (BTJ) chondrocytes onto spatial spots. In summary, we deciphered the cellular heterogeneity and the molecular dynamics during fibrocartilage differentiation. Combined with current spatial transcriptomic data, our results provide a transcriptional resource that will support future investigations of enthesis development at the mechanistic level and may shed light on the strategies for enhanced RC healing outcomes.