Neutrophil profiling illuminates anti-tumor antigen-presenting potency.

Neutrophils, the most abundant and efficient defenders against pathogens, exert opposing functions across cancer types. However, given their short half-life, it remains challenging to explore how neutrophils adopt specific fates in cancer. Here, we generated and integrated single-cell neutrophil transcriptomes from 17 cancer types (225 samples from 143 patients). Neutrophils exhibited extraordinary complexity, with 10 distinct states including inflammation, angiogenesis, and antigen presentation. Notably, the antigen-presenting program was associated with favorable survival in most cancers and could be evoked by leucine metabolism and subsequent histone H3K27ac modification. These neutrophils could further invoke both (neo)antigen-specific and antigen-independent T cell responses. Neutrophil delivery or a leucine diet fine-tuned the immune balance to enhance anti-PD-1 therapy in various murine cancer models. In summary, these data not only indicate the neutrophil divergence across cancers but also suggest therapeutic opportunities such as antigen-presenting neutrophil delivery.

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.

Single-cell proteomics and transcriptomics capture eosinophil development and identify the role of IL-5 in their lineage transit amplification.

The activities, ontogeny, and mechanisms of lineage expansion of eosinophils are less well resolved than those of other immune cells, despite the use of biological therapies targeting the eosinophilia-promoting cytokine interleukin (IL)-5 or its receptor, IL-5Rα. We combined single-cell proteomics and transcriptomics and generated transgenic IL-5Rα reporter mice to revisit eosinophilopoiesis. We reconciled human and murine eosinophilopoiesis and provided extensive cell-surface immunophenotyping and transcriptomes at different stages along the continuum of eosinophil maturation. We used these resources to show that IL-5 promoted eosinophil-lineage expansion via transit amplification, while its deletion or neutralization did not compromise eosinophil maturation. Informed from our resources, we also showed that interferon response factor-8, considered an essential promoter of myelopoiesis, was not intrinsically required for eosinophilopoiesis. This work hence provides resources, methods, and insights for understanding eosinophil ontogeny, the effects of current precision therapeutics, and the regulation of eosinophil development and numbers in health and disease.

An inducible genetic tool to track and manipulate specific microglial states reveals their plasticity and roles in remyelination.

Recent single-cell RNA sequencing studies have revealed distinct microglial states in development and disease. These include proliferative-region-associated microglia (PAMs) in developing white matter and disease-associated microglia (DAMs) prevalent in various neurodegenerative conditions. PAMs and DAMs share a similar core gene signature. However, the extent of the dynamism and plasticity of these microglial states, as well as their functional significance, remains elusive, partly due to the lack of specific tools. Here, we generated an inducible Cre driver line, Clec7a-CreERT2, that targets PAMs and DAMs in the brain parenchyma. Utilizing this tool, we profiled labeled cells during development and in several disease models, uncovering convergence and context-dependent differences in PAM and DAM gene expression. Through long-term tracking, we demonstrated microglial state plasticity. Lastly, we specifically depleted DAMs in demyelination, revealing their roles in disease recovery. Together, we provide a versatile genetic tool to characterize microglial states in CNS development and disease.

Organogenetic transcriptomes of the Drosophila embryo at single cell resolution.

To gain insight into the transcription programs activated during the formation of Drosophila larval structures, we carried out single cell RNA sequencing during two periods of Drosophila embryogenesis: stages 10-12, when most organs are first specified and initiate morphological and physiological specialization; and stages 13-16, when organs achieve their final mature architectures and begin to function. Our data confirm previous findings with regards to functional specialization of some organs - the salivary gland and trachea - and clarify the embryonic functions of another - the plasmatocytes. We also identify two early developmental trajectories in germ cells and uncover a potential role for proteolysis during germline stem cell specialization. We identify the likely cell type of origin for key components of the Drosophila matrisome and several commonly used Drosophila embryonic cell culture lines. Finally, we compare our findings with other recent related studies and with other modalities for identifying tissue-specific gene expression patterns. These data provide a useful community resource for identifying many new players in tissue-specific morphogenesis and functional specialization of developing organs.

BirthSeq, a new method to isolate and analyze dated cells in different vertebrates.

Embryonic development is a complex and dynamic process that unfolds over time and involves the production and diversification of increasing numbers of cells. The impact of developmental time on the formation of the central nervous system is well documented, with evidence showing that time plays a crucial role in establishing the identity of neuronal subtypes. However, the study of how time translates into genetic instructions driving cell fate is limited by the scarcity of suitable experimental tools. We introduce BirthSeq, a new method for isolating and analyzing cells based on their birth date. This innovative technique allows for in vivo labeling of cells, isolation via fluorescence-activated cell sorting, and analysis using high-throughput techniques. We calibrated the BirthSeq method for developmental organs across three vertebrate species (mouse, chick and gecko), and utilized it for single-cell RNA sequencing and novel spatially resolved transcriptomic approaches in mouse and chick, respectively. Overall, BirthSeq provides a versatile tool for studying virtually any tissue in different vertebrate organisms, aiding developmental biology research by targeting cells and their temporal cues.

Akt is a mediator of artery specification during zebrafish development.

The dorsal aorta (DA) is the first major blood vessel to develop in the embryonic cardiovascular system. Its formation is governed by a coordinated process involving the migration, specification, and arrangement of angioblasts into arterial and venous lineages, a process conserved across species. While vascular endothelial growth factor a (VEGF-A) drives DA specification and formation, the kinases involved in this process remain ambiguous. Thus, we investigated the role of protein kinase B, Akt, in zebrafish by generating a quadruple mutant (aktΔ/Δ), where expression and activity of all akt genes-akt 1, 2, 3a, and 3b are strongly decreased. Live imaging of developing aktΔ/Δ DA uncovers early arteriovenous malformations. Single-cell RNA sequencing analysis of aktΔ/Δ endothelial cells corroborates the impairment of arterial, yet not venous, cell specification. Notably, endothelial specific expression of ligand-independent activation of Notch or constitutively active Akt1 were sufficient to reestablish normal arterial specification in aktΔ/Δ. The Akt-loss-of-function mutant unveils that Akt kinase can act upstream of Notch in arterial endothelial cells, and is involved in proper embryonic artery specification. This sheds light on cardiovascular development, revealing a mechanism behind congenital malformations.

Single-cell profiling of African swine fever virus disease in the pig spleen reveals viral and host dynamics.

African swine fever, one of the major viral diseases of swine, poses an imminent threat to the global pig industry. The high-efficient replication of the causative agent African swine fever virus (ASFV) in various organs in pigs greatly contributes to the disease. However, how ASFV manipulates the cell population to drive high-efficient replication of the virus in vivo remains unclear. Here, we found that the spleen reveals the most severe pathological manifestation with the highest viral loads among various organs in pigs during ASFV infection. By using single-cell-RNA-sequencing technology and multiple methods, we determined that macrophages and monocytes are the major cell types infected by ASFV in the spleen, showing high viral-load heterogeneity. A rare subpopulation of immature monocytes represents the major population infected at late infection stage. ASFV causes massive death of macrophages, but shifts its infection into these monocytes which significantly arise after the infection. The apoptosis, interferon response, and antigen-presentation capacity are inhibited in these monocytes which benefits prolonged infection of ASFV in vivo. Until now, the role of immature monocytes as an important target by ASFV has been overlooked due to that they do not express classical monocyte marker CD14. The present study indicates that the shift of viral infection from macrophages to the immature monocytes is critical for maintaining prolonged ASFV infection in vivo. This study sheds light on ASFV tropism, replication, and infection dynamics, and elicited immune response, which may instruct future research on antiviral strategies.

Multiscale spatial mapping of cell populations across anatomical sites in healthy human skin and basal cell carcinoma.

Our understanding of how human skin cells differ according to anatomical site and tumour formation is limited. To address this, we have created a multiscale spatial atlas of healthy skin and basal cell carcinoma (BCC), incorporating in vivo optical coherence tomography, single-cell RNA sequencing, spatial global transcriptional profiling, and in situ sequencing. Computational spatial deconvolution and projection revealed the localisation of distinct cell populations to specific tissue contexts. Although cell populations were conserved between healthy anatomical sites and in BCC, mesenchymal cell populations including fibroblasts and pericytes retained signatures of developmental origin. Spatial profiling and in silico lineage tracing support a hair follicle origin for BCC and demonstrate that cancer-associated fibroblasts are an expansion of a POSTN+ subpopulation associated with hair follicles in healthy skin. RGS5+ pericytes are also expanded in BCC suggesting a role in vascular remodelling. We propose that the identity of mesenchymal cell populations is regulated by signals emanating from adjacent structures and that these signals are repurposed to promote the expansion of skin cancer stroma. The resource we have created is publicly available in an interactive format for the research community.

Single-cell RNA sequencing unveils unique transcriptomic signatures of endothelial cells and role of ENO1 in response to disturbed flow.

Flow patterns exert significant effects on vascular endothelial cells (ECs) to lead to the focal nature of atherosclerosis. Using a step flow chamber to investigate the effects of disturbed shear (DS) and pulsatile shear (PS) on ECs in the same flow channel, we conducted single-cell RNA sequencing analyses to explore the distinct transcriptomic profiles regulated by DS vs. PS. Integrated analysis identified eight cell clusters and demonstrated that DS induces EC transition from atheroprotective to proatherogenic phenotypes. Using an automated cell type annotation algorithm (SingleR), we showed that DS promoted endothelial-to-mesenchymal transition (EndMT) by inducing the transcriptional phenotypes for inflammation, hypoxia responses, transforming growth factor-beta (TGF-ß) signaling, glycolysis, and fatty acid synthesis. Enolase 1 (ENO1), a key gene in glycolysis, was one of the top-ranked genes in the DS-induced EndMT cluster. Pseudotime trajectory analysis revealed that the kinetic expression of ENO1 was significantly associated with EndMT and that ENO1 silencing repressed the DS- and TGF-ß-induced EC inflammation and EndMT. Consistent with these findings, ENO1 was highly expressed in ECs at the inner curvature of the mouse aortic arch (which is exposed to DS) and atherosclerotic lesions, suggesting its proatherogenic role in vivo. In summary, we present a comprehensive single-cell atlas of ECs in response to different flow patterns within the same flow channel. Among the DS-regulated genes, ENO1 plays an important role in DS-induced EC inflammation and EndMT. These results provide insights into how hemodynamic forces regulate vascular endothelium in health and disease.

Single-cell analysis of refractory anti-SRP necrotizing myopathy treated with anti-BCMA CAR-T cell therapy.

Immune-mediated necrotizing myopathy (IMNM) is an autoimmune disorder associated with the presence of autoantibodies, characterized by severe clinical presentation with rapidly progressive muscular weakness and elevated levels of creatine kinase, while traditional pharmacological approaches possess varying and often limited effects. Considering the pathogenic role of autoantibodies, chimeric antigen receptor (CAR)-T cells targeting B cell maturation antigen (BCMA) have emerged as a promising therapeutic strategy. We reported here a patient with anti-signal recognition particle IMNM refractory to multiple available therapies, who was treated with BCMA-targeting CAR-T cells, exhibited favorable safety profiles, sustained reduction in pathogenic autoantibodies, and persistent clinical improvements over 18 mo. Longitudinal single-cell RNA, B cell receptor, T cell receptor sequencing analysis presented the normalization of immune microenvironment after CAR-T cell infusion, including reconstitution of B cell lineages, replacement of T cell subclusters, and suppression of overactivated immune cells. Analysis on characteristics of CAR-T cells in IMNM demonstrated a more active expansion of CD8+ CAR-T cells, with a dynamic phenotype shifting pattern similar in CD4+ and CD8+ CAR-T cells. A comparison of CD8+ CAR-T cells in patients with IMNM and those with malignancies collected at different timepoints revealed a more NK-like phenotype with enhanced tendency of cell death and neuroinflammation and inhibited proliferating ability of CD8+ CAR-T cells in IMNM while neuroinflammation might be the distinct characteristics. Further studies are warranted to define the molecular features of CAR-T cells in autoimmunity and to seek higher efficiency and longer persistence of CAR-T cells in treating autoimmune disorders.

High-resolution omics of vascular ageing and inflammatory pathways in neurodegeneration.

High-resolution omics, particularly single-cell and spatial transcriptomic profiling, are rapidly enhancing our comprehension of the normal molecular diversity of gliovascular cells, as well as their age-related changes that contribute to neurodegeneration. With more omic profiling studies being conducted, it is becoming increasingly essential to synthesise valuable information from the rapidly accumulating findings. In this review, we present an overview of the molecular features of neurovascular and glial cells that have been recently discovered through omic profiling, with a focus on those that have potentially significant functional implications and/or show cross-species differences between human and mouse, and that are linked to vascular deficits and inflammatory pathways in ageing and neurodegenerative disorders. Additionally, we highlight the translational applications of omic profiling, and discuss omic-based strategies to accelerate biomarker discovery and facilitate disease course-modifying therapeutics development for neurodegenerative conditions.

Predicting prognosis and immunotherapy response in colorectal cancer by pericytes insights from single-cell RNA sequencing.

Immunotherapy has revolutionized the treatment of tumors, but there are still a large number of patients who do not benefit from immunotherapy. Pericytes play an important role in remodeling the immune microenvironment. However, how pericytes affect the prognosis and treatment resistance of tumors is still unknown. This study jointly analyzed single-cell RNA sequencing (scRNA-seq) data and bulk RNA sequencing data of multiple cancers to reveal pericyte function in the colorectal cancer microenvironment. Analyzing over 800 000 cells, it was found that colorectal cancer had more pericyte enrichment in tumor tissues than other cancers. We then combined the TCGA database with multiple public datasets and enrolled more than 1000 samples, finding that pericyte may be closely related to poor prognosis due to the higher epithelial-mesenchymal transition (EMT) and hypoxic characteristics. At the same time, patients with more pericytes have higher immune checkpoint molecule expressions and lower immune cell infiltration. Finally, the contributions of pericyte in poor treatment response have been demonstrated in multiple immunotherapy datasets (n = 453). All of these observations suggest that pericyte can be used as a potential biomarker to predict patient disease progression and immunotherapy response.

Characterizing efficient feature selection for single-cell expression analysis.

Unsupervised feature selection is a critical step for efficient and accurate analysis of single-cell RNA-seq data. Previous benchmarks used two different criteria to compare feature selection methods: (i) proportion of ground-truth marker genes included in the selected features and (ii) accuracy of cell clustering using ground-truth cell types. Here, we systematically compare the performance of 11 feature selection methods for both criteria. We first demonstrate the discordance between these criteria and suggest using the latter. We then compare the distribution of selected genes in their means between feature selection methods. We show that lowly expressed genes exhibit seriously high coefficients of variation and are mostly excluded by high-performance methods. In particular, high-deviation- and high-expression-based methods outperform the widely used in Seurat package in clustering cells and data visualization. We further show they also enable a clear separation of the same cell type from different tissues as well as accurate estimation of cell trajectories.

scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.

LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.

Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs).

The complicated process of neuronal development is initiated early in life, with the genetic mechanisms governing this process yet to be fully elucidated. Single-cell RNA sequencing (scRNA-seq) is a potent instrument for pinpointing biomarkers that exhibit differential expression across various cell types and developmental stages. By employing scRNA-seq on human embryonic stem cells, we aim to identify differentially expressed genes (DEGs) crucial for early-stage neuronal development. Our focus extends beyond simply identifying DEGs. We strive to investigate the functional roles of these genes through enrichment analysis and construct gene regulatory networks to understand their interactions. Ultimately, this comprehensive approach aspires to illuminate the molecular mechanisms and transcriptional dynamics governing early human brain development. By uncovering potential links between these DEGs and intelligence, mental disorders, and neurodevelopmental disorders, we hope to shed light on human neurological health and disease. In this study, we have used scRNA-seq to identify DEGs involved in early-stage neuronal development in hESCs. The scRNA-seq data, collected on days 26 (D26) and 54 (D54), of the in vitro differentiation of hESCs to neurons were analyzed. Our analysis identified 539 DEGs between D26 and D54. Functional enrichment of those DEG biomarkers indicated that the up-regulated DEGs participated in neurogenesis, while the down-regulated DEGs were linked to synapse regulation. The Reactome pathway analysis revealed that down-regulated DEGs were involved in the interactions between proteins located in synapse pathways. We also discovered interactions between DEGs and miRNA, transcriptional factors (TFs) and DEGs, and between TF and miRNA. Our study identified 20 significant transcription factors, shedding light on early brain development genetics. The identified DEGs and gene regulatory networks are valuable resources for future research into human brain development and neurodevelopmental disorders.

irGSEA: the integration of single-cell rank-based gene set enrichment analysis.

irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA sequencing data. This package incorporates six distinct scoring methods that rely on the expression ranks of genes, emphasizing relative expression levels over absolute values. The implemented methods include AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Previous studies have demonstrated the robustness of these methods to variations in dataset size and composition, generating enrichment scores based solely on the relative gene expression of individual cells. By employing the robust rank aggregation algorithm, irGSEA amalgamates results from all six methods to ascertain the statistical significance of target gene sets across diverse scoring methods. The package prioritizes user-friendliness, allowing direct input of expression matrices or seamless interaction with Seurat objects. Furthermore, it facilitates a comprehensive visualization of results. The irGSEA package and its accompanying documentation are accessible on GitHub (https://github.com/chuiqin/irGSEA).

scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference.

Trajectory inference is a crucial task in single-cell RNA-sequencing downstream analysis, which can reveal the dynamic processes of biological development, including cell differentiation. Dimensionality reduction is an important step in the trajectory inference process. However, most existing trajectory methods rely on cell features derived from traditional dimensionality reduction methods, such as principal component analysis and uniform manifold approximation and projection. These methods are not specifically designed for trajectory inference and fail to fully leverage prior information from upstream analysis, limiting their performance. Here, we introduce scCRT, a novel dimensionality reduction model for trajectory inference. In order to utilize prior information to learn accurate cells representation, scCRT integrates two feature learning components: a cell-level pairwise module and a cluster-level contrastive module. The cell-level module focuses on learning accurate cell representations in a reduced-dimensionality space while maintaining the cell-cell positional relationships in the original space. The cluster-level contrastive module uses prior cell state information to aggregate similar cells, preventing excessive dispersion in the low-dimensional space. Experimental findings from 54 real and 81 synthetic datasets, totaling 135 datasets, highlighted the superior performance of scCRT compared with commonly used trajectory inference methods. Additionally, an ablation study revealed that both cell-level and cluster-level modules enhance the model's ability to learn accurate cell features, facilitating cell lineage inference. The source code of scCRT is available at https://github.com/yuchen21-web/scCRT-for-scRNA-seq.