Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID

BCG vaccination alters the epigenetic landscape of progenitor cells in human bone marrow to influence innate immune responses

Although the Bacille-Calmette-Guérin (BCG) vaccine is used to prevent tuberculosis, it also offers protection against a diverse range of non-mycobacterial infections. However, the underlying protective mechanisms in humans are not yet fully understood. Here, we surveyed at single-cell resolution the gene expression and chromatin landscape of human bone marrow, aspirated before and 90 days after BCG vaccination or placebo. We showed that BCG alters both the gene expression and epigenetic profiles of human hematopoietic stem and progenitor cells (HSPCs). Changes in gene expression occurred primarily within uncommitted stem cells. By contrast, changes in chromatin accessibility were most prevalent within differentiated progenitor cells at sites influenced by Kruppel-like factor (KLF) and early growth response (EGR) transcription factors and were highly correlated (r > 0.8) with the interleukin (IL)-1β secretion capacity of paired peripheral blood mononuclear cells (PBMCs). Our findings shed light on BCG vaccination's profound and lasting effects on HSPCs and its influence on innate immune responses and trained immunity.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Using clusterProfiler to characterize multiomics data

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .

Single-cell RNA sequencing reveals special basal cells and fibroblasts in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is the most predominant type of idiopathic interstitial pneumonia and has an increasing incidence, poor prognosis, and unclear pathogenesis. In order to investigate the molecular mechanisms underlying IPF further, we performed single-cell RNA sequencing analysis on three healthy controls and five IPF lung tissue samples. The results revealed a significant shift in epithelial cells (ECs) phenotypes in IPF, which may be attributed to the differentiation of alveolar type 2 cells to basal cells. In addition, several previously unrecognized basal cell subtypes were preliminarily identified, including extracellular matrix basal cells, which were increased in the IPF group. We identified a special population of fibroblasts that highly expressed extracellular matrix-related genes, POSTN, CTHRC1, COL3A1, COL5A2, and COL12A1. We propose that the close interaction between ECs and fibroblasts through ligand-receptor pairs may have a critical function in IPF development. Collectively, these outcomes provide innovative perspectives on the complexity and diversity of basal cells and fibroblasts in IPF and contribute to the understanding of possible mechanisms in pathological lung fibrosis.

Topical JAK inhibition ameliorates EGFR inhibitor-induced rash in rodents and humans

Epidermal growth factor receptor inhibitors (EGFRis) are used to treat many cancers, but their use is complicated by the development of a skin rash that may be severe, limiting their use and adversely affecting patient quality of life. Most studies of EGFRi-induced rash have focused on the fully developed stage of this skin disorder, and early pathological changes remain unclear. We analyzed high-throughput transcriptome sequencing of skin samples from rats exposed to the EGFRi afatinib and identified that keratinocyte activation is an early pathological alteration in EGFRi-induced rash. Mechanistically, the induction of S100 calcium-binding protein A9 (S100A9) occurred before skin barrier disruption and led to keratinocyte activation, resulting in expression of specific cytokines, chemokines, and surface molecules such as interleukin 6 (Il6) and C-C motif chemokine ligand 2 (CCL2) to recruit and activate monocytes through activation of the Janus kinase (JAK)-signal transducers and activators of transcription (STAT) pathway, further recruiting more immune cells. Topical JAK inhibition suppressed the recruitment of immune cells and ameliorated the severity of skin rash in afatinib-treated rats and mice with epidermal deletion of EGFR, while having no effect on EGFRi efficacy in tumor-bearing mice. In a pilot clinical trial (NCT05120362), 11 patients with EGFRi-induced rash were treated with delgocitinib ointment, resulting in improvement in rash severity by at least one grade in 10 of them according to the MASCC EGFR inhibitor skin toxicity tool (MESTT) criteria. These findings provide a better understanding of the early pathophysiology of EGFRi-induced rash and suggest a strategy to manage this condition.

SIMBA: single-cell embedding along with features

Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).

Direct conversion of cardiac fibroblasts into endothelial-like cells using Sox17 and Erg

Endothelial cells are a heterogeneous population with various organ-specific and conserved functions that are critical to organ development, function, and regeneration. Here we report a Sox17-Erg direct reprogramming approach that uses cardiac fibroblasts to create differentiated endothelial cells that demonstrate endothelial-like molecular and physiological functions in vitro and in vivo. Injection of these induced endothelial cells into myocardial infarct sites after injury results in improved vascular perfusion of the scar region. Furthermore, we use genomic analyses to illustrate that Sox17-Erg reprogramming instructs cardiac fibroblasts toward an arterial-like identity. This results in a more efficient direct conversion of fibroblasts into endothelial-like cells when compared to traditional Etv2-based reprogramming. Overall, this Sox17-Erg direct reprogramming strategy offers a robust tool to generate endothelial cells both in vitro and in vivo, and has the potential to be used in repairing injured tissue.

Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures

Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell-types and developmental stages remain underexplored. Here we harnessed the potential of heterogeneous differentiating cultures ( HDCs ), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell-types. We generated HDCs for 53 human donors and collected single-cell RNA-sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell-types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues, and dynamic regulatory effects associated with a range of complex traits.

Single-cell profiling of response to neoadjuvant chemo-immunotherapy in surgically resectable esophageal squamous cell carcinoma

BACKGROUND

The efficacy of neoadjuvant chemo-immunotherapy (NAT) in esophageal squamous cell carcinoma (ESCC) is challenged by the intricate interplay within the tumor microenvironment (TME). Unveiling the immune landscape of ESCC in the context of NAT could shed light on heterogeneity and optimize therapeutic strategies for patients.

METHODS

We analyzed single cells from 22 baseline and 24 post-NAT treatment samples of stage II/III ESCC patients to explore the association between the immune landscape and pathological response to neoadjuvant anti-PD-1 combination therapy, including pathological complete response (pCR), major pathological response (MPR), and incomplete pathological response (IPR).

RESULTS

Single-cell profiling identified 14 major cell subsets of cancer, immune, and stromal cells. Trajectory analysis unveiled an interesting link between cancer cell differentiation and pathological response to NAT. ESCC tumors enriched with less differentiated cancer cells exhibited a potentially favorable pathological response to NAT, while tumors enriched with clusters of more differentiated cancer cells may resist treatment. Deconvolution of transcriptomes in pre-treatment tumors identified gene signatures in response to NAT contributed by specific immune cell populations. Upregulated genes associated with better pathological responses in CD8 + effector T cells primarily involved interferon-gamma (IFNγ) signaling, neutrophil degranulation, and negative regulation of the T cell apoptotic process, whereas downregulated genes were dominated by those in the immune response-activating cell surface receptor signaling pathway. Natural killer cells in pre-treatment tumors from pCR patients showed a similar upregulation of gene expression in response to IFNγ but a downregulation of genes in the neutrophil-mediated immunity pathways. A decreased cellular contexture of regulatory T cells in ESCC TME indicated a potentially favorable pathological response to NAT. Cell-cell communication analysis revealed extensive interactions between CCL5 and its receptor CCR5 in various immune cells of baseline pCR tumors. Immune checkpoint interaction pairs, including CTLA4-CD86, TIGIT-PVR, LGALS9-HAVCR2, and TNFSF4-TNFRSF4, might serve as additional therapeutic targets for ICI therapy in ESCC.

CONCLUSIONS

This pioneering study unveiled an intriguing association between cancer cell differentiation and pathological response in esophageal cancer patients, revealing distinct subgroups of tumors for which neoadjuvant chemo-immunotherapy might be effective. We also delineated the immune landscape of ESCC tumors in the context of clinical response to NAT, which provides clinical insights for better understanding how patients respond to the treatment and further identifying novel therapeutic targets for ESCC patients in the future.

scAnnoX: an R package integrating multiple public tools for single-cell annotation

Background

Single-cell annotation plays a crucial role in the analysis of single-cell genomics data. Despite the existence of numerous single-cell annotation algorithms, a comprehensive tool for integrating and comparing these algorithms is also lacking.

Methods

This study meticulously investigated a plethora of widely adopted single-cell annotation algorithms. Ten single-cell annotation algorithms were selected based on the classification of either reference dataset-dependent or marker gene-dependent approaches. These algorithms included SingleR, Seurat, sciBet, scmap, CHETAH, scSorter, sc.type, cellID, scCATCH, and SCINA. Building upon these algorithms, we developed an R package named scAnnoX for the integration and comparative analysis of single-cell annotation algorithms.

Results

The development of the scAnnoX software package provides a cohesive framework for annotating cells in scRNA-seq data, enabling researchers to more efficiently perform comparative analyses among the cell type annotations contained in scRNA-seq datasets. The integrated environment of scAnnoX streamlines the testing, evaluation, and comparison processes among various algorithms. Among the ten annotation tools evaluated, SingleR, Seurat, sciBet, and scSorter emerged as top-performing algorithms in terms of prediction accuracy, with SingleR and sciBet demonstrating particularly superior performance, offering guidance for users. Interested parties can access the scAnnoX package at https://github.com/XQ-hub/scAnnoX.

Spatiotemporal analysis of mRNA-protein relationships enhances transcriptome-based developmental inference

Elucidating the complex relationships between mRNA and protein expression at high spatiotemporal resolution is critical for unraveling multilevel gene regulation and enhancing mRNA-based developmental analyses. In this study, we conduct a single-cell analysis of mRNA and protein expression of transcription factors throughout C. elegans embryogenesis. Initially, cellular co-presence of mRNA and protein is low, increasing to a medium-high level (73%) upon factoring in delayed protein synthesis and long-term protein persistence. These factors substantially affect mRNA-protein concordance, leading to potential inaccuracies in mRNA-reliant gene detection and specificity characterization. Building on the learned relationship, we infer protein presence from mRNA expression and demonstrate its utility in identifying tissue-specific genes and elucidating relationships between genes and cells. This approach facilitates identifying the role of sptf-1/SP7 in neuronal lineage development. Collectively, this study provides insights into gene expression dynamics during rapid embryogenesis and approaches for improving the efficacy of transcriptome-based developmental analyses.

The Alzheimer's disease risk gene BIN1 regulates activity-dependent gene expression in human-induced glutamatergic neurons

Bridging Integrator 1 (BIN1) is the second most important Alzheimer's disease (AD) risk gene, but its physiological roles in neurons and its contribution to brain pathology remain largely elusive. In this work, we show that BIN1 plays a critical role in the regulation of calcium homeostasis, electrical activity, and gene expression of glutamatergic neurons. Using single-cell RNA-sequencing on cerebral organoids generated from isogenic BIN1 wild type (WT), heterozygous (HET) and homozygous knockout (KO) human-induced pluripotent stem cells (hiPSCs), we show that BIN1 is mainly expressed by oligodendrocytes and glutamatergic neurons, like in the human brain. Both BIN1 HET and KO cerebral organoids show specific transcriptional alterations, mainly associated with ion transport and synapses in glutamatergic neurons. We then demonstrate that BIN1 cell-autonomously regulates gene expression in glutamatergic neurons by using a novel protocol to generate pure culture of hiPSC-derived induced neurons (hiNs). Using this system, we also show that BIN1 plays a key role in the regulation of neuronal calcium transients and electrical activity via its interaction with the L-type voltage-gated calcium channel Cav1.2. BIN1 KO hiNs show reduced activity-dependent internalization and higher Cav1.2 expression compared to WT hiNs. Pharmacological blocking of this channel with clinically relevant doses of nifedipine, a calcium channel blocker, partly rescues electrical and gene expression alterations in BIN1 KO glutamatergic neurons. Further, we show that transcriptional alterations in BIN1 KO hiNs that affect biological processes related to calcium homeostasis are also present in glutamatergic neurons of the human brain at late stages of AD pathology. Together, these findings suggest that BIN1-dependent alterations in neuronal properties could contribute to AD pathophysiology and that treatment with low doses of clinically approved calcium blockers should be considered as an option to slow disease-onset and progression.

Switch of innate to adaptative immune responses in the brain of patients with Alzheimer's disease correlates with tauopathy progression

Neuroinflammation is a key feature of Alzheimer's disease (AD). In this work, analysis of single- cell RNA-sequencing (scRNA-seq) data obtained from the brain of patients with AD provides evidence supporting a switch from an innate to an adaptative immune response during tauopathy progression, with both disease-associated microglia (DAM) and CD8+ T cells becoming more frequent at advanced Braak stages.

Comparative analysis of cell-cell communication at single-cell resolution

Inference of cell-cell communication from single-cell RNA sequencing data is a powerful technique to uncover intercellular communication pathways, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell-level information. Here we present Scriabin, a flexible and scalable framework for comparative analysis of cell-cell communication at single-cell resolution that is performed without cell aggregation or downsampling. We use multiple published atlas-scale datasets, genetic perturbation screens and direct experimental validation to show that Scriabin accurately recovers expected cell-cell communication edges and identifies communication networks that can be obscured by agglomerative methods. Additionally, we use spatial transcriptomic data to show that Scriabin can uncover spatial features of interaction from dissociated data alone. Finally, we demonstrate applications to longitudinal datasets to follow communication pathways operating between timepoints. Our approach represents a broadly applicable strategy to reveal the full structure of niche-phenotype relationships in health and disease.

Pro-inflammatory feedback loops define immune responses to pathogenic Lentivirus infection

BACKGROUND

The Lentivirus human immunodeficiency virus (HIV) causes chronic inflammation and AIDS in humans, with variable rates of disease progression between individuals driven by both host and viral factors. Similarly, simian lentiviruses vary in their pathogenicity based on characteristics of both the host species and the virus strain, yet the immune underpinnings that drive differential Lentivirus pathogenicity remain incompletely understood.

METHODS

We profile immune responses in a unique model of differential lentiviral pathogenicity where pig-tailed macaques are infected with highly genetically similar variants of SIV that differ in virulence. We apply longitudinal single-cell transcriptomics to this cohort, along with single-cell resolution cell-cell communication techniques, to understand the immune mechanisms underlying lentiviral pathogenicity.

RESULTS

Compared to a minimally pathogenic lentiviral variant, infection with a highly pathogenic variant results in a more delayed, broad, and sustained activation of inflammatory pathways, including an extensive global interferon signature. Conversely, individual cells infected with highly pathogenic Lentivirus upregulated fewer interferon-stimulated genes at a lower magnitude, indicating that highly pathogenic Lentivirus has evolved to partially escape from interferon responses. Further, we identify CXCL10 and CXCL16 as important molecular drivers of inflammatory pathways specifically in response to highly pathogenic Lentivirus infection. Immune responses to highly pathogenic Lentivirus infection are characterized by amplifying regulatory circuits of pro-inflammatory cytokines with dense longitudinal connectivity.

CONCLUSIONS

Our work presents a model of lentiviral pathogenicity where failures in early viral control mechanisms lead to delayed, sustained, and amplifying pro-inflammatory circuits, which in turn drives disease progression.

scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data

Accurate cell type annotation in single-cell RNA-sequencing data is essential for advancing biological and medical research, particularly in understanding disease progression and tumor microenvironments. However, existing methods are constrained by single feature extraction approaches, lack of adaptability to immune cell types with similar molecular profiles but distinct functions and a failure to account for the impact of cell label noise on model accuracy, all of which compromise the precision of annotation. To address these challenges, we developed a supervised approach called scMMT. We proposed a novel feature extraction technique to uncover more valuable information. Additionally, we constructed a multi-task learning framework based on the GradNorm method to enhance the recognition of challenging immune cells and reduce the impact of label noise by facilitating mutual reinforcement between cell type annotation and protein prediction tasks. Furthermore, we introduced logarithmic weighting and label smoothing mechanisms to enhance the recognition ability of rare cell types and prevent model overconfidence. Through comprehensive evaluations on multiple public datasets, scMMT has demonstrated state-of-the-art performance in various aspects including cell type annotation, rare cell identification, dropout and label noise resistance, protein expression prediction and low-dimensional embedding representation.

Transcriptome analysis in a humanized mouse model of familial dysautonomia reveals tissue-specific gene expression disruption in the peripheral nervous system

Familial dysautonomia (FD) is a rare recessive neurodevelopmental disease caused by a splice mutation in the Elongator acetyltransferase complex subunit 1 (ELP1) gene. This mutation results in a tissue-specific reduction of ELP1 protein, with the lowest levels in the central and peripheral nervous systems (CNS and PNS, respectively). FD patients exhibit complex neurological phenotypes due to the loss of sensory and autonomic neurons. Disease symptoms include decreased pain and temperature perception, impaired or absent myotatic reflexes, proprioceptive ataxia, and progressive retinal degeneration. While the involvement of the PNS in FD pathogenesis has been clearly recognized, the underlying mechanisms responsible for the preferential neuronal loss remain unknown. In this study, we aimed to elucidate the molecular mechanisms underlying FD by conducting a comprehensive transcriptome analysis of neuronal tissues from the phenotypic mouse model TgFD9; Elp1Δ20/flox. This mouse recapitulates the same tissue-specific ELP1 mis-splicing observed in patients while modeling many of the disease manifestations. Comparison of FD and control transcriptomes from dorsal root ganglion (DRG), trigeminal ganglion (TG), medulla (MED), cortex, and spinal cord (SC) showed significantly more differentially expressed genes (DEGs) in the PNS than the CNS. We then identified genes that were tightly co-expressed and functionally dependent on the level of full-length ELP1 transcript. These genes, defined as ELP1 dose-responsive genes, were combined with the DEGs to generate tissue-specific dysregulated FD signature genes and networks. Within the PNS networks, we observed direct connections between Elp1 and genes involved in tRNA synthesis and genes related to amine metabolism and synaptic signaling. Importantly, transcriptomic dysregulation in PNS tissues exhibited enrichment for neuronal subtype markers associated with peptidergic nociceptors and myelinated sensory neurons, which are known to be affected in FD. In summary, this study has identified critical tissue-specific gene networks underlying the etiology of FD and provides new insights into the molecular basis of the disease.

Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity

Advances in single-cell technology have enabled molecular dissection of heterogeneous biospecimens at unprecedented scales and resolutions. Cluster-centric approaches are widely applied in analyzing single-cell data, however they have limited power in dissecting and interpreting highly heterogenous, dynamically evolving data. Here, we present GSDensity, a graph-modeling approach that allows users to obtain pathway-centric interpretation and dissection of single-cell and spatial transcriptomics (ST) data without performing clustering. Using pathway gene sets, we show that GSDensity can accurately detect biologically distinct cells and reveal novel cell-pathway associations ignored by existing methods. Moreover, GSDensity, combined with trajectory analysis can identify curated pathways that are active at various stages of mouse brain development. Finally, GSDensity can identify spatially relevant pathways in mouse brains and human tumors including those following high-order organizational patterns in the ST data. Particularly, we create a pan-cancer ST map revealing spatially relevant and recurrently active pathways across six different tumor types.

To bind or not to bind: how AUXIN RESPONSE FACTORs select their target genes

Most plant growth and development processes are regulated in one way or another by auxin. The best-studied mechanism by which auxin exerts its regulatory effects is through the nuclear auxin pathway (NAP). In this pathway, Auxin Response Factors (ARFs) are the transcription factors that ultimately determine which genes become auxin regulated by binding to specific DNA sequences. ARFs have primarily been studied in Arabidopsis thaliana, but recent studies in other species have revealed family-wide DNA binding specificities for different ARFs and the minimal functional system of the NAP system, consisting of a duo of competing ARFs of the A and B classes. In this review, we provide an overview of key aspects of ARF DNA binding such as auxin response elements (TGTCNN) and tandem repeat motifs, and consider how structural biology and in vitro studies help us understand ARF DNA preferences. We also highlight some recent aspects related to the regulation of ARF levels inside a cell, which may alter the DNA binding profile of ARFs in different tissues. We finally emphasize the need to study minimal NAP systems to understand fundamental aspects of ARF function, the need to characterize algal ARFs to understand how ARFs evolved, how cutting-edge techniques can increase our understanding of ARFs, and which remaining questions can only be answered by structural biology.

Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census

MOTIVATION

Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin.

RESULTS

We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications.

AVAILABILITY AND IMPLEMENTATION

Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.

BCG vaccination impacts the epigenetic landscape of progenitor cells in human bone marrow

While the Bacille-Calmette-Guérin (BCG) vaccine is used to prevent tuberculosis, it also offers protection against a diverse range of non-mycobacterial infections. However, the underlying protective mechanisms in humans are not yet fully understood. Here, we surveyed at single-cell resolution the gene expression and chromatin landscape of human bone marrow, aspirated before and 90 days after BCG vaccination or placebo administration. We show that BCG vaccination significantly alters both the gene expression and epigenetic profiles of human hematopoietic stem and progenitor cells (HSPCs). Changes in gene expression occur primarily on the most uncommitted stem cells and are reflective of a persistent myeloid bias. In contrast, BCG-induced changes in chromatin accessibility are most prevalent within differentiated progenitor cells at sites influenced by Kruppel-like factor (KLF)/SP and EGR transcription factors (TFs). These TFs are also activated in the most uncommitted stem cells, indicating that activated TFs, which drive persistent changes in HSC gene expression, likely also drive chromatin dynamics appearing within downstream progenitor cells. This perspective contests the prevailing notion that epigenetic modifications linked to innate immune memory transfer directly from stem cells to their differentiated derivatives. Finally, we show that alterations in gene expression and chromatin accessibility in HSPCs due to BCG vaccination were highly correlated (r>0.8) with the IL-1β secretion capacity of paired PBMCs upon secondary immune challenge. Overall, our findings shed light on BCG vaccination's profound and lasting effects on HSPCs and its influence on innate immune responses.

Transcriptome analysis in a humanized mouse model of familial dysautonomia reveals tissue-specific gene expression disruption in the peripheral nervous system

Familial dysautonomia (FD) is a rare recessive neurodevelopmental disease caused by a splice mutation in the Elongator acetyltransferase complex subunit 1 ( ELP1 ) gene. This mutation results in a tissue-specific reduction of ELP1 protein, with the lowest levels in the central and peripheral nervous systems (CNS and PNS, respectively). FD patients exhibit complex neurological phenotypes due to the loss of sensory and autonomic neurons. Disease symptoms include decreased pain and temperature perception, impaired or absent myotatic reflexes, proprioceptive ataxia, and progressive retinal degeneration. While the involvement of the PNS in FD pathogenesis has been clearly recognized, the underlying mechanisms responsible for the preferential neuronal loss remain unknown. In this study, we aimed to elucidate the molecular mechanisms underlying FD by conducting a comprehensive transcriptome analysis of neuronal tissues from the phenotypic mouse model TgFD9 ; Elp1 Δ 20/flox . This mouse recapitulates the same tissue-specific ELP1 mis-splicing observed in patients while modeling many of the disease manifestations. Comparison of FD and control transcriptomes from dorsal root ganglion (DRG), trigeminal ganglion (TG), medulla (MED), cortex, and spinal cord (SC) showed significantly more differentially expressed genes (DEGs) in the PNS than the CNS. We then identified genes that were tightly co-expressed and functionally dependent on the level of full-length ELP1 transcript. These genes, defined as ELP1 dose-responsive genes, were combined with the DEGs to generate tissue-specific dysregulated FD signature genes and networks. Within the PNS networks, we observed direct connections between Elp1 and genes involved in tRNA synthesis and genes related to amine metabolism and synaptic signaling. Importantly, transcriptomic dysregulation in PNS tissues exhibited enrichment for neuronal subtype markers associated with peptidergic nociceptors and myelinated sensory neurons, which are known to be affected in FD. In summary, this study has identified critical tissue-specific gene networks underlying the etiology of FD and provides new insights into the molecular basis of the disease.

Gut microbiota aggravates neutrophil extracellular traps-induced pancreatic injury in hypertriglyceridemic pancreatitis

Hypertriglyceridemic pancreatitis (HTGP) is featured by higher incidence of complications and poor clinical outcomes. Gut microbiota dysbiosis is associated with pancreatic injury in HTGP and the mechanism remains unclear. Here, we observe lower diversity of gut microbiota and absence of beneficial bacteria in HTGP patients. In a fecal microbiota transplantation mouse model, the colonization of gut microbiota from HTGP patients recruits neutrophils and increases neutrophil extracellular traps (NETs) formation that exacerbates pancreatic injury and systemic inflammation. We find that decreased abundance of Bacteroides uniformis in gut microbiota impairs taurine production and increases IL-17 release in colon that triggers NETs formation. Moreover, Bacteroides uniformis or taurine inhibits the activation of NF-κB and IL-17 signaling pathways in neutrophils which harness NETs and alleviate pancreatic injury. Our findings establish roles of endogenous Bacteroides uniformis-derived metabolic and inflammatory products on suppressing NETs release, which provides potential insights of ameliorating HTGP through gut microbiota modulation.

Single-cell assignment using multiple-adversarial domain adaptation network with large-scale references

The rapid accumulation of single-cell RNA-seq data has provided rich resources to characterize various human cell populations. However, achieving accurate cell-type annotation using public references presents challenges due to inconsistent annotations, batch effects, and rare cell types. Here, we introduce SELINA (single-cell identity navigator), an integrative and automatic cell-type annotation framework based on a pre-curated reference atlas spanning various tissues. SELINA employs a multiple-adversarial domain adaptation network to remove batch effects within the reference dataset. Additionally, it enhances the annotation of less frequent cell types by synthetic minority oversampling and fits query data with the reference data using an autoencoder. SELINA culminates in the creation of a comprehensive and uniform reference atlas, encompassing 1.7 million cells covering 230 distinct human cell types. We substantiate its robustness and superiority across a multitude of human tissues. Notably, SELINA could accurately annotate cells within diverse disease contexts. SELINA provides a complete solution for human single-cell RNA-seq data annotation with both python and R packages.

An interpretable single-cell RNA sequencing data clustering method based on latent Dirichlet allocation

Single-cell RNA sequencing (scRNA-seq) detects whole transcriptome signals for large amounts of individual cells and is powerful for determining cell-to-cell differences and investigating the functional characteristics of various cell types. scRNA-seq datasets are usually sparse and highly noisy. Many steps in the scRNA-seq analysis workflow, including reasonable gene selection, cell clustering and annotation, as well as discovering the underlying biological mechanisms from such datasets, are difficult. In this study, we proposed an scRNA-seq analysis method based on the latent Dirichlet allocation (LDA) model. The LDA model estimates a series of latent variables, i.e. putative functions (PFs), from the input raw cell-gene data. Thus, we incorporated the 'cell-function-gene' three-layer framework into scRNA-seq analysis, as this framework is capable of discovering latent and complex gene expression patterns via a built-in model approach and obtaining biologically meaningful results through a data-driven functional interpretation process. We compared our method with four classic methods on seven benchmark scRNA-seq datasets. The LDA-based method performed best in the cell clustering test in terms of both accuracy and purity. By analysing three complex public datasets, we demonstrated that our method could distinguish cell types with multiple levels of functional specialization, and precisely reconstruct cell development trajectories. Moreover, the LDA-based method accurately identified the representative PFs and the representative genes for the cell types/cell stages, enabling data-driven cell cluster annotation and functional interpretation. According to the literature, most of the previously reported marker/functionally relevant genes were recognized.

A Dual-Targeted Metal-Organic Framework Based Nanoplatform for the Treatment of Rheumatoid Arthritis by Restoring the Macrophage Niche

Inflammatory infiltration and bone destruction are important pathological features of rheumatoid arthritis (RA), which originate from the disturbed niche of macrophages. Here, we identified a niche-disrupting process in RA: due to overactivation of complement, the barrier function of VSIg4⁺ lining macrophages is disrupted and mediates inflammatory infiltration within the joint, thereby activating excessive osteoclastogenesis and bone resorption. However, complement antagonists have poor biological applications due to superphysiologic dose requirements and inadequate effects on bone resorption. Therefore, we developed a dual-targeted therapeutic nanoplatform based on the MOF framework to achieve bone-targeted delivery of the complement inhibitor CRIg-CD59 and pH-responsive sustained release. The surface-mineralized zoledronic acid (ZA) of ZIF8@CRIg-CD59@HA@ZA targets the skeletal acidic microenvironment in RA, and the sustained release of CRIg-CD59 can recognize and prevent the complement membrane attack complex (MAC) from forming on the surface of healthy cells. Importantly, ZA can inhibit osteoclast-mediated bone resorption, and CRIg-CD59 can promote the repair of the VSIg4⁺ lining macrophage barrier to achieve sequential niche remodeling. This combination therapy is expected to treat RA by reversing the core pathological process, circumventing the pitfalls of traditional therapy.

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

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

Optimized cell type signatures revealed from single-cell data by combining principal feature analysis, mutual information, and machine learning

Machine learning techniques are excellent to analyze expression data from single cells. These techniques impact all fields ranging from cell annotation and clustering to signature identification. The presented framework evaluates gene selection sets how far they optimally separate defined phenotypes or cell groups. This innovation overcomes the present limitation to objectively and correctly identify a small gene set of high information content regarding separating phenotypes for which corresponding code scripts are provided. The small but meaningful subset of the original genes (or feature space) facilitates human interpretability of the differences of the phenotypes including those found by machine learning results and may even turn correlations between genes and phenotypes into a causal explanation. For the feature selection task, the principal feature analysis is utilized which reduces redundant information while selecting genes that carry the information for separating the phenotypes. In this context, the presented framework shows explainability of unsupervised learning as it reveals cell-type specific signatures. Apart from a Seurat preprocessing tool and the PFA script, the pipeline uses mutual information to balance accuracy and size of the gene set if desired. A validation part to evaluate the gene selection for their information content regarding the separation of the phenotypes is provided as well, binary and multiclass classification of 3 or 4 groups are studied. Results from different single-cell data are presented. In each, only about ten out of more than 30000 genes are identified as carrying the relevant information. The code is provided in a GitHub repository at https://github.com/AC-PHD/Seurat_PFA_pipeline.

Applications of single-cell RNA sequencing in drug discovery and development

Single-cell technologies, particularly single-cell RNA sequencing (scRNA-seq) methods, together with associated computational tools and the growing availability of public data resources, are transforming drug discovery and development. New opportunities are emerging in target identification owing to improved disease understanding through cell subtyping, and highly multiplexed functional genomics screens incorporating scRNA-seq are enhancing target credentialling and prioritization. ScRNA-seq is also aiding the selection of relevant preclinical disease models and providing new insights into drug mechanisms of action. In clinical development, scRNA-seq can inform decision-making via improved biomarker identification for patient stratification and more precise monitoring of drug response and disease progression. Here, we illustrate how scRNA-seq methods are being applied in key steps in drug discovery and development, and discuss ongoing challenges for their implementation in the pharmaceutical industry.

Adjustments to the reference dataset design improve cell type label transfer

The transfer of cell type labels from pre-annotated (reference) to newly collected data is an important task in single-cell data analysis. As the number of publicly available annotated datasets which can be used as reference, as well as the number of computational methods for cell type label transfer are constantly growing, rationals to understand and decide which reference design and which method to use for a particular query dataset are needed. Using detailed data visualisations and interpretable statistical assessments, we benchmark a set of popular cell type annotation methods, test their performance on different cell types and study the effects of the design of reference data (e.g., cell sampling criteria, inclusion of multiple datasets in one reference, gene set selection) on the reliability of predictions. Our results highlight the need for further improvements in label transfer methods, as well as preparation of high-quality pre-annotated reference data of adequate sampling from all cell types of interest, for more reliable annotation of new datasets.

Quantitatively Visualizing Bipartite Datasets

As experiments continue to increase in size and scope, a fundamental challenge of subsequent analyses is to recast the wealth of information into an intuitive and readily interpretable form. Often, each measurement conveys only the relationship between a pair of entries, and it is difficult to integrate these local interactions across a dataset to form a cohesive global picture. The classic localization problem tackles this question, transforming local measurements into a global map that reveals the underlying structure of a system. Here, we examine the more challenging bipartite localization problem, where pairwise distances are available only for bipartite data comprising two classes of entries (such as antibody-virus interactions, drug-cell potency, or user-rating profiles). We modify previous algorithms to solve bipartite localization and examine how each method behaves in the presence of noise, outliers, and partially observed data. As a proof of concept, we apply these algorithms to antibody-virus neutralization measurements to create a basis set of antibody behaviors, formalize how potently inhibiting some viruses necessitates weakly inhibiting other viruses, and quantify how often combinations of antibodies exhibit degenerate behavior.

Multi-modal quantification of pathway activity with MAYA

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

Severe hematopoietic stem cell inflammation compromises chronic granulomatous disease gene therapy

X-linked chronic granulomatous disease (CGD) is associated with defective phagocytosis, life-threatening infections, and inflammatory complications. We performed a clinical trial of lentivirus-based gene therapy in four patients (NCT02757911). Two patients show stable engraftment and clinical benefits, whereas the other two have progressively lost gene-corrected cells. Single-cell transcriptomic analysis reveals a significantly lower frequency of hematopoietic stem cells (HSCs) in CGD patients, especially in the two patients with defective engraftment. These two present a profound change in HSC status, a high interferon score, and elevated myeloid progenitor frequency. We use elastic-net logistic regression to identify a set of 51 interferon genes and transcription factors that predict the failure of HSC engraftment. In one patient, an aberrant HSC state with elevated CEBPβ expression drives HSC exhaustion, as demonstrated by low repopulation in a xenotransplantation model. Targeted treatments to protect HSCs, coupled to targeted gene expression screening, might improve clinical outcomes in CGD.

Cartography of Genomic Interactions Enables Deep Analysis of Single-Cell Expression Data

Remarkable advances in single cell genomics have presented unique challenges and opportunities for interrogating a wealth of biomedical inquiries. High dimensional genomic data are inherently complex because of intertwined relationships among the genes. Existing methods, including emerging deep learning-based approaches, do not consider the underlying biological characteristics during data processing, which greatly compromises the performance of data analysis and hinders the maximal utilization of state-of-the-art genomic techniques. In this work, we develop an entropy-based cartography strategy to contrive the high dimensional gene expression data into a configured image format, referred to as genomap, with explicit integration of the genomic interactions. This unique cartography casts the gene-gene interactions into the spatial configuration of genomaps and enables us to extract the deep genomic interaction features and discover underlying discriminative patterns of the data. We show that, for a wide variety of applications (cell clustering and recognition, gene signature extraction, single cell data integration, cellular trajectory analysis, dimensionality reduction, and visualization), the proposed approach drastically improves the accuracies of data analyses as compared to the state-of-the-art techniques.

Pancreatic microexons regulate islet function and glucose homeostasis

Pancreatic islets control glucose homeostasis by the balanced secretion of insulin and other hormones, and their abnormal function causes diabetes or hypoglycaemia. Here we uncover a conserved programme of alternative microexons included in mRNAs of islet cells, particularly in genes involved in vesicle transport and exocytosis. Islet microexons (IsletMICs) are regulated by the RNA binding protein SRRM3 and represent a subset of the larger neural programme that are particularly sensitive to SRRM3 levels. Both SRRM3 and IsletMICs are induced by elevated glucose levels, and depletion of SRRM3 in human and rat beta cell lines and mouse islets, or repression of particular IsletMICs using antisense oligonucleotides, leads to inappropriate insulin secretion. Consistently, mice harbouring mutations in Srrm3 display defects in islet cell identity and function, leading to hyperinsulinaemic hypoglycaemia. Importantly, human genetic variants that influence SRRM3 expression and IsletMIC inclusion in islets are associated with fasting glucose variation and type 2 diabetes risk. Taken together, our data identify a conserved microexon programme that regulates glucose homeostasis.

Neonatal imprinting of alveolar macrophages via neutrophil-derived 12-HETE

Resident-tissue macrophages (RTMs) arise from embryonic precursors1,2, yet the developmental signals that shape their longevity remain largely unknown. Here we demonstrate in mice genetically deficient in 12-lipoxygenase and 15-lipoxygenase (Alox15-/- mice) that neonatal neutrophil-derived 12-HETE is required for self-renewal and maintenance of alveolar macrophages (AMs) during lung development. Although the seeding and differentiation of AM progenitors remained intact, the absence of 12-HETE led to a significant reduction in AMs in adult lungs and enhanced senescence owing to increased prostaglandin E2 production. A compromised AM compartment resulted in increased susceptibility to acute lung injury induced by lipopolysaccharide and to pulmonary infections with influenza A virus or SARS-CoV-2. Our results highlight the complexity of prenatal RTM programming and reveal their dependency on in trans eicosanoid production by neutrophils for lifelong self-renewal.

A transcription factor atlas of directed differentiation

Transcription factors (TFs) regulate gene programs, thereby controlling diverse cellular processes and cell states. To comprehensively understand TFs and the programs they control, we created a barcoded library of all annotated human TF splice isoforms (>3,500) and applied it to build a TF Atlas charting expression profiles of human embryonic stem cells (hESCs) overexpressing each TF at single-cell resolution. We mapped TF-induced expression profiles to reference cell types and validated candidate TFs for generation of diverse cell types, spanning all three germ layers and trophoblasts. Targeted screens with subsets of the library allowed us to create a tailored cellular disease model and integrate mRNA expression and chromatin accessibility data to identify downstream regulators. Finally, we characterized the effects of combinatorial TF overexpression by developing and validating a strategy for predicting combinations of TFs that produce target expression profiles matching reference cell types to accelerate cellular engineering efforts.

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding

Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data. We present a series of benchmarking analyses on publicly available spatially resolved transcriptomics datasets, that demonstrate the superiority of Spatial-ID compared with state-of-the-art methods. Besides, we apply Spatial-ID on a self-collected mouse brain hemisphere dataset measured by Stereo-seq, that shows the scalability of Spatial-ID to three-dimensional large field tissues with subcellular spatial resolution.

Deciphering Transcriptional Networks during Human Cardiac Development

Human heart development is governed by transcription factor (TF) networks controlling dynamic and temporal gene expression alterations. Therefore, to comprehensively characterize these transcriptional regulations, day-to-day transcriptomic profiles were generated throughout the directed cardiac differentiation, starting from three distinct human- induced pluripotent stem cell lines from healthy donors (32 days). We applied an expression-based correlation score to the chronological expression profiles of the TF genes, and clustered them into 12 sequential gene expression waves. We then identified a regulatory network of more than 23,000 activation and inhibition links between 216 TFs. Within this network, we observed previously unknown inferred transcriptional activations linking IRX3 and IRX5 TFs to three master cardiac TFs: GATA4, NKX2-5 and TBX5. Luciferase and co-immunoprecipitation assays demonstrated that these five TFs could (1) activate each other's expression; (2) interact physically as multiprotein complexes; and (3) together, finely regulate the expression of SCN5A, encoding the major cardiac sodium channel. Altogether, these results unveiled thousands of interactions between TFs, generating multiple robust hypotheses governing human cardiac development.

Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics

The cell ecology and spatial niche implicated in the dynamic and sequential process of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and subsequent invasive adenocarcinoma (IAC) have not yet been elucidated. Here, we performed an integrative analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to characterize the cell atlas of the invasion trajectory of LUAD. We found that the UBE2C + cancer cell subpopulation constantly increased during the invasive process of LUAD with remarkable elevation in IAC, and its spatial distribution was in the peripheral cancer region of the IAC, representing a more malignant phenotype. Furthermore, analysis of the TME cell type subpopulation showed a constant decrease in mast cells, monocytes, and lymphatic endothelial cells, which were implicated in the whole process of invasive LUAD, accompanied by an increase in NK cells and MALT B cells from AIS to MIA and an increase in Tregs and secretory B cells from MIA to IAC. Notably, for AIS, cancer cells, NK cells, and mast cells were colocalized in the cancer region; however, for IAC, Tregs colocalized with cancer cells. Finally, communication and interaction between cancer cells and TME cell-induced constitutive activation of TGF-β signaling were involved in the invasion of IAC. Therefore, our results reveal the specific cellular information and spatial architecture of cancer cells and TME subpopulations, as well as the cellular interaction between them, which will facilitate the identification and development of precision medicine in the invasive process of LUAD from AIS to IAC.

Single-cell transcriptomic analysis of the tumor ecosystem of adenoid cystic carcinoma

Adenoid cystic carcinoma (ACC) is a malignant tumor that originates from exocrine gland epithelial cells. We profiled the transcriptomes of 49,948 cells from paracarcinoma and carcinoma tissues of three patients using single-cell RNA sequencing. Three main types of the epithelial cells were identified into myoepithelial-like cells, intercalated duct-like cells, and duct-like cells by marker genes. And part of intercalated duct-like cells with special copy number variations which altered with MYB family gene and EN1 transcriptomes were identified as premalignant cells. Developmental pseudo-time analysis showed that the premalignant cells eventually transformed into malignant cells. Furthermore, MYB and MYBL1 were found to belong to two different gene modules and were expressed in a mutually exclusive manner. The two gene modules drove ACC progression into different directions. Our findings provide novel evidence to explain the high recurrence rate of ACC and its characteristic biological behavior.

Efferocytosis drives myeloid NLRP3 dependent inflammasome signaling secretion of IL-1β to promote tumor growth

Caspase-1 signaling in myeloid suppressor cells can promote T-cell independent cancer progression, but the regulation of inflammasome signaling within the highly heterogeneous myeloid population in the tumor milieu remains elusive. To resolve this complexity, single cell transcriptomic profile of Head and Neck Squamous Cell Carcinoma (HNSCC) identified distinct inflammasome-associated genes within specific clusters of tumor-infiltrating myeloid cells. Among these myeloid cells, the sensor protein, NLRP3, and downstream effector IL-1β transcripts were enriched in discreet monocytic and macrophage subtypes in the TME. We showed that deletion of NLRP3, but not AIM2, phenocopied caspase-1/IL-1β dependent tumor progression in vivo. Paradoxically, we found myeloid-intrinsic caspase-1 signaling increased myeloid survival contrary to what would be predicted from the canonical pyroptotic function of caspase-1. This myeloid NLRP3/IL-1β signaling axis promotion of tumor growth was found to be gasdermin D independent. Mechanistically, we found that phagocyte-mediated efferocytosis of dying tumor cells in the TME directly activated NLRP3-dependent inflammasome signaling to drive IL-1β secretion. Subsequently we showed that NLRP3-mediated IL-1β production drives tumor growth in vivo. Dynamic RNA velocity analysis showed a robust directional flow from efferocytosis gene-set ^high macrophages to an inflammasome gene-set ^high macrophage population. We provide a novel efferocytosis-dependent inflammasome signaling pathway which mediates homeostatic tumor cell apoptosis that characterizes chronic inflammation-induced malignancy.

EPSTI1 as an immune biomarker predicts the prognosis of patients with stage III colon cancer

Objective

The poor prognosis and heterogeneity of stage III colon cancer (CC) suggest the need for more prognostic biomarkers. The tumor microenvironment (TME) plays a crucial role in tumor progression. We aimed to explore novel immune infiltration-associated molecules that serve as potential prognostic and therapeutic targets.

Methods

TME immune scores were calculated using “TMEscore” algorithm. Differentially expressed genes between the high and low TME immune score groups were identified and further investigated through a protein-protein interaction network and the Molecular Complex Detection algorithm. Cox regression, meta-analysis and immunohistochemistry were applied to identify genes significantly correlated with relapse-free survival (RFS). We estimated immune infiltration using three different algorithms (TIMER 2.0, CIBERSORTx, and TIDE). Single-cell sequencing data were processed by Seurat software.

Results

Poor RFS was observed in the low TME immune score groups (log-rank P < 0.05). EPSTI1 was demonstrated to be significantly correlated with RFS (P < 0.05) in stage III CC. Meta-analysis comprising 547 patients revealed that EPSTI1 was a protective factor (HR = 0.79, 95% CI, 0.65-0. 96; P < 0.05)). More immune infiltrates were observed in the high EPSTI1 group, especially M1 macrophage and myeloid dendritic cell infiltration (P < 0.05).

Conclusion

The TME immune score is positively associated with better survival outcomes. EPSTI1 could serve as a novel immune prognostic biomarker for stage III CC.

Cross-lineage potential of Ascl1 uncovered by comparing diverse reprogramming regulatomes

Direct reprogramming has revolutionized the fields of stem cell biology and regenerative medicine. However, the common mechanisms governing how reprogramming cells undergo transcriptome and epigenome remodeling (i.e., regulatome remodeling) have not been investigated. Here, by characterizing early changes in the regulatome of three different types of direct reprogramming, we identify lineage-specific features as well as common regulatory transcription factors. Of particular interest, we discover that the neuronal factor Ascl1 possesses cross-lineage potential; together with Mef2c, it drives efficient cardiac reprogramming toward a mature and induced cardiomyocyte phenotype. Through ChIP-seq and RNA-seq, we find that MEF2C drives the shift in ASCL1 binding away from neuronal genes toward cardiac genes, guiding their co-operative epigenetic and transcription activities. Together, these findings demonstrate the existence of common regulators of different direct reprogramming and argue against the premise that transcription factors possess only lineage-specific capabilities for altering cell fate - the basic premise used to develop direct reprogramming approaches.

Single-Cell RNA Sequencing Reveals Molecular Features of Heterogeneity in the Murine Retinal Pigment Epithelium

Transcriptomic analysis of the mammalian retinal pigment epithelium (RPE) aims to identify cellular networks that influence ocular development, maintenance, function, and disease. However, available evidence points to RPE cell heterogeneity within native tissue, which adds complexity to global transcriptomic analysis. Here, to assess cell heterogeneity, we performed single-cell RNA sequencing of RPE cells from two young adult male C57BL/6J mice. Following quality control to ensure robust transcript identification limited to cell singlets, we detected 13,858 transcripts among 2667 and 2846 RPE cells. Dimensional reduction by principal component analysis and uniform manifold approximation and projection revealed six distinct cell populations. All clusters expressed transcripts typical of RPE cells; the smallest (C1, containing 1-2% of total cells) exhibited the hallmarks of stem and/or progenitor (SP) cells. Placing C1-6 along a pseudotime axis suggested a relative decrease in melanogenesis and SP gene expression and a corresponding increase in visual cycle gene expression upon RPE maturation. K-means clustering of all detected transcripts identified additional expression patterns that may advance the understanding of RPE SP cell maintenance and the evolution of cellular metabolic networks during development. This work provides new insights into the transcriptome of the mouse RPE and a baseline for identifying experimentally induced transcriptional changes in future studies of this tissue.

ALKBH5/MAP3K8 axis regulates PD-L1+ macrophage infiltration and promotes hepatocellular carcinoma progression

Hepatocellular carcinoma is one of the most common malignant tumors.M6A is a novel epigenetic modification that have been emerged as vital regulators for the progression of HCC. However, the regulatory role, clinical significance and the details of the modification, such as the impact on the local tumor environment, remain largely unclear. Our study showed that ALKBH5 was highly expressed in HCC and high ALKBH5 expression predicted a worse prognosis of HCC patients. Prediction of ALKBH5 function by tissue samples and single cell sequencing Gene Set Variation Analysis. Primary CD3 + T lymphocytes and bone marrow-derived macrophages were used to evaluate the effect of ALKBH5 on immune microenvironment. The results indicated that ALKBH5 promote HCC cell proliferation, metastasis and PD-L1+macrophage recruitment. Mechanistically the results showed that ALKBH5 regulates MAP3K8 expression in a m6A dependent manner which mediates the proliferation and metastasis of HCC cells. ALKBH5 also promotes the activation of JNK and ERK pathways through upregulating MAP3K8, thus regulating the expression of IL-8 and promoting macrophage recruitment. Taken together, these data show that ALKBH5 promotes HCC growth, metastasis and macrophage recruitment through ALKBH5/MAP3K8 axis and it may serve as a potential diagnostic marker and target for treatment of HCC patients.

Comparative analysis of cell-cell communication at single-cell resolution

Inference of cell-cell communication (CCC) from single-cell RNA-sequencing data is a powerful technique to uncover putative axes of multicellular coordination, yet existing methods perform this analysis at the level of the cell type or cluster, discarding single-cell level information. Here we present Scriabin â€" a flexible and scalable framework for comparative analysis of CCC at single-cell resolution. We leverage multiple published datasets to show that Scriabin recovers expected CCC edges and use spatial transcriptomic data, genetic perturbation screens, and direct experimental manipulation of receptor-ligand interactions to validate that the recovered edges are biologically meaningful. We then apply Scriabin to uncover co-expressed programs of CCC from atlas-scale datasets, validating known communication pathways required for maintaining the intestinal stem cell niche and revealing species-specific communication pathways. Finally, we utilize single-cell communication networks calculated using Scriabin to follow communication pathways that operate between timepoints in longitudinal datasets, highlighting bystander cells as important initiators of inflammatory reactions in acute SARS-CoV-2 infection. Our approach represents a broadly applicable strategy to leverage single-cell resolution data maximally toward uncovering CCC circuitry and rich niche-phenotype relationships in health and disease.