Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data

Dynamic development of microglia and macrophages after spinal cord injury

JOURNAL/nrgr/04.03/01300535-202512000-00029/figure1/v/2025-01-31T122243Z/r/image-tiff Secondary injury following spinal cord injury is primarily characterized by a complex inflammatory response, with resident microglia and infiltrating macrophages playing pivotal roles. While previous studies have grouped these two cell types together based on similarities in structure and function, an increasing number of studies have demonstrated that microglia and macrophages exhibit differences in structure and function and have different effects on disease processes. In this study, we used single-cell RNA sequencing and spatial transcriptomics to identify the distinct evolutionary paths of microglia and macrophages following spinal cord injury. Our results showed that microglia were activated to a pro-inflammatory phenotype immediately after spinal cord injury, gradually transforming to an anti-inflammatory steady state phenotype as the disease progressed. Regarding macrophages, our findings highlighted abundant communication with other cells, including fibroblasts and neurons. Both pro-inflammatory and neuroprotective effects of macrophages were also identified; the pro-inflammatory effect may be related to integrin β2 ( Itgb2 ) and the neuroprotective effect may be related to the oncostatin M pathway. These findings were validated by in vivo experiments. This research underscores differences in the cellular dynamics of microglia and macrophages following spinal cord injury, and may offer new perspectives on inflammatory mechanisms and potential therapeutic targets.

Accurate Transcription Factor Activity Inference to Decipher Cell Identity from Single-Cell Transcriptomic Data with MetaTF

Cellular heterogeneity within cancer tissues determines cancer progression and treatment response. Single-cell RNA sequencing (scRNA-seq) has provided a powerful approach for investigating the cellular heterogeneity of both cancer cells and stroma cells in the tumor microenvironment. However, the common practice to characterize cell identity based on the similarity of their gene expression profiles may not really indicate distinct cellular populations with unique roles. Generally, the cell identity and function are orchestrated by the expression of given specific genes tightly regulated by transcription factors (TFs). Therefore, deciphering TF activity is essential for gaining a better understanding of the uniqueness and functionality of each cell type. Herein, metaTF, a computational framework designed to infer TF activity in scRNA-seq data, is introduced and existing methods are outperformed for estimating TF activity. It presents the improved effectiveness in characterizing cell identity during mouse hematopoietic stem cell development. Furthermore, metaTF provides a superior characterization of the functional identity of breast cancer epithelial cells, and identifies a novel subset of neural-regulated T cells within the tumor immune microenvironment, which potentially activates BCL6 in response to neural-related signals. Overall, metaTF enables robust TF activity analysis from scRNA-seq data, significantly enhancing the characterization of cell identity and function.

ARID1A mutations protect follicular lymphoma from FAS-dependent immune surveillance by reducing RUNX3/ETS1-driven FAS-expression

The cell death receptor FAS and its ligand (FASLG) play crucial roles in the selection of B cells during the germinal center (GC) reaction. Failure to eliminate potentially harmful B cells via FAS can lead to lymphoproliferation and the development of B cell malignancies. The classic form of follicular lymphoma (FL) is a prototypic GC-derived B cell malignancy, characterized by the t(14;18)(q32;q21)IGH::BCL2 translocation and overexpression of antiapoptotic BCL2. Additional alterations were shown to be clinically relevant, including mutations in ARID1A. ARID1A is part of the SWI/SNF nucleosome remodeling complex that regulates DNA accessibility ("openness"). However, the mechanism how ARID1A mutations contribute to FL pathogenesis remains unclear. We analyzed 151 FL biopsies of patients with advanced-stage disease at initial diagnosis and found that ARID1A mutations were recurrent and mainly disruptive, with an overall frequency of 18%. Additionally, we observed that ARID1A mutant FL showed significantly lower FAS protein expression in the FL tumor cell population. Functional experiments in BCL2-translocated lymphoma cells demonstrated that ARID1A is directly involved in the regulation of FAS, and ARID1A loss leads to decreased FAS protein and gene expression. However, ARID1A loss did not affect FAS promotor openness. Instead, we identified and experimentally validated a previously unknown co-transcriptional complex consisting of RUNX3 and ETS1 that regulates FAS expression, and ARID1A loss leads to reduced RUNX3 promotor openness and gene expression. The reduced FAS levels induced by ARID1A loss rendered lymphoma cells resistant to both soluble and T cell membrane-anchored FASLG-induced apoptosis, and significantly diminished CAR T cell killing in functional experiments. In summary, we have identified a functionally and clinically relevant mechanism how FL cells can escape FAS-dependent immune surveillance, which may also impact the efficacy of T cell-based therapies, including CAR T cells.

Selective inhibition of stromal mechanosensing suppresses cardiac fibrosis

Matrix-derived biophysical cues are known to regulate the activation of fibroblasts and their subsequent transdifferentiation into myofibroblasts1-6, but whether modulation of these signals can suppress fibrosis in intact tissues remains unclear, particularly in the cardiovascular system7-10. Here we demonstrate across multiple scales that inhibition of matrix mechanosensing in persistently activated cardiac fibroblasts potentiates-in concert with soluble regulators of the TGFβ pathway-a robust transcriptomic, morphological and metabolic shift towards quiescence. By conducting a meta-analysis of public human and mouse single-cell sequencing datasets, we identify the focal-adhesion-associated tyrosine kinase SRC as a fibroblast-enriched mechanosensor that can be targeted selectively in stromal cells to mimic the effects of matrix softening in vivo. Pharmacological inhibition of SRC by saracatinib, coupled with TGFβ suppression, induces synergistic repression of key profibrotic gene programs in fibroblasts, characterized by a marked inhibition of the MRTF-SRF pathway, which is not seen after treatment with either drug alone. Importantly, the dual treatment alleviates contractile dysfunction in fibrotic engineered heart tissues and in a mouse model of heart failure. Our findings point to joint inhibition of SRC-mediated stromal mechanosensing and TGFβ signalling as a potential mechanotherapeutic strategy for treating cardiovascular fibrosis.

Learning tissue representation by identification of persistent local patterns in spatial omics data

Spatial omics data provide rich molecular and structural information on tissues. Their analysis provides insights into local heterogeneity of tissues and holds promise to improve patient stratification by associating clinical observations with refined tissue representations. We introduce Kasumi, a method for identifying spatially localized neighborhood patterns of intra- and intercellular relationships that are persistent across samples and conditions. The tissue representation based on these patterns can facilitate translational tasks, as we show for stratification of cancer patients for disease progression and response to treatment using data from different experimental platforms. On these tasks, Kasumi outperforms related approaches and offers explanations of spatial coordination and relationships at the cell-type or marker level. We show that persistent patterns comprise regions of different sizes, and that non-abundant, localized relationships in the tissue are strongly associated with unfavorable outcomes.

Macrophage KDM2A promotes atherosclerosis via regulating FYN and inducing inflammatory response

Macrophage inflammatory response is the key driver in atherosclerosis development. However, transcriptional remodeling of macrophage inflammatory response remains largely unknown. In this study, transcriptional regulatory networks were constructed from human plaque microarray datasets. Differential analysis and subsequent machine learning algorithms were used to identify key transcriptional regulons. Multiple immune cell inference methods (including CIBERSORT, ssGSEA, MCP-counter, and xCell), single-cell RNA-seq of human plaques and immunofluorescence of human and mouse plaque samples reveal that the macrophage-specific transcriptional regulator, KDM2A, is critical for inflammatory response. Diagnostic analyses validate KDM2A expression in peripheral monocytes/macrophages is an excellent predictor of atherosclerosis development and progression. RNA-seq of mouse bone marrow-derived macrophages under oxidized low-density lipoprotein stimulation reveal KDM2A knockdown significantly represses pro-inflammatory, oxidative, and lipid uptake pathways. In vitro experiments confirmed KDM2A activates inflammation, oxidative stress and lipid accumulation in macrophages. Mechanistically, FYN was identified as a direct target of KDM2A by chromatin immunoprecipitation followed by sequencing and qPCR analysis. Specific inhibition of FYN restored the inflammatory response, oxidative stress, and intracellular lipid accumulation after transfection with KDM2A overexpression plasmid. Importantly, macrophage-specific knockdown of KDM2A in ApoE-/- mice fed a high-fat diet apparently attenuated plaque progression. Furthermore, the genetic association of KDM2A with atherosclerosis was validated by Mendelian randomization and colocalization analysis. A group of small molecules with the potential to target KDM2A has been identified through virtual screening, offering promising strategies for atherosclerosis treatment. The current study provides the novel role of KDM2A in macrophage inflammatory response of atherosclerosis through transcriptional regulation of FYN.

EGFR-mediated local invasiveness and response to Cetuximab in head and neck cancer

BACKGROUND

Recurrent/metastatic head and neck squamous cell carcinoma (R/M-HNSCC) is a severe, frequently lethal condition. Oncogene addiction to epidermal growth factor receptor (EGFR) is a hallmark of HNSCC, but the clinical efficacy of EGFR-targeted therapies remains low. Understanding molecular networks governing EGFR-driven progression is paramount to the exploration of (co)-treatment targets and predictive markers.

METHODS

We performed function-based mapping of differentially expressed genes in EGFR-mediated local invasion (fDEGs) using photoconvertible tracers and RNA-sequencing (RNA-seq) in a cellular 3D-model.

RESULTS

Upon alignment with public single-cell RNA-seq (scRNA-seq) datasets and HNSCC-specific regulons, a gene regulatory network of local invasion (invGRN) was inferred from gene expression data, which was overrepresented in budding tumors. InvGRN comprises the central hubs inhibin subunit beta alpha (INHBA) and snail family transcriptional repressor 2 (SNAI2), and druggable fDEGs integrin subunit beta 4 (ITGB4), laminin 5 (LAMB3/LAMC2), and sphingosine kinase 1 (SPHK1). Blockade of INHBA repressed local invasion and was reverted by activin A, laminin 5, and sphingosine-1-phosphate, demonstrating a functional interconnectivity of the invGRN. Epithelial-to-mesenchymal transition (EMT) of malignant cells and the invGRN are induced by newly defined EGFR-activity subtypes with prognostic value that are promoted by amphiregulin (AREG) and epiregulin (EREG). Importantly, co-inhibition of SPHK1 showed synthetic effects on Cetuximab-mediated invasion blockade and high expression of selected fDEGs was associated with response to Cetuximab in patient-derived xenotransplantation (PDX) and R/M-HNSCC patients.

CONCLUSIONS

We describe an actionable network of EGFR-mediated local invasion and define druggable effectors with predictive potential regarding the response of R/M-HNSCC to Cetuximab.

Identification of pro-fibrotic cellular subpopulations in fascia of gluteal muscle contracture using single-cell RNA sequencing

Fibrosis is a common and integral pathological feature in various chronic diseases, capable of affecting any tissue or organ. Fibrosis within deep fascia is implicated in many myofascial disorders, including gluteal muscle contracture (GMC), Dupuytren's disease, plantar fasciitis, iliotibial band syndrome, and chronic muscle pain. Despite its clinical significance, deep fascia fibrosis remains considerably under-researched compared to other fibrotic conditions. Single-cell RNA-sequencing (scRNA-seq) has been used to investigate cellular heterogeneity in fibrotic tissues. However, to our knowledge, only a few studies have applied scRNA-seq to explore cellular heterogeneity in deep fascia, and none have specifically examined fibrotic fascia. In this study, we performed scRNA-seq analysis on fibrotic fascia associated with GMC and compared them to nonfibrotic control fascial samples. Our findings show that fibroblast and macrophage cells play critical roles in pathological tissue remodeling within fibrotic deep fascia. We observed an upregulation of various collagens, proteoglycans, and extracellular matrix (ECM) glycoproteins in contracture deep fascia, attributed to the widespread activation of fibroblast subclusters. Additionally, two pro-fibrotic macrophage subpopulations, SPP1+ MP and ECM-like MP, appear to facilitate ECM deposition in fibrotic deep fascia by either regulating fibroblast activation or directly contributing to ECM production. The SPP1+ MP and ECM-like MP cells, as well as the signal interaction between SPP1+ MP and fibroblast cells, present potential therapeutic target for treating GMC and other related myofascial disorders.

In vivo regulation of the monocyte phenotype by Mycobacterium marinum and the ESX-1 type VII secretion system

Pathogenic mycobacteria require the conserved ESX-1 type VII secretion system to cause disease. In a murine Mycobacterium marinum infection model we previously demonstrated that infiltrating monocytes and neutrophils represent the major bacteria-harbouring cell populations in infected tissue. In the current study we use this model, in combination with scRNA sequencing, to assess the impact of M. marinum infection on the transcriptional profile of infiltrating Ly6C⁺MHCII⁺ monocytes in vivo. Our findings demonstrate that infection of infiltrating monocytes with M. marinum alters their cytokine expression profile, induces glycolytic metabolism, hypoxia-mediated signaling, nitric oxide synthesis, tissue remodeling, and suppresses responsiveness to IFNγ. We further show that the transcriptional response of bystander monocytes is influenced by ESX-1-dependent mechanisms, including a reduced responsiveness to IFNγ. These findings suggest that mycobacterial infection has pleiotropic effects on monocyte phenotype, with potential implications in bacterial growth restriction and granuloma formation.

Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research

In recent years, significant advancements in biochemistry, materials science, engineering, and computer-aided testing have driven the development of high-throughput tools for profiling genetic information. Single-cell RNA sequencing (scRNA-seq) technologies have established themselves as key tools for dissecting genetic sequences at the level of single cells. These technologies reveal cellular diversity and allow for the exploration of cell states and transformations with exceptional resolution. Unlike bulk sequencing, which provides population-averaged data, scRNA-seq can detect cell subtypes or gene expression variations that would otherwise be overlooked. However, a key limitation of scRNA-seq is its inability to preserve spatial information about the RNA transcriptome, as the process requires tissue dissociation and cell isolation. Spatial transcriptomics is a pivotal advancement in medical biotechnology, facilitating the identification of molecules such as RNA in their original spatial context within tissue sections at the single-cell level. This capability offers a substantial advantage over traditional single-cell sequencing techniques. Spatial transcriptomics offers valuable insights into a wide range of biomedical fields, including neurology, embryology, cancer research, immunology, and histology. This review highlights single-cell sequencing approaches, recent technological developments, associated challenges, various techniques for expression data analysis, and their applications in disciplines such as cancer research, microbiology, neuroscience, reproductive biology, and immunology. It highlights the critical role of single-cell sequencing tools in characterizing the dynamic nature of individual cells.

Single-cell profiling reveals a reduced epithelial defense system, decreased immune responses and the immune regulatory roles of different fibroblast subpopulations in chronic atrophic gastritis

PURPOSE

To identify key cellular changes and molecular events in atrophic mucosa, we aimed to elucidate the molecular mechanisms driving the occurrence of chronic atrophic gastritis (CAG).

METHODS

We used single-cell RNA sequencing (scRNA-seq) to characterize changes in the epithelial state and tissue microenvironment associated with CAG. The molecular changes were identified by comparing differentially expressed genes (DEGs) between the two mucosa states. Gene Ontology (GO) pathway enrichment analysis was used to explore the potential functional changes in each cell subtype in atrophic mucosa. Gene set score analysis was conducted to compare the functional roles of different fibroblast subtypes and functional changes in cell subtypes between the CAG and control groups. Metabolic analysis was performed to compare the metabolic activity of C1Q+ macrophages under different conditions. NichNet analysis was used to analyze the regulatory relationships between CCL11+APOE+ fibroblasts and C1Q+ macrophages and between CCL11+APOE+ fibroblasts and CD8+ effector T cells. Transcription factor (TF) analysis was performed to determine the transcription status of different T-cell subtypes in atrophic and normal mucosa.

RESULTS

We generated a single-cell transcriptomic atlas from 3 CAG biopsy samples and paired adjacent normal tissues. Our analysis revealed that chief cells and parietal cells exhibited a loss of detoxification ability and that surface mucous cells displayed a reduced antimicrobial defense ability in CAG lesions. The mucous neck cells in CAG lesions showed upregulation of genes related to cell cycle transition, which may lead to aberrant DNA replication. Additionally, cells with the T exhaustion phenotype infiltrated under CAG condition. C1Q+ macrophages exhibited reduced phagocytosis, downregulated expression of pattern recognition receptors and decreased metabolic activity. NichNet analysis revealed that a subpopulation of CXCL11+APOE+ fibroblasts regulated the inflammatory response in the pathogenesis of atrophic gastritis. APSN+CXCL11+APOE+ fibroblasts were found to be associated with gastric cancer (GC) development.

CONCLUSIONS

The main goal of this study was to comprehensively elucidate the cellular changes in CAG lesions. We observed an immune decline in the mucosal microenvironment during the development of CAG, including a reduced immune response of C1Q+ macrophages, reduced cytotoxicity of T cells, and increased infiltration of exhausted T cells. Specifically, we demonstrated that different epithelial subtypes aberrantly express genes related to susceptibility to external bacterial infection and aberrant cell cycle progression. Our study provides new insights into the functions of epithelial changes and immune alterations during the development of CAG.

Single-cell and spatial transcriptomes reveal the impact of maternal low protein diet on follicular cell composition and ovarian micro-environment in the offspring

Maternal low protein diet around pregnancy reduces the primordial follicles in offspring ovary. Resolving cellular and molecular mechanisms associated with low protein diet is therefore urgently needed for the guidance of dietary interventions. Here, we utilized single-cell and spatial RNA-seq to create transcriptomic atlases of offspring ovaries from maternal low protein diet mice. Analysis of cell type specific low protein diet associated transcriptional changes revealed increased unfolded protein and decreased oxidative phosphorylation defense as a hallmark of low protein diet effects. Altered pathways included hedgehog signaling in granulosa cells, BMP signaling in theca cells and PTN signaling in early theca cells. Notably, the disordered follicular cell function and ovarian microenvironment may closely corelated with decreased follicular number and quality. Collectively, our findings depict the transcriptomic atlases of the offspring ovary derived from maternal low protein diet group and provide candidate molecular mechanisms underlying the complex ovarian cell changes conferred by low protein diet.

Single-cell transcriptomic profiling of heart reveals ANGPTL4 linking fibroblasts and angiogenesis in heart failure with preserved ejection fraction

INTRODUCTION

Despite the high morbidity and mortality, the effective therapies for heart failure with preserved fraction (HFpEF) are limited as the poor understand of its pathophysiological basis.

OBJECTIVE

This study was aimed to characterize the cellular heterogeneity and potential mechanisms of HFpEF at single-cell resolution.

METHODS

An HFpEF mouse model was induced by a high-fat diet with N-nitro-L-arginine methyl ester. Cells from the hearts were subjected to single-cell sequencing. The key protein expression was measured with Immunohistochemistry and immunofluorescence staining.

RESULTS

In HFpEF hearts, myocardial fibroblasts exhibited higher levels of fibrosis. Furthermore, an increased number of fibroblasts differentiated into high-metabolism and high-fibrosis phenotypes. The expression levels of genes encoding certain pro-angiogenic secreted proteins were decreased in the HFpEF group, as confirmed by bulk RNA sequencing. Additionally, the proportion of the endothelial cell (EC) lineages in the HFpEF group was significantly downregulated, with low angiogenesis and high apoptosis phenotypes observed in these EC lineages. Interestingly, the fibroblasts in the HFpEF heart might cross-link with the EC lineages via over-secretion of ANGPTL4, thus displaying an anti-angiogenic function. Immunohistochemistry and immunofluorescence staining then revealed the downregulation of vascular density and upregulation of ANGPTL4 expression in HFpEF hearts. Finally, we predicted ANGPTL4as a potential druggable target using DrugnomeAI.

CONCLUSION

In conclusion, this study comprehensively characterized the angiogenesis impairment in HFpEF hearts at single-cell resolution and proposed that ANGPTL4 secretion by fibroblasts may be a potential mechanism underlying this angiogenic abnormality.

An integrated single-cell and spatial transcriptomic atlas of thyroid cancer progression identifies prognostic fibroblast subpopulations

Thyroid cancer progression from curable well-differentiated thyroid carcinoma to highly lethal anaplastic thyroid carcinoma is distinguished by tumor cell de-differentiation and recruitment of a robust stromal infiltrate. Combining an integrated thyroid cancer single-cell sequencing atlas with spatial transcriptomics and bulk RNA-sequencing, we define stromal cell subpopulations and tumor-stromal cross-talk occurring across the histologic and mutational spectrum of thyroid cancer. We identify distinct inflammatory and myofibroblastic cancer-associated fibroblast (iCAF and myCAF) populations and perivascular-like populations. The myCAF population is only found in malignant samples and is associated with tumor cell invasion, BRAF V600E mutation, lymph node metastasis, and disease progression. Tumor-adjacent myCAFs abut invasive tumor cells with a partial epithelial-to-mesenchymal phenotype. Tumor-distant iCAFs infiltrate inflammatory autoimmune thyroid lesions and anaplastic tumors. In summary, our study provides an integrated atlas of thyroid cancer fibroblast subtypes and spatial characterization at sites of tumor invasion and de-differentiation, defining the stromal reorganization central to disease progression.

Single-cell mapping of regulatory DNA:Protein interactions

Gene expression is coordinated by a multitude of transcription factors (TFs), whose binding to the genome is directed through multiple interconnected epigenetic signals, including chromatin accessibility and histone modifications. These complex networks have been shown to be disrupted during aging, disease, and cancer. However, profiling these networks across diverse cell types and states has been limited due to the technical constraints of existing methods for mapping DNA:Protein interactions in single cells. As a result, a critical gap remains in understanding where TFs or other chromatin remodelers bind to DNA and how these interactions are perturbed in pathological contexts. To address this challenge, we developed a transformative single-cell immuno-tethering DNA:Protein mapping technology. By coupling a species-specific antibody-binding nanobody to a cytosine base editing enzyme, this approach enables profiling of even weak or transient factor binding to DNA, a task that was previously unachievable in single cells. Thus, our Docking & Deamination followed by sequencing (D&D-seq) technique induces cytosine-to-uracil edits in genomic regions bound by the target protein, offering a novel means to capture DNA:Protein interactions with unprecedented resolution. Importantly, this technique can be seamlessly incorporated into common single-cell multiomics workflows, enabling multimodal analysis of gene regulation in single cells. We tested the ability of D&D-seq to record TF binding both in bulk and at the single-cell level by profiling CTCF and GATA family members, obtaining high specificity and efficiency, with clear identification of TF footprint and signal retention in the targeted cell subpopulations. Furthermore, the deamination reaction showed minimal off-target activity, with high concordance to bulk ChIP-seq reference data. Applied to primary human peripheral blood mononuclear cells (PBMCs), D&D-seq successfully identified CTCF binding sites and enabled integration with advanced machine-learning algorithms for predicting 3D chromatin structure. Furthermore, we integrated D&D-seq with single-cell genotyping to assess the impact of IDH2 mutations on CTCF binding in a human clonal hematopoiesis sample, uncovering altered binding and chromatin co-accessibility patterns in mutant cells. Altogether, D&D-seq represents an important technological advance enabling the direct mapping of TF or chromatin remodeler binding to the DNA in primary human samples, opening new avenues for understanding chromatin and transcriptional regulation in health and disease.

A specific inflammatory suppression fibroblast subpopulation characterized by MHCII expression in human dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is a significant cause of heart failure that requires heart transplantation. Fibroblasts play a central role in the fibro-inflammatory microenvironment of DCM. However, their cellular heterogeneity and interaction with immune cells have not been well identified. An integrative analysis was conducted on single-cell RNA sequencing (ScRNA-Seq) data from human left ventricle tissues, which comprised 4 hearts from healthy donors and 6 hearts with DCM. The specific antigen-presenting fibroblast (apFB) was explored as a subtype of fibroblasts characterized by expressing MHCII genes, the existence of which was confirmed by immunofluorescence staining of 3 cardiac tissues from DCM patients with severe heart failure. apFB highly expressed the genes that response to IFN-γ, and it also have a high activity of the JAK-STAT pathway and the transcription factor RFX5. In addition, the analysis of intercellular communication between apFBs and CD4+T cells revealed that the anti-inflammatory ligand-receptor pairs TGFB-TGFR, CLEC2B-KLRB1, and CD46-JAG1 were upregulated in DCM. The apFB signature exhibited a positive correlation with immunosuppression and demonstrated diagnostic and prognostic value when evaluated using a bulk RNA dataset comprising 166 donors and 166 DCM samples. In conclusion, the present study identified a novel subpopulation of fibroblasts that specifically expresses MHCII-encoding genes. This specific apFBs can suppress the inflammation occurring in DCM. Our findings further elucidate the composition of the fibro-inflammatory microenvironment in DCM, and provide a novel therapeutic target.

BACH1 as a key driver in rheumatoid arthritis fibroblast-like synoviocytes identified through gene network analysis

RNA-sequencing and differential gene expression studies have significantly advanced our understanding of pathogenic pathways underlying rheumatoid arthritis (RA). Yet, little is known about cell-specific regulatory networks and their contributions to disease. In this study, we focused on fibroblast-like synoviocytes (FLS), a cell type central to disease pathogenesis and joint damage in RA. We used a strategy that computed sample-specific gene regulatory networks to compare network properties between RA and osteoarthritis FLS. We identified 28 transcription factors (TFs) as key regulators central to the signatures of RA FLS. Six of these TFs are new and have not been previously implicated in RA through ex vivo or in vivo studies, and included BACH1, HLX, and TGIF1. Several of these TFs were found to be co-regulated, and BACH1 emerged as the most significant TF and regulator. The main BACH1 targets included those implicated in fatty acid metabolism and ferroptosis. The discovery of BACH1 was validated in experiments with RA FLS. Knockdown of BACH1 in RA FLS significantly affected the gene expression signatures, reduced cell adhesion and mobility, interfered with the formation of thick actin fibers, and prevented the polarized formation of lamellipodia, all required for the RA destructive behavior of FLS. This study establishes BACH1 as a central regulator of RA FLS phenotypes and suggests its potential as a therapeutic target to selectively modulate RA FLS.

The Low Tumorigenic Risk and Subtypes of Cardiomyocytes Derived from Human-induced Pluripotent Stem Cells

BACKGROUND

Clinical application of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) is a promising approach for the treatment of heart diseases. However, the tumorigenicity of hiPSC-CMs remains a concern for their clinical applications and the composition of the hiPSC-CM subtypes need to be clearly identified.

METHODS

In the present study, hiPSC-CMs were induced from hiPSCs via modulation of Wnt signaling followed by glucose deprivation purification. The structure, function, subpopulation composition, and tumorigenic risk of hiPSC-CMs were evaluated by single-cell RNA sequencing (scRNAseq), whole exome sequencing (WES), and integrated molecular biology, cell biology, electrophysiology, and/or animal experiments.

RESULTS

The high purity of hiPSC-CMs, determined by flow cytometry analysis, was generated. ScRNAseq analysis of differentiation day (D) 25 hiPSC-CMs did not identify the transcripts representative of undifferentiated hiPSCs. WES analysis showed a few newly acquired confidently identified mutations and no mutations in tumor susceptibility genes. Further, no tumor formation was observed after transplanting hiPSC-CMs into NOD-SCID mice for 3 months. Moreover, D25 hiPSC-CMs were composed of subtypes of ventricular-like cells (23.19%) and atrial-like cells (66.45%) in different cell cycle stages or mature levels, based on the scRNAseq analysis. Furthermore, a subpopulation of more mature ventricular cells (3.21%) was identified, which displayed significantly up-regulated signaling pathways related to myocardial contraction and action potentials. Additionally, a subpopulation of cardiomyocytes in an early differentiation stage (3.44%) experiencing nutrient stress-induced injury and heading toward apoptosis was observed.

CONCLUSIONS

This study confirmed the biological safety of hiPSC-CMs and described the composition and expression profile of cardiac subtypes in hiPSC-CMs which provide standards for quality control and theoretical supports for the translational applications of hiPSC-CMs.

Spatial Transcriptomics: Integrating Morphology and Molecular Mechanisms of Kidney Diseases

The recent arrival of high-resolution spatial transcriptomics (ST) technologies is generating a veritable revolution in life sciences, enabling biomolecules to be measured in their native spatial context. By integrating morphology and molecular biology, ST technologies offer the potential of improving the understanding of tissue biology and disease and may also provide meaningful clinical insights. This review describes the main ST technologies currently available and the computational analysis for data interpretation and visualization, and illustrate their scientific and potential medical interest in the context of kidney disease. Finally, we discuss the perspectives and challenges of these booming new technologies.

Pathway metrics accurately stratify T cells to their cells states

Pathway analysis is a powerful approach for elucidating insights from gene expression data and associating such changes with cellular phenotypes. The overarching objective of pathway research is to identify critical molecular drivers within a cellular context and uncover novel signaling networks from groups of relevant biomolecules. In this work, we present PathSingle, a Python-based pathway analysis tool tailored for single-cell data analysis. PathSingle employs a unique graph-based algorithm to enable the classification of diverse cellular states, such as T cell subtypes. Designed to be open-source, extensible, and computationally efficient, PathSingle is available at https://github.com/zurkin1/PathSingle under the MIT license. This tool provides researchers with a versatile framework for uncovering biologically meaningful insights from high-dimensional single-cell transcriptomics data, facilitating a deeper understanding of cellular regulation and function.

Age-Related ECM Stiffness Mediates TRAIL Activation in Muscle Stem Cell Differentiation

The stiffening of the extracellular matrix (ECM) with age hinders muscle regeneration by causing intrinsic muscle stem cell (MuSC) dysfunction through a poorly understood mechanism. Here, the study aims to study those age-related molecular changes in the differentiation of MuSCs due to age and/or stiffness. Hence, young and aged MuSCs are seeded onto substrates engineered to mimic a soft and stiff ECM microenvironment to study those molecular changes using single-cell RNA sequencing (scRNA). The trajectory of scRNA data of the MuSCs under four different conditions undergoing differentiation is analyzed as well as the active molecular pathways and transcription factors driving those differentiation fates. Data revealed the presence of a branching point within the trajectory leading to the emergence of an age-related fibroblastic population characterized by activation of the TNF-related apoptosis-inducing ligand (TRAIL) pathway, which is significantly activated in aged cells cultured on stiff substrates. Next, using the collagen cross-linking inhibitor β-aminopropionitrile (BAPN) in vivo, the study elucidates stiffness changes on TRAIL downstream apoptotic targets (caspase 8 and caspase 3) using immunostaining. TRAIL activity is significantly inhibited by BAPN in aged animals, indicating a complex mechanism of age-related declines in muscle function through inflammatory and apoptotic mediators.

RCDdb: A manually curated database and analysis platform for regulated cell death

Regulated cell death is a pivotal regulatory mechanism governing the development and homeostasis of multicellular organisms. A comprehensive understanding of RCD's regulatory mechanisms is crucial for developing novel therapeutic strategies against diseases associated with cell death, such as cancer and neurodegenerative diseases. However, existing data repositories support limited types of cell death data and lack comprehensive annotation and analytical functionalities. Thus, establishing an extensive cell death database is an urgent imperative. To address this gap, we developed the Regulated Cell Death Database (RCDdb, chenyclab.com/RCDdb), the first comprehensively manually annotated database designed to support annotations and analytical capabilities across all RCD types. We compiled 3090 marker gene annotations associated with 15 RCD types from 2180 relevant articles. The RCDdb includes annotation data on these marker genes concerning diseases, drugs, pathways, proteins, and gene expressions. Furthermore, it provides 49 diverse visualization methods to present this information. More importantly, the RCDdb features three online analysis tools for identifying and analyzing RCD-related features within user-submitted data. Furthermore, the RCDdb offers a user-friendly interface for querying, browsing, analysis, and visualization of detailed information associated with each RCD category. This resource promises to significantly aid researchers in better understanding the mechanisms of cell death, thereby accelerating progress in research and therapeutic strategies aimed at combating RCD-related diseases.

BIOTIC: a Bayesian framework to integrate single-cell multi-omics for transcription factor activity inference and improve identity characterization of cells

Understanding cell destiny requires unraveling the intricate mechanism of gene regulation, where transcription factors (TFs) play a pivotal role. However, the actual contribution of TFs, that is TF activity, is not only determined by TF expression, but also accessibility of corresponding chromatin regions. Therefore, we introduce BIOTIC, an advanced Bayesian model with a well-established gene regulation structure that harnesses the power of single-cell multi-omics data to model the gene expression process under the control of regulatory elements, thereby defining the regulatory activity of TFs with variational inference. We demonstrated that the TF activity inferred by BIOTIC can serve as a characterization of cell identity, and outperforms baseline methods for the tasks of cell typing, cell development tracking, and batch effect correction. Additionally, BIOTIC trained on multi-omics data can flexibly be applied to the scenario where merely single-cell transcriptome sequencing is available, to infer TF activity and annotate the cell type by mapping the query cell into the reference TF activity space, as an emerging application of cell atlases. The structure of BIOTIC has been determined to be adaptable for the inclusion of additional biological factors, allowing for flexible and more comprehensive gene regulation analysis. BIOTIC introduces a pioneering biological-mechanism-driven framework to infer TF activity and elucidate cell identity states at gene regulatory level, paving the way for a deeper understanding of the complex interplay between TFs and gene expression in living systems.

Neutrophils in nasal polyps exhibit transcriptional adaptation and proinflammatory roles that depend on local polyp milieu

Chronic rhinosinusitis with nasal polyps (CRSwNP) is an inflammatory upper airway disease, divided into eosinophilic CRSwNP (eCRSwNP) and noneosinophilic CRSwNP (neCRSwNP) according to eosinophilic levels. Neutrophils are major effector cells in CRSwNP, but their roles in different inflammatory environments remain largely unclear. We performed an integrated transcriptome analysis of polyp-infiltrating neutrophils from patients with CRSwNP, using healthy donor blood as a control. Additional experiments, including flow cytometry and in vitro epithelial cell and fibroblast culture, were performed to evaluate the phenotypic feature and functional role of neutrophils in CRSwNP. Single-cell RNA-sequencing analysis demonstrated that neutrophils could be classified into 5 functional subsets, with GBP5+ neutrophils occurring mainly in neCRSwNP and a high proportion of CXCL8+ neutrophils in both subendotypes. GBP5+ neutrophils exhibited significant IFN-I pathway activity in neCRSwNP. CXCL8+ neutrophils displayed increased neutrophil activation scores and mainly secreted oncostatin M (OSM), which facilitates communication with other cells. In vitro experiments showed that OSM enhanced IL-13- or IL-17-mediated immune responses in nasal epithelial cells and fibroblasts. Our findings indicate that neutrophils display transcriptional plasticity and activation when exposed to polyp tissue, contributing to CRSwNP pathogenesis by releasing OSM, which interacts with epithelial cells and fibroblasts depending on the inflammatory environment.

Uncovering disease-related multicellular pathway modules on large-scale single-cell transcriptomes with scPAFA

Pathway analysis is a crucial analytical phase in disease research on single-cell RNA sequencing (scRNA-seq) data, offering biological interpretations based on prior knowledge. However, currently available tools for generating cell-level pathway activity scores (PAS) exhibit computational inefficacy in large-scale scRNA-seq datasets. Additionally, disease-related pathways are often identified through cross-condition comparisons within specific cell types, overlooking potential patterns that involve multiple cell types. Here, we present single-cell pathway activity factor analysis (scPAFA), a Python library designed for large-scale single-cell datasets allowing rapid PAS computation and uncovering biologically interpretable disease-related multicellular pathway modules, which are low-dimensional representations of disease-related PAS alterations in multiple cell types. Application on colorectal cancer (CRC) datasets and large-scale lupus atlas over 1.2 million cells demonstrated that scPAFA can achieve over 40-fold reductions in the runtime of PAS computation and further identified reliable and interpretable multicellular pathway modules that capture the heterogeneity of CRC and transcriptional abnormalities in lupus patients, respectively. Overall, scPAFA presents a valuable addition to existing research tools in disease research, with the potential to reveal complex disease mechanisms and support biomarker discovery at the pathway level.

DKK1+ tumor cells inhibited the infiltration of CCL19+ fibroblasts and plasma cells contributing to worse immunotherapy response in hepatocellular carcinoma

Intra-tumor immune infiltration plays a pivotal role in the interaction with tumor cells in hepatocellular carcinoma (HCC). However, its phenotype and related spatial structure remained elusive. To address these limitations, we conducted a comprehensive study combining spatial data (38,191 spots from eight samples) and single-cell data (56,022 cells from 20 samples). Our analysis revealed two distinct infiltration patterns: immune exclusion and immune activation. Plasma cells emerged as the primary cell type within intra-tumor immune clusters. Notably, we observed the co-location of CCL19+ fibroblasts with plasma cells, which secrete chemokines and promote T-cell activation and leukocyte migration. Conversely, in immune-exclusion samples, this co-location was primarily observed in the adjacent normal area. This co-localization correlated with T cell infiltration and the formation of tertiary lymphoid structures, validated by multiplex immunofluorescence conducted on twenty HCC samples. Both CCL19+ fibroblasts and plasma cells were associated with favorable survival outcomes. In an immunotherapy cohort, HCC patients who responded favorably exhibited higher infiltration of CCL19+ fibroblasts and plasma cells. Additionally, we observed the accumulation of DKK1+ tumor cells within the tumor area in immune-exclusion samples, particularly at the tumor boundary, which inhibited the infiltration of CCL19+ fibroblasts and plasma cells into the tumor area. Furthermore, in immune-exclusion samples, the SPP1 signaling pathway demonstrated the highest activity in communication between tumor and immune clusters, and CCL19-CCR7 played a pivotal role in the self-communication of immune clusters. This study elucidates immune exclusion and immune activation patterns in HCC and identifies relevant factors contributing to immune resistance.

Systematic perturbation screens identify regulators of inflammatory macrophage states and a role for TNF mRNA m6A modification

Macrophages exhibit remarkable functional plasticity, a requirement for their central role in tissue homeostasis. During chronic inflammation, macrophages acquire sustained inflammatory 'states' that contribute to disease, but there is limited understanding of the regulatory mechanisms that drive their generation. Here we describe a systematic functional genomics approach that combines genome-wide phenotypic screening in primary murine macrophages with transcriptional and cytokine profiling of genetic perturbations in primary human macrophages to uncover regulatory circuits of inflammatory states. This process identifies regulators of five distinct states associated with key features of macrophage function. Among these regulators, loss of the N6-methyladenosine (m6A) writer components abolishes m6A modification of TNF transcripts, thereby enhancing mRNA stability and TNF production associated with multiple inflammatory pathologies. Thus, phenotypic characterization of primary murine and human macrophages describes the regulatory circuits underlying distinct inflammatory states, revealing post-transcriptional control of TNF mRNA stability as an immunosuppressive mechanism in innate immunity.

Distinct Patterns of Smooth Muscle Phenotypic Modulation in Thoracic and Abdominal Aortic Aneurysms

Thoracic and abdominal aortic aneurysms (TAAs and AAAs, respectively) share morphological features but have distinct clinical and hereditary characteristics. Studies using bulk RNA comparisons revealed distinct patterns of gene expression in human TAA and AAA tissues. However, given the summative nature of bulk RNA studies, these findings represent the totality of gene expression without regards to the differences in cellular composition. Single-cell RNA sequencing provides an opportunity to interrogate cell-type-specific transcriptomes. Single cell RNA sequencing datasets from mouse TAA (GSE153534) and AAA (GSE164678 and GSE152583) with respective controls were obtained from the Gene Expression Omnibus. Bioinformatic analysis was performed with the Seurat 4, clusterProfiler, and Connectome software packages (V1.0.1). Immunostaining was performed with standard protocols. Within normal and aneurysmal aortae, three unique populations of cells that express smooth muscle cell (SMC) markers were identified (SMC1, SMC2, and SMCmod). A greater proportion of TAA SMCs clustered as a unique population, SMCmod, relative to the AAA SMCs (38% vs. 10-12%). These cells exhibited transcriptional features distinct from other SMCs, which were characterized by Igfbp2 and Tnfrsf11b expression. Genes upregulated in TAA SMCs were enriched for the Reactome terms "extracellular matrix organization" and "insulin-like growth factor (IGF) transport and uptake by IGF binding proteins (IGFBPs)", indicating a role for Igfbp2 in TAA pathogenesis. Regulon analysis revealed transcription factors enriched in TAAs and AAAs. Validating these mouse bioinformatic findings, immunostaining demonstrated that both IGFBP2 and TNFRSF11B proteins increased in human TAAs compared to AAAs. These results highlight the unique cellular composition and transcriptional signature of SMCs in TAAs and AAAs. Future studies are needed to reveal the pathogenetic pathways of IGFBP2 and TNFRSF11B.

Scalable identification of lineage-specific gene regulatory networks from metacells with NetID

The identification of gene regulatory networks (GRNs) is crucial for understanding cellular differentiation. Single-cell RNA sequencing data encode gene-level covariations at high resolution, yet data sparsity and high dimensionality hamper accurate and scalable GRN reconstruction. To overcome these challenges, we introduce NetID leveraging homogenous metacells while avoiding spurious gene-gene correlations. Benchmarking demonstrates superior performance of NetID compared to imputation-based methods. By incorporating cell fate probability information, NetID facilitates the prediction of lineage-specific GRNs and recovers known network motifs governing bone marrow hematopoiesis, making it a powerful toolkit for deciphering gene regulatory control of cellular differentiation from large-scale single-cell transcriptome data.

Spatial analyses revealed S100P + TFF1 + tumor cells in spread through air spaces samples correlated with undesirable therapy response in non-small cell lung cancer

Spread through air spaces (STAS) is a recognized aggressive pattern in lung cancer, serving as a crucial risk factor for postoperative recurrence. However, its phenotype and related spatial structure have remained elusive. To address these limitations, we conducted a comprehensive study based on spatial data, analyzing over 30,000 spots from 14 non-STAS samples and one STAS sample. We observed increased proliferation activities and angiogenesis in STAS, identifying S100P as a potential biomarker for STAS. Furthermore, our investigation into the heterogeneity of STAS tumor cells revealed a subset identified as S100P + TFF1 +, exhibiting a negative impact on patients' survival in public datasets. This subtype exhibited the highest activities in the TGFb and hypoxia, suggesting its potential pro-tumor role within the tumor microenvironment. To assess the role of S100P + TFF1 + tumor cells in therapy response, we included data from two clinical trial cohorts (BPI-7711 for EGFR-TKI therapy and ORIENT-3 for immunotherapy). The presence of S100P + TFF1 + tumor cells correlated with worse responses to both EGFR-TKI therapy and immunotherapy. Notably, TFF1 emerged as a serum marker for predicting EGFR-TKI response. Cell-cell communication analysis revealed that the TGFb signaling pathway was the most activated in S100P + TFF1 + tumor cells, with TGFB2-TGFBR2 identified as the main ligand-receptor pair. This was further validated by multiplex immunofluorescence performed on twenty NSCLC samples. In summary, our study identified S100P as the biomarker for STAS and highlighted the adverse role of S100P + TFF1 + tumor cells in survival outcomes.

Functional gene signature offers a powerful tool for characterizing clinicopathological features and depicting tumor immune microenvironment of colorectal cancer

BACKGROUND

Colorectal cancer, a prevalent malignancy worldwide, poses a significant challenge due to the lack of effective prognostic tools. In this study, we aimed to develop a functional gene signature to stratify colorectal cancer patients into different groups with distinct characteristics, which will greatly facilitate disease prediction.

RESULTS

Patients were stratified into high- and low-risk groups using a prediction model built based on the functional gene signature. This innovative approach not only predicts clinicopathological features but also reveals tumor immune microenvironment types and responses to immunotherapy. The study reveals that patients in the high-risk group exhibit poorer pathological features, including invasion depth, lymph node metastasis, and distant metastasis, as well as unfavorable survival outcomes in terms of overall survival and disease-free survival. The underlying mechanisms for these observations are attributed to upregulated tumor-related signaling pathways, increased infiltration of pro-tumor immune cells, decreased infiltration of anti-tumor immune cells, and a lower tumor mutation burden. Consequently, patients in the high-risk group exhibit a diminished response to immunotherapy. Furthermore, the high-risk group demonstrates enrichment in extracellular matrix-related functions and significant infiltration of cancer-associated fibroblasts (CAFs). Single-cell transcriptional data analysis identifies CAFs as the primary cellular type expressing hub genes, namely ACTA2, TPM2, MYL9, and TAGLN. This finding is further validated through multiple approaches, including multiplex immunohistochemistry (mIHC), polymerase chain reaction (PCR), and western blot analysis. Notably, TPM2 emerges as a potential biomarker for identifying CAFs in colorectal cancer, distinguishing them from both colorectal cancer cell lines and normal colon epithelial cell lines. Co-culture of CAFs and colorectal cancer cells revealed that CAFs could enhance the tumorigenic biofunctions of cancer cells indirectly, which could be partially inhibited by knocking down CAF original TPM2 expression.

CONCLUSIONS

This study introduces a functional gene signature that effectively and reliably predicts clinicopathological features and the tumor immune microenvironment in colorectal cancer. Moreover, the identification of TPM2 as a potential biomarker for CAFs holds promising implications for future research and clinical applications in the field of colorectal cancer.

STAN, a computational framework for inferring spatially informed transcription factor activity

Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN ( S patially informed T ranscription factor A ctivity N etwork), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand‒receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.

Gene regulatory networks in disease and ageing

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Exploring public cancer gene expression signatures across bulk, single-cell and spatial transcriptomics data with signifinder Bioconductor package

Understanding cancer mechanisms, defining subtypes, predicting prognosis and assessing therapy efficacy are crucial aspects of cancer research. Gene-expression signatures derived from bulk gene expression data have played a significant role in these endeavors over the past decade. However, recent advancements in high-resolution transcriptomic technologies, such as single-cell RNA sequencing and spatial transcriptomics, have revealed the complex cellular heterogeneity within tumors, necessitating the development of computational tools to characterize tumor mass heterogeneity accurately. Thus we implemented signifinder, a novel R Bioconductor package designed to streamline the collection and use of cancer transcriptional signatures across bulk, single-cell, and spatial transcriptomics data. Leveraging publicly available signatures curated by signifinder, users can assess a wide range of tumor characteristics, including hallmark processes, therapy responses, and tumor microenvironment peculiarities. Through three case studies, we demonstrate the utility of transcriptional signatures in bulk, single-cell, and spatial transcriptomic data analyses, providing insights into cell-resolution transcriptional signatures in oncology. Signifinder represents a significant advancement in cancer transcriptomic data analysis, offering a comprehensive framework for interpreting high-resolution data and addressing tumor complexity.

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.

Associating transcription factors to single-cell trajectories with DREAMIT

Inferring gene regulatory networks from single-cell RNA-sequencing trajectories has been an active area of research yet methods are still needed to identify regulators governing cell transitions. We developed DREAMIT (Dynamic Regulation of Expression Across Modules in Inferred Trajectories) to annotate transcription-factor activity along single-cell trajectory branches, using ensembles of relations to target genes. Using a benchmark representing several different tissues, as well as external validation with ATAC-Seq and Perturb-Seq data on hematopoietic cells, the method was found to have higher tissue-specific sensitivity and specificity over competing approaches.

Prevalence of and gene regulatory constraints on transcriptional adaptation in single cells

BACKGROUND

Cells and tissues have a remarkable ability to adapt to genetic perturbations via a variety of molecular mechanisms. Nonsense-induced transcriptional compensation, a form of transcriptional adaptation, has recently emerged as one such mechanism, in which nonsense mutations in a gene trigger upregulation of related genes, possibly conferring robustness at cellular and organismal levels. However, beyond a handful of developmental contexts and curated sets of genes, no comprehensive genome-wide investigation of this behavior has been undertaken for mammalian cell types and conditions. How the regulatory-level effects of inherently stochastic compensatory gene networks contribute to phenotypic penetrance in single cells remains unclear.

RESULTS

We analyze existing bulk and single-cell transcriptomic datasets to uncover the prevalence of transcriptional adaptation in mammalian systems across diverse contexts and cell types. We perform regulon gene expression analyses of transcription factor target sets in both bulk and pooled single-cell genetic perturbation datasets. Our results reveal greater robustness in expression of regulons of transcription factors exhibiting transcriptional adaptation compared to those of transcription factors that do not. Stochastic mathematical modeling of minimal compensatory gene networks qualitatively recapitulates several aspects of transcriptional adaptation, including paralog upregulation and robustness to mutation. Combined with machine learning analysis of network features of interest, our framework offers potential explanations for which regulatory steps are most important for transcriptional adaptation.

CONCLUSIONS

Our integrative approach identifies several putative hits-genes demonstrating possible transcriptional adaptation-to follow-up on experimentally and provides a formal quantitative framework to test and refine models of transcriptional adaptation.

Distinct melanocyte subpopulations defined by stochastic expression of proliferation or maturation programs enable a rapid and sustainable pigmentation response

The ultraviolet (UV) radiation triggers a pigmentation response in human skin, wherein, melanocytes rapidly activate divergent maturation and proliferation programs. Using single-cell sequencing, we demonstrate that these 2 programs are segregated in distinct subpopulations in melanocytes of human and zebrafish skin. The coexistence of these 2 cell states in cultured melanocytes suggests possible cell autonomy. Luria-Delbrück fluctuation test reveals that the initial establishment of these states is stochastic. Tracking of pigmenting cells ascertains that the stochastically acquired state is faithfully propagated in the progeny. A systemic approach combining single-cell multi-omics (RNA+ATAC) coupled to enhancer mapping with H3K27 acetylation successfully identified state-specific transcriptional networks. This comprehensive analysis led to the construction of a gene regulatory network (GRN) that under the influence of noise, establishes a bistable system of pigmentation and proliferation at the population level. This GRN recapitulates melanocyte behaviour in response to external cues that reinforce either of the states. Our work highlights that inherent stochasticity within melanocytes establishes dedicated states, and the mature state is sustained by selective enhancers mark through histone acetylation. While the initial cue triggers a proliferation response, the continued signal activates and maintains the pigmenting subpopulation via epigenetic imprinting. Thereby our study provides the basis of coexistence of distinct populations which ensures effective pigmentation response while preserving the self-renewal capacity.

Tumour vasculature at single-cell resolution

Tumours can obtain nutrients and oxygen required to progress and metastasize through the blood supply1. Inducing angiogenesis involves the sprouting of established vessel beds and their maturation into an organized network2,3. Here we generate a comprehensive atlas of tumour vasculature at single-cell resolution, encompassing approximately 200,000 cells from 372 donors representing 31 cancer types. Trajectory inference suggested that tumour angiogenesis was initiated from venous endothelial cells and extended towards arterial endothelial cells. As neovascularization elongates (through angiogenic stages SI, SII and SIII), APLN+ tip cells at the SI stage (APLN+ TipSI) advanced to TipSIII cells with increased Notch signalling. Meanwhile, stalk cells, following tip cells, transitioned from high chemokine expression to elevated TEK (also known as Tie2) expression. Moreover, APLN+ TipSI cells not only were associated with disease progression and poor prognosis but also hold promise for predicting response to anti-VEGF therapy. Lymphatic endothelial cells demonstrated two distinct differentiation lineages: one responsible for lymphangiogenesis and the other involved in antigen presentation. In pericytes, endoplasmic reticulum stress was associated with the proangiogenic BASP1+ matrix-producing pericytes. Furthermore, intercellular communication analysis showed that neovascular endothelial cells could shape an immunosuppressive microenvironment conducive to angiogenesis. This study depicts the complexity of tumour vasculature and has potential clinical significance for anti-angiogenic therapy.

Single-cell RNA-seq reveals the transcriptional program underlying tumor progression and metastasis in neuroblastoma

Neuroblastoma (NB) is one of the most common childhood malignancies. Sixty percent of patients present with widely disseminated clinical signs at diagnosis and exhibit poor outcomes. However, the molecular mechanisms triggering NB metastasis remain largely uncharacterized. In this study, we generated a transcriptomic atlas of 15 447 NB cells from eight NB samples, including paired samples of primary tumors and bone marrow metastases. We used time-resolved analysis to chart the evolutionary trajectory of NB cells from the primary tumor to the metastases in the same patient and identified a common 'starter' subpopulation that initiates tumor development and metastasis. The 'starter' population exhibited high expression levels of multiple cell cycle-related genes, indicating the important role of cell cycle upregulation in NB tumor progression. In addition, our evolutionary trajectory analysis demonstrated the involvement of partial epithelial-to-mesenchymal transition (p-EMT) along the metastatic route from the primary site to the bone marrow. Our study provides insights into the program driving NB metastasis and presents a signature of metastasis-initiating cells as an independent prognostic indicator and potential therapeutic target to inhibit the initiation of NB metastasis.

Single-Cell RNA Sequencing and Its Applications in Pituitary Research

BACKGROUND

Mounting evidence underscores the significance of cellular diversity within the endocrine system and the intricate interplay between different cell types and tissues, essential for preserving physiological balance and influencing disease trajectories. The pituitary gland, a central player in the endocrine orchestra, exemplifies this complexity with its assortment of hormone-secreting and nonsecreting cells.

SUMMARY

The pituitary gland houses several types of cells responsible for hormone production, alongside nonsecretory cells like fibroblasts and endothelial cells, each playing a crucial role in the gland's function and regulatory mechanisms. Despite the acknowledged importance of these cellular interactions, the detailed mechanisms by which they contribute to pituitary gland physiology and pathology remain largely uncharted. The last decade has seen the emergence of groundbreaking technologies such as single-cell RNA sequencing, offering unprecedented insights into cellular heterogeneity and interactions. However, the application of this advanced tool in exploring the pituitary gland's complexities has been scant. This review provides an overview of this methodology, highlighting its strengths and limitations, and discusses future possibilities for employing it to deepen our understanding of the pituitary gland and its dysfunction in disease states.

KEY MESSAGE

Single-cell RNA sequencing technology offers an unprecedented means to study the heterogeneity and interactions of pituitary cells, though its application has been limited thus far. Further utilization of this tool will help uncover the complex physiological and pathological mechanisms of the pituitary, advancing research and treatment of pituitary diseases.

Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data

Gene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA sequencing (RNA-seq) data, which helps to decipher single-cell heterogeneity and cell type-specific variability by incorporating prior knowledge from functional gene sets. Single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) is a powerful technique for interrogating single-cell chromatin-based gene regulation, and genes or gene sets with dynamic regulatory potentials can be regarded as cell type-specific markers as if in single-cell RNA-seq (scRNA-seq). However, there are few GSS tools specifically designed for scATAC-seq, and the applicability and performance of RNA-seq GSS tools on scATAC-seq data remain to be investigated. Here, we systematically benchmarked ten GSS tools, including four bulk RNA-seq tools, five scRNA-seq tools, and one scATAC-seq method. First, using matched scATAC-seq and scRNA-seq datasets, we found that the performance of GSS tools on scATAC-seq data was comparable to that on scRNA-seq, suggesting their applicability to scATAC-seq. Then, the performance of different GSS tools was extensively evaluated using up to ten scATAC-seq datasets. Moreover, we evaluated the impact of gene activity conversion, dropout imputation, and gene set collections on the results of GSS. Results show that dropout imputation can significantly promote the performance of almost all GSS tools, while the impact of gene activity conversion methods or gene set collections on GSS performance is more dependent on GSS tools or datasets. Finally, we provided practical guidelines for choosing appropriate preprocessing methods and GSS tools in different application scenarios.

Computational single-cell methods for predicting cancer risk

Despite recent biotechnological breakthroughs, cancer risk prediction remains a formidable computational and experimental challenge. Addressing it is critical in order to improve prevention, early detection and survival rates. Here, I briefly summarize some key emerging theoretical and computational challenges as well as recent computational advances that promise to help realize the goals of cancer-risk prediction. The focus is on computational strategies based on single-cell data, in particular on bottom-up network modeling approaches that aim to estimate cancer stemness and dedifferentiation at single-cell resolution from a systems-biological perspective. I will describe two promising methods, a tissue and cell-lineage independent one based on the concept of diffusion network entropy, and a tissue and cell-lineage specific one that uses transcription factor regulons. Application of these tools to single-cell and single-nucleus RNA-seq data from stages prior to invasive cancer reveal that they can successfully delineate the heterogeneous inter-cellular cancer-risk landscape, identifying those cells that are more likely to turn cancerous. Bottom-up systems biological modeling of single-cell omic data is a novel computational analysis paradigm that promises to facilitate the development of preventive, early detection and cancer-risk prediction strategies.

Phosphoproteomic profiling of early rheumatoid arthritis synovium reveals active signalling pathways and differentiates inflammatory pathotypes

BACKGROUND

Kinases are intracellular signalling mediators and key to sustaining the inflammatory process in rheumatoid arthritis (RA). Oral inhibitors of Janus Kinase family (JAKs) are widely used in RA, while inhibitors of other kinase families e.g. phosphoinositide 3-kinase (PI3K) are under development. Most current biomarker platforms quantify mRNA/protein levels, but give no direct information on whether proteins are active/inactive. Phosphoproteome analysis has the potential to measure specific enzyme activation status at tissue level.

METHODS

We validated the feasibility of phosphoproteome and total proteome analysis on 8 pre-treatment synovial biopsies from treatment-naive RA patients using label-free mass spectrometry, to identify active cell signalling pathways in synovial tissue which might explain failure to respond to RA therapeutics.

RESULTS

Differential expression analysis and functional enrichment revealed clear separation of phosphoproteome and proteome profiles between lymphoid and myeloid RA pathotypes. Abundance of specific phosphosites was associated with the degree of inflammatory state. The lymphoid pathotype was enriched with lymphoproliferative signalling phosphosites, including Mammalian Target Of Rapamycin (MTOR) signalling, whereas the myeloid pathotype was associated with Mitogen-Activated Protein Kinase (MAPK) and CDK mediated signalling. This analysis also highlighted novel kinases not previously linked to RA, such as Protein Kinase, DNA-Activated, Catalytic Subunit (PRKDC) in the myeloid pathotype. Several phosphosites correlated with clinical features, such as Disease-Activity-Score (DAS)-28, suggesting that phosphosite analysis has potential for identifying novel biomarkers at tissue-level of disease severity and prognosis.

CONCLUSIONS

Specific phosphoproteome/proteome signatures delineate RA pathotypes and may have clinical utility for stratifying patients for personalised medicine in RA.

DOT: a flexible multi-objective optimization framework for transferring features across single-cell and spatial omics

Single-cell transcriptomics and spatially-resolved imaging/sequencing technologies have revolutionized biomedical research. However, they suffer from lack of spatial information and a trade-off of resolution and gene coverage, respectively. We propose DOT, a multi-objective optimization framework for transferring cellular features across these data modalities, thus integrating their complementary information. DOT uses genes beyond those common to the data modalities, exploits the local spatial context, transfers spatial features beyond cell-type information, and infers absolute/relative abundance of cell populations at tissue locations. Thus, DOT bridges single-cell transcriptomics data with both high- and low-resolution spatially-resolved data. Moreover, DOT combines practical aspects related to cell composition, heterogeneity, technical effects, and integration of prior knowledge. Our fast implementation based on the Frank-Wolfe algorithm achieves state-of-the-art or improved performance in localizing cell features in high- and low-resolution spatial data and estimating the expression of unmeasured genes in low-coverage spatial data.

Single-cell and spatial transcriptomics reveal a high glycolysis B cell and tumor-associated macrophages cluster correlated with poor prognosis and exhausted immune microenvironment in diffuse large B-cell lymphoma

BACKGROUND

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous malignancy characterized by varied responses to treatment and prognoses. Understanding the metabolic characteristics driving DLBCL progression is crucial for developing personalized therapies.

METHODS

This study utilized multiple omics technologies including single-cell transcriptomics (n = 5), bulk transcriptomics (n = 966), spatial transcriptomics (n = 10), immunohistochemistry (n = 34), multiple immunofluorescence (n = 20) and to elucidate the metabolic features of highly malignant DLBCL cells and tumor-associated macrophages (TAMs), along with their associated tumor microenvironment. Metabolic pathway analysis facilitated by scMetabolism, and integrated analysis via hdWGCNA, identified glycolysis genes correlating with malignancy, and the prognostic value of glycolysis genes (STMN1, ENO1, PKM, and CDK1) and TAMs were verified.

RESULTS

High-glycolysis malignant DLBCL tissues exhibited an immunosuppressive microenvironment characterized by abundant IFN_TAMs (CD68+CXCL10+PD-L1+) and diminished CD8+ T cell infiltration. Glycolysis genes were positively correlated with malignancy degree. IFN_TAMs exhibited high glycolysis activity and closely communicating with high-malignancy DLBCL cells identified within datasets. The glycolysis score, evaluated by seven genes, emerged as an independent prognostic factor (HR = 1.796, 95% CI: 1.077-2.995, p = 0.025 and HR = 2.631, 95% CI: 1.207-5.735, p = 0.015) along with IFN_TAMs were positively correlated with poor survival (p < 0.05) in DLBCL. Immunohistochemical validation of glycolysis markers (STMN1, ENO1, PKM, and CDK1) and multiple immunofluorescence validation of IFN_TAMs underscored their prognostic value (p < 0.05) in DLBCL.

CONCLUSIONS

This study underscores the significance of glycolysis in tumor progression and modulation of the immune microenvironment. The identified glycolysis genes and IFN_TAMs represent potential prognostic markers and therapeutic targets in DLBCL.

Construction of a pathological model of skin lesions in acute herpes zoster virus infection and its molecular mechanism

Varicella-zoster virus (VZV), a common pathogen with humans as the sole host, causes primary infection and undergoes a latent period in sensory ganglia. The recurrence of VZV is often accompanied by severe neuralgia in skin tissue, which has a serious impact on the life of patients. During the acute infection of VZV, there are few related studies on the pathophysiological mechanism of skin tissue. In this study, transcriptome sequencing data from the acute response period within 2 days of VZV antigen stimulation of the skin were used to explore a model of the trajectory of skin tissue changes during VZV infection. It was found that early VZV antigen stimulation caused activation of mainly natural immune-related signaling pathways, while in the late phase activation of mainly active immune-related signaling pathways. JAK-STAT, NFκB, and TNFα signaling pathways are gradually activated with the progression of infection, while Hypoxia is progressively inhibited. In addition, we found that dendritic cell-mediated immune responses play a dominant role in the lesion damage caused by VZV antigen stimulation of the skin. This study provides a theoretical basis for the study of the molecular mechanisms of skin lesions during acute VZV infection.

Dynamic Transcriptional Programs During Single NK Cell Killing: Connecting Form to Function in Cellular Immunotherapy

Introduction

Natural killer (NK) cell-based therapies are a promising new method for treating indolent cancer, however engineering new therapies is complex and progress towards therapy for solid tumors is slow. New methods for determining the underlying intracellular signaling driving the killing phenotype would significantly improve this progress.

Methods

We combined single-cell RNA sequencing with live cell imaging of a model system of NK cell killing to correlate transcriptomic data with functional output. A model of NK cell activity, the NK-92 cell line killing of HeLa cervical cancer cells, was used for these studies. NK cell killing activity was observed by microscopy during co-culture with target HeLa cells and killing activity subsequently manually mapped based on NK cell location and Annexin V expression. NK cells from this culture system were profiled by single-cell RNA sequencing using the 10× Genomics platform, and transcription factor activity inferred using the Viper and DoRothEA R packages. Luminescent microscopy of reporter constructs in the NK cells was then used to correlate activity of inferred transcriptional activity with killing activity.

Results

NK cells had heterogeneous killing activity during 10 h of culture with target HeLa cells. Analysis of the single cell sequencing data identified Nuclear Factor Kappa B (NF-κB), Signal Transducer and Activator of Transcription 1 (STAT1) and MYC activity as potential drivers of NK cell functional phenotype in our model system. Live cell imaging of the transcription factor activity found NF-κB activity was significantly correlated with past killing activity. No correlation was observed between STAT1 or MYC activity and NK cell killing.

Conclusions

Combining luminescent microscopy of transcription factor activity with single-cell RNA sequencing is an effective means of assigning functional phenotypes to inferred transcriptomics data.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-024-00812-3.

Clinicopathological role of Cyclin A2 in uterine corpus endometrial carcinoma: Integration of tissue microarrays and ScRNA-Seq

BACKGROUND

The comprehensive expression level and potential molecular role of Cyclin A2 (CCNA2) in uterine corpus endometrial carcinoma (UCEC) remains undiscovered.

METHODS

UCEC and normal endometrium tissues from in-house and public databases were collected for investigating protein and messenger RNA expression of CCNA2. The transcription factors of CCNA2 were identified by the Cistrome database. The prognostic significance of CCNA2 in UCEC was evaluated through univariate and multivariate Cox regression as well as Kaplan-Meier curve analysis. Single-cell RNA-sequencing (scRNA-seq) analysis was performed to explore cell types in UCEC, and the AUCell algorithm was used to investigate the activity of CCNA2 in different cell types.

RESULTS

A total of 32 in-house UCEC and 30 normal endometrial tissues as well as 720 UCEC and 165 control samples from public databases were eligible and collected. Integrated calculation showed that the CCNA2 expression was up-regulated in the UCEC tissues (SMD = 2.43, 95% confidence interval 2.23∼2.64). E2F1 and FOXM1 were identified as transcription factors due to the presence of binding peaks on transcription site of CCNA2. CCNA2 predicted worse prognosis in UCEC. However, CCNA2 was not an independent prognostic factor in UCEC. The scRNA-seq analysis disclosed five cell types: B cells, T cells, monocytes, natural killer cells, and epithelial cells in UCEC. The expression of CCNA2 was mainly located in B cells and T cells. Moreover, CCNA2 was active in T cells and B cells using the AUCell algorithm.

CONCLUSION

CCNA2 was up-regulated and mainly located in T cells and B cells in UCEC. Overexpression of CCNA2 predicted unfavorable prognosis of UCEC.

Flexible modeling of regulatory networks improves transcription factor activity estimation

Transcriptional regulation plays a crucial role in determining cell fate and disease, yet inferring the key regulators from gene expression data remains a significant challenge. Existing methods for estimating transcription factor (TF) activity often rely on static TF-gene interaction databases and cannot adapt to changes in regulatory mechanisms across different cell types and disease conditions. Here, we present a new algorithm - Transcriptional Inference using Gene Expression and Regulatory data (TIGER) - that overcomes these limitations by flexibly modeling activation and inhibition events, up-weighting essential edges, shrinking irrelevant edges towards zero through a sparse Bayesian prior, and simultaneously estimating both TF activity levels and changes in the underlying regulatory network. When applied to yeast and cancer TF knock-out datasets, TIGER outperforms comparable methods in terms of prediction accuracy. Moreover, our application of TIGER to tissue- and cell-type-specific RNA-seq data demonstrates its ability to uncover differences in regulatory mechanisms. Collectively, our findings highlight the utility of modeling context-specific regulation when inferring transcription factor activities.