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Disentangling covariate effects on single-cell-resolved epigenomes with DeepDive.

Understanding the effects of individual biological factors from single-cell-resolved epigenomic data is hindered by multicollinearity, particularly in human cohorts. We introduce DeepDive, a deep-learning framework designed to systematically disentangle known and unknown sources of variation in single-nucleus ATAC-seq data. DeepDive accurately reconstructs chromatin accessibility, outperforms state-of-the-art methods with incomplete covariate information, and robustly recovers true biological signals from even highly entangled covariates, unlocking counterfactual, "what-if," analyses. Applying DeepDive to pancreatic islet cells, we perform counterfactual analyses to prioritize covariates associated with a type 2 diabetes-linked beta-cell subtype and nominate transcription regulators. DeepDive offers a powerful and unbiased tool for mechanistic discovery in complex human disease cohorts.

disentanglement↗

Protocol to identify genes required for cardiomyocyte development using Perturb-Seq.

While Perturb-Seq combines CRISPR-based screening with single-cell RNA sequencing (scRNA-seq), large-scale experiments are costly and its application during development is complicated by differentiation heterogeneity. Here, we present a protocol to identify genes required for cardiomyocyte development using Perturb-Seq. We describe steps for sgRNA (single guide RNA) library cloning and infection, cardiomyocyte differentiation, cell hashing, super loading, and scRNA-seq. We then detail procedures for sequencing, mapping, and data analysis. For complete details on the use and execution of this protocol, please refer to Sivakumar et al.1.

CRISPR↗

Decoding the landscape of cell-type-specific co-expressed transcription factors in soybean.

Soybean (Glycine max) is an essential source of protein and oil with high nutritional value for human and animal consumption. To enhance our understanding of soybean biology, it is essential to have accurate information regarding the expression of each of its protein-coding genes. Here, we present Tabula Glycine max, a soybean single-cell resolution transcriptome atlas. This atlas comprises single-nucleus RNA-sequencing data from ten different G. max organs and morphological structures constituting the entire soybean plant. These nuclei are grouped into 156 different clusters based on their transcriptomic profiles. The breadth of various organs, tissues and cell types represented in Tabula Glycine max reveals that the pattern of co-expressed transcription factor genes is sufficient to define most cell types based on their function and organ of origin. Defining cell-type-specific co-expressed transcription factor genes offers a new perspective to engineer cell-type-specific programmes and enhance the biology of unique soybean cell types. This cellular resolution and breadth make the Tabula Glycine max an exceptional resource for the plant and soybean communities.

Journal Article↗

Endothelial cell-specific DNA methylation alterations in breast cancer.

DNA methylation alterations are well-established contributors to carcinogenesis, yet, in the tumor microenvironment (TME), patterns of lineage and cell-specific methylation alterations are not well understood. Single-cell DNA methylation profiling in the TME is limited by technical challenges and high costs. Here, we use bulk DNA methylation, cell type deconvolution (HiTIMED), and an interaction testing framework (CellDMC) to identify reproducible, computationally inferred lineage-specific epigenetic alterations in the TME supported by orthogonal data sources. Tumor endothelial cells (TECs), critical regulators of angiogenesis, vascular permeability, and immune cell trafficking, acquire structural and functional abnormalities that promote tumor growth. We hypothesize that TECs have altered DNA methylation compared with endothelial cells in non-tumor tissues. In genome-scale methylation data from discovery and validation datasets (tumor n = 1071; non-tumor n = 415), we identify and validate >4500 TEC-specific CpGs with altered methylation, many mapping to genes involved in angiogenesis and endothelial function. Integration with gene expression data indicates that TEC-specific methylation alterations may reprogram transcriptional networks controlling angiogenesis. High-resolution, cell lineage-specific epigenetic landscapes can be inferred from bulk methylation data, implicating TEC-specific DNA methylation alterations as potential drivers of cancer angiogenesis and vascular dysfunction and providing a framework for future mechanistic and translational studies of the tumor vasculature.

DNA Methylation↗

YAP1 induces hepatocellular carcinoma via DNA demethylation rather than by canonical driver gene mutations.

Large-scale genome sequencing analyses have identified driver gene mutations (DGMs) in most cancers as well as their associated tumorigenic mechanisms. However, a small fraction of cancers are not positive for these canonical DGMs, leaving the mechanisms underpinning their formation a mystery. We hypothesized that canonical DGM-negative cancers might be driven by activation of the transcriptional coactivator YAP1 that led to the induction of epigenetic changes. To test this theory, we established a mouse mosaic model of hepatocellular carcinoma (HCC) in which we induced YAP1-TEAD activation in a few hepatocytes. Whole-exome sequencing did not identify canonical DGMs in HCCs, but bisulfite sequencing revealed widespread DNA demethylation leading to the transcriptional activation of multiple oncogenes. Knockdown of the DNA demethylation-promoting gene, Tet1, attenuated HCC formation in these mice. Single-cell spatial transcriptomics identified a Tet1-high subpopulation of HCC cells that interacted with other hepatic cell types. Our mechanistic mouse data align with the observation that YAP1-TEAD-TET1-associated signatures were also elevated in hepatocytes from patients with Fontan-associated liver disease (FALD), a condition associated with the development of HCCs with lower frequencies of canonical DGMs. Our study suggests that the YAP1-TEAD-TET1 axis promotes canonical DGM-negative HCC development, and provides new insights into the molecular processes involved.

Animals↗

SISTEM: simulation of tumor evolution, metastasis, and DNA-seq data under genotype-driven selection.

SUMMARY: SISTEM is a software package and mathematical framework for simulating tumor evolution and cell migrations at single-cell resolution. Unlike existing frameworks which simulate cancer cell populations under the neutral coalescent or using simple birth-death models, SISTEM simulates tumor populations under somatic clonal selection using an agent-based framework. SISTEM can generate mutation profiles, read counts, and DNA sequencing reads along with ground truth cell lineages and migration graphs under a number of easily customizable mutation and selection models. For improved realism, SISTEM allows for cell fitness to be driven by genomic events of various scales including single nucleotide variants, segmental gains and losses, whole-chromosomal and chromosome-arm aberrations, and whole-genome duplications. SISTEM also includes numerous migration models to simulate metastatic cancers, facilitating the exploration and evaluation of diverse migration patterns. AVAILABILITY AND IMPLEMENTATION: SISTEM is written in Python and is freely available open-source under GNU GPLv3 from: https://github.com/samsonweiner/sistem.

Software↗

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.

SUMMARY: Image-based spatial transcriptomics (iST) deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in (iST) dataset, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. This highly scalable tool comprehensively segments tissue into spatial domains, aiding in biological interpretation of iST data and its underlying molecular microenvironments. AVAILABILITY AND IMPLEMENTATION: The SpatialRNA package is freely accessible from online repository https://github.com/ruqianl/spatialrna and can be installed via pip. Comprehensive tutorials, guidance on parameter selection, and complete workflows of case studies are available from the documentation website https://ruqianl.github.io/spatialrna_docs/, and uploaded on Zenodo with a DOI 10.5281/zenodo.17339575.

Neural Networks, Computer↗

Nested co-expression network analysis identifies compact gene clusters in a black box.

MOTIVATION: Digital analysis of biological systems requires methods capable of identifying both broad and nested gene modules reflecting complex biological processes. Existing transcriptomic methods often miss compact gene sets corresponding to subprocesses in specialized cell types, limiting insights into functional heterogeneity. RESULTS: We present Nested-WGCNA, a two-stage unsupervised network analysis algorithm designed to identify coarse-grained and fine-grained gene modules. Applied to bulk RNA-Seq data, Nested-WGCNA reveals stable modules reproducible across datasets. When validated against scRNA-Seq data, these modules correspond to both major and minor immune cell subtypes. Application to immunotherapy response datasets uncovers predictive and prognostic biomarkers, highlighting its utility in treatment stratification and biomarker discovery. AVAILABILITY: The NestedWGCNA source code and analysis pipeline are available on GitHub (https://github.com/ilyada/NestedWGCNA) and archived on Zenodo (https://doi.org/10.5281/zenodo.18959244).

Algorithms↗

ARX mutation-associated interneuron defects provide insights into mechanisms underlying developmental epilepsies.

Cortical interneuron (cIN) dysfunction is associated with various neurodevelopmental and neurological disorders, including developmental epilepsies, autism spectrum disorders and intellectual disabilities. Mutations in ARX (aristaless-related homeobox) are linked to these conditions, with or without accompanying structural brain anomalies. We previously demonstrated that the loss of Arx in the mouse ganglionic eminence, the birthplace of cINs, is associated with seizures, whereas its loss in cortical excitatory neuron progenitor cells results in structural anomalies but no seizures. To elucidate the pathophysiological role of ARX in cINs and its relationship to seizure phenotype, Arx conditional mutant mouse lines were investigated using Gad2- and Nkx2.1-Cre drivers to target distinct populations in the cIN lineage. Our data demonstrate that ARX abrogation results in defects in cIN density and distribution, as well as perinatal lethality. In these mice, we observed defects in cell cycle exit, a biased loss of the marginal zone migration stream of cINs, shifts in cell fate from caudal ganglionic eminence to medial ganglionic eminence identity, and a reduced number of parvalbumin⁺ and somatostatin⁺ cINs, with parvalbumin⁺ cINs being more severely affected. Single-cell RNA sequencing combined with chromatin immunoprecipitation and sequencing revealed that ARX regulates key processes involved in cell cycle progression, cIN subtype differentiation and cIN migration. Investigation of one downregulated target gene, Lmo1, uncovered a potential mechanism by which ARX regulates the number and distribution of cINs in the cortex. Cortical slice cultures demonstrate that LMO1 inhibits cIN migration by repressing Cxcr4 expression, which encodes a key receptor involved in cortical guidance. These data indicate that ARX positively regulates cIN migration by derepressing LMO1's repressive role. Consistent with our mouse model, we observed a significant loss of parvalbumin+ and somatostatin+ cINs in the brain of a patient carrying a pathogenic variant of ARX, who was diagnosed with developmental epileptic encephalopathy. Together, our data provide novel insights into how ARX and its target genes regulate cIN development and migration and into the pathogenic mechanisms underlying a spectrum of neurodevelopmental disorders linked to loss of ARX.

Animals↗

GBMdeconvoluteR accurately infers proportions of neoplastic and immune cell populations from bulk glioblastoma transcriptomics data.

BACKGROUND: Characterizing and quantifying cell types within glioblastoma (GBM) tumors at scale will facilitate a better understanding of the association between the cellular landscape and tumor phenotypes or clinical correlates. We aimed to develop a tool that deconvolutes immune and neoplastic cells within the GBM tumor microenvironment from bulk RNA sequencing data. METHODS: We developed an IDH wild-type (IDHwt) GBM-specific single immune cell reference consisting of B cells, T-cells, NK-cells, microglia, tumor associated macrophages, monocytes, mast and DC cells. We used this alongside an existing neoplastic single cell-type reference for astrocyte-like, oligodendrocyte- and neuronal progenitor-like and mesenchymal GBM cancer cells to create both marker and gene signature matrix-based deconvolution tools. We applied single-cell resolution imaging mass cytometry (IMC) to ten IDHwt GBM samples, five paired primary and recurrent tumors, to determine which deconvolution approach performed best. RESULTS: Marker-based deconvolution using GBM-tissue specific markers was most accurate for both immune cells and cancer cells, so we packaged this approach as GBMdeconvoluteR. We applied GBMdeconvoluteR to bulk GBM RNAseq data from The Cancer Genome Atlas and recapitulated recent findings from multi-omics single cell studies with regards associations between mesenchymal GBM cancer cells and both lymphoid and myeloid cells. Furthermore, we expanded upon this to show that these associations are stronger in patients with worse prognosis. CONCLUSIONS: GBMdeconvoluteR accurately quantifies immune and neoplastic cell proportions in IDHwt GBM bulk RNA sequencing data and is accessible here: https://gbmdeconvoluter.leeds.ac.uk.

Humans↗

Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type and sex specificity of gene expression with novel genetic risk for MERTK in female.

Alzheimer's disease, the most common age-related neurodegenerative disease, is closely associated with both amyloid-ß plaque and neuroinflammation. Two thirds of Alzheimer's disease patients are females and they have a higher disease risk. Moreover, women with Alzheimer's disease have more extensive brain histological changes than men along with more severe cognitive symptoms and neurodegeneration. To identify how sex difference induces structural brain changes, we performed unbiased massively parallel single nucleus RNA sequencing on Alzheimer's disease and control brains focusing on the middle temporal gyrus, a brain region strongly affected by the disease but not previously studied with these methods. We identified a subpopulation of selectively vulnerable layer 2/3 excitatory neurons that that were RORB-negative and CDH9-expressing. This vulnerability differs from that reported for other brain regions, but there was no detectable difference between male and female patterns in middle temporal gyrus samples. Disease-associated, but sex-independent, reactive astrocyte signatures were also present. In clear contrast, the microglia signatures of diseased brains differed between males and females. Combining single cell transcriptomic data with results from genome-wide association studies (GWAS), we identified MERTK genetic variation as a risk factor for Alzheimer's disease selectively in females. Taken together, our single cell dataset revealed a unique cellular-level view of sex-specific transcriptional changes in Alzheimer's disease, illuminating GWAS identification of sex-specific Alzheimer's risk genes. These data serve as a rich resource for interrogation of the molecular and cellular basis of Alzheimer's disease.

Journal Article↗

Unique growth and morphology properties of Clade 5 Clostridioides difficile strains revealed by single-cell time-lapse microscopy.

Clostridioides difficile is a gastrointestinal pathogen of both humans and agricultural animals and thus a major One Health threat. The C. difficile species consists of five main clades, with Clade 5 currently undergoing speciation from Clades 1-4. Clade 5 strains are highly prevalent in agricultural animals and can cause zoonotic infections, suggesting that these strains have evolved phenotypes that distinguish them from Clade 1-4 strains. Here, we compare the growth properties of Clade 5 strains to those of Clade 1-4 strains using anaerobic time-lapse microscopy coupled with automated image analysis. Our analyses indicate that Clade 5 strains grow faster and are more likely to form long chains of cells than Clade 1-4 strains. Using comparative genomic and CRISPRi analyses, we show that the chaining phenotype of Clade 5 strains is driven by the orientation of the invertible cmr switch sequence, with chaining strains exhibiting a bias to the cmr-ON state. Interestingly, Clade 5 strains with a bias towards the cmr-ON state shifted to a largely cmr-OFF state during murine infection, suggesting that the cmr-OFF state is under positive selection during infection. Collectively, our data reveal that Clade 5 strains have distinct growth properties, which may allow them to inhabit diverse ecological niches.

Journal Article↗

Phenotypic targeting using magnetic nanoparticles for rapid characterization of cellular proliferation regulators.

Genome-wide CRISPR screens have provided a systematic way to identify essential genetic regulators of a phenotype of interest with single-cell resolution. However, most screens use live/dead readout of viability to identify factors of interest. Here, we describe an approach that converts cell proliferation into the degree of magnetization, enabling downstream microfluidic magnetic sorting to be performed. We performed a head-to-head comparison and verified that the magnetic workflow can identify the same hits from a traditional screen while reducing the screening period from 4 weeks to 1 week. Taking advantage of parallelization and performance, we screened multiple mesenchymal cancer cell lines for their dependency on cell proliferation. We found and validated pan- and cell-specific potential therapeutic targets. The method presented provides a nanoparticle-enabled approach means to increase the breadth of data collected in CRISPR screens, enabling the rapid discovery of drug targets for treatment.

Humans↗

Tumor microbial burden drives immune responses through regulation of Interferon signaling.

Tumor microbes are increasingly recognized for modulating tumor behavior and therapy responses. Intratumoral microbial burden (ITMB) analysis across cancers revealed regulation of immune pathways, and activated mast cells, mostly in colorectal (CRC) and gastric (STAD) cancers. High ITMB CRC leads to interferon regulation and is associated with improved outcomes in advanced disease. Single-cell sequencing revealed induction of interferon-related genes (IRGs) within microbes-containing human CRC. GI-luminal mismatch repair deficiency (MMRd) tumors had higher ITMB than proficient tumors (MMRp). In a rectal MMRd cohort with 100% remission after immune checkpoint blockade (ICB), tumor microbes and microbes-containing mast cells increased. In ICB-sensitive syngeneic murine MMRd tumor models, local tumor microbial depletion, impaired ICB efficacy while downregulating IFN signaling. Forced upregulation of IRGs in ADAR1-deficient cancer cells restored immunotherapy responses during microbial ablation. These data highlight dynamic interplay between ITMB, host defense, and immunogenicity which seems key to determine therapy responses.

Journal Article↗

Follicular Lymphoma Transformation is Characterized by Cytokine-associated Remodeling of Stromal and Macrophage Compartments.

Across cancer, one of the most frequent examples of histologic transformation is the evolution of follicular lymphoma (FL) to an aggressive large cell lymphoma. Despite recent progress, understanding of the molecular and cellular underpinnings of transformation remains incomplete. Here, we dissect the interplay of tumor and microenvironment cell populations across transformation through a multimodal investigation of 95 FL and transformed FL (tFL) samples, including single-cell and bulk RNA-sequencing alongside spatial transcriptomics and proteomics, and validate findings across independent FL-tFL pairs. Upon transformation, fibroblasts and GPNMB+ macrophages increase while lymph-node organizing follicular dendritic and CCL21+ fibroblastic reticular cells were lost, resulting in an altered spatial distribution of cytokines that impacts T cell infiltration and macrophage differentiation and function. Secreted stromal and macrophage signals were further evident by non-invasive plasma proteomics. Taken together, our data reveal expansion of macrophages and fibroblasts as key features of transformation with potential diagnostic and therapeutic implications.

Journal Article↗

Impaired Glycolysis Leads to Defective Efferocytosis and Impaired Plaque Resolution in Tet2 Clonal Hematopoiesis.

BACKGROUND: Clonal hematopoiesis (CH) arising from mutations in hematopoietic genes has been identified as an important risk factor for atherosclerotic cardiovascular disease. Despite the established role of some CH mutations in promoting atherosclerosis progression, their role in clinically relevant LDL (low-density lipoprotein) lowering-induced plaque remodeling or regression has not been extensively studied. METHODS: To assess the effects of TET2 (tet methylcytosine dioxygenase 2) CH on plaque resolution, we prepared control or chimeric Tet2+/- CH mice with conditional deletion of Tet2 in hematopoietic stem cells during LDL lowering-induced plaque remodeling. After establishing atherosclerosis by Western diet feeding for 12 weeks in Ldlr-/- mice, Tet2 was deleted by tamoxifen injection, and hypercholesterolemia was either normalized to simulate clinical lipid management, or mice were continued on the Western diet. RESULTS: Unlike control mice, Tet2+/- CH mice failed to significantly reduce necrotic core area or increase fibrous cap thickness and showed impaired macrophage efferocytosis during LDL lowering. Single-cell RNA sequencing and gene set enrichment analysis of aortic cell populations revealed that Tet2 deficient monocyte/macrophage populations were defective in glycolysis, phagocytosis, and actin polymerization. Tet2-deficient bone marrow-derived macrophages and Tet2+/- induced pluripotent stem cell-derived human macrophages showed defective ability to sustain continuing rounds of efferocytosis. Bone marrow-derived macrophages displayed reduced apoptotic cell binding and internalization and impaired activity of Wiskott-Aldrich syndrome protein and SCAR (suppressor of cyclic AMP receptor) homolog complex mediated actin polymerization. We linked these defects to reduced anaerobic glycolysis and lactate levels and rescued them by lactate supplementation or by treatment with the HIF-1α (hypoxia-inducible factor 1α) activator molidustat. Molidustat treatment reversed the defects in necrotic core and fibrous cap formation during LDL lowering-induced plaque remodeling in Tet2+/- CH mice. Reduced plasma lactate levels were also shown in TET2 clonal hematopoiesis of indeterminate potential carriers in the UK Biobank. CONCLUSIONS: Our data identify impaired efferocytosis and glycolysis-lactate-actin polymerization pathways in advanced atherosclerosis as potential therapeutic targets to induce proresolving restructuring of the plaque immune cells and to promote beneficial atherosclerosis remodeling in subjects with TET2 CH.

LDL lowering↗

Single-cell transcriptomic atlas of Alzheimer's disease middle temporal gyrus reveals region, cell type, and sex specificity of gene expression with novel genetic risk for MERTK in female.

BackgroundAlzheimer's disease (AD), the most common age-related neurodegenerative disease, is closely associated with both amyloid-β plaque and neuroinflammation. Two thirds of AD patients are female, and they have a higher disease risk; women with AD have more extensive brain histological changes than men along with more severe cognitive symptoms and neurodegeneration.ObjectiveThis study aimed to determine how sex difference induces structural brain changes and molecular cell vulnerabilities in AD, with a focus on identifying sex-specific transcriptional alterations and genetic risk factors.MethodsWe performed single nucleus RNA sequencing on postmortem brains from individuals with AD and age- and sex-matched controls, focusing on the middle temporal gyrus, a cortical brain region strongly affected by the disease, and integrated single nucleus RNA sequencing results with genome-wide association study (GWAS) data using cell type-specific enrichment and generalized gene-set analysis approaches. The analysis pipeline is provided with threshold information.ResultsWe identified a selectively vulnerable subpopulation of layer 2/3 excitatory neurons that were RORB-negative and CDH9-expressing in both males and females. Disease-associated, but sex-independent, reactive astrocyte signatures were also present. In clear contrast, the microglia signatures of AD brains differed between males and females. Integrating single cell transcriptomic data with results from GWAS, we identified MERTK genetic variation as a candidate novel risk factor for AD selectively in females.ConclusionsTaken together, our single cell atlas of middle temporal gyrus revealed a unique cellular-level view of sex-specific transcriptional changes in AD, illuminating GWAS identification of sex-specific AD genes. These data serve as a rich resource for interrogation of the molecular and cellular basis of AD.

Alzheimer's disease↗

MitoTracer facilitates the identification of informative mitochondrial mutations for precise lineage reconstruction.

Mitochondrial (MT) mutations serve as natural genetic markers for inferring clonal relationships using single cell sequencing data. However, the fundamental challenge of MT mutation-based lineage tracing is automated identification of informative MT mutations. Here, we introduced an open-source computational algorithm called "MitoTracer", which accurately identified clonally informative MT mutations and inferred evolutionary lineage from scRNA-seq or scATAC-seq samples. We benchmarked MitoTracer using the ground-truth experimental lineage sequencing data and demonstrated its superior performance over the existing methods measured by high sensitivity and specificity. MitoTracer is compatible with multiple single cell sequencing platforms. Its application to a cancer evolution dataset revealed the genes related to primary BRAF-inhibitor resistance from scRNA-seq data of BRAF-mutated cancer cells. Overall, our work provided a valuable tool for capturing real informative MT mutations and tracing the lineages among cells.

Humans↗