Joint reconstruction of cis-regulatory interaction networks across multiple tissues using single-cell chromatin accessibility data.

scATAC-seq data presents unprecedented opportunities to identify somatic mutations in regulatory regions. However, existing tools struggle to reliably detect somatic mutations from scATAC-seq data due to the unbalanced coverage between tumor and normal cells. To address this, we developed scVar, a pipeline designed to detect somatic mutations from scATAC-seq data. Applying scVar to snATAC-Seq data from about 200 tumor samples spanning 10 cancer types, we identified over 70, 000 somatic mutations, with 95% located in non-coding regions. Comparison with bulk WGS and WXS data demonstrated that scVar achieves high accuracy in detecting somatic mutations. Notably, scVar effectively recaptured the UV mutational signature in skin cancer. Variant allele frequency (VAF) analysis revealed an increase in VAF in snATAC-seq data compared to WXS data. For tumor suppressor genes such as TP53, VHL, BAP1, CDH1, CBFB, TGIF1, and ZNF750, homozygous mutations with VAFs approaching 1 were consistently detected in snATAC-seq data, emphasizing the critical role of biallelic variants in tumor initiation. We also observed recurrent TERT promoter mutations across 20 samples, with VAFs close to 1, which were associated with increased chromatin accessibility and expression in glioblastoma, but not in skin cancer. Additionally, novel promoter mutations, such as in CDK12, were linked to increased gene expression. These findings highlight the utility of scVar in uncovering somatic mutations and their functional effects in non-coding regions from scATAC-seq data. Citation Format: Song Cao, Liting Zhang, Alla Karpova, Fernanda M. Rodrigues, Yingduo Song, Nadezhda V. Terekhanova, Benjamin J. Raphael, Ryan C. Fields, Feng Chen, Li Ding. Discovery of non-coding somatic driver mutations in single-cell chromatin accessibility data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2385.

Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.

Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.

Rationale: As sequencing methodologies continue to improve, and new methods such as spatial transcriptomics technologies rapidly emerge, we must develop and streamline methods to effectively and efficiently communicate these vast quantities of data. The process of using an atlas data mining website to showcase single cell and single nucleus transcriptomics data has been successfully applied in research. Our IPF cell atlas (https://www.ipfcellatlas.com) and more recently ILD immune cell atlas (https://ildimmunecellatlas.com) had over 16,000 users since 2020 and combined 1,650 unique users over the last 12 months. We have developed a seven-step pipeline that allows our fellow researchers to showcase their own single cell data in interactive and comprehensive websites. Methods: Our pipeline uses the R programming language and the shiny package to create a website hosted on shinyapps.io, while storing the data is on a MySQL database hosted on Amazon Web Services (AWS). The resulting website will be able to collect user engagement statistics as well as queries regarding the data explored by the user. This is done via Google Analytics in combination with logging tables stored on the MySQL database. The website has interactive functions that generate plots in real time using R to create gene expression, violin and box plots among others of data extracted from the database. The output plots are presented to the user to explore. Results: We developed a seven-step blueprint which can be implemented by researchers with little to no coding experience. Our pipeline generates a fully functional website that allows users to explore (transcriptomics) data with an intuitive, customizable, user-friendly interface. Data is presented in a multitude of ways: individual gene exploration, differential gene expression by gene type, transcriptomic signatures, cell type, gene ontology. Conclusions: We have developed for the first time an easy-to-use pipeline as a tool to aid researchers in creating a platform to share and showcase their work which works within existing software pipelines.

Study question Can we map the key maturation stages of human spermatogonial stem cells (SSCs) within their niche during infancy and puberty? Summary answer Using highplex spatial proteomics, we describe the key cellular events taking place during human testis maturation. What is known already The human testis is a complex “ecosystem” that matures in response to endocrine and exocrine signals and interactions between its diverse cellular components. Unlike animal models, in humans, testicular maturation is a slow process that takes place over more than a decade. In contrast to ovaries, the human testis retains its immature state until the onset of puberty. Single-cell RNA sequencing data has revealed significant developmental changes for germ cell and the somatic niche during infancy and puberty. Study design, size, duration Using the hyperplex imaging technique (CellDIVE) and a panel of 30 protein markers - selected to label key components of the developing human testis - we have systematically analyzed a time series of ∼75 testicular biopsies from boys aged 8 months to adulthood. Following sequential staining and capture of high-resolution images, our analysis pipeline involves the conversion of image into single-cell data, which allows a systematic description of the spatial SSC/niche microenvironment interactions across development. Participants/materials, setting, methods The panel of antibody markers was defined using integrated scRNAseq data and represents all key cell types, including germ cell markers (e.g., MAGEA4), Sertoli cell markers (e.g., SOX9), Leydig cell markers (e.g., INSL3), myoid cell markers (e.g., ACTA2) ... Main results and the role of chance To describe the events occurring during human testicular maturation, we build a Tissue Microarray (TMA) containing ∼75 biopsies of boys of different ages (0-16yrs old) and using an in-house panel of 30 markers for high-dimensional multiplex CELL DIVE images. Our analytical strategy uses Uniform Manifold Approximation and Projection (UMAP) maps to quantify and statistically compare single-cell data. Our marker panel allows us to phenotype testicular cells at different stages of development, including SSCs (state 0 to state 4), Sertoli cells, Leydig cells, and so on. Moreover, employing spatial-latent Dirichlet allocation (LDA), we identified recurrent cellular neighbourhoods (RCNs), revealing distinct cell clusters and their corresponding RCNs. These RCNs reflect recurring spatial arrangements of cells within testicular tissues, allowing us to infer the spatial organization and interplay among different cell types. Additionally, we explored spatial distances and neighbourhood frequencies across different tissues and conducted spatial co-occurrence analysis to assess the likelihood of finding adjacent cell types. The identification of RCNs offers valuable insights into the microenvironmental dynamics and cellular relationships taking place within the developing testis. Looking ahead, we aim to delve into the functional significance of RCNs in shaping the developmental trajectories and physiological processes within human testicular tissues. Limitations, reasons for caution Due to limitations in proteomics techniques, we could only include ∼30 protein markers on our panel. Although the biopsy samples used in our study are all from chemo-naïve patients, it possible that the conditions affecting these paediatric patients may impact the architecture or development of their testicular tissues. Wider implications of the findings Our study provides a systematic approach to study the development and organization of human testicular structures, which will allow to enumerate the cellular content of individual testicular biopsies and infer their stage of maturation, therefore providing a personalised baseline assessment for each patient at the time of cryopreservation. Trial registration number not applicable

Chronic pain is a complex clinical problem comprising multiple conditions that may share a common genetic profile. Genome-wide association studies (GWAS) have identified many risk loci whose cell-type context remains unclear. Here, we integrated GWAS data on chronic pain (N = 1,235,695) with single-cell RNA sequencing (scRNA-seq) data from human brain and dorsal root ganglia (hDRG), and single-cell chromatin accessibility data from human brain and mouse dorsal horn. Pain-associated variants were enriched in glutamatergic neurons; mainly in prefrontal cortex, hippocampal CA1–3, and amygdala. In hDRG, the hPEP.TRPV1/A1.2 neuronal subtype showed robust enrichment. Chromatin accessibility analyses revealed variant enrichment in excitatory and inhibitory neocortical neurons in brain and in midventral neurons and oligodendrocyte precursor cells in the mouse dorsal horn. Gene-level heritability in the brain highlighted roles for kinase activity, GABAergic synapses, axon guidance, and neuron projection development. In hDRG, implicated genes related to glutamatergic signaling and neuronal projection. In cervical DRG of patients with acute or chronic pain (N = 12), scRNA-seq data from neuronal or non-neuronal cells were enriched for chronic pain-associated genes (e.g., EFNB2, GABBR1, NCAM1, SCN11A). This cell-type-specific genetic architecture of chronic pain across central and peripheral nervous system circuits provides a foundation for targeted translational research.

Auditory hair cells transduce sound to the brain, and in mammals, these cells reside together with supporting cells in the sensory epithelium of the cochlea, called the organ of Corti. To establish the organ's delicate function during development and differentiation, spatiotemporal gene expression is strictly controlled by chromatin accessibility and cell type–specific transcription factors, jointly representing the regulatory landscape. Bulk sequencing technology and cellular heterogeneity obscured investigations on the interplay between transcription factors and chromatin accessibility in inner ear development. To study the formation of the regulatory landscape in hair cells, we collected single-cell chromatin accessibility profiles accompanied by single-cell RNA data from genetically labeled murine hair cells and supporting cells after birth. Using an integrative approach, we predicted cell type–specific activating and repressing functions of developmental transcription factors. Furthermore, by integrating gene expression and chromatin accessibility data sets, we reconstructed gene regulatory networks. Then, using a comparative approach, 20 hair cell–specific activators and repressors, including putative downstream target genes, were identified. Clustering of target genes resolved groups of related transcription factors and was used to infer their developmental functions. Finally, the heterogeneity in the single-cell data allowed us to spatially reconstruct transcriptional as well as chromatin accessibility trajectories, indicating that gradual changes in the chromatin accessibility landscape are lagging behind the transcriptional identity of hair cells along the organ's longitudinal axis. Overall, this study provides a strategy to spatially reconstruct the formation of a lineage-specific regulatory landscape using a single-cell multi-omics approach.

SummaryTo advance understanding of cellular heterogeneity in disease from single-cell sequencing data, we introduce residual principal-component analysis (ResidPCA), a robust method for identifying cell states that explicitly models cell-type heterogeneity. In simulations, ResidPCA achieved more than 4-fold higher accuracy than conventional PCA and over 3-fold higher accuracy than non-negative matrix factorization (NMF)-based methods in detecting states expressed across multiple cell types. Applied to single-cell RNA sequencing of light-stimulated mouse visual cortex cells, ResidPCA captured stimulus-driven variability with an accuracy more than 5-fold higher than NMF-based approaches. In single-nucleus datasets from an Alzheimer disease cohort, ResidPCA identified 44 chromatin accessibility-based states from single-nucleus ATAC-seq (snATAC-seq) and 42 transcriptional states from single-nucleus RNA-seq. Thirty snATAC-seq states were significantly enriched for Alzheimer disease heritability, often more so than established cell types such as microglia. The snATAC-seq state most significantly enriched for heritability further elucidates a recently implicated neuron-oligodendrocyte-microglial mechanistic axis, linking early amyloid production in neurons and oligodendrocytes with later microglial activation and immune response. These results highlight the ability of ResidPCA to uncover previously hidden biological variation in single-cell data and reveal disease-relevant cell states.

Transcriptome-wide association studies (TWAS) have successfully identified genes associated with complex traits and diseases, but most rely on bulk transcriptome data, overlooking cell-type-specific contexts. Population-scale single-cell RNA sequencing data now enable such analyses, but present unique challenges due to strong noises, technical variations, and high sparsity. Here, we propose scTWAS, a statistical method to conduct cell-type-specific TWAS using single-cell data. Leveraging a latent-variable model and moment-based estimation to address the challenges of single-cell data, scTWAS consistently improves the prediction of genetically regulated gene expression across cell types in both blood and brain tissues. Compared to existing methods, scTWAS identified substantially more gene-trait associations across 29 hematological traits and three immune-related diseases in immune cell types. An application to Alzheimer’s disease also revealed cell-subtype-specific associations, including MS4A6A in disease-associated microglia and PPP1R37 in both inflammatory microglial and astrocyte subtypes.

Immune checkpoint blockade (ICB) targeting PD-1/PD-L1 axis has transformed breast cancer treatment, yet how therapy reshapes the tumor microenvironment (TME) through cell-cell communication (CCC) remains unclear. Existing CCC inference methods relying on correlations have difficulty distinguishing genuine signaling from confounded associations. Here, we present a causal inference framework that uses single-cell data and leverages treatment as an instrumental variable to identify genuine CCC networks, referred to as scIVCCC, which infers causal signal transduction across cell types. Applying scIVCCC to single-cell RNA-seq data from 31 breast cancer patients before and after anti-PD-1 therapy, we constructed causal CCC networks linking exhausted T cells to tumor-associated macrophages (TAMs). Our analysis reveals a dual role of T cell-macrophage crosstalk: CD4+ and CD8+ exhausted T cells drive anti-tumor M1-like TAMs activation via TNF-TNFRSF1A, TNFSF14-LTBR, and ICAM1-ITGAL/ITGB2. Conversely, they also induce immunosuppressive M2-like polarization through pathways such as TNF-TNFRSF1B (TNFR2), TNFSF14-TNFRSF14 (HVEM), and RPS19-C5AR1, which likely contribute to therapeutic resistance. Our causal modeling suggests that receptors within these networks, such as C5AR1, TNFR2, and CSF1R, may serve as potential candidates for combination therapies to enhance anti-PD-1 efficacy. Collectively, these findings demonstrate that scIVCCC offers a robust framework for dissecting treatment-induced CCC dynamics and prioritizing actionable targets for clinical translation.

Whole exome and genome sequencing, coupled with refined bioinformatic pipelines, have enabled improved diagnostic yields for individuals with Mendelian conditions and have led to the rapid identification of novel syndromes. For many Mendelian neurodevelopmental disorders (NDDs), there is a lack of pre-existing model systems for mechanistic work. Thus, it is critical for translational researchers to have an accessible phenotype- and genotype-informed approach for model system selection. Single-cell RNA sequencing data can be informative in such an approach, as it can indicate which cell types express a gene of interest at the highest levels across time. For Mendelian NDDs, such data for the developing human brain is especially useful. A valuable single-cell RNA sequencing dataset of the second trimester developing human brain was produced by Bhaduri et al in 2021, but access to these data can be limited by computing power and the learning curve of single-cell data analysis. To reduce these barriers for translational research on Mendelian NDDs, we have built the web-based tool, Neurodevelopment in Trimester 2 - VIsualization of Single cell Data Online Tool (NeuroTri2-VISDOT), for exploring this single-cell dataset, and we have employed it in several different settings to demonstrate its utility for the translational research community.

A multi-phase approach using supervised algorithms and clinical models to generate high-accuracy signatures for pancreatic cancer.

Integrative analysis of large-scale single-cell data collected from diverse cell populations promises an improved understanding of complex biological systems. While several algorithms have been developed for single-cell RNA-sequencing data integration, many lack the scalability to handle large numbers of datasets and/or millions of cells due to their memory and run time requirements. The few tools that can handle large data do so by reducing the computational burden through strategies such as subsampling of the data or selecting a reference dataset to improve computational efficiency and scalability. Such shortcuts, however, hamper the accuracy of downstream analyses, especially those requiring quantitative gene expression information. We present SCEMENT, a SCalablE and Memory-Efficient iNTegration method, to overcome these limitations. Our new parallel algorithm builds upon and extends the linear regression model previously applied in ComBat to an unsupervised sparse matrix setting to enable accurate integration of diverse and large collections of single-cell RNA-sequencing data. Using tens to hundreds of real single-cell RNA-seq datasets, we show that SCEMENT outperforms ComBat as well as FastIntegration and Scanorama in runtime (upto 214× faster) and memory usage (upto 17.5× less). It not only performs batch correction and integration of millions of cells in under 25 min, but also facilitates the discovery of new rare cell types and more robust reconstruction of gene regulatory networks with full quantitative gene expression information. Source code freely available for download at https://github.com/AluruLab/scement, implemented in C++ and supported on Linux.

<h3>Abstract</h3> The recent advent of high-throughput single-cell molecular profiling is revolutionizing biology and medicine by unveiling the diversity of cell types and states contributing to development and disease. The identification...

Recently a biochemistry experiment named methyl-3C was developed to simultaneously capture the chromosomal conformations and DNA methylation levels on individual single cells. However, the number of data sets generated from this experiment is still small in the scientific community compared with the greater amount of single-cell Hi-C data generated from separate single cells. Therefore, a computational tool to predict single-cell methylation levels based on single-cell Hi-C data on the same individual cells is needed. We developed a graph transformer named scHiMe to accurately predict the base-pair-specific (bp-specific) methylation levels based on both single-cell Hi-C data and DNA nucleotide sequences. We benchmarked scHiMe for predicting the bp-specific methylation levels on all of the promoters of the human genome, all of the promoter regions together with the corresponding first exon and intron regions, and random regions on the whole genome. Our evaluation showed a high consistency between the predicted and methyl-3C-detected methylation levels. Moreover, the predicted DNA methylation levels resulted in accurate classifications of cells into different cell types, which indicated that our algorithm successfully captured the cell-to-cell variability in the single-cell Hi-C data. scHiMe is freely available at http://dna.cs.miami.edu/scHiMe/.