bims-sicarn 2025-04-20 papers

bims-sicarn

Biomed News

on scRNA-seq

Issue of 2025–04–20
twenty-six papers selected by
Anna Zawada, International Centre for Translational Eye Research

scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions.
Single-cell RNA Sequencing Analysis of Sputum Cell Transcriptomes Reveals Pathways and Communication Networks That Contribute to the Pathogenesis of Asthma.
Embryo-scale single-cell chemical transcriptomics reveals dependencies between cell types and signaling pathways.
Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.
A Spatial Multi-Omic Framework Identifies Gliomas Permissive to TIL Expansion.
Exploring Male-Specific Synaptic Plasticity in Major Depressive Disorder: A Single-Nucleus Transcriptomic Analysis Using Bioinformatics Methods.
Exploring a solid-state nanopore approach for single-molecule protein detection from single cells.
scHiCcompare: an R package for differential analysis of single-cell Hi-C data.
adverSCarial: assessing the vulnerability of single-cell RNA-sequencing classifiers to adversarial attacks.
GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data.
CeiTEA: Adaptive Hierarchy of Single Cells with Topological Entropy.
A statistical framework for inferring genetic requirements from embryo-scale single-cell sequencing experiments.
PM2.5 exposure reprograms cell cycle dynamics in uterine immune cells at single-cell resolution.
Evidence of off-target probe binding in the 10x Genomics Xenium v1 Human Breast Gene Expression Panel compromises accuracy of spatial transcriptomic profiling.
Cell-type specific epigenetic and transcriptional mechanisms in substance use disorder.
Tracing the Shared Foundations of Gene Expression and Chromatin Structure.
Protocol to identify proteins as regulators of gene expression noise.
Spatial synaptic connectivity underlies oligodendroglioma evolution and recurrence.
Disrupted MOS signaling alters meiotic cell cycle regulation and the egg transcriptome.
Multi-omics Analysis of Histone-related Genes in Osteosarcoma: A Multidimensional Integrated Study Revealing Drug Sensitivity and Immune Microenvironment Characteristics.
Airway Spatial Transcriptomics in Smoking.
Redefining the high variable genes by optimized LOESS regression with positive ratio.
High-fat diet alters retinal lipid composition and gene expression networks in mice.
Pdgfrβ marks distinct mesenchymal and pericyte populations within periosteum with overlapping cellular features.
Comparative transcriptomic insights into the evolution of vertebrate photoreceptor types.
Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction.

Nat Methods. 2025 Apr 17.

scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions.

Hechen Li, Ziqi Zhang, Michael Squires, Xi Chen, Xiuwei Zhang.

Simulated single-cell data are essential for designing and evaluating computational methods in the absence of experimental ground truth. Here we present scMultiSim, a comprehensive simulator that generates multimodal single-cell data encompassing gene expression, chromatin accessibility, RNA velocity and spatial cell locations while accounting for the relationships between modalities. Unlike existing tools that focus on limited biological factors, scMultiSim simultaneously models cell identity, gene regulatory networks, cell-cell interactions and chromatin accessibility while incorporating technical noise. Moreover, it allows users to adjust each factor's effect easily. Here we show that scMultiSim generates data with expected biological effects, and demonstrate its applications by benchmarking a wide range of computational tasks, including multimodal and multi-batch data integration, RNA velocity estimation, gene regulatory network inference and cell-cell interaction inference using spatially resolved gene expression data. Compared to existing simulators, scMultiSim can benchmark a much broader range of existing computational problems and even new potential tasks.

DOI: https://doi.org/10.1038/s41592-025-02651-0
bioRxiv. 2025 Apr 05. pii: 2025.03.31.646405. [Epub ahead of print]

Single-cell RNA Sequencing Analysis of Sputum Cell Transcriptomes Reveals Pathways and Communication Networks That Contribute to the Pathogenesis of Asthma.

Xiting Yan, Qing Liu, Taylor Adams, Jonas Schupp, Sihan Li, Stark Huang, Nicole Grant, Gabriella Wilson, Jose Gomez, Lauren Cohn, Naftali Kaminski, Scott T Weiss, Kelan Tantisira, Geoffrey L Chupp.

Background: Asthma is driven by complex interactions amongst structural airway cells, cells of the immune system, and the environmental. While sputum cell characterization has been instrumental in studying asthma pathogenesis and refining treatment strategies, the nuances of cellular transcriptomes and intercellular communication in asthmatic sputum remain poorly understood.
Methods: We employed single-cell RNA sequencing to analyze cells isolated form the sputum from 16 asthma patients and 8 non-asthmatic controls. Cell identities were established using curated marker genes and SingleR annotation. We compared cell-specific gene expression and communication networks between asthmatic and control groups, correlating findings with distinct pathways that were dysregulated in asthma.
Findings: 37,565 cellular transcriptomes were captured and analyzed. 15 distinct cell populations were identified, including various macrophages, monocytes, dendritic cells, and lymphocytes, along with rare cell types such as mast cells, innate lymphoid cells, bronchial epithelial cells, and eosinophils. Intercellular communication analysis indicated heightened signaling activity in asthma compared to controls, particularly in CD4+ T cells and dendritic cells which exhibited the most significant increases in RNA expression of outgoing signaling molecules. Notably, the ADAM12-SDC4 and CCL22-CCR4 ligand-receptor pathways demonstrated the strongest shifts between asthma and control subjects, particularly between dendritic cells and CD4 lymphocytes.
Interpretation: SC RNA seq profiling the asthma cellular transcriptome analysis of sputum highlights both innate and adaptive immune mechanisms that are significantly amplified in asthma. The elevated expression of ADAM12-SCD4 and CCL22-CC4 point to their critical role in asthma pathogenesis, suggesting potential avenues for targeted therapies and improved management of this chronic condition.
Research in context: Evidence Before This Study: Asthma is a chronic inflammatory disease of the airways driven by intricate interactions between airway structural and immune cells. Previous transcriptomic studies have focused on bulk RNA samples from the airway, leaving significant gaps in our understanding of the cellular dynamics that characterize the disease.Added Value of This Study: This study pioneers the use of single-cell RNA sequencing on sputum samples from patients with asthma, revealing a detailed landscape of cell phenotypes and dynamic communication patterns that distinguish asthmatic individuals from those without the disease. Notably, heightened intercellular communication was observed in asthma, particularly between CD4+ T cells and dendritic cells, confirming that there is a robust network of interactions between immune and structural cells. The notable increase of ADAM12-CCR4 communication from dendritic cells to other cell populations further emphasizes the dysregulation present in asthma.Implications of All Available Evidence: Our transcriptomic profiling illuminates distinct and amplified communication pathways involving CD4+ T cells and dendritic cells, aligning with established paradigms of both adaptive and innate immune responses in asthma pathogenesis. The identification of ADAM12 and CCR4 pathway dysregulation adds a critical layer to our understanding of the molecular mechanisms underpinning asthma, paving the way for potential therapeutic targets and personalized treatment strategies. Single cell profiling of the sputum has the capacity to characterize the breadth of cellular phenotypes, their functional status, and the communication in the airway at a level not previously attainable.

DOI: https://doi.org/10.1101/2025.03.31.646405
bioRxiv. 2025 Apr 03. pii: 2025.04.03.646423. [Epub ahead of print]

Embryo-scale single-cell chemical transcriptomics reveals dependencies between cell types and signaling pathways.

Eliza Barkan, Madeleine Duran, Nicholas Lammers, Amy Tresenrider, Dana Jackson, Heidi Lee, Benjamin Haagen, Lauren Saunders, Phil Abitua, David Kimelman, Cole Trapnell.

Organogenesis is a highly organized process that is conserved across vertebrates and is heavily dependent on intercellular signaling to achieve cell type identity. We lack a comprehensive understanding of how developing cell types in each organ and tissue depend on developmental signaling pathways. To address this gap in knowledge, we captured the molecular consequences of inhibiting each of the seven major developmental signaling pathways in zebrafish, using large-scale whole embryo single cell RNA-seq from over two million cells. This approach allowed us to detect signaling pathway regulation even in very rare cell types. By focusing on the development of the pectoral fin, we uncovered two new cell types (distal mesenchyme and tenocytes) and multiple novel signaling dependencies during pectoral fin development. This resource serves as a valuable tool for investigators seeking to rapidly assess the role of the major signaling pathways during the formation of their tissue of interest.

DOI: https://doi.org/10.1101/2025.04.03.646423
Nucleic Acids Res. 2025 Apr 10. pii: gkaf295. [Epub ahead of print]53(7):

Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.

Augustinas Sukys, Ramon Grima.

Bursty gene expression is characterized by two intuitive parameters, burst frequency and burst size, the cell-cycle dependence of which has not been extensively profiled at the transcriptome level. In this study, we estimate the burst parameters per allele in the G1 and G2/M cell-cycle phases for thousands of mouse genes by fitting mechanistic models of gene expression to messenger RNA count data, obtained by sequencing of single cells whose cell-cycle position has been inferred using a deep-learning method. We find that upon DNA replication, the median burst frequency approximately halves, while the burst size remains mostly unchanged. Genome-wide distributions of the burst parameter ratios between the G2/M and G1 phases are broad, indicating substantial heterogeneity in transcriptional regulation. We also observe a significant negative correlation between the burst frequency and size ratios, suggesting that regulatory processes do not independently control the burst parameters. We show that to accurately estimate the burst parameter ratios, mechanistic models must explicitly account for gene copy number variation and extrinsic noise due to the coupling of transcription to cell age across the cell cycle, but corrections for technical noise due to imperfect capture of RNA molecules in sequencing experiments are less critical.

DOI: https://doi.org/10.1093/nar/gkaf295
bioRxiv. 2025 Apr 12. pii: 2025.03.26.645566. [Epub ahead of print]

A Spatial Multi-Omic Framework Identifies Gliomas Permissive to TIL Expansion.

Kelly M Hotchkiss, Kenan Zhang, Anna M Corcoran, Elizabeth Owens, Pamela Noldner, Chelsea Railton, Kyra Van Batavia, Ying Zhou, Jodie Jepson, Kirit Singh, Roger McLendon, Kristen Batich, Anoop P Patel, Katayoun Ayasoufi, Michael C Brown, Evan Calabrese, Jichun Xie, Jose Conejo-Garcia, Beth H Shaz, John W Hickey, Mustafa Khasraw.

Tumor-infiltrating lymphocyte (TIL) therapy, recently approved by the FDA for melanoma, is an emerging modality for cell-based immunotherapy. However, its application in immunologically 'cold' tumors such as glioblastoma remains limited due to sparse T cell infiltration, antigenic heterogeneity, and a suppressive tumor microenvironment. To identify genomic and spatial determinants of TIL expandability, we performed integrated, multimodal profiling of high-grade gliomas using spectral flow cytometry, TCR sequencing, single-cell RNA-seq, Xenium in situ transcriptomics, and CODEX spatial proteomics. Comparative analysis of TIL-generating (TIL+) versus non-generating (TIL-) tumors revealed that IL7R expression, structured perivascular immune clustering, and tumor-intrinsic metabolic programs such as ACSS3 were associated with successful TIL expansion. In contrast, TIL-; tumors were enriched for neuronal lineage signatures, immunosuppressive transcripts including TOX and FERMT1, and tumor-connected macrophages. This study defines spatial and molecular correlates of TIL manufacturing success and establishes a genomics-enabled selection platform for adoptive T cell therapy. The profiling approach is now being prospectively implemented in the GIANT clinical trial ( NCT06816927 ), supporting its translational relevance and scalability across glioblastoma and other immune-excluded cancers.

DOI: https://doi.org/10.1101/2025.03.26.645566
Int J Mol Sci. 2025 Mar 28. pii: 3135. [Epub ahead of print]26(7):

Exploring Male-Specific Synaptic Plasticity in Major Depressive Disorder: A Single-Nucleus Transcriptomic Analysis Using Bioinformatics Methods.

Ji Chen, Xiumei Zhu, Fan Yang, Yanan Liu, Huajie Ba, Ping Huang, Hongyan Wang, Yingnan Bian, Chengtao Li, Suhua Zhang.

  Major depressive disorder (MDD) is a complex psychiatric illness, with synaptic plasticity playing a key role in its pathology. Our study aims to investigate the molecular basis of MDD by analyzing synaptic plasticity-related gene expression at the single-cell level. Utilizing a published snRNA-seq dataset (GSE144136), we identified Excitatory.neurons_1 as the cell cluster most associated with MDD and synaptic plasticity through cell clustering, gene set enrichment analysis (GSEA), and pseudotime analysis. Integrating the bulk RNA-seq data (GSE38206), we identified CASKIN1 and CSTB as hub genes via differential expression analysis and machine learning methods. Further exploration of the relevant mechanisms was performed via cell-cell communication and ligand-receptor interaction analysis, functional enrichment analysis, and the construction of molecular regulatory networks, highlighting miR-21-5p as a key biomarker. We propose that elevated miR-21-5p in MDD downregulates CASKIN1 in Excitatory.neurons_1 cells, resulting in decreased neural connectivity and altered synaptic plasticity. As our analyzed snRNA-seq dataset consists solely of male samples, these findings may be male-specific. Our findings shed light on potential mechanisms underlying synaptic plasticity in MDD, offering novel insights into the disorder's cellular and molecular dynamics.

Keywords:  hub gene; machine learning; major depressive disorder; single-nucleus RNA sequencing; synaptic plasticity

DOI:  https://doi.org/10.3390/ijms26073135
Chem Sci. 2025 Apr 07.

Exploring a solid-state nanopore approach for single-molecule protein detection from single cells.

Zi-Qi Zhou, Shao-Chuang Liu, Jia Wang, Ke-Le Chen, Bao-Kang Xie, Yi-Lun Ying, Yi-Tao Long.

Direct protein analysis from complex cellular samples is crucial for understanding cellular diversity and disease mechanisms. Here, we explored the potential of SiN x solid-state nanopores for single-molecule protein analysis from complex cellular samples. Using the LOV2 protein as a model, we designed a nanopore electrophoretic driver protein and fused it with LOV2, thereby enhancing the capture efficiency of the target protein. Then, we performed ex situ single-cell protein analysis by directly extracting the contents of individual cells using glass nanopipette-based single-cell extraction and successfully identified and monitored the conformational changes of the LOV2 protein from single-cell extracts using SiN x nanopores. Our results reveal significant differences between proteins measured directly from single cells and those obtained from purified samples. This work demonstrates the potential of solid-state nanopores as a powerful tool for single-cell, single-molecule protein analysis, opening avenues for investigating protein dynamics and interactions at the cellular level.

DOI: https://doi.org/10.1039/d5sc01764e
J Mol Biol. 2025 Apr 15. pii: S0022-2836(25)00221-9. [Epub ahead of print] 169155

scHiCcompare: an R package for differential analysis of single-cell Hi-C data.

My Nguyen, Brydon P G Wall, J Chuck Harrell, Mikhail G Dozmorov.

  Changes in the three-dimensional (3D) structure of the human genome are associated with various conditions, such as cancer and developmental disorders. Techniques like chromatin conformation capture (Hi-C) have been developed to study these global 3D structures, typically requiring millions of cells and an extremely high sequencing depth (around 1 billion reads per sample) for bulk Hi-C. In contrast, single-cell Hi-C (scHi-C) captures 3D structures at the individual cell level but faces significant data sparsity, characterized by a high proportion of zeros. scHi-C data enable the identification of cell types with distinct 3D structures; consequently, identifying differential chromatin interactions between such groups may offer insights into cell type-specific regulation. While differential analysis methods exist for bulk Hi-C data, they are limited for scHi-C data. To address this, we developed a method for differential scHi-C analysis, extending HiCcompare R package. Our approach optionally imputes sparse scHi-C data by considering genomic distances and creates pseudo-bulk Hi-C matrices by summing condition-specific data. The data are normalized using locally estimated scatterplot smoothing (LOESS) regression, and differential chromatin interactions are detected via Gaussian Mixture Model (GMM) clustering. Our workflow outperforms existing methods in identifying differential chromatin interactions across various genomic distances, fold changes, resolutions, and sample sizes in both simulated and experimental contexts. This enables the effective detection of cell type-specific differences in chromatin structure and shows expected associations with biological and epigenetic features. Our method is implemented in the scHiCcompare R package, available at https://github.com/dozmorovlab/scHiCcompare.

Keywords:  chromosome conformation capture; differential chromatin interactions; normalization; scHi-C; scHiCcompare; single cell

DOI:  https://doi.org/10.1016/j.jmb.2025.169155
Bioinformatics. 2025 Apr 15. pii: btaf168. [Epub ahead of print]

adverSCarial: assessing the vulnerability of single-cell RNA-sequencing classifiers to adversarial attacks.

Ghislain Fievet, Julien Broséus, David Meyre, Sébastien Hergalant.

MOTIVATION: Several machine learning (ML) algorithms dedicated to the detection of healthy and diseased cell types from single-cell RNA sequencing (scRNA-seq) data have been proposed for biomedical purposes. This raises concerns about their vulnerability to adversarial attacks, exploiting threats causing malicious alterations of the classifiers' output with defective and well-crafted input.
RESULTS: With adverSCarial, adversarial attacks of single-cell transcriptomic data can easily be simulated in a range of ways, from expanded but undetectable modifications to aggressive and targeted ones, enabling vulnerability assessment of scRNA-seq classifiers to variations of gene expression, whether technical, biological, or intentional. We exemplify the usefulness and performance with a panel of attack modes proposed in adverSCarial by assessing the robustness of five scRNA-seq classifiers, each belonging to a distinct class of ML algorithm, and explore the potential unlocked by exposing their inner workings and sensitivities on four different datasets. These analyses can guide the development of more reliable models, with improved interpretability, usable in biomedical research and future clinical applications.
AVAILABILITY: adverSCarial is a freely available R package accessible from Bioconductor: https://bioconductor.org/packages/adverSCarial/ or https://doi.org/10.18129/B9.bioc.adverSCarial. A development version is available at https://github.com/GhislainFievet/adverSCarial.
SUPPLEMENTARY INFORMATION: Main algorithms for the adversarial attack functions, Bioconductor vignette and package tutorials for adverSCarial, an overall vulnerability analysis, examples of data and classifier preparations, examples of explorations, computing performance analyses, uncovered gene signatures and differential statistics are available as supplementary material along this article.

DOI: https://doi.org/10.1093/bioinformatics/btaf168
BMC Bioinformatics. 2025 Apr 18. 26(1): 108

GRLGRN: graph representation-based learning to infer gene regulatory networks from single-cell RNA-seq data.

Kai Wang, Yulong Li, Fei Liu, Xiaoli Luan, Xinglong Wang, Jingwen Zhou.

   BACKGROUND: A gene regulatory network (GRN) is a graph-level representation that describes the regulatory relationships between transcription factors and target genes in cells. The reconstruction of GRNs can help investigate cellular dynamics, drug design, and metabolic systems, and the rapid development of single-cell RNA sequencing (scRNA-seq) technology provides important opportunities while posing significant challenges for reconstructing GRNs. A number of methods for inferring GRNs have been proposed in recent years based on traditional machine learning and deep learning algorithms. However, inferring the GRN from scRNA-seq data remains challenging owing to cellular heterogeneity, measurement noise, and data dropout.
RESULTS: In this study, we propose a deep learning model called graph representational learning GRN (GRLGRN) to infer the latent regulatory dependencies between genes based on a prior GRN and data on the profiles of single-cell gene expressions. GRLGRN uses a graph transformer network to extract implicit links from the prior GRN, and encodes the features of genes by using both an adjacency matrix of implicit links and a matrix of the profile of gene expression. Moreover, it uses attention mechanisms to improve feature extraction, and feeds the refined gene embeddings into an output module to infer gene regulatory relationships. To evaluate the performance of GRLGRN, we compared it with prevalent models and performed ablation experiments on seven cell-line datasets with three ground-truth networks. The results showed that GRLGRN achieved the best predictions in AUROC and AUPRC on 78.6% and 80.9% of the datasets, and achieved an average improvement of 7.3% in AUROC and 30.7% in AUPRC. The interpretation discussion and the network visualization were conducted.
CONCLUSIONS: The experimental results and case studies illustrate the considerable performance of GRLGRN in predicting gene interactions and provide interpretability for the prediction tasks, such as identifying hub genes in the network and uncovering implicit links.

Keywords:  Gene expression data; Gene regulatory network; Graph representation learning; Implicit links

DOI:  https://doi.org/10.1186/s12859-025-06116-1
Adv Sci (Weinh). 2025 Apr 17. e2503539

CeiTEA: Adaptive Hierarchy of Single Cells with Topological Entropy.

Bowen Tan, Shiying Li, Mengbo Wang, Shuai Cheng Li.

  Advances in single-cell RNA sequencing (scRNA-seq) enable detailed analysis of cellular heterogeneity, but existing clustering methods often fail to capture the complex hierarchical structures of cell types and subtypes. CeiTEA is introduced, a novel algorithm for adaptive hierarchical clustering based on topological entropy (TE), designed to address this challenge. CeiTEA constructs a multi-nary partition tree that optimally represents relationships and diversity among cell types by minimizing TE. This method combines a bottom-up strategy for hierarchy construction with a top-down strategy for local diversification, facilitating the identification of smaller hierarchical structures within subtrees. CeiTEA is evaluated on both simulated and real-world scRNA-seq datasets, demonstrating superior clustering performance compared to state-of-the-art tools like Louvain, Leiden, K-means, and SEAT. In simulated multi-layer datasets, CeiTEA demonstrated superior performance in retrieving hierarchies with a lower average clustering information distance of 0.15, compared to 0.39 from SEAT and 0.67 from traditional hierarchical clustering methods. On real datasets, the CeiTEA hierarchy reflects the developmental potency of various cell populations, validated by gene ontology enrichment, cell-cell interaction, and pseudo-time analysis. These findings highlight CeiTEA's potential as a powerful tool for understanding complex relationships in single-cell data, with applications in tumor heterogeneity and tissue specification.

Keywords:  entropy; hierarchical clustering; single cell

DOI:  https://doi.org/10.1002/advs.202503539
bioRxiv. 2025 Apr 03. pii: 2025.04.03.646654. [Epub ahead of print]

A statistical framework for inferring genetic requirements from embryo-scale single-cell sequencing experiments.

Madeleine Duran, Eliza Barkan, Amy Tresenrider, Heidi Lee, Ryan Z Friedman, Nicholas Lammers, Marazzano Colón, Jennifer Franks, Brent Ewing, David Kimelman, Cole Trapnell.

Improvements in single-cell sequencing protocols have democratized their use for phenotyping at organism-scale and molecular resolution, but interpreting such experiments poses computational challenges. Identifying the genes and cell types directly impacted by genetic, chemical, or environmental perturbations requires explicit modeling of lineage relationships amongst many cell types, over time, from datasets with millions of cells collected from thousands of specimens. We describe two software tools, "Hooke" and "Platt", which exploit the rich statistical patterns within single-cell datasets to characterize the direct molecular and cellular consequences of experimental perturbations. We apply Hooke and Platt to a single-cell atlas of thousands of perturbed zebrafish embryos to synthesize a coherent map of lineage dependencies and leverage it to reveal previously unappreciated roles for fate-determining transcription factors. We show that the co-variation between cell types in single-cell datasets is a powerful source of information for inferring how cells depend on genes and one another in the program of vertebrate development.

DOI: https://doi.org/10.1101/2025.04.03.646654
Front Immunol. 2025 ;16 1561290

PM2.5 exposure reprograms cell cycle dynamics in uterine immune cells at single-cell resolution.

Lin Zhang, Jiaqi Tian, Yongfei Zheng, Shuyin Duan.

   Background: Fine particulate matter (PM2.5) exposure has been associated with adverse effects on reproduction, yet the underlying cellular mechanisms remain poorly understood.
Methods: Using single-cell RNA sequencing, we systematically investigated cell cycle dynamics of immune cell populations in the mouse uterus following PM2.5 exposure. Analysis of 9,000 balanced cells was performed to identify distinct cell populations and characterize changes in cell cycle distribution and gene expression profiles.
Results: PM2.5 exposure induced distinct alterations in immune cell composition and cell cycle distributions. Notably, we observed significant changes in immune cell populations, including reductions in macrophages (510 to 58 cells), NK cells (445 to 91 cells), and granulocytes (1597 to 1 cells). Cell cycle analysis demonstrated cell type-specific responses to PM2.5 exposure: macrophages showed increased G1 phase representation (53.45%, +7.37%) with decreased G2M phase cells (18.97%, -12.79%), while NK cells exhibited relatively modest cell cycle alterations (G1: 28.6%, +2.5%; G2M: 45.1%, +2.6%; S: 26.4%, -5.1%). Differential gene expression analysis further identified crucial regulatory genes involved in cell cycle control, including Cd81 and Nrp1 in macrophages, Vps37b in NK cells. Integration of cell cycle markers with differentially expressed genes revealed distinctive phase-specific perturbations across immune cell types.
Conclusion: PM2.5 exposure induces cell type-specific alterations in cell cycle progression of uterine immune cells, which provides novel mechanistic insights into environmental pollution-induced reproductive dysfunction.

Keywords:  cell cycle progression; fine particulate matter; immune cell heterogeneity; reproductive toxicity; single-cell RNA sequencing

DOI:  https://doi.org/10.3389/fimmu.2025.1561290
bioRxiv. 2025 Apr 03. pii: 2025.03.31.646342. [Epub ahead of print]

Evidence of off-target probe binding in the 10x Genomics Xenium v1 Human Breast Gene Expression Panel compromises accuracy of spatial transcriptomic profiling.

Caleb Hallinan, Hyun Joo Ji, Steven L Salzberg, Jean Fan.

The accuracy of spatial gene expression profiles generated by probe-based in situ spatially-resolved transcriptomic technologies depends on the specificity with which probes bind to their intended target gene. Off-target binding, defined as a probe binding to something other than the target gene, can distort a gene's true expression profile, making probe specificity essential for reliable transcriptomics. Here, we investigate off-target binding in the 10x Genomics Xenium v1 Human Breast Gene Expression Panel. We developed a software tool, Off-target Probe Tracker (OPT), to identify putative off-target binding via alignment of probe sequences and found at least 21 out of the 280 genes in the panel impacted by off-target binding to protein-coding genes. To substantiate our predictions, we leveraged a previously published Xenium breast cancer dataset generated using this gene panel and compared results to orthogonal spatial and single-cell transcriptomic profiles from Visium CytAssist and 3' single-cell RNA-seq derived from the same tumor block. Our findings indicate that for some genes, the expression patterns detected by Xenium demonstrably reflect the aggregate expression of the target and predicted off-target genes based on Visium and single-cell RNA-seq rather than the target gene alone. Overall, this work enhances the biological interpretability of spatial transcriptomics data and improves reproducibility in spatial transcriptomics research.

DOI: https://doi.org/10.1101/2025.03.31.646342
Front Cell Neurosci. 2025 ;19 1552032

Cell-type specific epigenetic and transcriptional mechanisms in substance use disorder.

Bin Wang, Jiale Wang, Nicholas J Beacher, Da-Ting Lin, Yan Zhang.

  Substance use disorder (SUD) is a chronic and relapse-prone neuropsychiatric disease characterized by impaired brain circuitry within multiple cell types and neural circuits. Recent advancements in single-cell transcriptomics, epigenetics, and neural circuit research have unveiled molecular and cellular alterations associated with SUD. These studies have provided valuable insights into the transcriptional and epigenetic regulation of neuronal and non-neuronal cells, particularly in the context of drug exposure. Critical factors influencing the susceptibility of individuals to SUD include the regulation of gene expression during early developmental stages, neuroadaptive responses to psychoactive substances, and gene-environment interactions. Here we briefly review some of these mechanisms underlying SUD, with an emphasis on their crucial roles in in neural plasticity and maintenance of addiction and relapse in neuronal and non-neuronal cell-types. We foresee the possibility of integrating multi-omics technologies to devise targeted and personalized therapeutic strategies aimed at both the prevention and treatment of SUD. By utilizing these advanced methodologies, we can gain a deeper understanding of the fundamental biology of SUD, paving the way for more effective interventions.

Keywords:  cell-type specific modifications; epigenetic regulation; neuroadaptive mechanisms; substance use disorder; transcriptional adaptations

DOI:  https://doi.org/10.3389/fncel.2025.1552032
bioRxiv. 2025 Apr 02. pii: 2025.03.31.646349. [Epub ahead of print]

Tracing the Shared Foundations of Gene Expression and Chromatin Structure.

Huan Liang, Bonnie Berger, Rohit Singh.

The three-dimensional organization of chromatin into topologically associating domains (TADs) may impact gene regulation by bringing distant genes into contact. However, many questions about TADs' function and their influence on transcription remain unresolved due to technical limitations in defining TAD boundaries and measuring the direct effect that TADs have on gene expression. Here, we develop consensus TAD maps for human and mouse with a novel "bag-of-genes" approach for defining the gene composition within TADs. This approach enables new functional interpretations of TADs by providing a way to capture species-level differences in chromatin organization. We also leverage a generative AI foundation model computed from 33 million transcriptomes to define contextual similarity, an embedding-based metric that is more powerful than co-expression at representing functional gene relationships. Our analytical framework directly leads to testable hypotheses about chromatin organization across cellular states. We find that TADs play an active role in facilitating gene co-regulation, possibly through a mechanism involving transcriptional condensates. We also discover that the TAD-linked enhancement of transcriptional context is strongest in early developmental stages and systematically declines with aging. Investigation of cancer cells show distinct patterns of TAD usage that shift with chemotherapy treatment, suggesting specific roles for TAD-mediated regulation in cellular development and plasticity. Finally, we develop "TAD signatures" to improve statistical analysis of single-cell transcriptomic data sets in predicting cancer cell-line drug response. These findings reshape our understanding of cellular plasticity in development and disease, indicating that chromatin organization acts through probabilistic mechanisms rather than deterministic rules.
Software availability: https://singhlab.net/tadmap.

DOI: https://doi.org/10.1101/2025.03.31.646349
STAR Protoc. 2025 Apr 11. pii: S2666-1667(25)00169-8. [Epub ahead of print]6(2): 103763

Protocol to identify proteins as regulators of gene expression noise.

Óscar García-Blay, Maike M K Hansen.

  Noise regulatory proteins are key to understanding the dynamic regulation of cell-to-cell heterogeneity in gene expression. Here, we present a protocol for identifying novel candidate proteins with noise regulatory functions. We describe steps for inhibiting translation in cells, performing single-cell RNA sequencing and liquid chromatography-tandem mass spectrometry (LC-MS/MS), and utilizing known regulator-target interactions to integrate obtained data in a regulator enrichment analysis. This protocol has the potential to be applied in any cell line and under culture conditions of choice. For complete details on the use and execution of this protocol, please refer to García-Blay et al.1.

Keywords:  Cell-based Assays; Gene Expression; High Throughput Screening; RNAseq; Sequence analysis; Sequencing; Single Cell; Systems biology

DOI:  https://doi.org/10.1016/j.xpro.2025.103763
Res Sq. 2025 Apr 04. pii: rs.3.rs-6299872. [Epub ahead of print]

Spatial synaptic connectivity underlies oligodendroglioma evolution and recurrence.

David Raleigh, Kanish Mirchia, Sena Oten, Thiebaud Picart, Minh Nguyen, Vardhaan Ambati, Harish Vasudevan, Jacob Young, Jennie Taylor, Saritha Krishna, David Brang, Joanna Phillips, Arie Perry, Mitchel Berger, Susan Chang, John de Groot, Shawn Hervey-Jumper.

Oligodendrogliomas are initially slow-growing brain tumors that are prone to malignant transformation despite surgery and cytotoxic therapy. Understanding of oligodendroglioma evolution and new treatments for patients have been encumbered by a paucity of patient-matched newly diagnosed and recurrent tumor samples for multiplatform analyses, and by a lack of preclinical models for interrogation of therapeutic vulnerabilities that drive oligodendroglioma growth. Here we integrate spatial and functional analyses of tumor samples and patient-derived organoid co-cultures to show that synaptic connectivity is a hallmark of oligodendroglioma evolution and recurrence. We find that patient-matched recurrent oligodendrogliomas are enriched in synaptic gene expression programs irrespective of previous therapy or histologic grade. Analyses of spatial, single-cell, and clinical data reveal epigenetic misactivation of synaptic genes that are concentrated in regions of cortical infiltration and can be used to predict eventual oligodendroglioma recurrence. To translate these findings to patients, we show that local field potentials from tumor-infiltrated cortex at the time of resection and neuronal hyperexcitability and synchrony in patient-derived organoid co-cultures are associated with oligodendroglioma proliferation and recurrence. In preclinical models, we find that neurophysiologic drugs block oligodendroglioma growth and pathologic electrophysiology. These results elucidate mechanisms underlying oligodendroglioma evolution from an indolent tumor to a fatal disease and shed light on new biomarkers and new treatments for patients.

DOI: https://doi.org/10.21203/rs.3.rs-6299872/v1
bioRxiv. 2025 Apr 03. pii: 2025.04.02.646881. [Epub ahead of print]

Disrupted MOS signaling alters meiotic cell cycle regulation and the egg transcriptome.

Gisela Cairo, Olha Kholod, Olivia Palmer, Brittany A Goods, Soni Lacefield.

Mammalian female meiosis is tightly regulated to produce a developmentally competent egg. Oocytes enter meiosis in the fetal ovary and then arrest at prophase I until sexual maturation. Upon hormonal stimulation, a subset of oocytes resumes meiosis at which time, new transcription is halted. Oocytes then complete meiosis I, enter metaphase II, and arrest until fertilization, a process essential for egg competency and fertility. The MOS kinase is a key regulator of the metaphase II arrest, activating the MAPK signaling cascade. Loss of MOS in female mice disrupts the maintenance of the metaphase II arrest, leading some eggs to extrude two polar bodies and some to divide beyond anaphase II. To investigate the consequences of the Mos deletion, we performed live imaging and found that mos -/- eggs exhibit transient chromosome separation events in meiosis I, suggesting a role for MOS in coordinating the timing of meiotic divisions. Further analysis showed that new transcription is required for mos -/- eggs to undergo additional divisions but not for second polar body extrusion. Surprisingly, single-egg sequencing revealed extensive differences in gene expression between wildtype and mos -/- eggs, including those with only one polar body. Many of the differentially expressed genes were involved in cell cycle regulation, including Aurka , Bub3 , and Cdk7 . Other upregulated pathways included metabolism of RNA, transcription, and neddylation. Furthermore, the gene expression profile of mos -/- eggs was markedly different from that of wildtype eggs chemically activated to undergo embryo-like divisions. Our findings demonstrate that MOS plays a crucial role in meiotic cell cycle regulation and helps ensure that the egg maintains the proper transcriptome necessary for developmental competence.

DOI: https://doi.org/10.1101/2025.04.02.646881
Technol Cancer Res Treat. 2025 Jan-Dec;24:24 15330338251336275

Multi-omics Analysis of Histone-related Genes in Osteosarcoma: A Multidimensional Integrated Study Revealing Drug Sensitivity and Immune Microenvironment Characteristics.

Yang Yang, Xinqiao Tang, Zhong Liu.

  IntroductionOsteosarcoma (OS) is a highly aggressive primary bone malignancy with poor prognosis. Histone modifications play crucial roles in tumor progression, but their systematic investigation in OS remains unexplored.MethodsThis study integrated single-cell RNA sequencing data and large-scale clinical information to systematically analyze the spatial heterogeneity of histone modifications in OS and their clinical significance. We employed Seurat for single-cell data analysis, CellChat for cell-cell communication network analysis, and LASSO Cox regression to construct a prognostic model. Additionally, we conducted functional enrichment analysis, immune characteristics analysis, and drug sensitivity prediction.ResultsWe identified five major cell types in the OS microenvironment and discovered significant differences in histone modification levels among different cell types, with osteosarcoma cells and endothelial cells exhibiting higher modification levels. Cell-cell communication network analysis revealed the importance of signaling pathways such as SPP1, CypA, MIF, IGFBP, and VEGF in OS. Based on nine histone modification-related genes, we constructed an efficient prognostic model (AUC values of 0.713, 0.845, and 0.888 for 1-, 3-, and 5-year predictions, respectively), which was validated in an external cohort (AUC = 0.808). Immune microenvironment analysis showed significantly higher proportions of CD8+ T cells and Treg cells in the low-risk group. Drug sensitivity analysis revealed that the low-risk group was more sensitive to Imatinib, Rapamycin, and Sunitinib, while the high-risk group was more sensitive to MAPK pathway inhibitors.ConclusionThis study systematically revealed the spatial heterogeneity of histone modifications in OS and their clinical significance for the first time, proposing an "epigenetic-immune" regulatory network hypothesis and developing a histone modification-based prognostic model. Our proposed "epigenetic-guided personalized medication strategy" provides new insights for precision treatment of OS, potentially significantly improving patient prognosis.

Keywords:  epigenetic-immune regulation; histone modifications; osteosarcoma; personalized therapy; prognostic model; single-cell RNA sequencing; tumor microenvironment

DOI:  https://doi.org/10.1177/15330338251336275
medRxiv. 2025 Apr 03. pii: 2025.04.01.25325047. [Epub ahead of print]

Airway Spatial Transcriptomics in Smoking.

Jarrett D Morrow, Zaid W El-Husseini, Jeong H Yun, Craig P Hersh.

Background: Cigarette smoking has a significant impact on global health. Although cessation has positive health benefits, some molecular changes to intercellular communications may persist in the lung. In this study we created a framework to generate hypotheses by predicting altered cell-cell communication in smoker lungs using single-cell and spatial transcriptomic data.
Methods: We integrated publicly available lung single-cell transcriptomic data with spatial transcriptomic data from never-smoker and current-smoker lung tissue samples to create spatial transcriptomic data at virtual single-cell resolution by mapping individual cells from our lung scRNA-seq atlas to spots in the spatial transcriptomic data. Cell-cell communications altered in smoking were identified using the virtual single-cell transcriptomic data.
Results: We identified pathways altered in the three current-smoker samples compared with the three never-smoker samples, including the up-regulated collagen pathway. We observed increased collagen pathway activity involving the ligands COL1A1 and COL1A2 in adventitial fibroblasts and decreased activity involving COL1A2 and COL6A3 in pericytes and myofibroblasts, respectively. We also identified other pathways with structural (e.g. Fibronectin-1), immune-related (e.g. MHC-II), growth factor (e.g. Pleiotrophin) and immunophilin (e.g. Cyclophilin A) roles.
Conclusions: In this study we inferred spatially proximal cell-cell communication between interacting cell types from spatial transcriptomics at virtual single-cell resolution to identify lung intercellular signaling altered in smoking. Our findings further implicate several pathways previously identified, and provide additional molecular context to inform future functional experiments and therapeutic avenues to mitigate pathogenic effects of smoking.

DOI: https://doi.org/10.1101/2025.04.01.25325047
BMC Bioinformatics. 2025 Apr 15. 26(1): 104

Redefining the high variable genes by optimized LOESS regression with positive ratio.

Yue Xie, Zehua Jing, Hailin Pan, Xun Xu, Qi Fang.

   BACKGROUND: Single-cell RNA sequencing allows for the exploration of transcriptomic features at the individual cell level, but the high dimensionality and sparsity of the data pose substantial challenges for downstream analysis. Feature selection, therefore, is a critical step to reduce dimensionality and enhance interpretability.
RESULTS: We developed a robust feature selection algorithm that leverages optimized locally estimated scatterplot smoothing regression (LOESS) to precisely capture the relationship between gene average expression level and positive ratio while minimizing overfitting. Our evaluations showed that our algorithm consistently outperforms eight leading feature selection methods across three benchmark criteria and helps improve downstream analysis, thus offering a significant improvement in gene subset selection.
CONCLUSIONS: By preserving key biological information through feature selection, GLP provides informative features to enhance the accuracy and effectiveness of downstream analyses.

Keywords:  Feature selection; High variable genes; Single cell transcriptome

DOI:  https://doi.org/10.1186/s12859-025-06112-5
BMC Biol. 2025 Apr 17. 23(1): 103

High-fat diet alters retinal lipid composition and gene expression networks in mice.

Rong Zou, Jinrui Cai, Tianyu Chen, Wenhui Mo, Hao Qian, Xianjun Zhu, Lin Zhang.

   BACKGROUND: High-fat diet (HFD) was suggested to be associated with several retinal diseases, including age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR). Nevertheless, our understanding of the mechanisms governing retinal lipid metabolic homeostasis remains limited, with little attention focused on the influence of HFD on different retinal cell types. To address this gap, we established a high-fat model using mice fed with HFD for a duration of 6 months. Then, we conducted a comparative analysis of the retinal lipidome and proteome between normal diet (ND) and HFD-fed mice to explore the impacts of HFD on retinal lipid metabolism and gene expression network. Furthermore, we also investigated the impacts of HFD on retina in single-cell resolution by single-cell transcriptome sequencing.
RESULTS: We found that a long-term HFD significantly altered the lipid composition of the retina, with a dramatically elevated cholesterylesters (CE), phosphatidylcholine (PC), and phosphatidylglycerol (PG) level and a decreased eicosanoid level. Proteomic analysis revealed that the primary bile acid biosynthesis pathway was over-activated in HFD retinas. By using single-cell transcriptome analysis, we identified different regulation of gene expression in MG and rod cells in a high-fat environment, whereas the previously identified activation of the bile acid synthesis pathway was predominantly found in MG cells, and may be regulated by alternative pathways of bile acid synthesis, suggesting the critical roles of MG cells in retinal lipid metabolism.
CONCLUSIONS: Taken together, by multi-omics studies, we unveiled that HFD leading to the development of retinal diseases may be regulated by alternative pathways of bile acid synthesis, and our study will shed light on the treatment of these diseases.

Keywords:  Cholesterol; HFD; Metabolic homeostasis; Retina

DOI:  https://doi.org/10.1186/s12915-025-02212-z
Stem Cells. 2025 Apr 16. pii: sxaf020. [Epub ahead of print]

Pdgfrβ marks distinct mesenchymal and pericyte populations within periosteum with overlapping cellular features.

Ziyi Wang, Qizhi Qin, Neelima Thottappillil, Mario Gomez Salazar, Masnsen Cherief, Mary Archer, Deva Balaji, Aaron W James.

  Platelet derived growth factor receptor β (Pdgfrβ) is a cell surface marker often present on mesenchymal progenitor cells, playing a key role in regulating cell proliferation, migration, and survival. In the skeleton, Pdgfrβ-positive cells have significant osteogenic potential, differentiating into osteoblasts after injury to promote bone repair and homeostasis. However, multiple cell types within bone tissue express Pdgfrβ and their overlapping or distinct cellular features remain incompletely understood. Using a combination of single-cell RNA sequencing and transgenic Pdgfrβ-CreERT2-mT/mG reporter mice, we examined Pdgfrβ+ cells in mouse long bone periosteum. By single-cell analysis, Pdgfrb expression was found among a subset of mesenchymal cells and universally among pericytes within the periosteum. Histologic analysis of Pdgfrβ reporter activity confirmed a combination of perivascular and non-perivascular Pdgfrβ-expressing cell types. When isolated, Pdgfrβ reporter+ skeletal periosteal cells showed enhanced colony-forming, proliferative, migratory, and osteogenic capacities. Pdgfrβ reporter+ cells were further distinguished by co-expression of the pericyte marker CD146, which yielded Pdgfrβ+CD146+ pericytes and Pdgfrβ+CD146- skeletal mesenchymal cells. Colony forming and proliferative capacity were most highly enriched among Pdgfrβ+CD146+ pericytes, while osteogenic differentiation was similarly enriched across both Pdgfrβ+ cell fractions. In summary, Pdgfrβ expression identifies multiple subsets of progenitor cells within long bone periosteum with or without perivascular distribution and with overlapping cellular features.

Keywords:  Osteogenesis; Pdgfrβ; Periosteum progenitor cells

DOI:  https://doi.org/10.1093/stmcls/sxaf020
Curr Biol. 2025 Apr 11. pii: S0960-9822(25)00376-8. [Epub ahead of print]

Comparative transcriptomic insights into the evolution of vertebrate photoreceptor types.

Dario Tommasini, Takeshi Yoshimatsu, Teresa Puthussery, Tom Baden, Karthik Shekhar.

  To explore the molecular similarities and potential evolutionary origins of vertebrate photoreceptor types, we analyzed single-cell and -nucleus transcriptomic atlases from six vertebrate species: zebrafish, chicken, lizard, opossum, ground squirrel, and human. Comparative analyses identified conserved transcriptional signatures for the five ancestral photoreceptor types: red, blue, green, and UV cones, as well as rods. We further identified and validated molecular markers of the principal and accessory members of the tetrapod double cone. Comparative transcriptomics suggests that the principal member originated from ancestral red cones, although the origin of the accessory member is less clear. The gene expression variation among cone types mirrors their spectral order (red → green → blue → UV). We find that rods are highly dissimilar to all cone types, suggesting that rods may have diverged prior to the spectral diversification of cones.

Keywords:  accessory; cones; evolution; opsin; principal; retina; rods; single-cell RNA-seq; tetrapod double cones; vertebrate photoreceptors

DOI:  https://doi.org/10.1016/j.cub.2025.03.060
Front Cardiovasc Med. 2025 ;12 1516043

Neutrophil-related IL1R2 gene predicts the occurrence and early progression of myocardial infarction.

Jieqiong Tang, Xierenayi Tudi, Tianxiang Zhang, Jingbo Zhu, Tongtong Shen.

   Introduction: Myocardial infarction (MI) is a leading cause of death worldwide. Immune cells play a significant role in the MI development. This study aims to identify a marker related to neutrophil for the diagnosis and early progression of MI.
Methods: Key genes were screened using three machine learning algorithms to establish a diagnostic model. A gene associated with the early progression of MI was identified based on single cell RNA sequencing data. To further validate the predictive value of the gene, the mouse models of MI were constructed. Immunofluorescence (IF) analysis demonstrated the co-expression of the gene with neutrophils. Immunohistochemistry (IHC) was performed to validate the role of the gene in the progression of MI.
Results: Neutrophils were identified and verified as the key infiltrating immune cells (IICs) involved in the onset of MI. A diagnostic panel with superior performance was developed using five key genes related to neutrophils in MI (AUC = 0.887). Among the panel, IL1R2 was found to early phase of MI, which was further corroborated by IHC in mouse models of MI.
Conclusions: This study suggests that IL1R2, which is specific to neutrophils, can predict the diagnosis and early progression of MI, providing new insights into the clinical management of MI.

Keywords:  diagnosis; early progression; mouse model; myocardial infarction; neutrophil; single cell RNA analysis

DOI:  https://doi.org/10.3389/fcvm.2025.1516043