GSEApy: a comprehensive package for performing gene set enrichment analysis in Python

Prenatally derived macrophages support choroidal health and decline in age-related macular degeneration

Hallmark findings in age-related macular degeneration (AMD) include the accumulation of extracellular lipid and vasodegeneration of the choriocapillaris. Choroidal inflammation has long been associated with AMD, but little is known about the immune landscape of the human choroid. Using 3D multiplex immunofluorescence, single-cell RNA sequencing, and flow cytometry, we unravel the cellular composition and spatial organization of the human choroid and the immune cells within it. We identify two populations of choroidal macrophages with distinct FOLR2 expression that account for the majority of myeloid cells. FOLR2+ macrophages predominate in the nondiseased eye, express lipid-handling machinery, uptake lipoprotein particles, and contain high amounts of lipid. In AMD, FOLR2+ macrophages are decreased in number and exhibit dysfunctional lipoprotein metabolism. In mice, FOLR2+ macrophages are negative for the postnatal fate-reporter Ms4a3, and their depletion causes an accelerated AMD-like phenotype. Our results show that prenatally derived resident macrophages decline in AMD and are implicated in multiple hallmark functions known to be compromised in the disease.

Pro-Inflammatory Molecules Implicated in Multiple Sclerosis Divert the Development of Human Oligodendrocyte Lineage Cells

BACKGROUND AND OBJECTIVES

Oligodendrocytes (OLs) and their myelin-forming processes are lost during the disease course of multiple sclerosis (MS), targeted by infiltrating leukocytes and their effector cytokines. Myelin repair is considered to be dependent on recruitment and differentiation of oligodendrocyte progenitor cells (OPCs). The basis of remyelination failure during the disease course of MS remains to be defined. The aim of this study was to determine the impact of the proinflammatory molecules tumor necrosis factor-⍺ (TNF⍺) and interferon-γ (IFNγ) on the differentiation of human OPCs.

METHODS

We generated human OPCs from induced pluripotent stem cells with a reporter gene under the OL-specific transcription factor SOX10. We treated the cells in vitro with TNF⍺ or IFNγ and evaluated effects regarding cell viability, expression of OL lineage markers, and coexpression of astrocyte markers. To relate our findings to the molecular properties of OPCs as found in the MS brain, we reanalyzed publicly available single-nuclear RNA sequencing (RNAseq) datasets.

RESULTS

Our analysis indicated that both TNF⍺ and IFNγ decreased the proportion of cells differentiating into the OL lineage, consistent with previous reports. Uniquely, we now observe that the TNF⍺ effect is linked to aberrant OPC differentiation in that a subset of O4+, reporter-positive cells coexpressing the astrocytic marker aquaporin-4. At the transcriptomic level, the cells acquire an astrocyte-like signature alongside a conserved reactive phenotype while downregulating OL lineage genes. Analysis of single-nuclear RNAseq datasets from the human MS brain revealed a subset of OPCs expressing an astrocytic signature.

DISCUSSION

In the context of MS, these results imply that OPCs are present but inhibited from differentiating along the OL lineage, with a subset acquiring a reactive and stem cell-like phenotype, reducing their capacity to contribute toward repair. These findings help define a potential basis for the impaired myelin repair in MS and provide a prospective route for regenerative treatment.

Transcriptional substrates of cortical thickness alterations in anhedonia of major depressive disorder

BACKGROUND

Anhedonia is a core symptom of major depressive disorder (MDD), which has been shown to be associated with abnormalities in cortical morphology. However, the correlation between cortical thickness (CT) changes with anhedonia in MDD and gene expression remains unclear.

METHODS

We investigated the link between brain-wide gene expression and CT correlates of anhedonia in individuals with MDD, using 7 Tesla neuroimaging and a publicly available transcriptomic dataset. The interest-activity score was used to evaluation MDD with high anhedonia (HA) and low anhedonia (LA). Nineteen patients with HA, nineteen patients with LA, and twenty healthy controls (HC) were enrolled. We investigated CT alterations of anhedonia subgroups relative to HC and related cortical gene expression, enrichment and specific cell types. We further used Neurosynth and von Economo-Koskinas atlas to assess the meta-analytic cognitive functions and cytoarchitectural variation associated with anhedonia-related cortical changes.

RESULTS

Both patient subgroups exhibited widespread CT reduction, with HA manifesting more pronounced changes. Gene expression related to anhedonia had significant spatial correlations with CT differences. Transcriptional signatures related to anhedonia-associated cortical thinning were connected to mitochondrial dysfunction and enriched in adipogenesis, oxidative phosphorylation, mTORC1 signaling pathways, involving neurons, astrocytes, and oligodendrocytes. These CT alterations were significantly correlated with meta-analytic terms involving somatosensory processing and pain perception. HA had reduced CT within the somatomotor and ventral attention networks, and in agranular cortical regions.

LIMITATIONS

These include measuring anhedonia using interest-activity score and employing a cross-sectional design.

CONCLUSIONS

This study sheds light on the molecular basis underlying gene expression associated with anhedonia in MDD, suggesting directions for targeted therapeutic interventions.

Interpretable niche-based cell‒cell communication inference using multi-view graph neural networks

Cell‒cell communication (CCC) is a fundamental biological process for the harmonious functioning of biological systems. Increasing evidence indicates that cells of the same type or cluster may exhibit different interaction patterns under varying niches, yet most prevailing methods perform CCC inference at the cell type or cluster level while disregarding niche heterogeneity. Here we introduce the Spatial Transcriptomics-based cell‒cell Communication And Subtype Exploration (STCase) tool, which can describe CCC events at the single-cell/spot level based on spatial transcriptomics (ST). STCase includes an interpretable multi-view graph neural network via CCC-aware attention to identify niches for each cell type and uncover niche-specific CCC events. We show that STCase outperforms state-of-the-art approaches and accurately captures reported immune-related CCC events in human bronchial glands. We also identify three distinct niches of oral squamous cell carcinoma that may be obscured by agglomerative methods, and discover niche-specific CCC events that could influence tumor prognosis.

Single-cell multimodal analysis reveals tumor microenvironment predictive of treatment response in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) constitutes over 80% of lung cancer cases and remains a leading cause of cancer-related mortality worldwide. Despite the advent of immune checkpoint inhibitors, their efficacy is limited to 27 to 45% of patients. Identifying likely treatment responders is essential for optimizing healthcare and improving quality of life. We generated multiplex immunofluorescence (mIF) images, histopathology, and RNA sequencing data from human NSCLC tissues. Through the analysis of mIF images, we characterized the spatial organization of 1.5 million cells based on the expression levels for 33 biomarkers. To enable large-scale characterization of tumor microenvironments, we developed NucSegAI, a deep learning model for automated nuclear segmentation and cellular classification in histology images. With this model, we analyzed the morphological, textural, and topological phenotypes of 45.6 million cells across 119 whole-slide images. Through unsupervised phenotype discovery, we identified specific lymphocyte phenotypes predictive of immunotherapy response. Our findings can improve patient stratification and guide selection of effective therapeutic regimens.

Joint analysis of chromatin accessibility and gene expression in the same single cells reveals cancer-specific regulatory programs

Biological analyses conducted at the single-cell scale have revealed profound impacts of heterogeneity and plasticity of chromatin states and gene expression on physiology and cancer. Here, we developed Parallel-seq, a technology for simultaneously measuring chromatin accessibility and gene expression in the same single cells. By combining combinatorial cell indexing and droplet overloading, Parallel-seq generates high-quality data in an ultra-high-throughput fashion and at a cost two orders of magnitude lower than alternative technologies (10× Multiome and ISSAAC-seq). We applied Parallel-seq to 40 lung tumor and tumor-adjacent clinical samples and obtained over 200,000 high-quality joint scATAC-and-scRNA profiles. Leveraging this large dataset, we characterized copy-number variations (CNVs) and extrachromosomal circular DNA (eccDNA) heterogeneity in tumor cells, predicted hundreds of thousands of cell-type-specific regulatory events, and identified enhancer mutations affecting tumor progression. Our analyses highlight Parallel-seq's power in investigating epigenetic and genetic factors driving cancer development at the cell-type-specific level and its utility for revealing vulnerable therapeutic targets.

A multi-kingdom genetic barcoding system for precise clone isolation

Cell-tagging strategies with DNA barcodes have enabled the analysis of clone size dynamics and clone-restricted transcriptomic landscapes in heterogeneous populations. However, isolating a target clone that displays a specific phenotype from a complex population remains challenging. Here we present a multi-kingdom genetic barcoding system, CloneSelect, which enables a target cell clone to be triggered to express a reporter gene for isolation through barcode-specific CRISPR base editing. In CloneSelect, cells are first stably tagged with DNA barcodes and propagated so that their subpopulation can be subjected to a given experiment. A clone that shows a phenotype or genotype of interest at a given time can then be isolated from the initial or subsequent cell pools stored during the experiment using CRISPR base editing. CloneSelect is scalable and compatible with single-cell RNA sequencing. We demonstrate the versatility of CloneSelect in human embryonic kidney 293T cells, mouse embryonic stem cells, human pluripotent stem cells, yeast cells and bacterial cells.

Human and Mouse Bone Marrow CD45+ Erythroid Cells Have a Constitutive Expression of Antibacterial Immune Response Signature Genes

Introduction: Recent studies have shown that Erythroid progenitor cells exhibit a distinct immunosuppressive and immunoregulatory phenotype associated with the response to bacteria. Methods: The objective of this study was to comprehensively explore the traits of human bone marrow Erythroid cells through protein-protein interaction network analysis using cytokine secretion analysis, and single-cell immunoproteomic analysis using flow cytometry, as well as the re-analysis of publicly available human and mouse bone marrow Erythroid-cell transcriptomic data. Results: Our protein-protein interaction network analysis of human bone marrow Erythroid-cell protein-coding genes identified enrichment in the immune response to lipopolysaccharide, with Calprotectin and Cathepsin G being the main factors. We then mapped the Calprotectin to the CD45+ Erythroid cells of both humans and mice via the analysis of the publicly available scRNA-seq data. Additionally, we observed that human bone marrow Erythroid cells secrete cytokines and chemokines, such as IL-1b, IL-8, and IL-18, which are also mainly involved in the immune response to lipopolysaccharide. We also found that human and mouse bone marrow Erythroid-cell conditional media inhibit bacterial growth in vitro. Discussion: These findings suggest that both human and mouse bone marrow CD45+ Erythroid cells possess the potential to combat pathogenic microbes and thus play a role in innate antimicrobial immunity. Conclusions: CD45+ Erythroid cells are a potent immunoregulatory cell population in both humans and mice.

Human proteome distribution atlas for tissue-specific plasma proteome dynamics

The plasma proteome is maintained by the influx and efflux of proteins from surrounding organs and cells. To quantify the extent to which different organs and cells impact the plasma proteome in healthy and diseased conditions, we developed a mass-spectrometry-based proteomics strategy to infer the tissue origin of proteins detected in human plasma. We first constructed an extensive human proteome atlas from 18 vascularized organs and the 8 most abundant cell types in blood. The atlas was interfaced with previous RNA and protein atlases to objectively define proteome-wide protein-organ associations to infer the origin and enable the reproducible quantification of organ-specific proteins in plasma. We demonstrate that the resource can determine disease-specific quantitative changes of organ-enriched protein panels in six separate patient cohorts, including sepsis, pancreatitis, and myocardial injury. The strategy can be extended to other diseases to advance our understanding of the processes contributing to plasma proteome dynamics.

Phenotypic heterogeneity and plasticity in colorectal cancer metastasis

Phenotypic heterogeneity and plasticity in colorectal cancer (CRC) has a crucial role in tumor progression, metastasis, and therapy resistance. However, the regulatory factors and the extrinsic signals driving phenotypic heterogeneity remain unknown. Using a combination of single-cell multiomics and spatial transcriptomics data from primary and metastatic CRC patients, we reveal cancer cell states with regenerative and inflammatory phenotypes that closely resemble metastasis-initiating cells in mouse models. We identify an intermediate population with a hybrid regenerative and stem phenotype. We reveal the transcription factors AP-1 and nuclear factor κB (NF-κB) as their key regulators and show localization of these states in an immunosuppressive niche both at the invasive edge in primary CRC and in liver metastasis. We uncover ligand-receptor interactions predicted to activate the regenerative and inflammatory phenotype in cancer cells. Together, our findings reveal regulatory and signaling factors that mediate distinct cancer cell states and can serve as potential targets to impair metastasis.

Mitochondrial dysfunction fuels drug resistance in adult T-cell acute lymphoblastic leukemia

BACKGROUND

T-cell acute lymphoblastic leukemia (T-ALL) is a relatively rare hematological malignancy, characterized by the uncontrolled proliferation of immature T lymphoblasts and associated with a generally unfavorable prognosis. Our previous research has demonstrated that decreased mitochondrial activity is associated with the aggressiveness of T-ALL tumors. However, the mechanisms underlying this phenomenon and its contribution to treatment resistance remain largely elusive.

METHODS

We have built up the largest known T-ALL tumor bank, with a median follow-up of 32 months, including our transcriptomic data from 79 newly sequenced tumors that adds to the 54 publicly accessible samples. Computational analyses and a series of functional assays were performed to investigate the molecular links between altered mitochondrial activity and drug resistance.

RESULTS

The transcriptomic analysis revealed that down-regulation of mitochondrial activity is a potent driver of ABCB1 activation, a gene strongly associated with multidrug resistance. In tumors with low mitochondrial activity, the impaired fatty acids β-oxidation leads to intracellular lipid accumulation, which is directly involved in ABCB1 activation. Indeed, our data show that lipid neo-synthesis and accumulation promotes the activation of lipogenic transcription factors, liver X receptors (LXRs), which act as drivers of ABCB1 expression. Tumor data analyses confirmed that high ABCB1 expression in tumour samples is indeed associated with reduced mitochondrial gene expression, lipid droplet enrichment, increased tumour aggressiveness, and significantly shorter patient survival.

CONCLUSIONS

Our study demonstrates that reduced mitochondrial activity drives multidrug resistance in adult T-ALL via lipid-mediated activation of ABCB1. These findings enhance our understanding of the biology of aggressive T-ALL and provide insight into mechanisms of resistance to conventional chemotherapy. Consequently, we propose that targeting de novo lipogenesis and restricting dietary fats, such as caprylic acid, may help overcome treatment resistance in patients with T-ALL exhibiting low mitochondrial activity.

TRIAL REGISTRATION

The clinical trial was registered under the identifiers ChiCTR-ONRC-14004968 and ChiCTR2000031553 at ClinicalTrials.gov.

Single-cell meta-analysis of T cells reveals clonal dynamics of response to checkpoint immunotherapy

Despite the crucial role of T cell clones in anti-tumor activity, their characterization and association with clinical outcomes following immune checkpoint inhibitors are lacking. Here, we analyzed paired single-cell RNA sequencing/T cell receptor sequencing of 767,606 T cells across 460 samples spanning 6 cancer types. We found a robust signature of response based on expanded CD8+ clones that differentiates responders from non-responders. Analysis of persistent clones showed transcriptional changes that are differentially induced by therapy in the different response groups, suggesting an improved reinvigoration capacity in responding patients. Moreover, a gene trajectory analysis revealed changes in the pseudo-temporal state of de novo clones that are associated with clinical outcomes. Lastly, we found that clones shared between tumor and blood are more abundant in non-responders and execute distinct transcriptional programs. Overall, our results highlight differences in clonal transcriptional states that are linked to patient response, offering valuable insights into the mechanisms driving effective anti-tumor immunity.

O-GlcNAcylation of NONO regulates paraspeckle component assembly and contributes to colon cancer cell proliferation

Non-POU domain-containing octamer-binding protein (NONO) is a multifunctional member of the Drosophila behavior/human splicing (DBHS) protein family with DNA- and RNA-binding activity. NONO is highly expressed in various types of cancer, and excessive O-GlcNAcylation has also been implicated in tumorigenesis. Although recent studies revealed that NONO is O-GlcNAcylated and that this modification is involved in DNA damage repair, it remains unknown whether O-GlcNAcylation of NONO regulates cancer cell proliferation. Additionally, little is known about the effect of O-GlcNAcylation on other biological properties of NONO. In this study, we identify Thr440 as the primary NONO O-GlcNAcylation site and demonstrates its crucial role in the assembly of paraspeckles, an important subnuclear compartment that facilitates NONO-dependent transcriptional regulation in mammalian cells. Moreover, we found that O-GlcNAcylation of NONO is required to maintain the expression of genes related to microtubule cytoskeleton organization involved in mitosis and to suppress the expression of genes related to cellular response to type I interferon. Regarding the regulation of these genes, depletion of NONO O-GlcNAcylation at Thr440 significantly inhibited the proliferation of colon cancer cells. Collectively, our findings highlight NONO O-GlcNAcylation as a key regulator modulating paraspeckle formation and as a candidate therapeutic target in colon cancer.

Distinct systemic impacts of Aβ42 and Tau revealed by whole-organism snRNA-seq

Both neuronal and peripheral tissues become disrupted in Alzheimer's disease (AD). However, a comprehensive understanding of how AD impacts different tissues across the whole organism is lacking. Using Drosophila, we generated an AD Fly Cell Atlas (AD-FCA) based on whole-organism single-nucleus transcriptomes of 219 cell types from flies expressing AD-associated proteins, either human amyloid-β 42 peptide (Aβ42) or Tau, in neurons. We found that Aβ42 primarily affects the nervous system, including sensory neurons, while Tau induces accelerated aging in peripheral tissues. We identified a neuronal cluster enriched in Aβ42 flies, which has high lactate dehydrogenase (LDH) expression. This LDH-high cluster is conserved in 5XFAD mouse and human AD datasets. We found a conserved defect in fat metabolism from both fly and mouse tauopathy models. The AD-FCA offers new insights into how Aβ42 or Tau systemically and differentially affects a whole organism and provides a valuable resource for understanding brain-body communication in neurodegeneration.

Drug repurposing candidates for amyotrophic lateral sclerosis using common and rare genetic variants

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative condition for which novel disease modifying therapies are urgently needed. Given the increasing bottlenecks in drug discovery pipelines, repurposing existing drugs for ALS may represent a path to expedite translation and improve disease outcomes. However, ALS is a heterogeneous disease for which the aetiology remains poorly characterized, complicating efforts to effectively repurpose drugs. We propose that the polygenic architecture of ALS genetic liability, which ranges from ultra-rare, high-impact variation to common frequency loci of small-individual effect, could be leveraged to prioritize drug repurposing candidates which are more generalizable to the ALS clinical population. Here, we utilize common and rare frequency ALS genetic risk with a novel approach to uncover therapeutic classes that may be prospective repurposing opportunities in ALS. The common variant-led analyses integrated both positional-based and functional gene-based tests on SNP-genotype data from a genome-wide association study of ALS and implicated mitogen-activated protein kinase signalling related downregulation through B-Raf inhibitors as a prospective target for repurposing. The rare variant-led approaches leveraged rare variant burden testing of exonic variation on whole genome-sequencing data from a subset of the common variant genome-wide association study cohort and prioritized B-vitamin related candidates, such as cobalamin and niacin. Clinical characterization of these putative repurposing opportunities revealed genetic support to existing biology for which related compounds are actively proceeding through ALS clinical studies. Moreover, leveraging transcriptomic data from ALS derived cell lines carrying a selection of pathogenic variants in genes that cause familial forms of ALS (C9orf72, SOD1, FUS and TARDBP) suggested that the action of B-Raf inhibitors may be of particular relevance to C9orf72 carriers, whilst the signal for B-vitamin signalling related targets was strongest in SOD1 carriers. In summary, we demonstrate the importance of considering the therapeutic actionability of both common and rare-variant mediated risk for ALS given the immense biological heterogeneity of this disorder. Future pre-clinical and clinical studies are now warranted to further characterize the tractability of these prioritized compounds.

ONTraC characterizes spatially continuous variations of tissue microenvironment through niche trajectory analysis

Recent technological advances enable mapping of tissue spatial organization at single-cell resolution, but methods for analyzing spatially continuous microenvironments are still lacking. We introduce ONTraC, a graph neural network-based framework for constructing spatial trajectories at niche-level. Through benchmarking analyses using multiple simulated and real datasets, we show that ONTraC outperforms existing methods. ONTraC captures both normal anatomical structures and disease-associated tissue microenvironment changes. In addition, it identifies tissue microenvironment-dependent shifts in gene expression, regulatory network, and cell-cell interaction patterns. Taken together, ONTraC provides a useful framework for characterizing the structural and functional organization of tissue microenvironments.

Immunotherapy-related cognitive impairment after CAR T cell therapy in mice

Immunotherapies have revolutionized cancer care for many tumor types, but their potential long-term cognitive impacts are incompletely understood. Here, we demonstrated in mouse models that chimeric antigen receptor (CAR) T cell therapy for both central nervous system (CNS) and non-CNS cancers impaired cognitive function and induced a persistent CNS immune response characterized by white matter microglial reactivity, microglial chemokine expression, and elevated cerebrospinal fluid (CSF) cytokines and chemokines. Consequently, oligodendroglial homeostasis and hippocampal neurogenesis were disrupted. Single-nucleus sequencing studies of human frontal lobe from patients with or without previous CAR T cell therapy for brainstem tumors confirmed reactive states of microglia and oligodendrocytes following treatment. In mice, transient microglial depletion or CCR3 chemokine receptor blockade rescued oligodendroglial deficits and cognitive performance in a behavioral test of attention and short-term memory function following CAR T cell therapy. Taken together, these findings illustrate targetable neural-immune mechanisms underlying immunotherapy-related cognitive impairment.

Epigenetic deprogramming by disruption of CIZ1-RNA nuclear assemblies in early-stage breast cancers

CIZ1 is part of the RNA-dependent supramolecular assemblies that form around the inactive X-chromosome (Xi) in female cells and smaller assemblies throughout the nucleus in both sexes. Here, we show that CIZ1 C-terminal anchor domain (AD) is elevated in human breast tumor transcriptomes, even at stage I. Elevation correlates with deprotection of chromatin and upregulation of lncRNA-containing gene clusters in ∼10 Mb regions enriched in cancer-associated genes. We modeled the effect of AD on endogenous CIZ1-Xi assemblies and observed dominant-negative interference with their reformation after mitosis, leading to abnormal assemblies similar to those in breast cancer cells, and depletion of H2AK119ub1, H3K27me3, and Xist. Consistent alterations in gene expression were evident across the genome, showing that AD-mediated interference has a destabilizing effect, likely by unscheduled exposure of underlying chromatin to modifying enzymes. The data argue for a dominant, potent, and rapid effect of CIZ1 AD that can deprogram gene expression patterns and which may predispose incipient tumors to epigenetic instability.

Inhibition of DKK-1 by WAY262611 Inhibits Osteosarcoma Metastasis

Osteosarcoma is the most common primary malignant bone tumor in childhood. Patients who present with metastatic disease at diagnosis or relapse have a very poor prognosis, and this has not changed over the past four decades. The Wnt signaling pathway plays a role in regulating osteogenesis and is implicated in osteosarcoma pathogenesis. DKK-1 inhibits the canonical Wnt signaling pathway, causing inhibition of osteoblast differentiation and disordered bone repair. Our lab previously demonstrated that an mAb against DKK-1 prevented metastatic disease in a mouse model. This study expands upon those findings by demonstrating similar results with a small-molecule inhibitor of DKK-1, WAY262611, both in vitro and in vivo. WAY262611 was evaluated in vitro on osteosarcoma cell lines, including proliferation, caspase activation, cell-cycle analysis, and signaling pathway activation. We utilized our orthotopic implantation/amputation model of osteosarcoma metastasis in vivo to determine the impact of WAY262611 on primary tumor progression and metastatic outgrowth of disseminated tumor cells. Differentiation status was determined using single-cell RNA sequencing. We show here that WAY262611 activates canonical Wnt signaling, enhances nuclear localization and transcriptional activity of β-catenin, and slows proliferation of osteosarcoma cell lines. We also show that WAY262611 induces osteoblastic differentiation of a patient-derived xenograft of osteosarcoma in vivo, as well as inhibiting metastasis. This work credentials DKK-1 as a therapeutic target in osteosarcoma, allowing for manipulation of the Wnt signaling pathway and providing preclinical justification for the development of new biologics for the prevention of osteosarcoma metastasis.

STExplore: An Integrated Online Platform for Comprehensive Analysis and Visualization of Spatial Transcriptomics Data

Spatial transcriptomics revolutionizes the understanding of tissue organization and cellular interactions by combining high-resolution spatial information with gene expression profiles. Existing spatial transcriptomics analysis platforms face challenges in accommodating diverse techniques, integrating multi-omics data, and providing comprehensive analytical workflows. STExplore, an advanced online platform, is developed to address these limitations. STExplore supports a wide range of technologies, including sequencing-based and image-based methods, and offers a complete analysis workflow encompassing preprocessing, integration with single-cell RNA sequencing (scRNA-seq), cluster-level and gene-level analyses, and cell-cell communication studies. The platform features dynamic parameter adjustments and interactive visualizations at each analytical stage, enabling users to gain deeper insights into the spatial transcriptomic landscape. Case studies on neurogenesis in embryonic brain development, Alzheimer's disease, and brain tissue architecture demonstrate STExplore's capabilities in enhancing gene expression analysis, revealing cellular spatial organizations, and uncovering intercellular communication patterns. STExplore provides a comprehensive and user-friendly solution for the expanding demands of spatial transcriptomics research. The platform is accessible at http://120.77.47.2:3000/.

Persistence of a Proteomic Signature After a Hypertensive Disorder of Pregnancy

BACKGROUND

A hypertensive disorder of pregnancy is associated with a higher risk of cardiovascular disease later in life, but the potential mechanistic links are unknown.

METHODS

We recruited 2 groups of women, 1 during pregnancy and another at least 2 years after delivery. Cases had a hypertensive disorder of pregnancy, and controls had a normotensive pregnancy. The pregnancy cohort had study visits antepartum and postpartum; the mid-life group made a single study visit. We assayed 7288 plasma proteins, applied machine learning to identify proteomics signatures at each time point, and performed enrichment analyses to identify relevant biological pathways.

RESULTS

The pregnancy cohort (58 cases and 46 controls) had a mean age of 33.8 years, and the mid-life group (71 cases and 74 controls) had a mean age of 40.8 years. Protein levels differed significantly between cases and controls at each time point: 6233 antepartum, 189 postpartum, and 224 in mid-life. The postpartum protein signature discriminated well between cases and controls (c-index=0.78), and it also discriminated well in the independent mid-life samples (c-index=0.72). Pathway analyses identified differences in the complement and coagulation cascades that persisted across the antepartum, postpartum, and mid-life samples. The 28 proteins present in both the postpartum and mid-life signatures included 5 complement factors (3, B, H, H-related-1, and C1r-subcomponent-like) and coagulation factor IX.

CONCLUSIONS

Differences in protein expression persist for years after a hypertensive disorder of pregnancy. The consistent differences in the complement and coagulation pathways may contribute to the increased risk of later life cardiovascular disease.

Multiomic spatial atlas shows deleted in malignant brain tumors 1 (DMBT1) glycoprotein is lost in colonic dysplasia

Colorectal cancer (CRC) is responsible for over 900,000 annual deaths worldwide. Emerging evidence supports pro-carcinogenic bacteria in the colonic microbiome are at least promotional in CRC development and may be causal. We previously showed toxigenic C. difficile from human CRC-associated bacterial biofilms accelerates tumorigenesis in ApcMin/+ mice, both in specific pathogen-free mice and in gnotobiotic mice colonized with a defined consortium of bacteria. To further understand host-microbe interactions during colonic tumorigenesis, we combined single-cell RNA-sequencing (scRNA-seq), spatial transcriptomics, and immunofluorescence to define the molecular spatial organization of colonic dysplasia in our consortium model with or without C. difficile. Our data show a striking bipartite regulation of Deleted in Malignant Brain Tumors 1 (DMBT1) in the inflamed versus dysplastic colon. From scRNA-seq, differential gene expression analysis of normal absorptive colonocytes at 2 weeks postinoculation showed DMBT1 upregulated by C. difficile compared to colonocytes from mice without C. difficile exposure. In contrast, our spatial transcriptomic analysis showed DMBT1 dramatically downregulated in dysplastic foci compared with normal-adjacent tissue. We further integrated our datasets to generate custom colonic dysplasia scores and ligand-receptor mapping. Validation with immunofluorescence showed DMBT1 protein downregulated in dysplastic foci from three mouse models of colonic tumorigenesis and in adenomatous dysplasia from human samples. Finally, we used mouse and human organoids to implicate WNT signaling in the downregulation of DMBT1 mRNA and protein. Together, our data reveal cell type-specific regulation of DMBT1, a potential mechanistic link between bacteria and colonic tumorigenesis. Published 2025. This article is a U.S. Government work and is in the public domain in the USA. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

An integrated transcriptomic cell atlas of human endoderm-derived organoids

Human pluripotent stem cells and tissue-resident fetal and adult stem cells can generate epithelial tissues of endodermal origin in vitro that recapitulate aspects of developing and adult human physiology. Here, we integrate single-cell transcriptomes from 218 samples covering organoids and other models of diverse endoderm-derived tissues to establish an initial version of a human endoderm-derived organoid cell atlas. The integration includes nearly one million cells across diverse conditions, data sources and protocols. We compare cell types and states between organoid models and harmonize cell annotations through mapping to primary tissue counterparts. Focusing on the intestine and lung, we provide examples of mapping data from new protocols and show how the atlas can be used as a diverse cohort to assess perturbations and disease models. The human endoderm-derived organoid cell atlas makes diverse datasets centrally available and will be valuable to assess fidelity, characterize perturbed and diseased states, and streamline protocol development.

Replicability of bulk RNA-Seq differential expression and enrichment analysis results for small cohort sizes

The high-dimensional and heterogeneous nature of transcriptomics data from RNA sequencing (RNA-Seq) experiments poses a challenge to routine downstream analysis steps, such as differential expression analysis and enrichment analysis. Additionally, due to practical and financial constraints, RNA-Seq experiments are often limited to a small number of biological replicates. In light of recent studies on the low replicability of preclinical cancer research, it is essential to understand how the combination of population heterogeneity and underpowered cohort sizes affects the replicability of RNA-Seq research. Using 18'000 subsampled RNA-Seq experiments based on real gene expression data from 18 different data sets, we find that differential expression and enrichment analysis results from underpowered experiments are unlikely to replicate well. However, low replicability does not necessarily imply low precision of results, as data sets exhibit a wide range of possible outcomes. In fact, 10 out of 18 data sets achieve high median precision despite low recall and replicability for cohorts with more than five replicates. To assist researchers constrained by small cohort sizes in estimating the expected performance regime of their data sets, we provide a simple bootstrapping procedure that correlates strongly with the observed replicability and precision metrics. We conclude with practical recommendations to alleviate problems with underpowered RNA-Seq studies.

Quasi-spatial single-cell transcriptome based on physical tissue properties defines early aging associated niche in liver

Aging is associated with the accumulation of senescent cells, which are triggered by tissue injury response and often escape clearance by the immune system. The specific traits and diversity of these cells in aged tissues, along with their effects on the tissue microenvironment, remain largely unexplored. Despite the advances in single-cell and spatial omics technologies to understand complex tissue architecture, senescent cell populations are often neglected in general analysis pipelines due to their scarcity and the technical bias in current omics toolkits. Here we used the physical properties of tissue to enrich the age-associated fibrotic niche and subjected them to single-cell RNA sequencing and single-nuclei ATAC sequencing (ATAC-seq) analysis and named this method fibrotic niche enrichment sequencing (FiNi-seq). Fibrotic niche of the tissue was selectively enriched based on its resistance to enzymatic digestion, enabling quasi-spatial analysis. We profiled young and old livers of male mice using FiNi-seq, discovered Wif1- and Smoc1-producing mesenchymal cell populations showing senescent phenotypes, and investigated the early immune responses within this fibrotic niche. Finally, FiNi-ATAC-seq revealed age-associated epigenetic changes enriched in fibrotic niche cells. Thus, our quasi-spatial, single-cell profiling method allows the detailed analysis of initial aging microenvironments, providing potential therapeutic targets for aging prevention.

Atf4 Protects Islet β-Cell Identity and Function Under Acute Glucose-Induced Stress but Promotes β-Cell Failure in the Presence of Free Fatty Acid

Glucolipotoxicity, caused by combined hyperglycemia and hyperlipidemia, results in β-cell failure and type 2 diabetes via cellular stress-related mechanisms. Activating transcription factor 4 (Atf4) is an essential effector of stress response. We show here that Atf4 expression in β-cells is minimally required for glucose homeostasis in juvenile and adolescent mice but it is needed for β-cell function during aging and under obesity-related metabolic stress. Henceforth, Atf4-deficient β-cells older than 2 months after birth display compromised secretory function under acute hyperglycemia. In contrast, they are resistant to acute free fatty acid-induced dysfunction and reduced production of several factors essential for β-cell identity. Atf4-deficient β-cells downregulate genes involved in protein translation. They also upregulate several lipid metabolism or signaling genes, likely contributing to their resistance to free fatty acid-induced dysfunction. These results suggest that Atf4 activation is required for β-cell identity and function under high glucose. But Atf4 activation paradoxically induces β-cell failure in high levels of free fatty acids. Different transcriptional targets of Atf4 could be manipulated to protect β-cells from metabolic stress-induced failure.

ARTICLE HIGHLIGHTS

Habitual Exercise Modulates Neuroimmune Interaction to Mitigate Aortic Stiffness

BACKGROUND

Exercise augments hemodynamic shear to activate mechano-sensitive molecular transducers in the vascular endothelium. Recently, the central nervous system has been reported to mediate neuroimmune interaction in the aortic adventitia (AA). Whether exercise modulates the sympathetic nerve interaction with the immune cells to mitigate aortic stiffness remains unknown.

METHODS AND RESULTS

Four weeks of Ang II (angiotensin II) infusion to C57BL/6 mice increased neural activation to increase the expression of TH (tyrosine hydroxylase) for sympathetic nerve axons and norepinephrine levels along with the colocalization of synapsin and β2-AR (β2-adrenergic receptor)-positive macrophages in the AA. This Ang II-mediated sympathetic nerve and macrophage interaction activated fibroblasts to increase vascular fibrosis and arterial pulse wave velocity. Sympathetic denervation with celiac ganglionectomy or 6-hydroxydopamine treatment abrogated Ang II-mediated TH+, AA thickness, and pulse wave velocity. Single-cell RNA sequencing analyses of the AA revealed that Ang II increased the circulating monocyte-derived macrophages (Ccr2+CD80) but reduced the resident macrophages (Lyve1+CD163). Gene ontology analysis of differentially expressed genes unveiled that voluntary wheel running mitigated Ang II-mediated increase in Ccr2+CD80 macrophages, cytokine-mediated signaling pathways in macrophages, and extracellular matrix deposition in fibroblasts. Macrophage depletion with Ki20227 (colony stimulating factor 1 receptor inhibitor) reduced Ang II-mediated synapsin+ macrophages. Using the Ccr2 knock-in (Ccr2gfp)/knock-out (Ccr2KO) mice, we observed that Ang II-mediated increases in Ccr2+ macrophages were expressed in Ccr2gfp mice but were absent in Ccr2KO mice. Also, Ang II-induced increases in synapsin expression, neighboring Ccr2+ cells, AA thickness, and pulse wave velocity were reduced in Ccr2KO mice. Both Ki20227 and Ccr2KO reduced the Ang II-mediated increase in TH levels. Furthermore, voluntary wheel running-mediated reduction in vascular fibrosis and aortic stiffness were mitigated by a β2-AR agonist, terbutaline, indicating β2-AR in neuroimmune modulation.

CONCLUSIONS

Exercise mitigates Ang II-mediated sympathetic axon interaction with the circulating monocyte-derived macrophages in the AA to attenuate vascular fibrosis and aortic stiffness.

Drug-targeted Mendelian randomization analysis combined with transcriptome sequencing to explore the molecular mechanisms associated with cognitive impairment

BackgroundCurrent therapies for cognitive impairment, including Alzheimer's disease (AD) and mild cognitive impairment, are limited by a lack of universal treatment and adverse effects associated with polypharmacy. Investigating genetic and molecular mechanisms underlying cognitive decline is critical for the development of targeted therapeutics.ObjectiveTo identify causal genes and potential therapeutic targets for cognitive impairment through integrative genomic analyses.MethodsGenome-wide association study data on cognitive impairment were combined with the expression quantitative trait loci (eQTL) data from the eQTLGen consortium. Mendelian randomization (MR) and colocalization analyses were employed to infer causal relationships. Gene Set Enrichment Analysis and Gene Set Variation Analysis evaluated the pathway and functional differences. Immune cell infiltration patterns and the immunometabolic pathways were assessed, followed by drug target prediction.ResultsMR analysis identified seven gene-eQTL pairs significantly associated with cognitive impairment. SMR colocalization prioritized three key genes: HNMT (histamine metabolism), TNFSF8 (inflammatory signaling), and S1PR5 (sphingolipid signaling). HNMT, TNFSF8, and S1PR5 had 39, 24, and 30 predicted targeted drugs, respectively, including arsenic trioxide, aspirin, and immunomodulators.ConclusionsThis study implicates HNMT, TNFSF8, and S1PR5 as potential therapeutic targets for cognitive impairment. Further validation is required to confirm their clinical relevance.

Histology-Based Virtual RNA Inference Identifies Pathways Associated with Metastasis Risk in Colorectal Cancer

Colorectal cancer (CRC) remains a major health concern, with over 150,000 new diagnoses and more than 50,000 deaths annually in the United States, underscoring an urgent need for improved screening, prognostication, disease management, and therapeutic approaches. The tumor microenvironment (TME)-comprising cancerous and immune cells interacting within the tumor's spatial architecture-plays a critical role in disease progression and treatment outcomes, reinforcing its importance as a prognostic marker for metastasis and recurrence risk. However, traditional methods for TME characterization, such as bulk transcriptomics and multiplex protein assays, lack sufficient spatial resolution. Although spatial transcriptomics (ST) allows for the high-resolution mapping of whole transcriptomes at near-cellular resolution, current ST technologies (e.g., Visium, Xenium) are limited by high costs, low throughput, and issues with reproducibility, preventing their widespread application in large-scale molecular epidemiology studies. In this study, we refined and implemented Virtual RNA Inference (VRI) to derive ST-level molecular information directly from hematoxylin and eosin (H&E)-stained tissue images. Our VRI models were trained on the largest matched CRC ST dataset to date, comprising 45 patients and more than 300,000 Visium spots from primary tumors. Using state-of-the-art architectures (UNI, ResNet-50, ViT, and VMamba), we achieved a median Spearman's correlation coefficient of 0.546 between predicted and measured spot-level expression. As validation, VRI-derived gene signatures linked to specific tissue regions (tumor, interface, submucosa, stroma, serosa, muscularis, inflammation) showed strong concordance with signatures generated via direct ST, and VRI performed accurately in estimating cell-type proportions spatially from H&E slides. In an expanded CRC cohort controlling for tumor invasiveness and clinical factors, we further identified VRI-derived gene signatures significantly associated with key prognostic outcomes, including metastasis status. Although certain tumor-related pathways are not fully captured by histology alone, our findings highlight the ability of VRI to infer a wide range of "histology-associated" biological pathways at near-cellular resolution without requiring ST profiling. Future efforts will extend this framework to expand TME phenotyping from standard H&E tissue images, with the potential to accelerate translational CRC research at scale.

CeiTEA: Adaptive Hierarchy of Single Cells with Topological Entropy

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.

HHIP protein interactions in lung cells provide insight into COPD pathogenesis

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. The primary causes of COPD are environmental, including cigarette smoking; however, genetic susceptibility also contributes to COPD risk. Genome-Wide Association Studies (GWASes) have revealed more than 80 genetic loci associated with COPD, leading to the identification of multiple COPD GWAS genes. However, the biological relationships between the identified COPD susceptibility genes are largely unknown. Genes associated with a complex disease are often in close network proximity, i.e. their protein products often interact directly with each other and/or similar proteins. In this study, we use affinity purification mass spectrometry (AP-MS) to identify protein interactions with HHIP, a well-established COPD GWAS gene which is part of the sonic hedgehog pathway, in two disease-relevant lung cell lines (IMR90 and 16HBE). To better understand the network neighborhood of HHIP, its proximity to the protein products of other COPD GWAS genes, and its functional role in COPD pathogenesis, we create HUBRIS, a protein-protein interaction network compiled from 8 publicly available databases. We identified both common and cell type-specific protein-protein interactors of HHIP. We find that our newly identified interactions shorten the network distance between HHIP and the protein products of several COPD GWAS genes, including DSP, MFAP2, TET2, and FBLN5. These new shorter paths include proteins that are encoded by genes involved in extracellular matrix and tissue organization. We found and validated interactions to proteins that provide new insights into COPD pathobiology, including CAVIN1 (IMR90) and TP53 (16HBE). The newly discovered HHIP interactions with CAVIN1 and TP53 implicate HHIP in response to oxidative stress.

Enteric glia regulate Paneth cell secretion and intestinal microbial ecology

Glial cells of the enteric nervous system (ENS) interact closely with the intestinal epithelium and secrete signals that influence epithelial cell proliferation and barrier formation in vitro. Whether these interactions are important in vivo, however, is unclear because previous studies reached conflicting conclusions (Prochera and Rao, 2023). To better define the roles of enteric glia in steady state regulation of the intestinal epithelium, we characterized the glia in closest proximity to epithelial cells and found that the majority express the gene Proteolipid protein 1 (PLP1) in both mice and humans. To test their functions using an unbiased approach, we genetically depleted PLP1+ cells in mice and transcriptionally profiled the small and large intestines. Surprisingly, glial loss had minimal effects on transcriptional programs and the few identified changes varied along the gastrointestinal tract. In the ileum, where enteric glia had been considered most essential for epithelial integrity, glial depletion did not drastically alter epithelial gene expression but caused a modest enrichment in signatures of Paneth cells, a secretory cell type important for innate immunity. In the absence of PLP1+ glia, Paneth cell number was intact, but a subset appeared abnormal with irregular and heterogenous cytoplasmic granules, suggesting a secretory deficit. Consistent with this possibility, ileal explants from glial-depleted mice secreted less functional lysozyme than controls with corresponding effects on fecal microbial composition. Collectively, these data suggest that enteric glia do not exert broad effects on the intestinal epithelium but have an essential role in regulating Paneth cell function and gut microbial ecology.

Unveiling chromatin dynamics with virtual epigenome

The three-dimensional organization of chromatin is essential for gene regulation and cellular function, with epigenome playing a key role. Hi-C methods have expanded our understanding of chromatin interactions, but their high cost and complexity limit their use. Existing models for predicting chromatin interactions rely on limited ChIP-seq inputs, reducing their accuracy and generalizability. In this work, we present a computational approach, EpiVerse, which leverages imputed epigenetic signals and advanced deep learning techniques. EpiVerse significantly improves the accuracy of cross-cell-type Hi-C prediction, while also enhancing model interpretability by incorporating chromatin state prediction within a multitask learning framework. Moreover, EpiVerse predicts Hi-C contact maps across an array of 39 human tissues, which provides a comprehensive view of the complex relationship between chromatin structure and gene regulation. Furthermore, EpiVerse facilitates unprecedented in silico perturbation experiments at the "epigenome-level" to unveil the chromatin architecture under specific conditions. EpiVerse is available on GitHub: https://github.com/jhhung/EpiVerse .

Cross-talks between Metabolic and Translational Controls during Beige Adipocyte Differentiation

Whether and how regulatory events at the translation stage shape the cellular and metabolic features of thermogenic adipocytes is hardly understood. In this study, we report two hitherto unidentified cross-talk pathways between metabolic and translational regulation in beige adipocytes. By analysing temporal profiles of translation activity and protein level changes during precursor-to-beige differentiation, we found selective translational down-regulation of OXPHOS component-coding mRNAs. The down-regulation restricted to Complexes I, III, IV, and V, is coordinated with enhanced translation of TCA cycle genes, engendering distinct stoichiometry of OXPHOS and TCA cycle components and altering the related metabolic activities in mitochondria of thermogenic adipocytes. Our high-resolution description of ribosome positioning unveiled potentiated ribosome pausing at glutamate codons. The increased stalling is attributable to remodelled glutamate metabolism that decreases glutamates for tRNA charging during pan-adipocyte differentiation. The ribosome pauses decrease protein synthesis and mRNA stability of glutamate codon-rich genes, such as actin cytoskeleton-associated genes.

MIDAA: deep archetypal analysis for interpretable multi-omic data integration based on biological principles

High-throughput multi-omic molecular profiling allows the probing of biological systems at unprecedented resolution. However, integrating and interpreting high-dimensional, sparse, and noisy multimodal datasets remains challenging. Deriving new biological insights with current methods is difficult because they are not rooted in biological principles but prioritise tasks like dimensionality reduction. Here, we introduce a framework that combines archetypal analysis, an approach grounded in biological principles, with deep learning. Using archetypes based on evolutionary trade-offs and Pareto optimality, MIDAA finds extreme data points that define the geometry of the latent space, preserving the complexity of biological interactions while retaining an interpretable output. We demonstrate that these extreme points represent cellular programmes reflecting the underlying biology. Moreover, we show that, compared to alternative methods, MIDAA can identify parsimonious, interpretable, and biologically relevant patterns from real and simulated multi-omics.

Sphingolipid metabolism orchestrates establishment of the hair follicle stem cell compartment

Sphingolipids serve as building blocks of membranes to ensure subcellular compartmentalization and facilitate intercellular communication. How cell type-specific lipid compositions are achieved and what is their functional significance in tissue morphogenesis and maintenance has remained unclear. Here, we identify a stem cell-specific role for ceramide synthase 4 (CerS4) in orchestrating fate decisions in skin epidermis. Deletion of CerS4 prevents the proper development of the adult hair follicle bulge stem cell (HFSC) compartment due to altered differentiation trajectories. Mechanistically, HFSC differentiation defects arise from an imbalance of key ceramides and their derivate sphingolipids, resulting in hyperactivation of noncanonical Wnt signaling. This impaired HFSC compartment establishment leads to disruption of hair follicle architecture and skin barrier function, ultimately triggering a T helper cell 2-dominated immune infiltration resembling human atopic dermatitis. This work uncovers a fundamental role for a cell state-specific sphingolipid profile in stem cell homeostasis and in maintaining an intact skin barrier.

Hypergraph-based analysis of weighted gene co-expression hypernetwork

Background

With the rapid advancement of gene sequencing technologies, Traditional weighted gene co-expression network analysis (WGCNA), which relies on pairwise gene relationships, struggles to capture higher-order interactions and exhibits low computational efficiency when handling large, complex datasets.

Methods

To overcome these challenges, we propose a novel Weighted Gene Co-expression Hypernetwork Analysis (WGCHNA) based on weighted hypergraph, where genes are modeled as nodes and samples as hyperedges. By calculating the hypergraph Laplacian matrix, WGCHNA generates a topological overlap matrix for module identification through hierarchical clustering.

Results

Results on four gene expression datasets show that WGCHNA outperforms WGCNA in module identification and functional enrichment. WGCHNA identifies biologically relevant modules with greater complexity, particularly in processes like neuronal energy metabolism linked to Alzheimer's disease. Additionally, functional enrichment analysis uncovers more comprehensive pathway hierarchies, revealing potential regulatory relationships and novel targets.

Conclusion

WGCHNA effectively addresses WGCNA's limitations, providing superior accuracy in detecting gene modules and deeper insights for disease research, making it a powerful tool for analyzing complex biological systems.

Cell Storage Conditions Impact Single-Cell Proteomic Landscapes

Single cell transcriptomics (SCT) has revolutionized our understanding of cellular heterogeneity, yet the emergence of single cell proteomics (SCP) promises a more functional view of cellular dynamics. A challenge is that not all mass spectrometry facilities can perform SCP, and not all laboratories have access to cell sorting equipment required for SCP, which together motivate an interest in sending bulk cell samples through the mail for sorting and SCP analysis. Shipping requires cell storage, which has an unknown effect on SCP results. This study investigates the impact of cell storage conditions on the proteomic landscape at the single cell level, utilizing Data-Independent Acquisition (DIA) coupled with Parallel Accumulation Serial Fragmentation (diaPASEF). Three storage conditions were compared in 293T cells: (1) 37 °C (control), (2) 4 °C overnight, and (3) -196 °C storage followed by liquid nitrogen preservation. Both cold and frozen storage induced significant alterations in the cell diameter, elongation, and proteome composition. By elucidating how cell storage conditions alter cellular morphology and proteome profiles, this study contributes foundational technical information about SCP sample preparation and data quality.

Pre-existing stem cell heterogeneity dictates clonal responses to the acquisition of leukemic driver mutations

Cancer cells display wide phenotypic variation even across patients with the same mutations. Differences in the cell of origin provide a potential explanation, but traditional assays lack the resolution to distinguish clonally heterogeneous subsets of stem and progenitor cells. To address this challenge, we developed simultaneous tracking of recombinase activation and clonal kinetics (STRACK), a method to trace clonal dynamics and gene expression before and after the acquisition of cancer mutations. Using mouse models, we studied two leukemic mutations, Dnmt3a-R878H and Npm1c, and found that their effect was highly variable across different stem cell states. Specifically, a subset of differentiation-primed stem cells, which normally becomes outcompeted with time, expands with both mutations. Intriguingly, Npm1c mutations reversed the intrinsic bias of the clone of origin, with differentiation-primed stem cells giving rise to more primitive malignant states. Thus, we highlight the relevance of single-cell lineage tracing to unravel early events in cancer evolution and posit that different cellular histories carry distinct cancer phenotypic potential.

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Most human transcription factor (TF) genes encode multiple protein isoforms differing in DNA-binding domains, effector domains, or other protein regions. The global extent to which this results in functional differences between isoforms remains unknown. Here, we systematically compared 693 isoforms of 246 TF genes, assessing DNA binding, protein binding, transcriptional activation, subcellular localization, and condensate formation. Relative to reference isoforms, two-thirds of alternative TF isoforms exhibit differences in one or more molecular activities, which often could not be predicted from sequence. We observed two primary categories of alternative TF isoforms: "rewirers" and "negative regulators," both of which were associated with differentiation and cancer. Our results support a model wherein the relative expression levels of, and interactions involving, TF isoforms add an understudied layer of complexity to gene regulatory networks, demonstrating the importance of isoform-aware characterization of TF functions and providing a rich resource for further studies.

Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina

Most genetic risk variants linked to ocular diseases are non-protein coding and presumably contribute to disease through dysregulation of gene expression, however, deeper understanding of their mechanisms of action has been impeded by an incomplete annotation of the transcriptional regulatory elements across different retinal cell types. To address this knowledge gap, we carried out single-cell multiomics assays to investigate gene expression, chromatin accessibility, DNA methylome and 3D chromatin architecture in human retina, macula, and retinal pigment epithelium (RPE)/choroid. We identified 420,824 unique candidate regulatory elements and characterized their chromatin states in 23 sub-classes of retinal cells. Comparative analysis of chromatin landscapes between human and mouse retina cells further revealed both evolutionarily conserved and divergent retinal gene-regulatory programs. Leveraging the rapid advancements in deep-learning techniques, we developed sequence-based predictors to interpret non-coding risk variants of retina diseases. Our study establishes retina-wide, single-cell transcriptome, epigenome, and 3D genome atlases, and provides a resource for studying the gene regulatory programs of the human retina and relevant diseases.

Niche-derived Semaphorin 4A safeguards functional identity of myeloid-biased hematopoietic stem cells

Somatic stem cell pools comprise diverse, highly specialized subsets whose individual contribution is critical for the overall regenerative function. In the bone marrow, myeloid-biased hematopoietic stem cells (myHSCs) are indispensable for replenishment of myeloid cells and platelets during inflammatory response but, at the same time, become irreversibly damaged during inflammation and aging. Here we identify an extrinsic factor, Semaphorin 4A (Sema4A), which non-cell-autonomously confers myHSC resilience to inflammatory stress. We show that, in the absence of Sema4A, myHSC inflammatory hyper-responsiveness in young mice drives excessive myHSC expansion, myeloid bias and profound loss of regenerative function with age. Mechanistically, Sema4A is mainly produced by neutrophils, signals via a cell surface receptor, Plexin D1, and safeguards the myHSC epigenetic state. Our study shows that, by selectively protecting a distinct stem cell subset, an extrinsic factor preserves functional diversity of somatic stem cell pool throughout organismal lifespan.

Tumor Microenvironment On-A-Chip and Single-Cell Analysis Reveal Synergistic Stromal-Immune Crosstalk on Breast Cancer Progression

Solid tumors develop within a complex environment called the tumor microenvironment (TME), which is sculpted by the presence of other cells, such as cancer-associated fibroblasts (CAFs) and immune cells like macrophages (Mφs). Despite the presence of immune cells, tumor cells orchestrate a tumor-supportive environment through intricate interaction with the components of the TME. However, the specific mechanism by which this intercellular dialogue is regulated is not fully understood. To that end, the development of an organotypic 3D breast TME-on-a-chip (TMEC) model, integrated with single-cell RNA sequencing analysis, is reported to mechanistically evaluate the progression of triple-negative breast cancer (TNBC) cells in the presence of patient-derived CAFs and Mφs. Extensive functional assays, including invasion and morphometric characterization, reveal the synergistic influence of CAFs and Mφs on tumor cells. Furthermore, gene expression and pathway enrichment analyses identify the involvement of the KYNU gene, suggesting a potential immune evasion mechanism through the kynurenine pathway. Lastly, the pharmacological targeting of the identified pathway is investigated.

Transcriptomic signatures and network-based methods uncover new senescent cell anti-apoptotic pathways and senolytics

Cellular senescence is an irreversible cell cycle arrest caused by various stressors that damage cells. Over time, senescent cells accumulate and contribute to the progression of multiple age-related degenerative diseases. It is believed that these cells accumulate partly due to their ability to evade programmed cell death through the development and activation of survival and antiapoptotic resistance mechanisms; however, many aspects of how these survival mechanisms develop and activate are still unknown. By analyzing transcriptomic signature profiles generated by the LINCS L1000 project and using network-based methods, we identified various genes that could represent new senescence-related survival mechanisms. Additionally, employing the same methodology, we identified over 600 molecules with potential senolytic activity. Experimental validation of our computational findings confirmed the senolytic activity of Fluorouracil, whose activity would be mediated by a multitarget mechanism, revealing that its targets AURKA, EGFR, IRS1, SMAD4, and KRAS are new senescent cell antiapoptotic pathways (SCAPs). The development of these pathways could depend on the stimulus that induces cellular senescence. The SCAP development and activation mechanisms proposed in this work offer new insights into how senescent cells survive. Identifying new antiapoptotic resistance targets and drugs with potential senolytic activity paves the way for developing new pharmacological therapies to eliminate senescent cells selectively.

Deciphering recent transposition patterns in plants through comparison of 811 genome assemblies

Transposable elements (TEs) are significant drivers of genome evolution, yet their recent dynamics and impacts within and among species, as well as the roles of host genes and non-coding RNAs in the transposition process, remain elusive. With advancements in large-scale pan-genome sequencing and the development of open data sharing, large-scale comparative genomics studies have become feasible. Here, we performed complete de novo TE annotations and identified active TEs in 310 plant genome assemblies across 119 species and seven crop populations. Using 811 high-quality genomes, we detected 13 844 553 TE-induced structural variants (TE-SVs), providing unprecedented resolution in delineating recent TE activities. Our integrative analysis revealed a mutual evolutionary relationship between TEs and host genomes. On one hand, host genes and ncRNAs are involved in the transposition process, as evidenced by their colocalization and coactivation with TEs, and may play a role in chromatin regulation. On the other hand, TEs drive genetic innovation by promoting the duplication of host genes and inserting into regulatory regions. Moreover, genes influenced by active TEs are linked to plant growth, nutrient absorption, storage metabolism and environmental adaptation, aiding in crop domestication and adaptation. This TE dynamics atlas not only reveals evolutionary and functional features linked to transposition activity but also highlights the role of TEs in crop domestication and adaptation, paving the way for future exploration of TE-mediated genome evolution and crop improvement strategies.

SOAPy: a Python package to dissect spatial architecture, dynamics, and communication

Advances in spatial omics enable deeper insights into tissue microenvironments while posing computational challenges. Therefore, we developed SOAPy, a comprehensive tool for analyzing spatial omics data, which offers methods for spatial domain identification, spatial expression tendency, spatiotemporal expression pattern, cellular co-localization, multi-cellular niches, cell-cell communication, and so on. SOAPy can be applied to diverse spatial omics technologies and multiple areas in physiological and pathological contexts, such as tumor biology and developmental biology. Its versatility and robust performance make it a universal platform for spatial omics analysis, providing diverse insights into the dynamics and architecture of tissue microenvironments.

Routine CT Diagnostics Cause Dose-Dependent Gene Expression Changes in Peripheral Blood Cells

The increasing use of computed tomography (CT) has led to a rise in cumulative radiation dose due to medical imaging, raising concerns about potential long-term adverse effects. Large-scale epidemiological studies indicate a higher tumor incidence associated with CT examinations, but the underlying biological mechanisms remain largely unexplained. To gain further insights into the cellular response triggered by routine CT diagnostics, we investigated CT-induced changes of gene expression in peripheral blood cells using whole transcriptome sequencing. RNA was isolated from peripheral blood cells of 40 male patients with asymptomatic microhematuria, sampled before and after multi-phase abdominal CT (CTDIvol: 3.75-26.95 mGy, median: 6.55 mGy). On average, 22.11 million sequence reads (SD 5.71) per sample were generated to identify differentially expressed genes 6 h post-exposure by means of DESeq2. To assess the dose dependency of CT-induced effects, we additionally divided samples into three categories: low exposure (≤6.55 mGy, n = 20), medium exposure (>6.55 mGy and <12 mGy, n = 16), and high exposure (≥12 mGy, n = 4), and repeated gene expression analysis for each subset and their corresponding prae-exposure sample. CT exposure caused consistent and dose-dependent upregulation of six genes (EDA2R, AEN, FDXR, DDB2, PHLDA3, and MIR34AHG; padj < 0.1). These genes share several functional commonalities, including regulation by TP53 and involvement in the DNA damage response. The biological pathways highlighted by Gene Set Enrichment Analysis (GSEA) suggest a dose-dependent increase of cellular damage and metabolic particularities in the low-exposure subset, which may be related to a potential adaptive cellular response to low-dose irradiation. Irrespective of applied dose, AEN emerged as the most robust biomarker for CT exposure among all genes. Routine abdominal CT scans cause dose-dependent gene deregulation in association with DNA damage in peripheral blood cells after in vivo exposure. Regarding risk assessment of CT, our results support the commonly applied "As Low-As -Reasonably Achievable (ALARA)" principle. Evidence of additional gene expression changes associated with metabolic processes indicates a rather complex molecular response beyond DNA damage after CT exposure, and emphasizes the need for further targeted investigations.

PyEvoCell: an LLM-augmented single-cell trajectory analysis dashboard

MOTIVATION

Several methods have been developed for trajectory inference in single-cell studies. However, identifying relevant lineages among several cell types and interpreting the results of downstream analysis remains a challenging task that requires deep understanding of various cell type transitions and progression patterns. Therefore, there is a need for methods that can aid researchers in the analysis and interpretation of such trajectories.

RESULTS

We developed PyEvoCell, a dashboard for trajectory interpretation and analysis that is augmented by large language model (LLM) capabilities. PyEvoCell applies the LLM to the outputs of trajectory inference methods such as Monocle3, to suggest biologically relevant lineages. Once a lineage is defined, users can conduct differential expression and functional analyses which are also interpreted by the LLM. Finally, any hypothesis or claim derived from the analysis can be validated using the veracity filter, a feature enabled by the LLM, to confirm or reject claims by providing relevant PubMed citations.

AVAILABILITY AND IMPLEMENTATION

The software is available at https://github.com/Sanofi-Public/PyEvoCell. It contains installation instructions, user manual, demo datasets, as well as license conditions. https://doi.org/10.5281/zenodo.15114803.

Systematic analysis of the effects of splicing on the diversity of post-translational modifications in protein isoforms using PTM-POSE

Post-translational modifications (PTMs) and splicing are important regulatory processes for controlling protein function and activity. Despite examples of interplay between alternative splicing and cell signaling in literature, there have been few detailed analyses of the impacts of alternative splicing on PTMs, partly due to difficulties in extracting PTM information from splicing measurements. We developed a computational pipeline, PTM Projection Onto Splice Events (PTM-POSE), to identify "prospective" PTM sites in alternative isoforms and splice events recorded in databases using only the genomic coordinates of a splice event or isoform of interest. Importantly, PTM-POSE integrates various PTM-specific databases and tools to allow for deeper analysis of the individual and global impact of spliced PTMs on isoform function, protein interactions, and regulation by enzymes like kinases. Using PTM-POSE, we performed a systematic analysis of PTM diversification across isoforms annotated in the Ensembl database. We found that 32% of PTMs are excluded from at least one Ensembl isoform, with palmitoylation being most likely to be excluded (49%) and glycosylation and crotonylation exhibiting the highest constitutive rates (75% and 94%, respectively). Further, approximately 2% of prospective PTM sites exhibited altered regulatory sequences surrounding the modification site, suggesting that regulatory or binding interactions might be different in these proteoforms. When comparing splicing of phosphorylation sites to measured phosphorylation abundance in KRAS-expressing lung cells, differential inclusion of phosphorylation sites correlated with phosphorylation levels, particularly for larger changes in inclusion (> 20%). To better understand how splicing diversification of PTMs may alter protein function and regulatory networks in specific biological contexts, we applied PTM-POSE to exon utilization measurements from TCGASpliceSeq of prostate tumor samples from The Cancer Genome Atlas (TCGA) and identified 1,489 PTMs impacted by ESRP1-correlated splicing, a splicing factor associated with worsened prognosis. We identified protein interaction and regulatory networks that may be rewired as a result of differential inclusion of PTM sites in ribosomal and cytoskeletal proteins. We also found instances in which ESRP1-mediated splicing impacted PTMs by altering flanking residues surrounding specific phosphorylation sites that may be targets of 14-3-3 proteins and SH2 domains. In addition, SGK1 signaling was found to be influenced by ESRP1 expression through increased inclusion of SGK1 substrates in ESRP1-expressing patients. Based on validation in a separate prostate cancer cohort from the Chinese Prostate Cancer Genome and EpiGenome Atlas (CPGEA), this correlated with increased phosphorylation of SGK1 substrates, particularly when SGK1 was predicted to be active. From this work, we highlighted the extensive splicing-control of PTM sites across the transcriptome and the novel information that can be gained through inclusion of PTMs in the analysis of alternative splicing. Importantly, we have provided a publicly available python package (PTM-POSE: https://github.com/NaegleLab/PTM-POSE) and all associated data for use by the broader scientific community to allow for continued exploration of the relationship between splicing and PTMs.

SIMVI disentangles intrinsic and spatial-induced cellular states in spatial omics data

Spatial omics technologies enable analysis of gene expression and interaction dynamics in relation to tissue structure and function. However, existing computational methods may not properly distinguish cellular intrinsic variability and intercellular interactions, and may thus fail to reliably capture spatial regulations. Here, we present Spatial Interaction Modeling using Variational Inference (SIMVI), an annotation-free deep learning framework that disentangles cell intrinsic and spatial-induced latent variables in spatial omics data with rigorous theoretical support. By this disentanglement, SIMVI enables estimation of spatial effects at a single-cell resolution, and empowers various downstream analyses. We demonstrate the superior performance of SIMVI across datasets from diverse platforms and tissues. SIMVI illuminates the cyclical spatial dynamics of germinal center B cells in human tonsil. Applying SIMVI to multiome melanoma data reveals potential tumor epigenetic reprogramming states. On our newly-collected cohort-level CosMx melanoma data, SIMVI uncovers space-and-outcome-dependent macrophage states and cellular communication machinery in tumor microenvironments.