Azacitidine, a hypomethylating agent, is currently represented as initial therapy in most cases of high-risk myelodysplastic syndromes (MDS), yet only 40-60% of patients achieve therapeutic response. Non-responders face poor outcomes and disease progression to secondary acute myeloid leukemia. To improve treatment outcomes, understanding the pre-treatment molecular determinants of azacitidine response is critical. Despite the fundamental role of epigenetic regulation and cellular composition in MDS, the complex interplay between epigenetic regulation and therapeutic response has not been investigated. Here, we analyzed chromatin accessibility landscapes of 23 high-risk MDS patients before getting azacitidine, using Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq). We identified 1, 940 distinct chromatin accessibility features strongly associated with azacitidine response, suggesting gene regulatory elements like enhancers as key determinants of it. Also, non-responders exhibited significant myeloid lineage commitment, while responders showed elevated accessibility in regions governing immune activation and T cell differentiation pathways. Notably, T cell subsets of non-responders displayed distinct chromatin states associated with impaired cellular differentiation compared to responders and healthy donors. This study first establishes chromatin accessibility features as potential biomarkers for azacitidine response in high-risk MDS and provides novel insights for personalized treatment optimization. Dayoung Kim, Silvia Park, Yoo Jin Kim, Ryan Corces, Seung Woo Cho. Analysis of chromatin accessibility profiling reveals epigenetic features of azacitidine response in high-risk myelodysplastic syndrome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 217.

Azacitidine, used in the treatment of higher-risk myelodysplastic neoplasms, is a DNA methyltransferase inhibitor that modifies epigenetic regulatory programs. The efficacy of azacitidine varies among patients, with approximately 50% of patients failing to respond. However, whether epigenomic factors affect responses to azacitidine has not been investigated. We examined chromatin accessibility in bone marrow cells from 23 treatment-naive patients with higher-risk myelodysplastic syndrome, suggesting azacitidine response is strongly associated with distinct hematopoietic cell states. Chromatin-accessible regions in non-responders were enriched for myeloid progenitor signatures, whereas those in responders were enriched for T cell signatures. Notably, CD8+ T cells from non-responders exhibited reduced chromatin accessibility at TBX/EOMES-binding sites, bridging T cell differentiation state and azacitidine response. These findings suggest that immune cell function contributes to the responses to hypomethylating agents in myelodysplastic neoplasms and that chromatin accessibility could be used to predict drug responses in high-risk myelodysplastic syndrome patients.

Triggering cancer cell death by inducing DNA damage is the primary aim of radiotherapy; however, normal cells are also damaged. In this study, we showed that delivery of only four synthetic guide RNAs with Cas9 endonuclease efficiently induced simultaneous DNA double-strand breaks, resulting in efficient cell death in a cell type-specific manner. Off-target effects of Cas9 endonuclease were prevented by using Cas9-nickase to induce DNA single-strand breaks and blocking their repair with PARP inhibitors (PARPi). When recombinant Cas9-nickase protein and multiple synthetic guide RNAs were delivered with PARPis into cultured cells, in vivo xenografts, and patient-derived cancer organoids via lipid nanoparticles, cancer cells were unable to tolerate the induced DNA damage even in the presence of a functional BRCA2 gene. This approach has the potential to expand the use of PARPis with verified safety and thus is a potentially powerful tool for personalized genome-based anticancer therapy. SIGNIFICANCE:Targeting cancer-specific variants with CRISPR/Cas9-nickase induces cancer-specific cell death in combination with DNA repair pathway inhibitors, demonstrating the potential of CRISPR cancer therapy for treating a broad range of cancers.

Transcription factors (TFs) and transcriptional coregulators are emerging therapeutic targets. Gene regulatory networks (GRNs) can evaluate pharmacological agents and identify drivers of disease, but methods that rely solely on gene expression often neglect post-transcriptional modulation of TFs. We present Epiregulon, a method that constructs GRNs from single-cell ATAC-seq and RNA-seq data for accurate prediction of TF activity. This is achieved by considering the co-occurrence of TF expression and chromatin accessibility at TF binding sites in each cell. ChIP-seq data allows motif-agonistic activity inference of transcriptional coregulators or TF harboring neomorphic mutations. Epiregulon accurately predicted the effects of AR inhibition across different drug modalities including an AR antagonist and an AR degrader, delineated the mechanisms of a SMARCA4 degrader by identifying context-dependent interaction partners, and prioritized drivers of lineage reprogramming and tumorigenesis. By mapping gene regulation across various cellular contexts, Epiregulon can accelerate the discovery of therapeutics targeting transcriptional regulators.

Clustering is a critical step in the analysis of single-cell data, enabling the discovery and characterization of cell types and states. However, most popular clustering tools do not subject results to statistical inference testing, leading to risks of overclustering or underclustering data and often resulting in ineffective identification of cell types with widely differing prevalence. To address these challenges, we present CHOIR (cluster hierarchy optimization by iterative random forests), which applies a framework of random forest classifiers and permutation tests across a hierarchical clustering tree to statistically determine clusters representing distinct populations. We demonstrate the performance of CHOIR through extensive benchmarking against 15 existing clustering methods across 230 simulated and five real single-cell RNA sequencing, assay for transposase-accessible chromatin sequencing, spatial transcriptomic and multi-omic datasets. CHOIR can be applied to any single-cell data type and provides a flexible, scalable and robust solution to the challenge of identifying biologically relevant cell groupings within heterogeneous single-cell data.

Microglia are implicated in aging, neurodegeneration and Alzheimer’s disease (AD). Low-plex protein imaging does not capture cellular states and interactions in the human brain, which differs from rodent models. Here we used multiplexed ion beam imaging to spatially map cellular states and niches in cognitively normal human brains, identifying a spectrum of proteomic microglial profiles. Defined by immune activation states that were skewed across brain regions and compartmentalized according to microenvironments, this spectrum enables the identification of proteomic trends across the microglia of ten cognitively normal individuals and orthogonally with single-nuclei epigenetic analysis, revealing associated molecular functions. Notably, AD tissues exhibit regulatory shifts in the immunologically active cells at the end of the proteomic spectrum, including enrichment of CD33 and CD44 and decreases in HLA-DR, P2RY12 and ApoE expression. These findings establish an in situ, single-cell spatial proteomic framework for AD-specific microglial states. In this Resource paper, the authors use MIBI spatial proteomics to map microglial cell states in brains from cognitively normal humans and those with Alzheimer’s disease.

Introduction While genome-wide association studies have identified GBA1 as a key gene contributing to disease severity and cognitive decline in PD, its molecular effects remain poorly understood. Methods We used integrative bulk ATAC-seq across six brain regions from autopsied individuals with PD and varying genetic risk to characterize region- and cell type-specific molecular differences. Using Cellformer, an AI-based bulk ATAC-seq-deconvolution tool, we determined cell type-specific effects of GBA1 on PD disease progression and then validated our findings using whole transcriptome data from blood samples. Results Epigenomic differences between PD with (“GBA+”; n = 15) and without (“GBA-”, n = 15) GBA1 variants were localized in substantia nigra. Nineteen chromatin-accessible regions strictly separated GBA+ from GBA-, including the promoter sites of key genes such as CACNA1C, EHMT1, and SLC25A48. The effect in GBA + spanned the main cell types in brain, and chromatin differences between GBA- and GBA + increased with neuropathologic progression of disease. Significant differences in the epigenomic profile in GBA+ were observed in neuronal cells (AUROC = 0.8, AUPRC = 0.8, P-value<0.0001). Validation in blood samples distinguished between GBA+ and GBA-subtypes, achieving AUROC values of 0.99. Over 5000 transcripts in blood cells distinguished GBA+ from GBA-, validating key genes and pathways from our epigenomic analysis of brain regions. Conclusion Our study provides novel insights into the cell type-specific epigenomic and transcriptomic landscape of GBA+ and its molecular divergence from other PD subtypes, and highlights potential therapeutic targets for this genetically defined subset of PD.

Genome conformation underlies transcriptional regulation by distal enhancers, and genomic rearrangements in cancer can alter critical regulatory interactions. Here we profiled the three-dimensional genome architecture and enhancer connectome of 69 tumor samples spanning 15 primary human cancer types from The Cancer Genome Atlas. We discovered the following three archetypes of enhancer usage for over 100 oncogenes across human cancers: static, selective gain or dynamic rewiring. Integrative analyses revealed the enhancer landscape of noncancer cells in the tumor microenvironment for genes related to immune escape. Deep whole-genome sequencing and enhancer connectome mapping provided accurate detection and validation of diverse structural variants across cancer genomes and revealed distinct enhancer rewiring consequences from noncoding point mutations, genomic inversions, translocations and focal amplifications. Extrachromosomal DNA promoted more extensive enhancer rewiring among several types of focal amplification mechanisms. These results suggest a systematic approach to understanding genome topology in cancer etiology and therapy.

Telomerase is intimately associated with stem cells and cancer, because it catalytically elongates telomeres-nucleoprotein caps that protect chromosome ends(1). Overexpression of telomerase reverse transcriptase (TERT) enhances the proliferation of cells in a telomere-independent manner(2-8), but so far, loss-of-function studies have provided no evidence that TERT has a direct role in stem cell function. In many tissues, homeostasis is shaped by stem cell competition, a process in which stem cells compete on the basis of inherent fitness. Here we show that conditional deletion of Tert in the spermatogonial stem cell (SSC)-containing population in mice markedly impairs competitive clone formation. Using lineage tracing from the Tert locus, we find that TERT-expressing SSCs yield long-lived clones, but that clonal inactivation of TERT promotes stem cell differentiation and a genome-wide reduction in open chromatin. This role for TERT in competitive clone formation occurs independently of both its reverse transcriptase activity and the canonical telomerase complex. Inactivation of TERT causes reduced activity of the MYC oncogene, and transgenic expression of MYC in the TERT-deleted pool of SSCs efficiently rescues clone formation. Together, these data reveal a catalytic-activity-independent requirement for TERT in enhancing stem cell competition, uncover a genetic connection between TERT and MYC and suggest that a selective advantage for stem cells with high levels of TERT contributes to telomere elongation in the male germline during homeostasis and ageing.

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type–specific features. Using organ-matched healthy tissues, we identified the “nearest healthy” cell types in diverse cancers, demonstrating that the chromatin signature of basal-like–subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms. RNA binding proteins (RBPs) regulate various RNA processes, yet their interactions remain poorly understood. Here, authors generate a comprehensive map of RBP interactions using multimodal data, uncovering context-specific functions and revealing complex post-transcriptional regulatory networks.

Relapse of acute myeloid leukemia (AML) is highly aggressive and often treatment refractory. We analyzed previously published AML relapse cohorts and found that 40% of relapses occur without changes in driver mutations, suggesting that non-genetic mechanisms drive relapse in a large proportion of cases. We therefore characterized epigenetic patterns of AML relapse using 26 matched diagnosis-relapse samples with ATAC-seq. This analysis identified a relapse-specific chromatin accessibility signature for mutationally stable AML, suggesting that AML undergoes epigenetic evolution at relapse independent of mutational changes. Analysis of leukemia stem cell (LSC) chromatin changes at relapse indicated that this leukemic compartment underwent significantly less epigenetic evolution than non-LSCs, while epigenetic changes in non-LSCs reflected overall evolution of the bulk leukemia. Finally, we used single-cell ATAC-seq paired with mitochondrial sequencing (mtscATAC) to map clones from diagnosis into relapse along with their epigenetic features. We found that distinct mitochondrially-defined clones exhibit more similar chromatin accessibility at relapse relative to diagnosis, demonstrating convergent epigenetic evolution in relapsed AML. These results demonstrate that epigenetic evolution is a feature of relapsed AML and that convergent epigenetic evolution can occur following treatment with induction chemotherapy.

Genome-wide association studies (GWASs) provide a key foundation for elucidating the genetic underpinnings of common polygenic diseases. However, these studies have limitations in their ability to assign causality to particular genetic variants, especially those residing in the noncoding genome. Over the past decade, technological and methodological advances in both analytical and empirical prioritization of noncoding variants have enabled the identification of causative variants by leveraging orthogonal functional evidence at increasing scale. In this review, we present an overview of these approaches and describe how this workflow provides the groundwork necessary to move beyond associations toward genetically informed studies on the molecular and cellular mechanisms of polygenic disease.

Over the past decade, single-cell genomics technologies have allowed scalable profiling of cell-type-specific features, which has substantially increased our ability to study cellular diversity and transcriptional programs in heterogeneous tissues. Yet our understanding of mechanisms of gene regulation or the rules that govern interactions between cell types is still limited. The advent of new computational pipelines and technologies, such as single-cell epigenomics and spatially resolved transcriptomics, has created opportunities to explore two new axes of biological variation: cell-intrinsic regulation of cell states and expression programs and interactions between cells. Here, we summarize the most promising and robust technologies in these areas, discuss their strengths and limitations and discuss key computational approaches for analysis of these complex datasets. We highlight how data sharing and integration, documentation, visualization and benchmarking of results contribute to transparency, reproducibility, collaboration and democratization in neuroscience, and discuss needs and opportunities for future technology development and analysis.

Patient sample features including cytogenetic alterations and mutations

Table of median survival in TCGA data by predicted clusters

RNA-seq derived signatures and per-cluster weights for TCGA clustering

Supplementary and expanded descriptions of methods and figure legends for supplementary data

Supplementary Figures S1-S6, and Table S1-S3