Nat Protoc. 2025 Mar 21. doi: 10.1038/s41596-025-01145-9. Online ahead of print.

ABSTRACT

The epigenome of a cell is tightly correlated with gene transcription, which controls cell identity and diverse biological activities. Recent advances in spatial technologies have improved our understanding of tissue heterogeneity by analyzing transcriptomics or epigenomics with spatial information preserved, but have been mainly restricted to one molecular layer at a time. Here we present procedures for two spatially resolved sequencing methods, spatial-ATAC-RNA-seq and spatial-CUT&Tag-RNA-seq, that co-profile transcriptome and epigenome genome wide. In both methods, transcriptomic readouts are generated through tissue fixation, permeabilization and in situ reverse transcription. In spatial-ATAC-RNA-seq, Tn5 transposase is used to probe accessible chromatin, and in spatial-CUT&Tag-RNA-seq, the tissue is incubated with primary antibodies that target histone modifications, followed by Protein A-fused Tn5-induced tagmentation. Both methods leverage a microfluidic device that delivers two sets of oligonucleotide barcodes to generate a two-dimensional mosaic of tissue pixels at near single-cell resolution. A spatial-ATAC-RNA-seq or spatial-CUT&Tag-RNA-seq library can be generated in 3-5 d, allowing researchers to simultaneously investigate the transcriptomic landscape and epigenomic landscape of an intact tissue section. This protocol is an extension of our previous spatially resolved epigenome sequencing protocol and provides opportunities in multimodal profiling.

PMID:40119005 | DOI:10.1038/s41596-025-01145-9

Nat Methods. 2025 Mar 13. doi: 10.1038/s41592-024-02563-5. Online ahead of print.

ABSTRACT

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Human Reference Atlas (HRA) of the healthy adult body. Experts from 20+ consortia collaborate to develop a Common Coordinate Framework (CCF), knowledge graphs and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes and biomarkers) and to use the HRA to characterize changes that occur with aging, disease and other perturbations. HRA v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types and 2,089 biomarkers (such as genes, proteins and lipids) from 33 ASCT+B tables and 65 3D Reference Objects linked to ontologies. New experimental data can be mapped into the HRA using (1) cell type annotation tools (for example, Azimuth), (2) validated antibody panels or (3) by registering tissue data spatially. This paper describes HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interfaces, flexible hybrid cloud infrastructure and previews atlas usage applications.

PMID:40082611 | DOI:10.1038/s41592-024-02563-5

Nat Commun. 2025 Mar 5;16(1):2229. doi: 10.1038/s41467-025-57229-3.

ABSTRACT

Genomic instability and inflammation are distinct hallmarks of aging, but the connection between them is poorly understood. Here we report a mechanism directly linking genomic instability and inflammation in senescent cells through a mitochondria-regulated molecular circuit involving p53 and cytoplasmic chromatin fragments (CCF) that are enriched for DNA damage signaling marker γH2A.X. We show that p53 suppresses CCF accumulation and its downstream inflammatory phenotype. p53 activation suppresses CCF formation linked to enhanced DNA repair and genome integrity. Activation of p53 in aged mice by pharmacological inhibition of MDM2 reverses transcriptomic signatures of aging and age-associated accumulation of monocytes and macrophages in liver. Mitochondrial ablation in senescent cells suppresses CCF formation and activates p53 in an ATM-dependent manner, suggesting that mitochondria-dependent formation of γH2A.X + CCF dampens nuclear DNA damage signaling and p53 activity. These data provide evidence for a mitochondria-regulated p53 signaling circuit in senescent cells that controls DNA repair, genome integrity, and senescence- and age-associated inflammation, with relevance to therapeutic targeting of age-associated disease.

PMID:40044657 | PMC:PMC11882782 | DOI:10.1038/s41467-025-57229-3

Geroscience. 2025 Feb 25. doi: 10.1007/s11357-025-01568-y. Online ahead of print.

ABSTRACT

Cellular senescence gene sets have been leveraged to overcome the inadequate sensitivity or specificity of single markers. However, growing evidence of heterogeneity among tissues in senescent cell phenotypes and gene expression profiles has highlighted the need for tissue-specific gene sets. SenSkin™ was curated by an expert review of literature on cellular senescence in the skin and characterized with pathway analysis. To validate SenSkin™, it was evaluated for enrichment with chronological aging in a bulk RNA-sequencing (RNA-seq) dataset and a pseudobulk RNA-seq dataset. Further, changes to SenSkin™ in different skin cell types with photoaging were evaluated in two single-cell RNA-seq datasets. SenSkin™ predominantly included genes related to the senescence-associated secretory phenotype (SASP), which were associated with metabolism and multiple aspects of immune responses. SenSkin™ was more enriched in chronologically aged skin than other commonly used cellular senescence and aging gene sets. In scRNA-seq, SenSkin™ displayed significant upregulation due to photoaging in ten skin cell types. In conclusion, SenSkin™ is a human skin-specific senescence gene set validated in chronological aging and photoaging, which may be more effective at detecting senescent cells in the skin than non-tissue-specific gene sets.

PMID:39998731 | DOI:10.1007/s11357-025-01568-y

Anal Chem. 2025 Mar 11;97(9):5029-5037. doi: 10.1021/acs.analchem.4c05661. Epub 2025 Feb 24.

ABSTRACT

Human cerebral organoids have become valuable tools in neurodevelopment research, holding promise for investigating neurological diseases and reducing drug development costs. However, clinical translation and large-scale production of brain organoids face challenges due to invasive methodologies such as immunohistochemistry and omics that are traditionally used for their investigation. These hinder real-time monitoring of organoids and highlight the need for a nondestructive approach to promote resource-efficient production and standardization and enable dynamic studies for drug testing and developmental monitoring. Here, we propose a label-free methodology utilizing Raman spectroscopy (RS) and machine learning to discern cortical organoid maturation stages and to observe their biochemical variations. We validated the method's robustness by analyzing both pluripotent stem cell-derived organoids and embryonic stem cell-derived organoids, revealing also significant biochemical variability between the two. This finding paves the way for the use of RS for longitudinal studies to observe dynamic changes in brain organoids, offering a promising tool for advancing our understanding of brain development and accelerating drug discovery.

PMID:39993137 | PMC:PMC11912127 | DOI:10.1021/acs.analchem.4c05661