Biology subjects

Abila, E.

Publications and source records attributed to Abila, E..

4 recordsLinked to original sources

bioRxiv2025

Sequential PIDD1 auto-processing is essential for ploidy control in liver and heart

Polyploidization refers to the balanced increase in gene copy number and is a feature of specialized cells in different mammalian tissues, including the liver and the heart. During organogenesis, hepatocytes and cardiomyocytes undergo scheduled polyploidization events to increase their cellular or nuclear DNA content. This is thought to improve cellular output and enable for rapid genetic adaptation in response to stress. Yet, excessive increases in ploidy can also be disadvantageous and increase the risk of genome instability. Hence, a dedicated machinery, the PIDDosome multi-protein complex, has evolved to prevent exacerbated increases in DNA content. Using targeted mutagenesis in mice, we show that the PIDDosome controls hepatocyte ploidy in a cell-autonomous manner and that sequential and quantitative auto-processing of PIDD1 is key for accurate control of ploidy in postnatal development of liver and heart. Stoichiometric imbalances in bioactive PIDD1-fragments impair p53-dependent and independent cell cycle arrest responses during organogenesis, as well as caspase-2-dependent apoptosis caused by centrosome amplification. Strikingly, targeted mutagenesis of the caspase cleavage motif in the critical E3-ligase controlling p53 protein levels, Mdm2, impairs ploidy control in hepatocytes, but not in cardiomyocytes, indicative of the existence of alternative caspase-2 substrates that help to restrict ploidy in the heart.

cell biology↗

bioRxiv2025

The Spatial Atlas of Human Anatomy (SAHA): A Multimodal Subcellular-Resolution Reference Across Human Organs

The Spatial Atlas of Human Anatomy (SAHA) represents the first multimodal, subcellular- resolution reference of healthy adult human tissues across multiple organ systems. Integrating spatial transcriptomics, proteomics, and histological features across over 15 million cells from more than 100 donors, SAHA maps conserved and organ-specific cellular niches in gastrointestinal and immune tissues. High-resolution profiling using CosMx SMI, 10x Xenium, RNAscope, GeoMx DSP, and single-nucleus RNA-seq reveals spatially organized cell states, rare adaptive immune populations, and tissue-specific cell-cell interactions and ligand-receptor pairs. Comparative analyses with colorectal cancer and inflammatory bowel disease demonstrate the power of SAHA to detect disease-associated spatial disruptions, including crypt dedifferentiation, perineural invasion, and therapy-resistant immune remodeling. All data are openly accessible through a FAIR-compliant interactive portal to support exploration, benchmarking, and machine learning model training. Through SAHA, we provide a foundational framework for spatial diagnostics and next-generation precision medicine grounded in a comprehensive human tissue atlas, enabling the development of context-aware models that simulate tissue behavior, decode complex pathologies, and accelerate therapeutic innovation at unprecedented scale.

systems biology↗

bioRxiv2025

LazySlide: accessible and interoperable whole slide image analysis

Histopathological data are foundational in both biological research and clinical diagnostics but remain siloed from modern multimodal and single-cell frameworks. We introduce LazySlide, an open-source Python package built on the scverse ecosystem for efficient whole-slide image (WSI) analysis and multimodal integration. By leveraging vision-language foundation models and adhering to scverse data standards, LazySlide bridges histopathology with omics workflows. It supports tissue and cell segmentation, feature extraction, cross-modal querying, and zero-shot classification, with minimal setup. Its modular design empowers both novice and expert users, lowering the barrier to advanced histopathology analysis and accelerating AI-driven discovery in tissue biology and pathology.

bioinformatics↗

bioRxiv2024

Tissue clocks derived from histological signatures of biological aging enable tissue-specific aging predictions from blood

Aging, the leading risk factor for numerous diseases, manifests through diverse structural and architectural changes in human tissues, providing an opportunity to quantify and interpret tissue-specific aging. To address this, we present a comprehensive assessment of tissue changes occurring during human aging, utilizing a vast array of whole slide histopathological images from the Genotype-Tissue Expression Project (GTEx), primarily reflecting non-diseased tissue samples. Using deep learning, we analyzed 25,712 images from 40 distinct tissue types across 983 individuals, quantifying nuanced morphological changes that tissues undergo with age. We developed tissue clocks--predictors of biological age based on tissue images--which achieved a mean prediction error of 4.9 years. These clocks were associated with established aging markers, including telomere attrition, subclinical pathologies, and comorbidities. In a systematic assessment of biological age rates across organs, we identified pervasive non-uniform rates of aging across the human lifespan, with some organs exhibiting earlier changes (20-40 years old) and others showing bimodal patterns of age-related changes. We also uncovered several associations between demographic, lifestyle, and medical history factors and tissue-specific acceleration or deceleration of biological age, highlighting potential modifiable risk factors that influenced the aging process at the tissue level. Finally, by combining paired histological images and gene expression data, we developed a strategy to predict tissue-specific age gaps from blood samples. This approach was validated in independent cohorts covering eight diseases, ranging from acute conditions like stroke to chronic diseases such as cystic fibrosis and Alzheimers disease. It successfully recovered significant associations with disease-relevant organs and revealed patterns of systemic and tissue-specific aging that may reflect broader physiological changes in health and disease. This work offers a new perspective on the aging process by positioning tissue structure as an integrator of cellular and molecular changes that reflect the physiological state of organs in health and disease. It underscores the value of histopathological imaging as a tool for understanding human aging and provides a foundation for the monitoring of tissue-specific aging processes in age-associated diseases.

physiology↗