Journalists have a reputation of taking the juiciest parts out of context and publishing them. Science journalist and Big-Science groupie, Elizabeth Pennisi, did exactly the opposite. After...

Based on the notion that histology images capture architecture features critical for tissue function, we developed a computational framework called PathStAR (Fig. 1a–c), that quantify how and when this tissue structure changes during aging from high-resolution hematoxylin and eosin (H&E) images of large-scale post-mortem biopsies. PathStAR was applied on the GTEx cohort23, post-mortem biopsies from 970 nondiseased individuals (age range 21–70 years, 66.4% male donors; Extended Data Fig. 1), spanning 40 tissue types (Fig. 1d). GTEx provided a total of 25,306 organ biopsy whole-slide scans (×20 magnification), from which we extracted 30.3 million patch images (512 × 512 pixels). Pathology annotations from GTEx for these images confirmed canonical aging-related increase in atrophy, fibrosis, cysts and atherosclerosis and decrease in ovum, spermatogenesis and protective layers including mucosa, endometrium and myometrium (Supplementary Figs. 1 and 2), demonstrating quality to develop a systematic method. The tissue-specific quality control analysis is presented in Supplementary Note 1. PathStAR comprises three steps (see details in the Methods): (1) Step 1: feature extraction from whole-slide images. After background removal, each slide is segmented into patches, each encoded into a 1,024-dimensional embedding via UNI24, a vision transformer (ViT) pretrained on more than 100,000 whole-slide images. Mean pooling yields...

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Axy turns your papers into a living scientific knowledge graph — mapped at the concept level, so your lab can see the whole field and inherit what came before.

To study the impact of aging on tissue structure in humans, we used large-scale whole-slide histopathological images (WSIs) from 983 individuals in the Genotype-Tissue Expression (GTEx) project25,26,27 (Fig. 1a). The individuals profiled in this cohort died primarily from accidents, suicide or natural death, with ages ranging from 20 to 70 years (mean of 52.77 years; Supplementary Fig. 1a and Supplementary Table 1). Tissue was collected under a rapid autopsy protocol from a total of 40 different tissues in 29 organs, making up 25,713 WSIs (Supplementary Fig. 1b–e), which have been reviewed by pathologists and include a description of subclinical levels of pathology (Supplementary Fig. 1f–h), with demographic, lifestyle and clinical information also available (Supplementary Fig. 1i). Modern artificial intelligence vision models are powerful extractors of deep features from images (Fig. 1b) that can then be linked to information such as clinical outcomes21,22,23,24. For our aim, to numerically quantify the morphological features in these WSIs, we first fine-tuned vision models on a set balanced for tissue, sex and age brackets (Extended Data Fig. 1a, b) and then used them to extract a feature vector representing the morphological features of the WSI (Extended Data Fig. 1c). We observed that these features were...

Whole-slide histopathological images from 40 tissue types reveal morphological changes associated with aging, which, when paired with transcriptomic data from blood, are used to develop aging clocks t...

In 2010, the US National Institutes of Health launched an ambitious project: collect tissue samples from hundreds of deceased donors, sequence the RNA from each tissue, and build a comprehensive map of how genes are turned on and off across the human body. More than a decade later, the Genotype-Tissue Expression (GTEx) program has transformed

Take advantage of more than 3,000 publicly available genomics data sets spanning millions of samples in the free academic R2 data science and discovery platform. R2 is designed for biomedical scientis...

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