Dalal, B. | bioRxiv Science

bioRxiv2026

Organoid-like Functional Adrenal Gland Cortex Derived From Human Pluripotent Stem Cells

The adrenal cortex is a critical for life endocrine system. It manages metabolic homeostasis, electrolyte balance, stress response, and sex development. This is accomplished through the release of various steroids from a dynamically changing landscape of concentric cellular zones/layers. Adrenocortical dysfunction is implicated in pathologies ranging from adrenal insufficiency to hypertension. The in-depth investigation of adrenal gland biology, pathology and drug discovery has been hampered by a lack of human, experimentally tractable models. Particularly missing are models that recapitulate the cellular diversity of the adrenal cortex with representation of all fetal and adult cell layers and the capsule. Here, we employ human pluripotent stem cells (hPSCs) to generate cells of all three cortex zones alongside capsular cells in a single 2D platform. This platform mimics the cellular diversity expected from an organoid, yet it provides the simplicity of monolayer cultures, that are better suited for drug discovery and high-throughput settings. These cultures secrete zone-specific steroids (cortisol, aldosterone, and DHEA-S) and exhibit robust, physiologically relevant ACTH stimulation responses. Transcriptomic analysis revealed sequential acquisition of profiles consistent with adrenocortical development, zonation, and signaling programs consistent with zone maintenance. Our platform is validated by a literature meta-analysis that defines transcriptomic signatures for each human cortical cell type, spanning fetal and adult stages. Together, this novel adrenocortical platform enables investigation of adrenal development, disease mechanisms, and therapeutic strategies. Significance StatementWe describe a hPSC-based 2D differentiation strategy with the cell type complexity of an organoid and the technical simplicity necessary for high-throughput assays. The platform contains all cortical subtypes that mediate electrolyte regulation, stress response, sex development and self-maintenance of the tissue. This co-differentiation offers a unique opportunity to study human adrenal development, biology, pathology and enables drug discovery.

developmental biology↗

bioRxiv2024

Federated deep learning enables cancer subtyping by proteomics

Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a Federated Deep Learning (FDL) approach (ProCanFDL), training local models on simulated sites containing data from a pan-cancer cohort (n=1,260) and 29 cohorts held behind private firewalls (n=6,265), representing 19,930 replicate data-independent acquisition mass spectrometry (DIA-MS) runs. Local parameter updates were aggregated to build the global model, achieving a 43% performance gain on the hold-out test set (n=625) in 14 cancer subtyping tasks compared to local models, and matching centralized model performance. The approachs generalizability was demonstrated by retraining the global model with data from two external DIA-MS cohorts (n=55) and eight acquired by tandem mass tag (TMT) proteomics (n=832). ProCanFDL presents a solution for internationally collaborative machine learning initiatives using proteomic data, e.g., for discovering predictive biomarkers or treatment targets, while maintaining data privacy. Statement of SignificanceA federated deep learning approach applied to human proteomic data, acquired using two distinct proteomic technologies from 40 tumor cohorts from eight countries, enabled accurate cancer histopathological subtyping while preserving data privacy. This approach will enable privacy-compliant development of large-scale proteomic AI models, including foundation models, across institutions globally.

cancer biology↗