Hargreaves, R. | bioRxiv Science

bioRxiv2025

comBO: A combined human bone and lympho-myeloid bone marrow organoid for pre-clinical modelling of haematopoietic disorders

The bone marrow supports lifelong blood and immune cell production. Current human bone marrow organoid models do not include both lymphoid and myeloid elements and lack the complexity of stromal cell types present in native haematopoietic tissues, precluding the accurate ex vivo modelling of human pathologies. Here we introduce "comBOs" (combined bone and lympho-myeloid bone marrow organoids) that include osteolineage, vascular, lymphoid and myeloid cells. comBOs are generated by the differentiation of induced pluripotent stem cells guided by physiologically-relevant oxygen and cytokine exposures within an innovative granular microgel scaffold to increase scalability and reproducibility. We demonstrate that comBOs can be used to generate "chimeroids" - incorporating healthy or aberrant cells from adult donors - and recapitulate features of diseased microenvironments. ComBOs are one of the most physiologically-relevant human organoid systems to date, and this study showcases the potential of 3D in vitro disease models for discovery science and translational studies.

cancer biology↗

bioRxiv2024

Deep Learning Analysis on Images of iPSC-derived Motor Neurons Carrying fALS-genetics Reveals Disease-Relevant Phenotypes

Amyotrophic lateral sclerosis (ALS) is a devastating condition with very limited treatment options. It is a heterogeneous disease with complex genetics and unclear etiology, making the discovery of disease-modifying interventions very challenging. To discover novel mechanisms underlying ALS, we leverage a unique platform that combines isogenic, induced pluripotent stem cell (iPSC)-derived models of disease-causing mutations with rich phenotyping via high-content imaging and deep learning models. We introduced eight mutations that cause familial ALS (fALS) into multiple donor iPSC lines, and differentiated them into motor neurons to create multiple isogenic pairs of healthy (wild-type) and sick (mutant) motor neurons. We collected extensive high-content imaging data and used machine learning (ML) to process the images, segment the cells, and learn phenotypes. Self-supervised ML was used to create a concise embedding that captured significant, ALS-relevant biological information in these images. We demonstrate that ML models trained on core cell morphology alone can accurately predict TDP-43 mislocalization, a known phenotypic feature related to ALS. In addition, we were able to impute RNA expression from these image embeddings, in a way that elucidates molecular differences between mutants and wild-type cells. Finally, predictors leveraging these embeddings are able to distinguish between mutant and wild-type both within and across donors, defining cellular, ML-derived disease models for diverse fALS mutations. These disease models are the foundation for a novel screening approach to discover disease-modifying targets for familial ALS.

neuroscience↗