技术资料

Respiratory organoids have emerged as a powerful in vitro model for studying respiratory diseases and drug discovery. However,the high-throughput analysis of organoid images remains a challenge due to the lack of automated and accurate segmentation tools. This study presents a semi-automatic algorithm for image analysis of respiratory organoids (nasal and lung organoids),employing the U-Net architecture and CellProfiler for organoids segmentation. The algorithm processes bright-field images acquired through z-stack fusion and stitching. The model demonstrated a high level of accuracy,as evidenced by an intersection-over-union metric (IoU) of 0.8856,F1-score = 0.937 and an accuracy of 0.9953. Applied to forskolin-induced swelling assays of lung organoids,the algorithm successfully quantified functional differences in Cystic Fibrosis Transmembrane conductance Regulator (CFTR)-channel activity between healthy donor and cystic fibrosis patient-derived organoids,without fluorescent dyes. Additionally,an open-source dataset of 827 annotated respiratory organoid images was provided to facilitate further research. Our results demonstrate the potential of deep learning to enhance the efficiency and accuracy of high-throughput respiratory organoid analysis for future therapeutic screening applications. Author summaryIn this study,we developed a semi-automated tool to analyze images of respiratory organoids—3D cell structures that mimic the human respiratory system. These organoids are vital for studying diseases like cystic fibrosis and testing potential drugs,but manually analyzing their images is time-consuming and prone to errors. Our tool uses artificial intelligence (AI) to quickly and accurately measure organoid size and shape from bright-field microscope images,eliminating the need for fluorescent dyes that can harm cells. We trained our AI model on a publicly shared dataset of 827 annotated organoid images,achieving high accuracy in detecting and quantifying organoids. When applied to cystic fibrosis research,the tool successfully measured differences in organoid swelling (forskolin-induced swelling - a key test for drug response) between healthy and patient-derived samples. By making our dataset and method openly available,we hope to support further research into respiratory diseases. Our work bridges the gap between complex lab techniques and practical applications,offering a faster,more reliable way to study human health and disease. View Publication

In stem cell biology,a long-held structure–function relationship is the domed colony morphology and naïve pluripotency for mouse or human pluripotent stem cells. This link has provided a convenient way to recognize bona fide naïve pluripotent cells during derivation,passaging and characterization. However,the molecular basis of this link remains poorly understood. Results: We show that a loss of domed morphology may not impact the overall genetic architecture of naïve pluripotency in mouse embryonic stem cells (mESCs). We first generated stable mESC lines by knocking out Myh9 that encodes non-muscle myosin heavy chain IIA,resulting in colonies deprived of the typical domed morphology,but competent to differentiate into the three germ layers and chimeric mice. Modulating cell morphologies with inhibitors against kinases known to regulate myosin pathway also phenocopy the knockout in wild type mESCs. Conclusions: These results provide evidence that the domed morphology and potency can be uncoupled and suggest that domed structure is not a pre-requisite for acquiring and maintaining naïve pluripotency. View Publication

Chimeric antigen receptor (CAR) T cell therapy has transformed the treatment landscape of hematologic cancers by engineering T cells to specifically target and destroy cancer cells. Monitoring CAR T cell activity and function is essential for optimizing therapeutic outcomes,but existing tools for CAR detection are often limited in specificity and functional assessment capability. Methods: We developed dextran multimers by conjugating multiple CAR-specific antigens to a dextran backbone. The multimers were compared to previously reported antigen tetramers for their ability to stain and detect CAR T cells. Because these multimers incorporate the CAR target antigen,they uniquely enable assessment of CAR T cell functionality. We tested the staining and functional properties of the multimers across a range of CAR constructs with different affinities,using flow cytometry and microscopy. Results: The dextran multimers demonstrated high specificity and sensitivity in staining CAR T cells,with adjustable antigen density to optimize binding. Dextran multimers also enabled effective clustering and subsequent activation of CARs,showing their utility as both a staining and functional assessment tool. The multimers revealed that CARs with different affinities and clustering tendencies displayed varied binding and activation in response to different antigen densities. Conclusions: Dextran multimers offer a dual advantage as versatile reagents for both staining and functional analysis of CAR T cells. Their capacity to engage CARs with the specific antigen provides a valuable platform for evaluating CAR functionality,informing CAR design improvements,and enhancing therapeutic precision. View Publication

MEF2C encodes a transcription factor that is critical in nervous system development. Here,to examine disease-associated functions of MEF2C in human microglia,we profiled microglia differentiated from isogenic MEF2C-haploinsufficient and MEF2C-knockout induced pluripotent stem cell lines. Complementary transcriptomic and functional analyses revealed that loss of MEF2C led to a hyperinflammatory phenotype with broad phagocytic impairment,lipid accumulation,lysosomal dysfunction and elevated basal inflammatory cytokine secretion. Genome-wide profiling of MEF2C-bound sites coupled with the active regulatory landscape enabled inference of its transcriptional functions and potential mechanisms for MEF2C-associated cellular functions. Transcriptomic and epigenetic approaches identified substantial overlap with idiopathic autism datasets,suggesting a broader role of human microglial MEF2C dysregulation in idiopathic autism. In a mouse xenotransplantation model,loss of MEF2C led to morphological,lysosomal and lipid abnormalities in human microglia in vivo. Together,these studies reveal mechanisms by which reduced microglial MEF2C could contribute to the development of neurological diseases. Coufal and colleagues generated microglia from human iPS cells to examine mechanistic roles of the transcription factor MEF2C and how these roles might relate to the autism phenotype seen following the loss of MEF2C in human microglia. View Publication