Human iPSC-derived renal collecting duct organoid model cystogenesis in ADPKD.

In autosomal dominant polycystic kidney disease (ADPKD), renal cyst lesions predominantly arise from collecting ducts (CDs). However, relevant CD cyst models using human cells are lacking. Although previous reports have generated in vitro renal tubule cyst models from human induced pluripotent stem cells (hiPSCs), therapeutic drug candidates for ADPKD have not been identified. Here, by establishing expansion cultures of hiPSC-derived ureteric bud tip cells, an embryonic precursor that gives rise to CDs, we succeed in advancing the developmental stage of CD organoids and show that all CD organoids derived from PKD1-/- hiPSCs spontaneously develop multiple cysts, clarifying the initiation mechanisms of cystogenesis. Moreover, we identify retinoic acid receptor (RAR) agonists as candidate drugs that suppress in vitro cystogenesis and confirm the therapeutic effects on an ADPKD mouse model in vivo. Therefore, our in vitro CD cyst model contributes to understanding disease mechanisms and drug discovery for ADPKD.

Quality Control of Targeted Plasma Lipids in a Large-Scale Cohort Study Using Liquid Chromatography-Tandem Mass Spectrometry.

High-throughput metabolomics has enabled the development of large-scale cohort studies. Long-term studies require multiple batch-based measurements, which require sophisticated quality control (QC) to eliminate unexpected bias to obtain biologically meaningful quantified metabolomic profiles. Liquid chromatography-mass spectrometry was used to analyze 10,833 samples in 279 batch measurements. The quantified profile included 147 lipids including acylcarnitine, fatty acids, glucosylceramide, lactosylceramide, lysophosphatidic acid, and progesterone. Each batch included 40 samples, and 5 QC samples were measured for 10 samples of each. The quantified data from the QC samples were used to normalize the quantified profiles of the sample data. The intra- and inter-batch median coefficients of variation (CV) among the 147 lipids were 44.3% and 20.8%, respectively. After normalization, the CV values decreased by 42.0% and 14.7%, respectively. The effect of this normalization on the subsequent analyses was also evaluated. The demonstrated analyses will contribute to obtaining unbiased, quantified data for large-scale metabolomics.