Development and Validation of Novel and Efficient Methods to Characterize Cellular Markers from Stem-Cell Derived Pancreatic Organoids and Their Translatability to Other Lineages

Document Type

Article

Publication Date

2026

JAX Location

NYSCF

Abstract

Type 2 diabetes (T2D) and Parkinson's disease together affect hundreds of millions of people worldwide. Both diseases require improved human-relevant disease models. Human induced pluripotent stem cell (hiPSC)-derived organoids offer a platform for studying these diseases, but confirming the presence of desired cell types before use remains a challenge. Flow cytometry, the current gold standard for organoid characterization, requires complete dissociation of organoids into single cells, leading to significant consumption of biological material. This study validated three immunofluorescence (IF)-based alternatives to flow cytometry. These methods include: whole-mount staining, cryosectioning, and two-dimensional replating. They are used for characterizing pancreatic and midbrain organoid cellular markers. Whole-mount IF successfully confirmed marker expression in both hiPSC-derived pancreatic organoids (NKX6.1, C-peptide, glucagon, somatostatin, PPY) and midbrain organoids (TUJ1, TH, LMX1A) while preserving intact organoid structure, but was limited by a dark, antibody-inaccessible organoid core. Cryosectioning resolved this limitation, revealing positive marker signal throughout the internal organoid structure while retaining the original tissue architecture. Two-dimensional replating similarly overcame the internal visualization limitation by providing full cellular access, while also enabling longitudinal tracking of neuronal maturation and cell density over time. The same protocols developed for pancreatic organoids were directly translatable to midbrain organoids with only the antibody panel requiring modification. These results establish a validated, adaptable immunofluorescence-based toolkit for confirming hiPSC-derived organoid identity across disease models.

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