In vitro methods to ensure absence of residual undifferentiated human induced pluripotent stem cells intermingled in induced nephron progenitor cells.
Hiraku TsujimotoNaoko KatagiriYoshihiro IjiriBen SasakiYoshifumi KobayashiAkira MimaMakoto RyosakaKenichiro FuruyamaYoshiya KawaguchiKenji OsafunePublished in: PloS one (2022)
Cell therapies using human induced pluripotent stem cell (hiPSC)-derived nephron progenitor cells (NPCs) are expected to ameliorate acute kidney injury (AKI). However, using hiPSC-derived NPCs clinically is a challenge because hiPSCs themselves are tumorigenic. LIN28A, ESRG, CNMD and SFRP2 transcripts have been used as a marker of residual hiPSCs for a variety of cell types undergoing clinical trials. In this study, by reanalyzing public databases, we found a baseline expression of LIN28A, ESRG, CNMD and SFRP2 in hiPSC-derived NPCs and several other cell types, suggesting LIN28A, ESRG, CNMD and SFRP2 are not always reliable markers for iPSC detection. As an alternative, we discovered a lncRNA marker gene, MIR302CHG, among many known and unknown iPSC markers, as highly differentially expressed between hiPSCs and NPCs, by RNA sequencing and quantitative RT-PCR (qRT-PCR) analyses. Using MIR302CHG as an hiPSC marker, we constructed two assay methods, a combination of magnetic bead-based enrichment and qRT-PCR and digital droplet PCR alone, to detect a small number of residual hiPSCs in NPC populations. The use of these in vitro assays could contribute to patient safety in treatments using hiPSC-derived cells.
Keyphrases
- induced pluripotent stem cells
- single cell
- patient safety
- acute kidney injury
- stem cells
- long non coding rna
- endothelial cells
- clinical trial
- cell proliferation
- real time pcr
- high throughput
- cell therapy
- high glucose
- healthcare
- quality improvement
- mental health
- cardiac surgery
- randomized controlled trial
- gene expression
- drug induced
- high resolution
- genome wide
- dna methylation
- mesenchymal stem cells
- big data
- bone marrow
- cell death
- wastewater treatment
- cell cycle arrest
- deep learning
- stress induced