Genetic Variability Overrides the Impact of Parental Cell Type and Determines iPSC Differentiation Potential.
- Authors
- KyttΓ€lΓ€, Aija; Moraghebi, Roksana; Valensisi, Cristina; Kettunen, Johannes; Andrus, Colin; Pasumarthy, Kalyan Kumar; Nakanishi, Mahito; Nishimura, Ken; Ohtaka, Manami; Weltner, Jere; Van Handel, Ben; Parkkonen, Olavi; Sinisalo, Juha; Jalanko, Anu; Hawkins, R David; Woods, Niels-Bjarne; Otonkoski, Timo; Trokovic, Ras
- Year
- 2016
- Journal
- Stem cell reports
- PMID
- 26777058
- DOI
- 10.1016/j.stemcr.2015.12.009
- PMCID
- PMC4750096
Reports on the retention of somatic cell memory in induced pluripotent stem cells (iPSCs) have complicated the selection of the optimal cell type for the generation of iPSC biobanks. To address this issue we compared transcriptomic, epigenetic, and differentiation propensities of genetically matched human iPSCs derived from fibroblasts and blood, two tissues of the most practical relevance for biobanking. Our results show that iPSC lines derived from the same donor are highly similar to each other. However, genetic variation imparts a donor-specific expression and methylation profile in reprogrammed cells that leads to variable functional capacities of iPSC lines. Our results suggest that integration-free, bona fide iPSC lines from fibroblasts and blood can be combined in repositories to form biobanks. Due to the impact of genetic variation on iPSC differentiation, biobanks should contain cells from large numbers of donors.
iPSCs Derived from Fibroblasts and Blood Cells Are Transcriptionally Similar(A) Schematic representation of the study. Genetically matched iPSC were produced from fibroblasts and blood cells (PBMCs) from four female donors (T14, T42, T53, T55) using Sendai virus (SeVdp) mediated reprogramming. The cell lines used in this study are listed in Table 1. Fibroblast-derived iPSCs (F-iPS) and blood-derived iPSCs (B-iPS) are shown in black and red, respectively, throughout the figures.(B) PCA of global gene expression data of genetically matched F-iPSCs (n = 8) and B-iPSCs (n = 10) derived from four donors, two human embryonic stem cell lines (hESC), and somatic cells of origin (n = 4/4). Characterization of iPSC lines is presented in Figure S1.(C) Unsupervised hierarchical clustering of global DNA methylation profiles in genetically matched F-iPSC (n = 8) and B-iPSC (n = 10) lines, somatic cells of origin (n = 4/4), and two hESC lines (H1, H9) performed on a single-nucleotide level using reduced representation bisulfite sequencing.(D) Pairwise correlation of the genetically matched F- and B-iPSC lines for each donor (T14, n = 4; T42, n = 5; T53, n = 5; T55, n = 4) after local-pooled-error test. The entire list of genes for each donor is presented in Table S1. The direction of the correlation is visualized using thin lines inside boxes, and the magnitude of correlation using the colors. Darker color corresponds to the higher correlation. Isogenic iPSC lines derived from donor T42 display the lowest correlation.
Methylomic Analyses Demonstrate Minimal Contribution of Source-Cell-Specific Differences to iPSC Variability(A) Total number of donor-unique, differentially methylated cytosines (DMCs) between genetically matched iPSC lines derived from fibroblasts and blood cells. Analyses were performed for each donor (T14, n = 4; T42, n = 5; T53, n = 5; T55, n = 4). The table shows the number of DMCs per pairwise comparison as well as the number of hypermethylated CpGs and hypomethylated CpGs in B-iPSCs with respect to F-iPSCs and H1 hESCs with respect to H9. RRBS data for H1 hESCs were obtained from the ENCODE project (Meissner et al., 2008).(B) A heatmap representing methylation level of the 34 common DMCs across all pluripotent stem cells (n = 20) and somatic cell lines (n = 8). DMCs were annotated at the nearest transcription start site. ESC, embryonic stem cells; PBMC, blood cells; F-iPS, iPSCs derived from fibroblast; B-iPS, iPSCs derived from blood.(C) Aberrant methylation and somatic cell memory in genetically matched F- and B-iPSC lines (T14, n = 4; T42, n = 5; T53, n = 5; T55, n = 4) compared with embryonic stem cells (ESC). The bar chart represents the percentage of the total number of DMCs. The list of values can be found in (A). Donors are indicated above the bars.
Donor-Dependent Variability Affects Expression of Genes Related to Lineage Priming in iPSCs(A) Annotated heatmap showing expression of 167 genes across all cell lines (n = 34). The entire list of 167 genes can be found in Table S6. Individual cell lines used are indicated below the heatmap. The color bar on the right side demonstrates the log2 fold changes. B, blood cells; F, fibroblasts; EBs, embryoid bodies.(B) Donor-unique methylation signatures. The plot shows the distance of the donor-unique DMCs from the nearest transcription start site (TTS) in a Β±100-kb region. Genes (29) overlapping with donor-unique DMCs are listed. The density curve (black) shows the enrichment per position.(C) Verification of the genome-wide expression analysis with qPCR on selected genes (from A and B). Centerlines show the medians of MEG3, WNT3A, SOX17, and SNAI2 genes relative to human embryonic stem cell line (H9). Box limits indicate the 25th and 75th percentiles as determined by R software. Boxplots represent all iPSC lines for each donor (T14, n = 4; T42, n = 5; T53, n = 5; T55, n = 4; two technical replicates). Heatmaps from genome-wide expression analysis for each gene are shown below the boxplot.(D) Boxplots showing the mean values of gene expression (log2) of 167 genes (shown in A and listed in Table S6) in human embryonic stem cell lines (FES22, H9), and iPSC lines derived from four different donors (T14, n = 4; T42, n = 5; T53, n = 5; T55, n = 4).(Dβ²) Gene ontology analysis for the 167 genes analyzed using the PANTHER over-representation test. Bonferroni correction for multiple testing was applied. Only results with p < 0.05 are displayed.
Genetically Matched F- and B-iPSCs Have Similar Hematopoietic Differentiation Capacity(A) Representative FACS profiles of differentiated genetically matched F- and B-iPSC lines (F in black, B in red) from two donors. Green (T42) and blue (T55) colors mark iPSC lines derived from different donors. The plots show the percentage of hematopoietic cells (CD45+) and hematopoietic progenitors (CD45+ CD34+), immature (CD45+ CD43+), and more mature hematopoietic cells (CD45+ CD43β).(B) Assessment of variability resulting from cell type of origin. Percentages of hematopoietic cells, hematopoietic progenitors, and mature hematopoietic cells are shown in scatterplots.(C) Assessment of variability resulting from donor genetic background. Bar graphs show average percentage of hematopoietic cells, hematopoietic progenitors, and mature hematopoietic cells, differentiated from isogenic F- and B-iPSC lines. Donors are indicated in green (T42) and blue (T55). Values are mean Β± SEM. Statistics performed by Student's t test, in three or four biological replicates.
iPSCs Derived from Different Donors Show Variable Erythroid Lineage Differentiation Propensity(A) Representative micrographs showing the hematopoietic cells differentiated from iPSCs with reduced hemoglobinized erythroid cells in iPSCs derived from T42 (left panel). Scale bar, 100 ΞΌm.(B) Representative FACS plots of differentiated genetically matched F- and B-iPSC lines (n = 4) showing the percentage of erythroblasts (GPA+ CD71+ gated from CD45β CD33β).(C) Scatter graphs presenting percentage of erythroblasts generated from the genetically matched F- and B-iPSC lines. Each dot represents an independent experiment.(D) Percentage of erythroid colonies out of the total colony number generated from iPSC lines. Values are mean Β± SEM. Statistics performed by Student's t test, in three or four biological replicates.(E) Gene set enrichment plot and heatmap showing the genes constituting the core enrichment of overlap between multi-potent progenitors sorted from the bone marrow of DBA patients and iPSC lines derived from T42 cells. T42 iPSC lines show decreased expression levels of genes that are downregulated in Diamond-Blackfan anemia patients.
Implications for BiobankingiPSCs generated from genetically different donors show transcriptional and epigenetic variation, which is reflected in variable differentiation propensities. iPSC lines generated from genetically matched fibroblasts and blood cells are molecularly and functionally similar, implying that iPSCs derived from different tissues can be combined in repositories.
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| A Review of Research-Grade Human Induced Pluripotent Stem Cells Qualification and Biobanking Processes. | Ntai A et al. | β | 2017 | β |
| A Role for KLF4 in Promoting the Metabolic Shift via TCL1 during Induced Pluripotent Stem Cell Generation. | Nishimura K et al. | β | 2017 | β |
| Autologous Pluripotent Stem Cell-Derived Ξ²-Like Cells for Diabetes Cellular Therapy. | Millman JR et al. | β | 2017 | β |
| Capturing Human NaΓ―ve Pluripotency in the Embryo and in the Dish. | Zimmerlin L et al. | β | 2017 | β |
| Common genetic variation drives molecular heterogeneity in human iPSCs. | Kilpinen H et al. | β | 2017 | β |
| Differentiation-Defective Human Induced Pluripotent Stem Cells RevealΒ Strengths and Limitations of the Teratoma Assay and InΒ Vitro Pluripotency Assays. | Bouma MJ et al. | β | 2017 | β |
| Differentiation of Human Pluripotent Stem Cells into Mesodermal and Ectodermal Derivatives Is Independent of the Type of Isogenic Reprogrammed Somatic Cells. | Philonenko ES et al. | β | 2017 | β |
| Directly converted iNeuron as a screening model for pathogenic variants. | Lim SM et al. | β | 2017 | β |
| Divergent Levels of Marker Chromosomes in an hiPSC-Based Model ofΒ Psychosis. | Tcw J et al. | β | 2017 | β |
| Gene Editing and Human Pluripotent Stem Cells: Tools for Advancing Diabetes Disease Modeling and Beta-Cell Development. | Millette K et al. | β | 2017 | β |
| Genes Associated with Pancreas Development and Function Maintain Open Chromatin in iPSCs Generated from Human Pancreatic Beta Cells. | Thurner M et al. | β | 2017 | β |
| Integrating cardiomyocytes from human pluripotent stem cells in safety pharmacology: has the time come? | Sala L et al. | β | 2017 | β |
| Modeling of Autism Using Organoid Technology. | Choi H et al. | β | 2017 | β |
| Molecular and functional resemblance of differentiated cells derived from isogenic human iPSCs and SCNT-derived ESCs. | Zhao MT et al. | β | 2017 | β |
| Stem Cells for Modeling and Therapy of Parkinson's Disease. | Zhang Q et al. | β | 2017 | β |
| Term amniotic fluid: an unexploited reserve of mesenchymal stromal cells for reprogramming and potential cell therapy applications. | Moraghebi R et al. | β | 2017 | β |
| Transcriptomic and epigenomic differences in human induced pluripotent stem cells generated from six reprogramming methods. | Churko JM et al. | β | 2017 | β |
| Variability of human pluripotent stem cell lines. | Ortmann D et al. | β | 2017 | β |
| ZSCAN10 expression corrects the genomic instability of iPSCs from aged donors. | Skamagki M et al. | β | 2017 | β |
| Cell sources for inΒ vitro human liver cell culture models. | Zeilinger K et al. | β | 2016 | β |
| Genome-edited human stem cell-derived beta cells: a powerful tool for drilling down on type 2 diabetes GWAS biology. | Beer NL et al. | β | 2016 | β |
| Induced Pluripotent Stem Cells Meet Genome Editing. | Hockemeyer D et al. | β | 2016 | β |
| Insights into islet development and biology through characterization of a human iPSC-derived endocrine pancreas model. | van de Bunt M et al. | β | 2016 | β |
| Molecular Obstacles to Clinical Translation of iPSCs. | Tapia N et al. | β | 2016 | β |
| New tools for experimental diabetes research: Cellular reprogramming and genome editing. | Otonkoski T | β | 2016 | β |
| Novel Strategy to Control Transgene Expression Mediated by a Sendai Virus-Based Vector Using a Nonstructural C Protein and Endogenous MicroRNAs. | Sano M et al. | β | 2016 | β |
| Patient-derived induced pluripotent stem cells in cancer research and precision oncology. | Papapetrou EP | β | 2016 | β |
| Regulation of Human Pluripotent Stem Cell-Derived Hepatic Cell Phenotype by Three-Dimensional Hydrogel Models. | Toivonen S et al. | β | 2016 | β |
| STEM CELLS. Reprogrammed cells leave their past lives behind. | Nawy T | β | 2016 | β |
| Tankyrase inhibition promotes a stable human naΓ―ve pluripotent state with improved functionality. | Zimmerlin L et al. | β | 2016 | β |
| The silver lining of induced pluripotent stem cell variation. | Fossati V et al. | β | 2016 | β |
| Translational Prospects and Challenges in Human Induced Pluripotent Stem Cell Research in Drug Discovery. | Hosoya M et al. | β | 2016 | β |
| What Can hiPSC-Cardiomyocytes Teach Us about Modeling Complex Human Disease Phenotypes? | Broeckel U | β | 2016 | β |