Reprogramming of human peripheral blood mononuclear cells into … – Nature.com

OCT4 alone was insufficient to reprogram PBMCs into iMSCs directly

Previously, we reported that lentivirally expressed OCT4 could directly reprogram human cord blood CD34+ hematopoietic progenitor cells into iMSCs with very high efficiency11. Therefore, we first tried to convert human PBMCs into iMSCs by overexpressing OCT4 alone using a clinically relevant vector system. Isolated human PBMCs were cultured in a Stemline-based erythroid medium for six days to expand erythroid progenitors. Using the nucleofection method, 2106 expanded PBMCs were transfected with our modified oriP/EBNA1-based episomal vector, which expressed OCT4 under a strong SFFV promoter (Fig.1a), as we previously described11. Cells were then cultured in MSC medium11 supplemented with small molecules that promote reprogramming (3M CHIR99021, 10M forskolin, 10M ALK inhibitor (SB431542), and 5M tranylcypromine hydrochloride)15. However, there was no MSC-like colony formation 2 weeks later, indicating that OCT4 alone was insufficient to convert human PBMCs into iMSCs directly (Fig.1b).

a Schematic diagram of the episomal vector plasmids. SFFV is the spleen focus-forming virus U3 promoter; WPRE, posttranscriptional regulatory element; SV40PolyA, polyadenylation signal from SV40 virus; OriP, EBV (EpsteinBarr virus) origin of replication; EBNA1, EpsteinBarr nuclear antigen 1. b Colony formation at day 14 after nucleofection with 2106 PBMCs and maintenance in MSC culture conditions. c Reprogramming efficiency with different combinations of reprogramming factors. Error bars indicate standard deviation. n=3 biologically independent samples for each group. d Fluorescence-activated cell sorting (FACS) analysis of iMSCs 8 days after reprogramming with different factor combinations. SOX2 induced iPSCs generation (TRA-1-60+ cells). However, SOX9 did not induce detectable TRA-1-60+ cells. e Colony formation at day 14 after nucleofection with 1106 PBMCs (control) or CD34+-depleted PBMCs followed by maintenance in MSC culture conditions.

Our previous studies showed that BCL-XL is a critical reprogramming factor in blood cell reprogramming9,16, which increased the reprogramming efficiency by 10-fold when converting PBMCs into iPSCs using Yamanaka factors16. Here, we observed that transfection of PBMCs with OCT4, BCL-XL, and MYC (OBM) led to the formation of MSC-like colonies 2 weeks later (Fig.1b), albeit at low efficiency. The combination of any two of the OBM factors failed to generate iMSC colonies (Fig.1b). To improve the reprogramming efficiency further, we examined OBM with different combinations of other factors for generating iPSCs, including KLF4 and SOX2. KLF4 moderately improved iMSC generation, whereas SOX2 increased reprogramming efficiency by ~5-fold (Fig.1c and Supplementary Data1). However, the presence of SOX2 in the reprogramming cocktail resulted in ~12% of reprogrammed cells expressing iPSC markers, e.g., TRA-1-60 (Fig.1d) and NANOG (Supplementary Fig.1), even in MSC expansion culture conditions. Since iPSCs may induce teratomas, the SOX2-containing approach is not clinically prudent.

We decided to replace SOX2 with SOX9 because SOX9 plays an important role in skeletal development and chondrogenesis17,18. Surprisingly, SOX9 showed greater potency than SOX2 in iMSC reprogramming (Fig.1c). As expected, SOX9 virtually abolished the generation of TRA-1-60-expressing cells (Fig.1d). To ensure the absence of undetectable levels of iPSCs after reprogramming with SOX9, we cultured iMSCs in iPSC medium for 1 week. Phenotyping analysis of cultured cells showed no expression of iPSC markers. These data suggested that SOX9 restricted cell fate to iMSCs, whereas SOX2 would overshoot the reprogramming of a proportion of PBMCs beyond the stage of iMSCs. Moreover, after reprogramming with SOX9, PBMCs transformed morphologically to spindle-like cells resembling MSCs within 46 days, whereas SOX2-reprogrammed cells did not display spindle-like morphology (Supplementary Fig.2a).

Although PBMCs are composed of many different cell types, based on our previous studies3,16,19, we hypothesized that the CD34+ cell subset in peripheral blood was the most amenable to reprogramming to iMSCs. After six days of culture in hematopoietic stem cell expansion medium, the percentage of CD34+ cells in PBMCs increased from <1% to ~45%. When we depleted CD34+ cells from PBMCs before inducing reprogramming, no MSC-like colonies were observed (Fig.1e). These results suggested that the five reprogramming factors converted the CD34+ hematopoietic stem cells and progenitors but not the matureblood cells into iMSCs.

Having observed that the combination of OCT4, BCL-XL, MYC, KLF4, and SOX9 (named as 5F) induced the highest levels of PBMC conversion without overshooting the iMSC reprogramming process, we used the five factors (5F) for reprogramming in subsequent experiments. In all, 57 days after nucleofection of PBMCs with 5F, dozens of MSC-like colonies were observed. At approximately 2 weeks, reprogrammed cells resembled MSCs with typical spindle-like morphology (Fig.2a). The expression of MSC markers such as CD90 and CD73 increased from ~5% of reprogrammed cells by ~1 week to ~15% and 40% of the cells, respectively, by week 2 and >75% by week 3 (Fig.2b and Supplementary Data2). Four weeks after reprogramming with 5F, almost all cells expressed typical MSC markers: CD29 (99.7%), CD73 (95.3%), CD90 (96%), and CD166 (80%) (Fig.2c, d). The expression of hematopoietic markers such as CD45 and CD34 was negligible (Fig.2e). In addition, OCT4+ cells were not detectable (Supplementary Fig.3). Next, we evaluated the immunomodulatory potential of the iMSCs. We found that our 5F iMSCs were able to significantly suppress T-cell proliferation (CD4+ and CD8+ T-cell subsets) after 3 or 6 days (Fig.2f, Supplementary Fig.4a, and Supplementary Data3) co-culture with PBMCs. To further determine if the reprogramming to iMSCs or their expansion in culture may cause any chromosomal abnormalities, we performed digital karyotyping using SNP arrays. We did not identify any chromosomal abnormalities after either 1 week or 4 weeks of in vitro culture (Supplementary Figs.57). These data demonstrated that human PBMCs can be efficiently reprogrammed into iMSCs using our nonintegrating episomal vector system.

a Representative images of human PBMCs and iMSCs 14 days after reprogramming with five factors (5F). Scale bar represents 100m. b Changes in the percentage of cells expressing the MSC markers CD73 and CD90 as measured by flow cytometry of 5F-transfected PBMCs over time.c,d Flow cytometry plots of typical MSC marker expression (CD29, CD73, CD90, CD166)at 4 weeks after reprogramming. n=3 biologically independent samples for time point. eBlood cell markers (CD45 and CD34) were assessed 4 weeks after transfection of reprogramming factors. f iMSCs significantly inhibited T-cell proliferation after 3 days of co-culture with PBMCs. **P=0.0007. Error bars indicate standard deviation. n=3 biologically independent samples for each group.

To assess the essentiality of the five factors, we performed reprogramming by omitting a single factor in separate experiments. PBMCs from various donors were used. Surprisingly, we found that skipping OCT4, a critical factor for blood cell reprogramming, still allowed the generation of a considerable number of MSC-like colonies (Fig.3a and Supplementary Data4). In addition, PBMCs could be converted to iMSCs without KLF4, although at a ~35% decreased efficiency (Fig.3a). Omitting SOX9 not only significantly reduced the number of colonies formed but the reprogrammed cells were round in shape instead of spindle-like MSCs suggesting that SOX9 played a pivotal role in determining the MSC fate (Supplementary Fig.2b). By comparison, hardly any colonies were formed in the absence of BCL-XL or MYC. Taken together, SOX9, BCL-XL, and MYC were indispensable for reprogramming PBMCs into iMSCs.

a Reprogramming efficiency with the five-factor combination and removing one of the five factors. One-way ANOVA and Dunnetts multiple comparisons test, *P<0.05 vs. 5F group, ***P<0.001 vs. 5F group. ns: not significant. Error bars indicate standard deviation. n=5 for each group from biological independent donors. b Flow cytometry analysis of the MSC marker CD73 4 weeks after transfection with 5F, 4FnoO (no OCT4), and 4FnoK (no KLF4). c Flow cytometry analysis of the MSC markers CD73 and CD90 at 2, 3, and 4 weeks after transfection with 5F, 4FnoO (no OCT4), or 4FnoK (no KLF4). df RTqPCR analysis of osteogenesis-, adipogenesis-, and chondrogenesis-related genes in iMSCs reprogrammed with 5F, 4FnoO, and 4FnoK 2 weeks after multilineage differentiation. Tukeys multiple comparisons test, *P<0.05, 4FnoO vs. 5F and 4FnoK group. #P<0.05, 4FnoO vs. 4FnoK group. n=4 biologically independent samples for each group. Error bars indicate standard deviation (SD). g Multilineage differentiation of iMSCs reprogrammed with 5F, 4FnoO, or 4FnoK. Cells were cultured in osteogenic, adipogenic, or chondrogenic induction medium for 24 weeks and stained with Alizarin Red (osteogenesis), Oil Red O (adipogenesis), or Alcian blue (chondrogenesis), respectively. Scale bars represent 200m.

The iMSCs generated with the three different combinations of reprogramming factors, 5F, 4FnoO (5F minus OCT4), and 4FnoK (5F minus KLF4), were morphologically similar: they were all spindle-shaped, resembling MSCs (Supplementary Fig.2b). We evaluated the proliferation of the iMSCs generated from different conditions and compared it with primary human bone marrow MSCs (BMMSCs) (Supplementary Fig.2c). Primary human BMMSCs showed slowed proliferation after ~1 month in culture. The iMSCs reprogrammed from PBMCs displayed an enhanced in vitro proliferative capacity compared with BMMSCs. While the 5F iMSCs and 4FnoK iMSCs have similar proliferation ability, the 4FnoO iMSCs showed slower proliferation compared with the other two types of iMSCs (5F iMSCs and 4FnoK iMSCs). More than 100-fold more 5F iMSCs were generated than the human primary BMMSCs after ~1 month culture. In addition, >90% of the reprogrammed cells expressed the MSC marker CD73 (Fig.3b) 4 weeks after vector transfection. To monitor the reprogramming process in more detail, we evaluated the expression of the MSC markers CD73 and CD90 at 2-, 3-, and 4-week post-transfection (Fig.3c). We found that more than 60% of cells reprogrammed from either 5F or 4FnoK conditions became CD90+ by week 2, whereas only ~6% of cells from 4FnoO were CD90+, suggesting that OCT4 promoted the formation of CD90+ cells.

A characteristic feature of MSCs is the potential for trilineage differentiation into osteoblasts, adipocytes, and chondrocytes20. To assess the functionality of iMSCs reprogrammed with 5F, 4FnoO, or 4FnoK, we cultured iMSCs in three lineage-specific induction media, followed by RTqPCR analysis on the marker genes of osteogenesis, adipogenesis, and chondrogenesis.

The expression levels of runt-related transcription factor 2 (RUNX2), an early marker of osteogenic commitment, as well as the later osteogenic markers SP7 and alkaline phosphatase (ALP), were significantly decreased in the 4FnoO-reprogrammed iMSCs compared with 5F- or 4FnoK-reprogrammed iMSCs (P=0.01 and 0.03, respectively; Tukeys multiple comparisons test, Fig.3d and Supplementary Data5). To confirm the osteogenic commitment, we assessed calcium deposits by Alizarin Red S staining. Mineralization was observed in iMSCs reprogrammed with either 5F or 4FnoK but not in 4FnoO-reprogrammed iMSCs (Fig.3g).

Regarding chondrogenic differentiation, there was no significant difference in the expression of chondrogenic marker genes such as ACAN among the three groups (Fig.3e and Supplementary Data5). Alcian blue staining, which stains for aggrecans associated with MSC chondrogenic potential, also showed no significant difference among the three groups (Fig.3g). However, SOX9 expression was significantly reduced in 4FnoO iMSCs (4FnoO vs. 4FnoK, P=0.005). These data suggested that omitting OCT4 also impaired the chondrogenic differentiation potential of iMSCs. Taken together, these five factors were necessary for the generation of iMSCs with unbiased differentiation potential. Conversely, reprogramming without OCT4 led to the formation of dysfunctional iMSCs.

After the induction of adipogenic differentiation, lipoprotein lipase (LPL) and fatty acid-binding protein 4 (FADP4) were expressed at substantially lower levels in 4FnoO iMSCs than in either 5F or 4FnoK iMSCs (Fig.3f and Supplementary Data5). We used Oil Red O staining to visualize lipid droplets in functional adipocytes. Consistent with the adipogenic gene expression data, iMSCs reprogrammed without OCT4 failed to differentiate into functional adipocytes (Fig.3g). Of interest, omitting KLF4 led to the expression of higher levels of adipocyte markers and the formation of larger oil droplets, suggesting that KLF4 played a role in restricting adipogenic-biased MSCs.

To evaluate the immunomodulatory potentials of iMSCs reprogrammed with 5F, 4FnoO, or 4FnoK, we compared a list of major immunoregulatory cytokines, chemokines, and soluble factors secreted by MSCs21,22 using the normalized gene counts from the RNA-seq data (Supplementary Fig.4b and Supplementary Data6). We found that compared with 5F iMSCs, in addition to impaired trilineage differentiation potential, the 4FnoO iMSCs showed significantly reduced gene expression on many immunoregulatory cytokines/chemokines, such as IL-10, HGF, VCAM1, CCL2, CXCL14 (Supplementary Fig.4b). Both 5F and 4FnoK iMSCs showed comparable levels of immunoregulatory cytokines/chemokines gene expression compared to the primary human bone marrow-derived MSCs23.

To investigate the mechanisms underlying the distinct features of iMSCs reprogrammed with different factors (i.e., 5F, 4FnoO, and 4FnoK), we conducted transcriptome analysis 4 weeks after reprogramming factor transfection. We chose 4 weeks because >90% of the reprogrammed cells expressed MSC markers at this time point, and the nonintegrating episomal viral vectors were cleared from the reprogrammed cells7. First, we investigated the differentially expressed genes (DEGs) between the 5F, 4FnoO, or 4FnoK iMSCs. DEG analysis identified 827 significantly down- and 538 significantly upregulated genes in 4FnoO iMSCs compared to 5F iMSCs (FDR<0.05 and fold change (FC)>2, Fig.4a and Supplementary Data7). Of note, 5F and 4FnoK iMSCs showed similar transcriptomes with only 24 DEGs, consistent with their seemingly identical differentiation potentials (Supplementary Fig.8). Hierarchical clustering analysis identified a set of genes highly enriched in 5F and 4FnoK iMSCs, some of which were reported as MSC lineage signature genes, such as SRPX, S1PR3, ROBO2, NCAM1, COL5A1, and COL4A1 etc24,25,26 (Fig.4b and Supplementary Data7). Furthermore, the 4FnoO iMSCs displayed a significant decrease in the expression of mesoderm-regulating genes, including SOX4, SALL4, and TWIST1 (Supplementary Data7). We speculated that these downregulated genes might be associated with the impaired functionality of 4FnoO iMSCs. We then performed Gene Ontology (GO) enrichment analyses to explore the pathways associated with genes expressed at low levels in 4FnoO iMSCs. We found that 1365 DEGs were enriched in the biological processes of axonogenesis, extracellular structure organization, ossification, and cartilage development (Fig.4c). The top identified Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were the PI3K-Akt signaling and calcium signaling pathways (Fig.4d). These data helped explain the functional defects in osteogenesis of 4FnoO iMSCs and further understanding of the role of OCT4 in reprogramming PBMCs into iMSCs.

a Volcano plot showing differentially expressed genes identified in 4FnoO iMSCs compared with 5F iMSCs. Each dot represents a gene. The red dots are genes significantly upregulated (right) or downregulated (left) in 4FnoO iMSCs (Cutoff: P<10e6, fold change>2). b Heatmap showing the top 30 differentially expressed genes between 5F iMSCs and 4FnoO iMSCs (ranked by p-value). c, d Dot plots showing the top Gene Ontology (GO) biological process (BP) terms (c) and KEGG pathways (d) enriched from DEGs in 4FnoO iMSCs compared to 5F iMSCs. e PCA of RNA-seq from iMSCs 4 weeks after reprogramming with 5F, 4FnoO, or 4FnoK, primary human bone marrow-derived MSCs (BMMSC) and primary adipose-derived MSCs (AdMSC). For each condition, iMSCs were reprogrammed from PBMCs derived from three biologically independent donors. f Pearson correlation analysis of iMSCs and primary MSCs. g Comparison of twenty-four genes previously determined to be specific to the MSC lineage between primary MSCs and iMSCs.

To compare the iMSCs reprogrammed from PBMCs with primary human MSCs, we downloaded RNA-seq data generated from primary human bone marrow-derived MSCs (BMMSC)23 and primary human adipose-derived MSCs (AdMSC). First, we analyzed the transcriptional similarity of the iMSCs in our study to the primary human MSCs using principal component analysis (PCA) (Fig.4e). The reduction of the multi-dimensional dataset into two principal component (PC) dimensions enables the unbiased comparison and visualization of the transcriptomes between samples. As expected, the results showed that 4FnoO iMSCs were distinct from the other two iMSC groups (Fig.4e), consistent with the impaired differentiation potential of 4FnoO iMSCs when compared with 5F and 4FnoK iMSCs. The transcriptomes of human BMMSC and AdMSC were very similar to each other. Furthermore, the variation captured in PC1 demonstrated closer similarity of 5F and 4FnoK iMSCs with the primary MSCs compared to 4FnoO iMSCs, which tended to cluster further away from BMMSC and AdMSC (Fig.4e). Pearson correlation analysis confirmed that the 4FnoK and 5F iMSCs retained strong transcriptome correlation with the primary MSCs, while the 4FnoO iMSCs had less correlation with the primary MSCs (Fig.4f). A panel of 24 MSC lineage genes25,26 were compared between the primary MSCs and our iMSCs (Fig.4g). The 4FnoO iMSCs showed distinct expression patterns of these MSC signature genes that contrasted strongly with other groups. Noteworthy is that COL4A1, COL5A1, LOX, NNMT, which are known to be upregulated in MSCs versus fibroblasts24,27, were downregulated in 4FnoO iMSCs.

Genome-wide chromatin accessibility can provide mechanistic insights at the molecular level into cell fate decisions, especially during the reprogramming process. Thus, we performed ATAC-seq28 analysis on iMSCs 4 weeks after reprogramming PBMCs with 5F, 4FnoO, or 4FnoK. Open chromatin regions were identified as peaks in the ATAC-seq dataset. Furthermore, after peak calling, the relative genomic distribution of ATAC peaks showed reduced peaks within promoter regions in iMSCs generated without OCT4 (Fig.5a). In contrast, these cells had more open chromatin at intron regions. These results suggested that OCT4 may preferentially bind promoter regions to promote chromatin accessibility during reprogramming.

a Genomic location of ATAC-seq peaks from 5F, 4FnoO, and 4FnoK iMSCs. b PCA using normalized ATAC-seq counts from 5F, 4FnoO, and 4FnoK iMSCs, and two datasets from bone marrow-derived CD34+ cells (SRR2920489 and SRR2920490). For each condition, the chromatin accessibility was profiled from iMSCs that were reprogrammed from two biologically independent donors. c Heatmap showing ATAC-seq signals with the top 200 most different peaks (ranked by padj). Red represents chromatin regions with more mapped reads, suggesting possible chromatin openness. Gray represents chromatin regions with fewer mapped reads, suggesting closed chromatin. d Selected genomic views of the ATAC-seq data using IGV (2.8) for the indicated groups. For each gene, all genome views are on the same vertical scale. e The bar plot showing RNA-seq gene expression values for the respective genes shown above in the genome view. RNA-seq gene expression levels are shown as log2() normalized read counts. n=3 biologically independent samples for each group. *P0.05; error bars indicate standard deviation.

Similar to what was observed in the RNA-seq transcriptomic data, PCA of normalized ATAC-seq read counts showed that chromatin accessibility of three groups of iMSCs (5F, 4FnoO, and 4FnoK) were well-separated from each other, in which the accessible chromatin regions were mainly different in 4FnoO cells (PC1=52% variance, Supplementary Fig.9). However, in contrast to the similar transcriptomes between 5F and 4FnoK iMSCs (Supplementary Fig.8 and Fig.4a), ATAC-seq analysis showed that therewas aclear separation between 5F and 4FnoK iMSCs (PC2=19%, Supplementary Fig.9). These data suggested that both OCT4 and KLF4 facilitate chromatin remodeling during reprogramming. To compare the changes in chromatin accessibility during reprogramming, we downloaded the ATAC-seq data of primary CD34+ cells from bone marrow (SRR2920489, SRR2920490)29, which are similar to our reprogramming-initiating cells in this study. The datasets were processed using the same analysis pipeline. PCA revealed that CD34+ hematopoietic progenitor cells clustered separately from the three groups of reprogrammed iMSCs (Fig.5b), whereas 5F iMSCs and 4FnoK iMSCs were clustered closely with each other.

We also noticed that some chromatin regions remained closed in both CD34+ and 4FnoO iMSCs, whereas the same regions were in an open configuration in the 5F and 4FnoK iMSCs (Fig.5c). These data suggested that OCT4, but not KLF4, played a critical role in opening chromatin during the reprogramming process. More specifically, OCT4 opened the chromatin of the stemness-associated gene SALL4, Wnt signaling-related genes such as SFRP4, microtubule-binding and glutamate receptor binding-related genes JAKMIP2 and SYNDIG1, and MSC lineage signature gene NNMT (Fig.5d). These genes with reduced ATAC-seq peaks in 4FnoO iMSCs also showed significantly reduced mRNA expression, indicating a consistency between transcriptome and chromatin accessibility data (Fig.5e and Supplementary Data8).

DNA methylation is the most common epigenetic modification of the genome to control gene expression in mammalian cells30 and the differentiation or self-renewal of MSCs13. To determine the effects of reprogramming factors on methylation levels and patterns in iMSCs, we assessed genome-wide CpG methylation profiles in 5F, 4FnoO, and 4FnoK iMSCs at week four using RRBS. First, we profiled CpG methylation patterns on five different genomic features (all sites, promoters, exons, introns, and transcription start sites (TSSs) (Fig.6a, b and Supplementary Data9). We found that iMSCs reprogrammed without OCT4 showed a globally hypermethylated CpGs compared to iMSCs reprogrammed with OCT4 (Fig.6a, b). Specifically, when reprogramming in the absence of OCT4, we identified 10,760 differentially methylated cytosines (DMCs) (20%, q=0.1, Supplementary Data10), of which 9004 DMCs were hypermethylated and 1756 DMCs were hypomethylated (4FnoO vs. 5F). Among these sites, 7.7% were within promoter regions, and 7.9% werewithin exon regions (Fig.6c). In contrast, there was no significant difference in CpG methylation within all five genomics features in the iMSCs when reprogrammed in the absence of KLF4 (Fig.6a, b). Of the 3849 CpG sites significantly different (20%, q=0.1) between the 5F and 4FnoK groups, 3698 CpG sites were hypermethylated, and 151 sites were hypomethylated. When measuring the average methylation against the distance to the TSS, there was a global hypermethylation pattern in the iMSCs reprogrammed without OCT4 (Fig.6d, p<0.0001), suggesting that OCT4 was critical for global demethylation during reprogramming of PBMCs to iMSCs.

a The bar graph showing the methylation levels of all sites, promoters, exons, and intron regions from 5F, 4FnoO, and 4FnoK iMSCs. n=2 biologically independent samples for each group. b The methylation levels of the TSS region. n=2 biologically independent samples for each group. c The percentage of differentially methylated CpGs (DMCs) between 5F and 4FnoO iMSCs annotated within the promoter, exon, intron, and intergenic regions shown in the pie chart. d The average methylation levels surrounding the TSSs (5000 to +5000bp) in 5F, 4FnoO, and 4FnoK iMSCs. e Hierarchical clustering and heatmap analysis of 13,974 DMCs. f The bar plot showing the log2() normalized read counts from RNA-seq. n=3 biologically independent samples for each group. *P<0.05; error bars indicate standard deviation.

We performed hierarchical clustering on six RRBS datasets and generated a heatmap using the beta value of all common CpG sites. As expected, two datasets from 4FnoO clustered together, enriched a set of hypermethylated DMCs that were not observed in the 5F and 4FnoK datasets (Fig.6e). Since the cells reprogrammed from 5F and 4FnoK were very similar in their transcriptomes, chromatin openness, and methylation levels, we focused on our comparisons in the iMSCs programmed using 5F vs. 4FnoO. We annotated 10,760 DMCs and identified 665 differentially methylated genes (DMGs) between 5F and 4FnoO iMSCs (Supplementary Data10) which were subject to GO enrichment analysis (Supplementary Fig.10). Similar to the GO enrichment analysis based on RNA-seq data, DMGs were enriched in axonal guidance signaling and mesenchyme development. Of note, POU5F1, SALL4, NCAM1, HDAC4, and MSC lineage signature gene COL5A1 were significantly hypermethylated in iMSCs reprogrammed using 4FnoO compared with the iMSCs programmed using 5F (Supplementary Data10), suggesting that these genes might be associated with the impaired functionality in the 4FnoO iMSCs.

Demethylation may occur passively. DNMT1 is the most abundant DNA methyltransferase in mammalian cells and is considered the key methyltransferase responsible for DNA methylation maintenance, and its inhibition will result in passive demethylation. We found that the expression levels of DNMT1 in iMSCs reprogrammed with or without OCT4 were similar (Fig.6f and Supplementary Data8), suggesting minimal role of DNMT1 in OCT4-mediated demethylation. We then suspected that active DNA demethylation might have contributed to the global hypomethylation. Active DNA demethylation is mainly regulated by ten-eleven translocation (TET) enzymes31. We observed that the expression of TET1, but not TET2, was significantly reduced when reprogramming without OCT4 (Fig.6f), suggesting that TET1 might have contributed to OCT4-induced global demethylation. Meanwhile, the expression level of DNMT3B was significantly increased when reprogramming without KLF4, suggesting a role of KLF4 in regulating DNA methylation homeostasis via de novo DNA methyltransferase DNMT3B (Fig.6f).

To assess the influence of methylation on gene expression, we performed integration analysis of DMGs and DEGs datasets. We found the co-occurrence of 67 genes between 5F and 4FnoO iMSCs (Fig.7a and Supplementary Table1). Hypergeometric test was applied to show that the overlap is significant. Our analysis suggested that the observed difference in functionality between 5F and 4FnoO iMSCs might be a consequence of the difference in the methylation status of these 67 genes. Among these genes, ZFHX4, SLC8A2, NCAM1, TFPI2, and SALL4 were the most differentially expressed (Fig.7b). When PBMCs were reprogrammed without OCT4, not only were these genes significantly hypermethylated on either promoters or exons compared to PBMCs reprogrammed with OCT4 (Supplementary Data10), but some chromatin regions of these genes also remained inaccessible/closed (Fig.7c). Consistent with the hypermethylation of the four genes, their transcription levels were close to zero (Fig.7d and Supplementary Data8).

a Venn diagram illustrating the overlap between the differentially expressed genes (DEGs) and differentially methylated genes (DMGs) between 5F iMSCs and 4FnoO iMSCs. A total of 1365 DEGs and 665 DMGs were identified; 67 of these were both differentially expressed and differentially methylated. b Volcano plot showing 67 overlapping genes between the DEG and DMG. pCutoff=10e6, log2 FC>1). c Selected genomic views of the ATAC-seq data using IGV (2.8) for the indicated groups. For each gene, all genome views are on the same vertical scale. d The bar plot showing the RNA-seq gene expression values for the respective genes, which are shown above in the genome view. RNA-seq gene expression levels are shown as log2() normalized read counts. *, P<0.05; error bars indicate standard deviation. n=3 biologically independent samples for each group. e Heatmap showing the normalized gene read count after log2() transformation from RNA-seq.

ZFHX4, a transcription-related zinc finger protein involved in the mesodermal commitment pathway, is upregulated in both embryonic stem cell-derived and bone marrow (BM)-derived MSCs32,33. These reports, together with our findings, indicate that ZFHX4 may serve as an MSC marker. In addition, neural cell adhesion molecule (NCAM), also called CD56, is expressed on human MSCs and was proposed as a marker for human MSC isolation34,35. Also, CD56+ cells showed increased colony formation ability, suggesting CD56 expression enriches MSCs with self-renewal potency36. On the other hand, BM-MSCs from NCAM-deficient mice exhibited defective migratory ability and significantly impaired adipogenic and osteogenic differentiation potential37.

Many genes have been proposed as MSC surface marker genes, but no consensus has been reached yet. To screen possible trilineage differentiation function associated MSC markers, we compared ten well-established MSC surface markers between primary MSCs and our iMSCs (Fig.7e and Supplementary Fig.11). We found that other than NCAM1, four additional MSC surface markers (CD90, PDGFRB, CD82, and FZD5) were highly expressed in both primary MSCs and 5F/4FnoK iMSCs but downregulated in 4FnoO iMSCs (Fig.7e). Taken together, integrated analysis of multiomics data lead to the identification of putative functional MSC markers, and our dataset enables the mining for additional MSC surface markers that co-associate with functional potential.

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Novel microelectrode array system enables long-term cultivation and analyses of brain organoid – Medical Xpress

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A human brain organoid (colored red) grew on the hammock-like mesh structure of a Mesh-MEA (green) for one year. The scanning electron micrograph shows how the brain organoid has grown around the mesh filaments and microelectrodes. Credit: Max Planck Institute for Molecular Biomedicine

Brain organoids are self-organizing tissue cultures grown from patient cell-derived induced pluripotent stem cells. They form tissue structures that resemble the brain in vivo in many ways. This makes brain organoids interesting for studying both normal brain development and for the development of neurological diseases. However, organoids have been poorly studied in terms of neuronal activity, as measured by electrical signals from the cells.

A team of scientists led by Dr. Thomas Rauen from the Max Planck Institute for Molecular Biomedicine in Mnster, Germany, in collaboration with Dr. Peter Jones' group at the NMI (Natural and Medical Sciences Institute at the University of Tbingen, Germany), has now developed a novel microelectrode array system (Mesh-MEA) that not only provides optimal growth conditions for human brain organoids, but also allows non-invasive electrophysiological measurements throughout the entire growth period. This opens up new perspectives for the study of various brain diseases and the development of new therapeutic approaches.

The study is published in the journal Biosensors and Bioelectronics.

Nerve cells communicate through chemical signals (neurotransmitters), which are converted into electrical signals that pass information from one nerve cell to the next. This is also the way in which the neurons in the brain organoids communicate with each other.

"To find the causes of various brain diseases and new therapeutic approaches, it is not enough to simply look at nerve cells under the microscope. You also need to know how the nerve cells workhow they communicate with each other," says Thomas Rauen.

However, current systems for recording the communication between nerve cells in brain organoids have their limitations. In the relatively large brain organoids, the sensors either do not get close enough to the nerve cells or they destroy parts of the organoid tissue when they penetrate it.

Now, Dr. Thomas Rauen's team, in collaboration with Dr. Peter Jones' team, has developed a novel microelectrode array system (Mesh-MEA) that not only provides optimal growth conditions for human brain organoids, but also enables non-invasive electrophysiological measurements throughout the growth period of the brain organoids.

The scientists designed a kind of hammock for the brain organoids. "The hammock-like mesh structure provides 61 microelectrodes for electrophysiological measurements of neuronal network activity," explains Dr. Peter Jones.

The current study shows that brain organoids can not only be cultured on the newly developed Mesh MEA for up to one year but can also be continuously electrophysiologically analyzed during this period. "This is a great achievement because it allows us to study brain organoids for much longer than before. Normal human brain development takes a very long time, and neurodegenerative diseases also develop slowly," says Rauen.

The key to the current success is that the brain organoids enveloped the filaments and continued to grow on the spider web-like Mesh-MEA scaffold. Dr. Katherina Psathaki from CellNanOs at the University of Osnabrck was able to show this using an electron microscope. She analyzed brain organoids in their Mesh-MEA hammock one year after the start of cultivation.

"The images clearly confirm that the brain organoids develop in the suspended Mesh-MEA net structure. The microelectrodes are located in the center of the brain organoid tissue," adds Thomas Rauen.

The scientists observed spontaneous neuronal activity recorded by the microelectrodes in the brain organoids. "There was continuously recurring, synchronized neuronal activity throughout the recording phase, suggesting the formation of neuronal networks as seen in vivo," says Thomas Rauen.

Although brain organoids cannot represent all the functions of the human brain, Peter Jones and Thomas Rauen are convinced that the electrophysiological analysis of brain organoids using their newly developed Mesh-MEA system will open up the possibility of simulating specific functional aspects of human brain development and its diseases in the laboratory, which has not been possible until now.

More information: Matthew McDonald et al, A mesh microelectrode array for non-invasive electrophysiology within neural organoids, Biosensors and Bioelectronics (2023). DOI: 10.1016/j.bios.2023.115223

Journal information: Biosensors and Bioelectronics

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Novel microelectrode array system enables long-term cultivation and analyses of brain organoid - Medical Xpress

Tackling TDP43 proteinopathies – Nature.com

Cytoplasmic aggregates of the protein TDP43 are a feature of neurodegenerative diseases including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD) and Alzheimer disease, but the mechanisms linking TDP43 with neuropathology are not fully understood. A study in Science now shows that aberrant processing of stathmin 2 (STMN2) pre-mRNA, encoding a protein that promotes neuronal health and survival, is a key pathophysiological step downstream of aberrant TDP43 biology. Moreover, antisense oligonucleotides (ASOs) can be used to restore stathmin 2 expression in mouse models.

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doi: https://doi.org/10.1038/d41573-023-00056-2

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Tackling TDP43 proteinopathies - Nature.com

DUSP6 is a memory retention feedback regulator of ERK signaling … – Nature.com

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The synaptic hypothesis of schizophrenia version III: a master ... - Nature.com

Optimization of Cas9 activity through the addition of cytosine … – Nature.com

Cell culture

We cultured mESCs in t2iL medium containing Dulbeccos modified eagle medium (DMEM, Nacalai Tesque), 2mM Glutamax (Nacalai Tesque), 1 non-essential amino acids (Nacalai Tesque), 1mM sodium pyruvate (Nacalai Tesque), 100Uml1 penicillin, 100gml1 streptomycin (P/S) (Nacalai Tesque), 0.1mM 2-mercaptoethanol (Sigma) and 15% fetal bovine serum (FBS) (Gibco), supplemented with 0.2M PD0325901 (Sigma), 3M CHIR99021 (Cayman) and 1,000Uml1 recombinant mouse leukaemia inhibitory factor (Millipore)54. A higher PD0325901 concentration of 1M was used for the 2iL medium. mESC colonies were dissociated with trypsin (Nacalai Tesque) and plated on gelatin-coated dishes. Y-27632 (10M, Sigma) was added when cells were passaged. hiPSCs were cultured in mTeSR Plus medium (Veritas). hiPSC colonies were dissociated with Accutase (Nacalai Tesque) and plated on Matrigel-coated dishes (Corning, 3/250 dilution with DMEM). Y-27632 and 1% FBS were added when cells were passaged. WT hiPSCs (409B2, HPS0076) were provided by the RIKEN BioResource Research Centre (BRC)55. FOP hiPSCs (HPS0376) were provided by RIKEN BRC through the National BioResource Project of the Japan Ministry of Education, Culture, Sports, Science and Technology (MEXT) and the Agency for Medical Research and Development (AMED)43. Experiments using hiPSCs were approved by the Kyushu University Institutional Review Board for Human Genome/Gene Research. HEK293T cells and mouse embryonic fibroblasts were cultured in 10% FBS medium containing DMEM, 2mM l-glutamine (Nacalai Tesque), 100Uml1 penicillin, 100gml1 streptomycin (P/S) (Nacalai Tesque) and 10% FBS. hADSCs (Thermo Fisher) were cultured in MesenPRO RS medium (Thermo Fisher). Culture conditions of a HB-AIMS cell line are described in the Generation of AIMS cell lines and mice and AIMS analysis section. Cells were maintained at 37C and 5% CO2.

In this study, we used C57BL/6 mice (Clea Japan), ICR mice (Clea Japan) and R26RYFP/YFP mice (a gift from Frank Costantini at Columbia University, NY, USA)56. The experiments were approved by the Kyushu University Animal Experiment Committee, and the care and use of the animals were in accordance with institutional guidelines.

All primers, spacer linkers and ssODNs used in the present study are listed in Supplementary Table 3.

Mouse ES B6-5-2 and B6-D2-4 cell lines were established from E3.5 blastocysts of the C57BL/6 strain using 2iL and t2iL medium, respectively; an R26RYFP/+ mESC line was established using t2iL medium. Blastocysts were placed on feeders (mitomycin C-treated mouse embryonic fibroblasts) after removal of the zona pellucida. Inner cell mass outgrowths (passage number 0, p0) were dissociated with trypsin and plated on gelatin-coated plates (p1). After domed colonies formed, they were dissociated and passaged (p2). mESC lines were generated by repeating this procedure.

Knock-in (KI) template plasmids for Cdh1-AIMS were generated by attaching the 5 and 3 arms to plasmids containing P2A1:Venus or P2A1:tdTomato cassettes. P2A1 is identical to a widely used P2A sequence26. The 5 arm was designed such that the coding end was fused in-frame to the P2A sequence to allow independent production of both E-cadherin (CDH1) and fluorescence protein. KI plasmids for Tbx3-AIMS were constructed using the same strategy. The alternative P2A sequence P2A2 was constructed by introducing silent mutations to each codon of the original P2A sequence. The conventional CRISPR-Cas9 system was used to efficiently knock-in the dual-colour plasmids in a pair of alleles. A spacer linker was designed to induce a DSB downstream of the stop codon, then inserted into the BpiI sites of a pSpCas9(BB)-2A-Puro (PX459) V2.0 plasmid (Addgene, 62988; see the Plasmid construction section)57. All sgRNAs used in this study were designed using the CRISPR DESIGN (http://crispr.mit.edu/) or CRISPOR tool (http://crispor.tefor.net).

The constructed all-in-one CRISPR plasmids and dual-coloured KI plasmids were co-transfected into mESCs using Lipofectamine 3000 (Thermo Fisher). Dissociated mESCs were plated on gelatin-coated 24-well plates with 500l of (t)2iL+Y-27632 medium ((t)2iL+Y). Nucleic acidLipofectamine 3000 complexes were prepared in accordance with the standard Lipofectamine 3000 protocol. We added 1l of Lipofectamine 3000 reagent to 25l Opti-MEM medium; simultaneously, 250ng of each plasmid (all-in-one, Cdh1-P2A-tdTomato and Cdh1-P2A-Venus plasmid) plus 1l of P3000 reagent were mixed with 25l of Opti-MEM medium in a different tube. These mixtures were combined and incubated for 5min at room temperature, then added to the 24-well plate immediately after cells were seeded. At 24h after transfection, puromycin (1.5 or 2gml1) was added for 2d and then washed out. The transiently treated puromycin-resistant cells were cultured for several days; dual-colour-positive colonies were picked and passaged. Genotypes for the candidate dual KI clones were confirmed by PCR. In this study, transfection experiments for mouse and human cells were performed using this procedure, with passage steps added for an AIMS assay to avoid mosaicism (Fig. 1d). Fluorescence microscopes (BZ-X800 (Keyence) and IX73 (Olympus)) were used to analyse the AIMS data. To extract genomic DNA for clonal sequence analysis, single mESC and hiPSC colonies were suspended in 510l 50mM NaOH (Nacalai Tesque) and incubated at 99C for 10min. PCR was performed using the template genomic DNA, and the amplicons were sequenced by Sanger sequencing.

For generation of AIMS mice, the established dual KI mESC clone (Cdh1-P2A1-tdTomato/Venus AIMS) was dissociated with trypsin and 58 cells were injected into 8-cell embryos (E2.5) collected from pregnant ICR mice. Injected blastocysts were transferred into the uteri of pseudo-pregnant ICR mice and chimaeras were generated. Male chimaeras were mated with C57BL/6 females, and Cdh1-P2A1-tdTomato and Cdh1-P2A1-Venus KI mouse lines were obtained through germline transmission. After the two genotype mice were mated, homozygous AIMS mice were generated.

HB-AIMS cells were established from the E12.5 dual KI embryos according to the protocol of a previous work58 with some modifications. Briefly, the whole liver was mechanically dissociated and filtrated, and the dissociated cells were seeded onto a type I collagen-coated plate (Iwaki) with the HB medium. The HB medium is composed of a 1:1 mixture of DMEM and F-12 (Nacalai Tesque), supplemented with 10% FBS (Gibco), 1gml1 insulin (Wako), 0.1M dexamethasone (Sigma-Aldrich), 10mM nicotinamide (Sigma-Aldrich), 2mM l-glutamine (Nacalai Tesque), 50M -mercaptoethanol (Nacalai Tesque), 20ngml1 recombinant human hepatocyte growth factor (rhHGF) (PeproTech), 50ngml1 recombinant human epidermal growth factor (rhHGF) (Sigma), penicillin/streptomycin (Nacalai Tesque), and small molecules of 10M Y-27632 (Wako), 0.5M A8301 (Tocris) and 3M CHIR99021 (Tocris). After expansion of HBs, a single-cell-derived HB colony with homogeneous expression of tdTomato and Venus was picked and established as an HB-AIMS cell line.

To generate all-in-one CRISPR plasmids for [5C](3A), [10C](8A), [15C](13A), [20C](18C), [25C](23A) and [30C](28A)sgRNA expression, spacer linkers were inserted into the BpiI sites of a PX459 plasmid (Extended Data Fig. 2b). In the plasmids, the 3rd, 8th, 13th, 18th, 23rd or 28th cytosine was replaced with adenine because the overhang sequence of CACC is required for linker ligation. The standard spacer linkers (20nt) or longer spacer linkers (30nt or 40nt) were inserted into the BpiI sites of the [0C], [5C](3A), [10C](8A), [15C](13A), [20C](18A), [25C](23A) or [30C](28A) PX459 plasmid, leading to generation of [5C][30C]sgRNA-expressing all-in-one Cas9 plasmids applicable for puromycin selection. The same [C] linkers were also inserted into the BpiI sites of a PX458 plasmid (Addgene, 62988)57 for selection of GFP-positive transfected cells.

For the plasmid dilution assay, sgRNA-expressing plasmid was constructed by removing a Cas9-T2A-Puro cassette from a PX459 plasmid using the KpnI and NotI sites. Different amounts of sgRNA-expressing plasmid (0250ng) were co-transfected with an unmodified PX459 plasmid (250ng). In addition, [5C][30C] linkers including BpiI sites were inserted into this sgRNA-expressing plasmid to construct [5C][30C]sgRNA-expressing plasmids, which were used for the experiments of CRISPRa (Extended Data Fig. 4e) described below.

For the CRISPR inhibition experiments, the pCMVAcrIIA4 plasmid was generated from the anti-Cas9 AcrIIA4-expressing pCMV+AcrIIA4 plasmid, pCMV-T7-AcrIIA4-NLS(SV40) (KAC200) (Addgene, plasmid 133801)59, by truncating the AcrIIA4 cassette using the NotI and AgeI sites.

For the CRISPRi experiments, the [5C][30C] linkers including BsmBI sites were inserted into the BsmBI sites of an LV hU6-sgRNA hUbC-dCas9-KRAB-T2a-Puro (sgRNA-KRAB-Puro) plasmid (Addgene, 71236)60 to construct [C]sgRNA-expressing all-in-one CRISPRi plasmids. The sgRNA spacers targeting BRCA1 and CXCR4 used in previous studies61 were inserted into the BsmBI sites of the all-in-one plasmids. A puromycin-selectable all-in-one plasmid for CRISPRa was constructed by replacing a GFP cassette of a pLV hU6-gRNA(anti-sense) hUbC-VP64-dCas9-VP64-T2A-GFP (sgRNA-VP64-GFP) plasmid (Addgene, 66707) with a puromycin N-acetyl transferase (PuroR) cassette. A synthetic gene encoding VP64-T2A-PuroR (AZENTA) (Supplementary Table 3) was inserted into the sgRNA-KRAB-GFP plasmid using NheI and AgeI sites, resulting in an sgRNA-VP64-Puro plasmid. In Fig. 4e, the [1C][10C] spacer linkers for targeting ASCL162 were inserted into the sgRNA-VP64-Puro plasmid. In Extended Data Fig. 4e, spacer linkers for targeting ASCL1 and TTN62 were inserted into the BpiI sites of the [0C][30]sgRNA-expressing plasmids, and then they were co-transfected with the spacerless all-in-one CRISPRa plasmid.

To construct all-in-one AsCpf1 plasmids enabling puromycin selection, a synthetic DNA fragment encoding U6 promoter and two BpiI sites (AZENTA) (Supplementary Table 3) was inserted into a PX459 plasmid while removing a U6-gRNA cassette using PciI and XbaI sites. Next, a CBh-Cas9 region of the crRNA-Cas9-puro plasmid was replaced with a CBh-AsCpf1 fragment digested from a pY036_ATP1A1_G3_Array plasmid (Addgene, 86619)63 using KpnI and FseI, resulting in the construction of an all-in-one crRNA-AsCpf1-puro plasmid (PX459 plasmid backbone). The crRNA linkers (Supplementary Table 3) targeting P2A2 sites of AIMS are composed of 5 hairpin, 20nt-spacer and U4AU4 3-overhang, which is known to increase editing efficiency of AsCpf1 (ref. 64), and they were inserted into the BpiI sites of the crRNA-AsCpf1-puro plasmid.

pSpCas9(BB)-2A-Puro (PX459) V2.0 (Addgene, plasmid 62988; http://n2t.net/addgene:62988; RRID: Addgene_62988) and pSpCas9(BB)-2A-GFP (PX458) (Addgene, plasmid 48138; http://n2t.net/addgene:48138; RRID: Addgene_48138) were gifts from Feng Zhang. The pY036_ATP1A1_G3_Array was a gift from Yannick Doyon (Addgene, plasmid 86619; http://n2t.net/addgene:86619; RRID: Addgene_86619). pLV hU6-sgRNA hUbC-dCas9-KRAB-T2a-Puro was a gift from Charles Gersbach (Addgene, plasmid 71236; http://n2t.net/addgene:71236; RRID: Addgene_71236). pLV hU6-gRNA(anti-sense) hUbC-VP64-dCas9-VP64-T2A-GFP was a gift from Charles Gersbach (Addgene, plasmid 66707; http://n2t.net/addgene:66707; RRID: Addgene_66707). pCMV-T7-AcrIIA4-NLS(SV40) (KAC200) was gifted by Joseph Bondy-Denomy and Benjamin Kleinstiver (Addgene, plasmid 133801; http://n2t.net/addgene:133801; RRID: Addgene_133801)59.

To detect sgRNAs complexed with Cas9, 1l of Cas9 (1M) (Alt-R S.p. Cas9 Nuclease V3, IDT) and 1l of synthetic sgRNAs (3M, 1M or 0.3M; IDT) were mixed with 8l of distilled water (total reaction volume of 10l) and reacted on ice for 30min. Samples were loaded onto Bullet PAGE One Precast gels (6%) (Nacalai Tesque) in Tris-borate-ethylenediaminetetraacetic acid (Tris-Borate-EDTA) buffer. RNA was transferred to a Hybond N+ membrane (GE Healthcare) and cross-linked using CX-2000 (Analytik Jena). An sgRNA tracer probe was labelled with an alkali-labile digoxigenin (DIG)-11-deoxyuridine triphosphate (dUTP) using a PCR DIG Probe Synthesis kit (Roche); DNA fragments were amplified using PCR and primers (Supplementary Table 3). After hybridization, specific bands were visualized with the CDP-Star reagent (Roche) using a luminescent image analyser (LAS-3000, FUJIFILM).

To detect DNA fragments complexed with sgRNA-dCas9, we mixed 1l of dCas9 (1M) (Alt-R S.p. dCas9 Nuclease V3, IDT) and 1l of synthetic sgRNAs (1M; IDT) with distilled water for a final reaction volume of 10l, then reacted the mixture at room temperature for 10min. After the reaction, the RNP complex was mixed with 100ng of DNA fragment and 1l of 10 Cas9 reaction buffer (1M HEPES, 3M NaCl, 1M MgCl2 and 250mM EDTA (pH 6.5)), then reacted at room temperature for 10min. The resulting 10l samples were loaded onto 2% agarose gels in Tris-acetate-EDTA buffer; DNA bands were detected by staining with ethidium bromide. The target DNA fragment (647bp) was prepared by PCR amplification from a Tbx3-P2A1-Venus KI plasmid using primers (Supplementary Table 3).

The sgRNA-Cas9-DNA complex was formed using most of the gel shift assay procedure, although its formation also included Cas9 and 3M of synthetic sgRNA. The samples were reacted at 37C for 90min, denatured at 70C for 10min and loaded onto Bullet PAGE One Precast gels (6%) (Nacalai Tesque).

A 20l sgRNA-Cas9-DNA complex was prepared via the procedure used in the gel shift assay. A cleavage reaction was performed at 37C for 30min; a 10l volume was kept on ice while the other 10l volume was denatured at 70C for 10min. The products were loaded onto 2% agarose gels.

Total RNAs were extracted from mESCs at 68h after transfection with P2A1-[C]sgRNA1-PX459 plasmids. Transfected cells were selected by 2d of treatment with puromycin (1.5gml1), then resuspended with ISOGEN II (NIPPON GENE). The samples were incubated for 10min at room temperature, then heated at 55C for 10min. Total RNA was isolated following the manufacturers protocol. After reaction at 70C for 10min, 30g RNAs were loaded onto Extra PAGE One Precast gels (520%) (Nacalai Tesque) in Tris-borate-EDTA buffer. RNA transfer, DIG-probe hybridization and signal detection were performed following the procedure used in the gel shift assay. The DIG probe was labelled by PCR amplification of the DNA fragment (primers shown in Supplementary Table 3). The mU6 DIG-probe was prepared by amplifying the DNA fragment from mESC complementary DNA using specific primers (Supplementary Table 3). cDNA was synthesized using a specific primer that targeted U6 small nuclear RNA65.

Template DNA fragments required for IVT were amplified from a P2A1-gRNA1-PX459 plasmid by PCR (primers shown in Supplementary Table 3). The T7 promoter sequence and cytosine tails were added to the 5-end of the forward primer. We synthesized [0C], [10C] and [25C]sgRNAs using the T7 RiboMAX Express large-scale RNA production system (Promega) following the manufacturers protocol.

FIJI software was used to quantify band signals for the gel shift, DNA cleavage and northern blot assays.

The PX458-based all-in-one plasmids (250ng) for targeting VEGFA1 gene were transfected into hADSCs using Lipofectamine 3000 upon 80% confluency. Immediately after adding the plasmid:Lipofectamine mixture into the cells, the plates were centrifuged at 700g at 35C for 10min to increase transfection efficiency. The cells were cultured for 7d without passaging to allow continuous expression of the plasmid, and then GFP-positive single cells were picked using a hand-made capillary and transferred to PCR tubes (1 cell per tube). To enable sequence analysis for a pair of alleles from a single cell, whole genomic DNA were amplified using PicoPLEX (TAKARA) according to the manufacturers instructions. The genomic locus targeted by Cas9 was amplified by PCR using primers (Supplementary Table 3) and the PCR amplicons were sequenced.

At 24h after transfection with the all-in-one Cas9 plasmid, mESCs were treated with the Cas9 inhibitor BRD0539 (TOCRIS) during puromycin selection and subsequent culture until analysis.

pCMV+AcrIIA4 plasmid was co-transfected with 250ng of the all-in-one Cas9 plasmid in different amounts (2.52,500ng for 24-well plates). For the BRD0539 and AcrIIA4 experiments, puromycin selection and indel analysis were performed using the same procedure as described above (Generation of AIMS cell lines and mice and AIMS analysis section) and in Fig. 1d.

A day before transfection, 3104 HEK293T cells were seeded onto a 96-well plate. The all-in-one CRISPRa/i plasmids (50ng, 1/5 scale of the 24-well plate version) were transfected and cultured for 24h. Then, puromycin (5.0gml1) was treated for 2d to exclude untransfected cells. After removal of puromycin, the transfected cells were cultured for 1d and 2d for CRISPRa and CRISPRi, respectively, and total RNAs were extracted using ISOGEN II as described above (Northern blotting section).

The cDNAs were synthesized from total RNAs using SuperScript III Reverse Transcriptase (Thermo Fisher) according to the manufacturers instructions. RTqPCR was conducted using a THUNDERBIRD SYBR qPCR Mix (Toyobo) and CFX Connect real-time PCR detection system (BIO RAD) according to the manufacturers instructions. Primers for ASCL1, TTN, BRCA1 and CXCR4 used in previous studies61,62, and for GAPDH are listed in Supplementary Table 3. The values for GAPDH were used as normalization controls.

A Tbx3-P2A1-tdTomato KI plasmid was co-transfected with Tbx3-sgRNA1-expressing PX459 to the mESCs. After transient puromycin selection, colonies were dissociated and passaged; the resulting colonies were analysed. Colonies with mosaic tdTomato expression were excluded from data analysis. After the colonies had been counted, positive tdTomato colonies were selected and genomic DNA was extracted for sequencing.

The neomycin (Neo) KI plasmid was constructed by replacing the tdTomato cassette of the Tbx3-P2A1-tdTomato KI plasmid with a P2A1-Neo cassette. The KI plasmid was co-transfected with P2A1 sgRNA1-expressing PX459 to a Tbx3-P2A1-AIMS clone. When puromycin was removed, geneticin (400gml1, Gibco) was added to select KI clones. All eight clones were confirmed to possess KI genotypes; geneticin-resistant colonies were identified as KI.

PCR reactions to amplify specific on-target or off-target sites were performed using KOD-Plus-ver.2 DNA polymerase (Toyobo) in accordance with the manufacturers protocol. The resulting PCR amplicons were denatured and re-annealed in 1 NEB buffer 2 (NEB) in a total volume of 9l under the following conditions: 95C for 5min, reduction from 95C to 25C at a rate of 0.1Cs1 and indefinite incubation at 4C. After re-annealing had been performed, 1l of T7 endonuclease I (NEB, 10Ul1) was added and the product was incubated at 37C for 15min.

Purified PCR products to amplify specific on-target or off-target sites were inserted into a T-easy vector (Promega) and transformed into DH5- bacterial cells. For rapid and efficient indel detection, plasmids were directly isolated from each white colony after blue/white screening; the inserted DNA fragment was amplified by PCR. The PCR amplicons were mixed with PCR products amplified from a WT DNA template such as KI plasmid or unedited genomic DNA; a T7E1 assay was then performed. Sanger sequencing was also performed for PCR amplicons that were not digested by T7E1 to determine the total number of colonies that harbour indels. The Bac[P] value was calculated as follows: Bac[P]=Indel/Total.

Bac[P] values for both WT and R206H alleles were determined through indel induction experiments using various [C]sgRNAs in the mESC clone of the FOP model. The targeting sites of both WT and R206H alleles were amplified by PCR, then cloned into a T-easy vector. Sanger sequencing was performed for each PCR product that had been derived from single bacterial clones, as described above. Similarly, Bac[P] values for both R206H (pf) and WT (1mm) alleles were determined by inducing indels in FOP hiPSCs; a corrected cell line (WT/Corrected) was used to determine the Bac[P] value of the corrected allele (2mm). Some PCR products did not contain a G/A hallmark because of intermediate-sized deletions (12~50 nucleotides); it was therefore impossible to determine which allele was edited for these PCR products. We observed that the fraction of such products with intermediate-sized deletions was generally constant (~20% in experiments shown in Fig. 6 and 1020% in experiments shown in Fig. 7) and did not decrease with [C] extension, suggesting that such intermediate-sized deletions are byproducts of the short indel induction processes. Therefore, we assigned products with intermediate-sized deletions to two alleles using the ratio of PCR products with convincingly confirmed origins. For the analysis shown in Fig. 7, we calculated the means of Bac[P] for WT (1mm) alleles on the basis of comparisons of R206H (pf) to WT (1mm) alleles and WT (1mm) to corrected (2mm) alleles for subsequent computational analyses.

Using the transfection protocol described above (Generation of AIMS cell lines and mice and AIMS analysis), 2105 WT hiPSCs or 4104 HEK293T cells were seeded onto 48-well plates and transfected with 100ng of all-in-one CRISPR plasmids (2/5 scale of the 24-well plate version). hiPSCs were dissociated and counted using trypan blue at 3 or 4d after transient puromycin treatment (1.5gml1); HEK293T cells were counted at 4d after transient puromycin treatment (3gml1). The data obtained by this procedure are indicated as Cell number in the Figures.

Biochemical assays were also performed using Cell Count Reagent SF reagent according to the manufacturers instructions (Nacalai Tesque). The Cdh1-P2A1-AIMS mESCs (2104 cells) were seeded onto 96-well plates and transfected with 50ng of all-in-one plasmids (1/5 scale of the 24-well plate version). Two days after puromycin selection, absorbance at 450nm was measured by Multiskan FC (Thermo Fisher). The data obtained from the biochemical assay are indicated as Cell viability (%) in Fig. 4d and Extended Data Fig. 4c by setting the data for [0C] and 0mM as a reference value (1.0), respectively.

For the AcrIIA4 experiments (Fig. 4c and Extended Data Fig. 4b), the Cdh1-P2A1-AIMS mESCs (3104 cells) were seeded onto 96-well plates and 50ng of all-in-one plasmids were co-transfected with different amounts of pCMV+AcrIIA4 and/or pCMVAcrIIA4 plasmids (1/5 scale of the 24-well plate version). In Fig. 4c and Extended Data Fig. 4b, we observed cytotoxicity for higher doses of AcrIIA4 expression plasmids. Similar cytotoxicity profiles were obtained in the absence of the Cdh1-P2A1-sgRNA1 target sequence in WT mESCs.

The transfection protocol for the 24-well plate experiment was performed as described above (Generation of AIMS cell lines and mice and AIMS analysis). For HDR induction in mESCs, WT hiPSCs and HEK293T cells, 1l of 10M ssODN (Eurofins) was added to the plasmidLipofectamine complex; for hiPSC transfection, 1l of 3M ssODN was added because a concentration of 10M induced severe toxicity. After transient puromycin selection, colonies were dissociated and plated at low density to avoid mosaicism. Single colonies were selected and genomic DNA was extracted. Sequence analysis was performed to identify G to A replacement with or without indels. To correct the FOP hiPSCs, clones that underwent HDR were screened by digesting the PCR product using the BstUI restriction enzyme (NEB); BstUI-positive PCR products were then sequenced. A silent mutation was inserted into the ssODN to generate the BstUI site and to distinguish an HDR-corrected (Corrected) allele from an original WT allele. Without this hallmark, WT/ clones, in which PCR amplicons from the R206H allele cannot to be obtained because of large deletions or more complex genomic rearrangement, would be misidentified as WT/Corrected clones.

For p53 staining, we performed transfection for HDR induction (1/5 scale of the 24-well plate version), using the protocol described above. In this assay, 6104 hiPSCs were seeded on a Matrigel-coated 96-well plate in triplicate. Puromycin selection was performed to examine p53 activity solely in transfected cells. The surviving cells were fixed with 4% paraformaldehyde at 2d after puromycin removal. For pSmad1/5/8 staining, 5103 cells were plated on a Matrigel-coated 96-well plate without Y-27632 and with 1% FBS. After 2.5h of culture, activin-A (100ngml1) (R & D Systems) was administered for 30min; cells were fixed with 4% paraformaldehyde. Antibody reactions were performed in accordance with standard protocols. Rabbit polyclonal p53 (FL-393, Santa Cruz, 1:200) and rabbit monoclonal pSmad1/5/8 (D5B10, Cell Signaling Technology, 1:1,000) antibodies were reacted overnight at 4C. Donkey anti-rabbit Alexa Fluor 488 secondary antibody (Thermo Fisher, 1:1,000) was reacted at room temperature for 30min. Data analysis was performed using a cell count application associated with a fluorescent microscope to select cells with p53 and pSmad1/5/8 activation by means of fluorescence intensity thresholds (BZ-X800, Keyence).

An mESC clone of an FOP model (R26RYFP/+ mESC line) was dissociated with trypsin and 58 cells were injected into 8-cell embryos (E2.5) collected from pregnant ICR mice. Injected blastocysts were transferred into the uteri of pseudo-pregnant ICR mice. Chimaeric contribution was confirmed by coat colour and YFP fluorescence. YFP was observed using a fluorescence stereo microscope (M165FC, Leica).

In this study, the probability of single-allele editing (P) was determined using AIMS and a Bac[P] assay, on the basis of a T7E1 assay, complemented by sequence validation. AIMS-based P (AIMS[P]) was determined as follows:

$$begin{array}{*{20}{c}} {mathrm{AIMS}left[ {mathrm{P}} right] = frac{{left( {2Fleft( {mathrm{Bi}} right) + Fleft( {mathrm{Mono}} right)} right)}}{2}} end{array}$$

(1)

where F(Bi) and F(Mono) are the experimental frequencies of cells with bi-allelic and mono-allelic genome editing, respectively.

The efficiency of the single-allele editing P (P(pf), where pf denotes perfect match) can be described as follows:

$${{mathrm{P}}left( {pf} right) = frac{S}{{K + S}}}$$

(2)

where the concentration of effective sgRNA-Cas9 complexes and the dissociation constant between the sgRNA and its target site are defined as S and K, respectively. On the basis of high editing efficiency without [C] extension (P=approximately 1), we assumed that the recovery rate from single-site damage was very low; therefore, it was neglected in subsequent analyses. To mechanistically understand the effects of [C] extension and 1mm, we assumed that [C] extension and 1mm decreased S and increased K, respectively. By setting S=1 for each sgRNA sequence without [C] extension, we approximated K values for each of eight sgRNA sequences. When P (AIMS[P] or Bac[P]) was 1, P was set to 0.99. Next, the relative S concentrations were determined using K and AIMS[P] for sgRNAs with [C] extension. Despite variation in the relationships between [C] extension and AIMS[P] among sgRNA sequences (Fig. 2f), we found clear and similar inverse relationships between [C] extension and relative S values for different sgRNA sequences (Extended Data Fig. 3d). Linear regression analysis demonstrated a good fit for the logarithm of the ratio of S to the length of [C] extension for all sgRNA sequences (Fig. 2g). Analysis of covariance (ANCOVA) indicated that the linear regression slopes did not significantly differ among various sgRNA sequences (Fig. 2h). This finding suggests that [C] extension exerts uniform suppression effects on diverse sgRNA sequences.

Since we observed that [C] extension modestly decreased target cleavage (Fig. 3c), we also performed similar analysis by gradually increasing K according to the length of [C] extension and observed that [C] extension gradually decreases S in a similar manner. In this setting, the effects on S became weaker. However, we observed that the dynamic range of suppression in northern blot analysis (Fig. 3f, ~6,000-fold change at [30C]) was more comparable to the range of change in S with constant K (~2,000-fold change at [30C]) relative to the range of change in S with increased K (~400-fold and 200-fold change with 5-fold and 10-fold increases in K at [30C], respectively). Therefore, this suggests that the effects on complex formation may be dominant, allowing determination of the single-allele editing probability in the cells.

In the initial phase of this study, we compared matched AIMS[P] and Bac[P] values for nine sgRNAs (that is, Cdh1-P2A1-sgRNA1 with different [C] extension lengths) and observed that AIMS[P] was strongly correlated with Bac[P] (Extended Data Fig. 5a). In our subsequent analyses, we used AIMS[P] to model indel insertion frequency (Figs. 2 and 5, and Extended Data Fig. 6) and Bac[P] to model HDR frequency (Figs. 6 and 7).

AIMS error was calculated as the difference between raw AIMS[P] and adjusted AIMS[P] (adjusted AIMS[P]AIMS[P]) (Fig. 1h). The raw AIMS[P] is simply based on fluorescence patterns. Therefore, in Fig. 1e, rare tdTomato+/Venusindel and tdTomatoindel/Venus+ heterozygous clones were grouped into mono-allelic clones. To determine the exact number of bi-allelic indel clones, these ostensibly heterozygous clones were analysed for sequencing (Seq-indel data). When sequencing these clones, most (86%) of these ostensibly heterozygous clones turned out to be homozygous. Adjusted AIMS[P] incorporates Seq-indel data together with fluorescence patterns. In most analyses, we used raw AIMS[P].

T7E1 error was calculated as Bac[P]T7E1:Bac[P] (Fig. 1i,j). T7E1:Bac[P] is the indel probability calculated from the rate of T7E1 sensitive clones, while Bac[P] is the indel probability calculated considering the Seq-indel data. The Seq-indel data were the exact numbers of indel clones that were not digested by T7E1, as determined by sequencing PCR products.

We performed extensive analyses using a combination of AIMS and sgRNAs with various types of [C] extensions. When editing efficiency was homogeneous across the cell population, we estimated the frequencies of cells with bi-allelic, mono-allelic or no genome editing (that is, F(Bi), F(Mono) or F(No)) as follows:

$${F(mathrm{Bi}) = mathrm{AIMS}[{mathrm{P}}]^2}$$

(3)

$${F(mathrm{Mono}) = 2mathrm{AIMS}[{mathrm{P}}]left( {1 - mathrm{AIMS}left[ {mathrm{P}} right]} right)}$$

(4)

$${Fleft( {mathrm{No}} right) = left( {1 - mathrm{AIMS}left[{mathrm{P}}right]} right)^2}$$

(5)

Using these equations, we observed that actual F(Mono) was lower than estimated F(Mono), particularly at intermediate AIMS[P] levels (AIMS[P]=~0.5). Therefore, we considered genome editing frequency heterogeneity at the single-cell level, which we modelled using a beta distribution. The probability density functions of P and mean P (E(P)) were calculated as follows:

$${fleft( {{mathrm{P}};alpha ,beta } right) = frac{{{mathrm{P}}^{alpha - 1}left( {1 - {mathrm{P}}} right)^{beta - 1}}}{{Bleft( {alpha ,beta } right)}}}$$

(6)

$${Eleft( {mathrm{P}} right) = frac{alpha }{{alpha + beta }}}$$

(7)

where the mean P corresponds to AIMS[P] (or Bac[P]) and and are exponents of P and its complement to 1. Using the beta distribution, F(Bi), F(Mono) and F(No) were described as follows:

$${F(mathrm{Bi}) = mathop {smallint }limits_0^1 {mathrm{P}}^2fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(8)

$${F(mathrm{Mono}) = mathop {smallint }limits_0^1 2{mathrm{P}}(1 - {mathrm{P}})fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(9)

$${Fleft( {mathrm{No}} right) = mathop {smallint }limits_0^1 (1 - {mathrm{P}})^2fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(10)

Using these equations, we determined values for each experiment that minimized the squared residuals between experimental F(Bi), F(Mono) and F(No), and simulated F(Bi), F(Mono) and F(No) (Extended Data Fig. 5b). As shown in Extended Data Fig. 5b, we observed that optimized values were generally constant for a wide range of AIMS[P] (0.1

As described above, 1mm (or 2mm) increases K in equation (2). The efficiency of the single-gene editing P on the 1mm (or 2mm) target can be described as follows:

$${{mathrm{P}}left( {1mathrm{mm};or;2mathrm{mm}} right) = frac{S}{{mK + S}}}$$

(11)

where m is the ratio of K for the 1mm target to K for the perfect match target. Thus, the single-gene editing P for 1mm (or 2mm) can be expressed as the function of P(pf), as follows:

$${{mathrm{P}}left( {1mathrm{mm};or;2mathrm{mm}} right) = frac{{{mathrm{P}}left( {pf} right)}}{{left( {1 - m} right){mathrm{P}}left( {pf} right) + m}}}$$

(12)

For the results shown in Figs. 6 and 7, we determined values of m that fit P(pf) and P(1mm or 2mm), using SSR as the error function (Fig. 6g). The ratios of P(pf) and P(1mm or 2mm) can also be described as functions of P(pf), as follows:

$${frac{{{mathrm{P}}(1mathrm{mm};or;2mathrm{mm})}}{{{mathrm{P}}(pf)}} = frac{1}{{left( {1 - m} right){mathrm{P}}left( {pf} right) + m}}}$$

(13)

$${frac{{{mathrm{P}}left( {pf} right)}}{{{mathrm{P}}left( {1mathrm{mm};or;2mathrm{mm}} right)}} = left( {1 - m} right){mathrm{P}}left( {pf} right) + m}$$

(14)

As shown in Fig. 6h, decreasing P(pf) contributes to the reduction in relative off-target ratio and enhancement of specificity. Thus, reduction in CRISPR-Cas9 activity through [C] extension is beneficial for reducing the relative off-target activity and enhancing specificity.

Using the beta distribution, the frequencies of the various HDR clones shown in Fig. 6 were determined as follows (Extended Data Fig. 7c,d):

$${F(mathrm{WT}/mathrm{R206H}) = mathop {smallint }limits_0^1 2h{mathrm{P}}(1 - {mathrm{P}})(1 - (1 - h){mathrm{P}}^{prime} )fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(15)

$${F(mathrm{WT}/mathrm{R206H} + mathrm{indel}) = mathop {smallint }limits_0^1 2h(1 - h){mathrm{P}}(1 - {mathrm{P}}){mathrm{P}}^{prime} fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(16)

$${F(mathrm{indel}/mathrm{R206H}) = mathop {smallint }limits_0^1 2h(1 - h){mathrm{P}}^2(1 - (1 - h){mathrm{P}}^{prime})fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(17)

$${F(mathrm{indel}/mathrm{R206H} + mathrm{indel}) = mathop {smallint }limits_0^1 2h(1 - h)^2{mathrm{P}}^2{mathrm{P}}^{prime} fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(18)

$${F(mathrm{R206H}/mathrm{R206H}) = mathop {smallint }limits_0^1 h^2{mathrm{P}}^2fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(19)

$${Fleft( {mathrm{overall};mathrm{HDR}} right) = mathop {smallint }limits_0^1 left( { - h^2{mathrm{P}}^2 + 2hP} right)fleft( {{mathrm{P}};alpha ,beta } right)dP}$$

(20)

where the efficiency of HDR on the Cas9-cleaved single allele is defined as h. The probability of single-gene editing on the edited (that is, 1mm) target is P' (Extended Data Fig. 7d), which is described in a manner similar to equation (12), as follows:

$${mathrm{P}^{prime} = frac{{mathrm{P}}}{{left( {1 - m} right){mathrm{P}} + m}}}$$

(21)

where m=1.723. P is decreased according to the [C] extension length (Extended Data Fig. 7e).

For simplicity, we considered h to be constant across the cell population in each experiment. On the basis of the experimental overall HDR frequency results and equation (20), we estimated h for each [C] extension (Fig. 6f). Although h was very low for sgRNAs without [C] extension (2.07%), h for sgRNAs with [C] extension was generally high (~11%). This result suggests that the conventional system without [C] extension suppresses HDR; [C] extension releases this suppression to allow HDR to reach its upper limit. On the basis of these findings, we used the mean estimated h (10.99%) for [C]-extended sgRNAs; we estimated the frequencies of distinct HDR patterns, overall HDR and precise HDR (Fig. 6i,j). For sgRNAs without [C] extension, we used the estimated h (2.07%). The simulated data adequately fit the experimental results (Fig. 6ik). To predict continuous HDR outcomes, we designed a hypothetical function for h for the range of P, such that h=2.07% for P>0.9 and h=10.99% for P<0.9 (Extended Data Fig. 7f); we estimated the frequencies of distinct HDR patterns, overall HDR and precise HDR (Extended Data Fig. 7g). In the simulation, precise HDR reached a maximum at P=0.313 (Extended Data Fig. 7e,g).

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