已有 7 次阅读2026-6-4 12:49 |个人分类:medicine
多伦多大学 移植巴胺能神经元 改善帕金森病运动功能障碍
利用神经模式形成过程中 FZD5 的时空调控实现高效的腹侧中脑特化
2024 年 3 月 4 日
https://pubmed.ncbi.nlm.nih.gov/38358799/
结论
本研究揭示了一种此前未报道的机制,该机制调控Wnt受体(FZD5)在前后轴模式形成过程中的时空表达。利用该调控机制对该受体的选择性激活以及对Wnt/β-catenin信号通路的严格控制,我们开发了一种新型的体外hPSC定向分化方案,该方案可产生功能性多巴胺能神经元,移植到帕金森病啮齿动物模型后,能够有效改善其运动功能障碍。我们的方案引入了一种定制的合成生长因子,该因子可选择性激活FZD5:LRP6受体复合物,并忠实地将神经祖细胞(NPCs)模式化为中脑神经元,从而取代了传统的GSK3抑制剂。我们预期,深入了解控制 Wnt/β-catenin 信号传导和其他信号通路的潜在遗传调控机制,并开发能够忠实模拟内源性细胞间通讯机制的合成替代生长因子,将在释放再生医学的潜力方面发挥关键作用。
Exploiting spatiotemporal regulation of FZD5 during neural patterning for efficient ventral midbrain specification
MAR 4 2024
https://pubmed.ncbi.nlm.nih.gov/38358799/
Next, we employed flow cytometry to profile the cell surface expression of each of the ten FZD receptors in regionally patterned NPCs using selective FZD antibodies (Fig. S1E). Intriguingly, FZD5 was not expressed in the pluripotent state, but after 4 days of neural induction, this receptor became ubiquitously expressed at the cell membrane of neural-induced cells without posterior patterning (Fig. S1F). Importantly, FZD5 cell surface expression remained upregulated in anterior neural progenitors after ventral patterning conditions (
FZD5 cell surface expression is uniquely upregulated in anterior neural progenitors. (A) Schematic outlining the derivation of regional neural progenitors from hPSCs and antibody-based profiling of surface expression of FZD receptor. (B) The percentages of expression of each FZD in H1 hESCs, and in dorsal patterned FB (forebrain), MB (midbrain) and HB (hindbrain) NPCs at day 4 and day 11 quantified using flow cytometry. Data are presented as the mean percentage of three replicates. (C) The dynamics of FZD5 surface expression in the first 4 days of neural induction without Wnt stimulation. n=3 independent experiments; data are mean±s.e.m. (D) The spatial expression of FZD5 in patterned regional NPCs at day 11. n=3 independent experiments, data are mean±s.e.m. **P<0.01 (two-tailed Student's t-test). (E) The histogram of cell surface expression of FZD5 in ventral patterned regional NPCs at day 4. Data are representative of three independent experiments.
A genome-wide CRISPR screen identifies regulators of FZD5 expression during neural induction
Intrigued with the unique spatial expression of FZD5 in the anterior NPC, we sought to understand the mechanisms governing its expression by conducting a genome-wide CRISPR screen during neural induction (Fig. S2A). The screen was performed in an H1 hESC line harboring doxycycline-inducible Cas9, which was transduced with the Toronto Knockout Library (TKOv3), consisting of 71,090 pooled gRNAs targeting ∼18,000 protein-coding genes (Fig. S2B). Moreover, gRNAs targeting FZD5 were the most highly enriched in the FZD5-low population (Table S1). gRNAs targeting DVL2 and CTNNB1, which are important mediators of Wnt/β-catenin signaling that promote posterior patterning, were expectedly identified in the FZD5-high sorted population. Interestingly, LDB1 and OTX2, which have established roles as forebrain developmental regulators, were uncovered as positive regulators of FZD5 expression (
A genome-wide CRISPR screen identifies regulators of FZD5 expression during neural induction. (A) Volcano plot depicting the gene level summary of gRNAs that regulate FZD5 surface expression identified in the CRISPR screen. Red dots represent regulators with FDR<0.1. The bottom panel is the individual gRNA enrichment of the highlighted genes. (B) Percentage of FZD5 surface expression in OTX2-KO cells and LDB1-KO cells at day 4 of neural induction compared with control AAVS1-KO cells. Two different gRNAs targeting OTX2 and LDB1 were used for validation. n=3 independent experiments. Data are mean±s.e.m. A two-tailed Student's t-test was used to test for significance. (C) FZD5 mRNA expression at day 4 of neural induction in OTX2-KO cells and LDB1-KO cells. n=3 independent experiments. Data are mean±s.e.m. A two-tailed Student's t-test was used to compare control with AAVS1-KO cells. (D,E) FOXG1 (D) and HOXA2 (E) mRNA expression at day 4 in AAVS1-KO cells compared with OTX2-KO cells and LDB1-KO cells. n=3 independent experiments. Data are mean±s.e.m. A two-tailed Student's t-test was used to test for significance. *P<0.05, **P<0.01.
To understand how LDB1 and OTX2 regulate FZD5 expression, we selectively knocked out these genes in hESCs using CRISPR-Cas9. We generated two individual polyclonal knockout (KO) lines for each regulator with different gRNAs and quantified KO efficiency using TIDE (Fig. S2C). The KO cells were subjected to neural induction for 4 days and stained for FZD5 expression. The differentiated cells from both LDB1-KO and OTX2-KO cells had ∼35-50% reduced FZD5 surface expression compared with control cells (Fig. S2D). Overall, these results elucidate the dynamic regulation of FZD5 cell surface expression along the anterior-posterior axis and indicate that FZD5 gene expression is dependent on the anterior forebrain regulators OTX2 and LDB1.
Based on the FZD profiling data and our finding that FZD5 expression is dynamically regulated during anterior-posterior neural patterning, we hypothesized that selectively activating FZD5 would efficiently induce midbrain patterning. In contrast, activating FZD2 or FZD7, which exhibit constant cell-surface expression, could result in posterior fates due to sustained signaling. To test this, we first performed a dose titration of a selective FZD5 FLAg, F5L6.13, and compared AP patterning of NPCs obtained with CHIR99021 by monitoring expression of various region-specific markers using qPCR (Fig. S3A). Strikingly, treatment with 0.5 nM F5L6.13 induced expression of the mesencephalic floor-plate markers LMX1A, OTX2 and FOXA2 with a similar efficiency to 1 µM CHIR99021, with minimal induction of the posterior marker HOXA2 (Fig. S3B). Both CHIR99021 and F5L6.13 treatments led to reporter expression in well over 80% of the cells (Fig. S3C). Moreover, 0.5 nM F5L6.13 patterned VM progenitors efficiently from H9 hESCs and WTC11 iPSCs (Fig. S3D). We conclude that the spatiotemporal regulation of FZD5 cell surface expression during AP patterning provides a previously unreported regulatory mechanism for reinforcing midbrain specification while minimizing hindbrain fate in hPSC differentiation.
F5L6.13 activates Wnt signaling to pattern VM progenitors from hPSCs. (A) Schematic of VM progenitor differentiation protocol from hPSCs. (B) qPCR gene marker expression of LMX1A, FOXA2 and OTX2 in day 16 differentiated cells patterned with CHIR99021 or F5L6.13, and H1 ESCs (ES). Data are mean±s.e.m. n=5 independent experiments. (C) qPCR gene marker expression of hindbrain marker HOXA2 in differentiated cells patterned with CHIR99021 F5L6.13, F2L6.13 or F7L6.13 at the indicated dose. Data are mean±s.e.m. n=3 independent experiments. (D,E) Immunofluorescence staining of VM markers LMX1A, FOXA2 and EN1 (D) and LMX1A, OTX2 and EN1 (E) on day 16. Images are representative of three independent experiments. Scale bar: 100 μm. (F) PCA plot of H1 hESCs and patterned VM progenitors from CHIR99021 or F5L6.13. (G) Heatmap of scaled gene expression of VM markers across treatment conditions. (H) Volcano plot portraying RNA-seq differential expression analysis comparing day 16 patterned VM progenitor. Significantly regulated genes that are associated with midbrain development are highlighted in red with adjusted P<0.1 and fold change>1.5.
Next, we performed bulk RNA-Seq to examine gene expression variations between VM progenitors derived from F5L6.13 and CHIR99021 treatments at day 16. This is the timepoint when progenitor cells can be engrafted in preclinical models of PD to restore motor deficits (Fig. S3E). Furthermore, both treatment conditions led to equivalent increase in VM marker gene expression (S3F). These genes are highly expressed in the caudal midbrain and correlate with higher DA neuron differentiation efficiency in transplantation studies (Fig. S4A). Next, using immunostaining, we observed the presence of MAP2+ neuron-rich cultures that were also positive for tyrosin hydroxylase (TH) and FOXA2, confirming DA neuron identity, in both CHIR99021 and F5L6.13 treatment conditions at D45 (Fig. S4B). In addition, a high induction of TH mRNA expression was observed in the differentiated cell populations (Fig. S4D). Next, to examine whether the F5L6.13-patterned cell population possess functional characteristics of DA neurons and are excitable in vitro, we performed whole-cell patch-clamp electrophysiological recordings in cells exhibiting neuronal morphology with attachments to neuron-like processes at day 45. The neurons showed evoked action potentials upon depolarization and rebound action potentials after brief periods of hyperpolarization, consistent with DA neuron electrophysiological characteristics (Fig. S4C).
F5L6.13-patterned VM progenitors give rise to DA neurons in vitro and rescue the motor dysfunction in the 6-OHDA rat model in vivo. (A) Immunostaining for TH, MAP2 and FOXA2 in day 45 differentiated neuronal cultures from VM progenitors patterned with CHIR99021 or F5L6.13 in H1 hESCs. Images are representative of three independent experiments. Scale bars: 100 μm. (B) qPCR gene expression of TH in day 45 differentiated neuronal cultures normalized to H1 hESCs. Data are mean±s.e.m. n=4 independent experiments. (C) Phase-contrast image of a patch-clamped neuron and representative traces of evoked action potentials in F5L6.13 patterned neuronal culture at day 45. Thirteen out of 13 patch-clamped neurons showed traces with action potential. (D) Example of rebound action potentials after brief hyperpolarization in the patch-clamped neurons. (E) Schematic summary of an in vivo study with athymic nude rats. (F) Amphetamine-induced rotations of the 6-OHDA lesioned animals in sham-engrafted (n=8 rats) or F5L6.13 VM NPC-engrafted (n=7 rats) groups. Unpaired multiple t-tests were used for comparing engrafted F5L6.13 VM NPCs with sham controls at each timepoint (**P<0.01). (G) Immunostaining of TH and human NCAM in sectioned brain slices of the intact and 6-OHDA lesioned side of the striatum in sham-engrafted and F5L6.13 VM NPC-engrafted animals at 8 months post-transplantation. Scale bar: 100 μm. Images are representative of all the animals in each cohort.
To investigate whether VM progenitors patterned with F5L6.13 can give rise to functional DA neurons in vivo, we employed the 6-OHDA neurotoxin-induced PD rat model that is widely used to test behavioral alterations upon DA depletion (Fig. S4E,F). In addition, immunofluorescence staining revealed that both conditions yielded TH+ cells expressing MAP2, revealing cells with DA neuron identity (Fig. S4G). Moreover, these organoids contain cells with LMX1A and OTX2 expression, indicating midbrain identity (Fig. S4H). These results demonstrate that selective activation of Wnt/β-catenin signaling with F5L6.13 is sufficient to generate midbrain organoids harboring DA neurons.
Minimizing cellular heterogeneity during hPSC differentiation is a desired objective in advancing regenerative medicine and transplantation therapies. To investigate the cellular heterogeneity of differentiated VM progenitor populations obtained with F5L6.13 and CHIR99021, and to assess whether VM patterning is successfully achieved by F5L6.13 in comparison with conventional protocols, we conducted single-cell RNA sequencing (scRNA-seq) of cell populations. We employed the 10x CellPlex platform to simultaneously profile the differentiated VM progenitors patterned by F5L6.13 and CHIR99021 at day 11 in parallel within the same batch using one biological replicate (Fig. S5A). Both treatment conditions gave rise to a high distribution of cells expressing LMX1A, FOXA2 and OTX2, confirming successful VM patterning (
scRNA sequencing of day 11 VM progenitors patterned by F5L6.13 and CHIR99021. (A) Schematic of the workflow in 10x Cellplex pipeline labelling the differentiated cells with CMOS for barcoding before sample processing and sequencing. This experiment was performed using one biological replicate in parallel. (B) Violin plots displaying distribution of expression levels of LMX1A, FOXA2 and OTX2 across all cells. (C) UMAP plot showing clustering of single cells from both treatment conditions. (D) UMAP plot showing the cell identity of treatment conditions contributing the clusters. (E) Bar plot showing the number of cells from each treatment condition contributing to each cluster. (F) Volcano plots of differentially expressed genes comparing cluster 0 versus cluster 1, 2 and 5. Each dot represents a gene. Red dots represent significantly regulated genes with fold change>1.5. (G) Normalized expression levels of indicated genes that were identified in the analysis superimposed onto the UMAPs. Data are colored according to expression levels in each cell. (H) Enrichment of pathways identified using GSEA with a curated ranked gene list from differential expression analysis comparing cluster 0 with clusters 1, 2 and 5. NES and FDR q-values are indicated in the pathway. Bottom right panel indicates the pleiotropic effect of GSK3 in the signaling pathways.
Next, we employed Uniform Manifold Approximation and Projection (UMAP)-based embedding to visualize the clustering of the cell populations. The UMAP segregated the dataset into 10 distinct clusters (Fig. S5B). We first examined whether our treatment conditions produced cell clusters that corresponded to day 11 cell populations derived from floor plate induction protocols in the literature. We compiled a list of VM progenitor marker genes (Table S2) and then applied the UCell method for scoring the expression of the resulting gene signature in individual cells (Fig. S5C). In addition, we compared our dataset with another scRNA dataset from day 11 VM progenitor patterned with CHIR99021 that identified three separate clusters of floor-plate progenitors (Table S2). High expression scores for each of the three floor plate gene signatures was observed in our dataset, highlighting the efficiency of VM patterning in our differentiation protocol (Fig. S5D). Moreover, the obtained cell populations expressed high levels of NES and SOX2 (neural progenitor markers), with minimal ASCL1 (a neurogenesis marker) and SNAP25 (a neuronal marker) expression, indicating that the day 11 cells from the dataset were in the neural progenitor stage (Fig. S5E).
To identify the differences between CHIR99021- and F5L6.13-patterned VM progenitors, we focused on the clusters that showed distinct enrichment in each treatment (Fig. S5B). Specifically, CHIR99021-patterned cells were over-represented in clusters 1, 2 and 5 but under-represented in cluster 0 when compared with F5L6.13-patterned cells. The canonical VM markers LMX1A, FOXA2 and OTX2 were consistently expressed across these clusters, indicating that these differences did not arise from improper VM patterning (Fig. S5F). To examine whether the observed differences could be attributed to differences in cell cycle status, we employed the CellCycleScoring function in Seurat to classify cell cycle phases in the population. The UMAP with cell cycle classification revealed that clusters 6 and 7 are associated with cells in G2M phase, while cluster 3 includes cells in S phase (Fig. S5G). Moreover, a similar proportion of cells in each cell cycle phase was observed across the two treatments, suggesting that the differences in cluster abundance is not merely due to varying cell cycle status. Next, we performed differential gene expression analysis comparing clusters 0 with cluster 1, 2 and 5, and identified 11 significantly upregulated genes and 57 downregulated genes with a fold change greater than 1.5 (Fig. S6A). UMAP-based embedding partitioned the cell populations into 15 clusters (Fig. S6B). To examine cell cycle status, we again used the cell-cycle related gene set to calculate and assign cell-cycle scores to each cell. This revealed that both treatments gave rise to cells with a similar proportion of cell-cycle states and, as expected for this stage, a trend reflecting the progression from proliferating progenitors to postmitotic neuronal cell population (Fig. S6C).
scRNA sequencing of day 30 VM neuronal population patterned by F5L6.13 and CHIR99021. (A) UMAP plot showing clustering of the day 30 single cells from one biological replicate. (B) UMAP plot showing the cell identity of treatment conditions (CHIR99021 or F5L6.13) contributing the clusters. (C) Bar plot showing the number of cells from each treatment condition contributing to each cluster. (D) Normalized expression levels of ASCL1 and SNAP25 superimposed onto the UMAPs. Data are colored according to scaled expression in each cell. (E) UMAP plot showing subclustered neuronal population. (F,G) Normalized expression levels of TH (F) and NR4A2 (G) superimposed onto the neuronal subclustered UMAP. Data are colored according to expression levels in each cell. (H) Pie chart displaying the proportion of neuronal subclusters contributed from each treatment condition with the DA+ clusters highlighted. The percentage of DA+ cluster in each condition is indicated. (I) HiDDEN continuous scoring of the DA neuronal population in CHIR and F5L6.13 conditions. (J) Classification of the DA subpopulation from the continuous score generated from HiDDEN. (K) Gene ontology enrichment analysis of the 394 marker genes of the F5L6_L1 subpopulation. **P<0.01, ***P<0.001, ****P<0.0001 (Fisher's Exact test).
To identify the clusters representing the neuronal populations, we assessed the expression of neural progenitor markers NES and SOX2, neurogenesis marker ASCL1, and neuronal marker SNAP25 (Fig. S6D). Clusters 1, 2, 4 and 6 displayed pronounced neuronal gene expression while demonstrating minimal expression of genes related to progenitor cells. Interestingly, 40.8% and 30.4% of the analyzed cells were attributed to the neuronal clusters when VM progenitors were patterned with GSK3i and F5L6.13, respectively. This suggests that, overall, treatment with GSK3i may be more effective at promoting neuronal differentiation. We then refined the analysis and subjected the neuronal clusters to another UMAP embedding to examine heterogeneity, focusing on the neuronal populations. The subclustered neuronal population parsed into 14 distinct neuronal clusters (N0-N13) (Fig. S6E). Feature plots of TH and NR4A2 display that some of the neuronal clusters have DA neuronal identity (Fig. S6F). In F5L6.13-patterned cultures, the DA+ clusters represent 50% of the neuronal population, while CHIR99021-patterned neuronal cultures exhibit 46% DA+ clusters (Fig. S6H). Based on these results, we conclude that F5L6.13 can achieve similar efficiency to GSK3i in DA neuron differentiation.
To further investigate the transcriptional differences between DA neurons generated from CHIR and F5L6.13-patterned cells, we deployed HiDDEN, a computational method designed towards identifying unique subpopulations of cells in cross-condition experiments (Fig. S6I), suggesting that the F5L6.13 agonist produces a DA neuronal subpopulation that is very similar to the current protocol using GSK3i. However, the F5L6_L1 population significantly differs from CHIR and CHIR-like F5L6_L0 cells, as evidenced by 597 significant DE genes (Fig. S6I, DE genes are listed in Table S3). Gene ontology (GO) enrichment analysis of the 394 DE genes enriched in F5L6_L1 DA neurons revealed that this subpopulation is enriched in GO terms of ‘substantia nigra development’ and ‘neuronal differentiation processes’ (Fig. S6J). This analysis suggests that a subpopulation of DA neurons generated by F5L6.13 aligns more closely with the anatomical origin of DA neurons. Overall, F5L6.13 is as efficient as CHIR99021 in directing neural progenitors toward the DA neuron fate.
Successful PSC differentiation into functional cell types has been guided by knowledge of the endogenous developmental signals regulating differentiation of progenitor cells into tissue-specific cell types. Wnt proteins are one such important class of evolutionarily conserved signaling molecules, activating β-catenin signaling to mediate embryonic and tissue stem cell self-renewal and differentiation (Table S4) and Power SYBR Green according to manufacturer's protocol (ThermoFisher) in triplicates. Samples were run and analyzed on a CFX384 Real-Time PCR Detection System (BioRad) using the following thermocycling conditions: 95°C for 10 min, followed by 40 cycles of 95°C for 15 s, 60°C for 1 min and 72°C for 30 s. Mean relative gene expression was analyzed with the ΔΔCt method using CFX Maestro software and normalized to the housekeeping gene PPIB.
For single gene knockout studies, individual gRNAs (see Table S5 for gRNA sequence list) were ligated into Esp3I digested LentiCRISPRv2, a generous gift from Feng Zheng (Addgene, 52961), by following established protocol (Table S5. INDELs and sgRNA cutting efficiency were measured by comparing .ab1 files of targeted amplified genomic regions of the wild-type cells with the knockout cells using the online TIDE software (113631), was used. Synthetic design of pJ151-LMX1A-IRES-EGFP was planned using SnapGene cloning software. Briefly, a IRES-GFP fragment was subcloned into the pJ151 vector downstream of the HDR sites using Gibson Assembly (New England Biolabs). Left and right homology arms (LHA and RHA) from genomic DNA of H1 hESCs were amplified using PCR (Kapa Biosystems), designed for integration immediately after the stop exon of the human LMX1A gene (sequence accessed by GenBank file from NCBI). The homology arms were cloned into the backbone using restriction cloning. The LHA was cleaved using enzymes AscI and BAMHI (New England Biolabs), and the RHA was cleaved using SpeI and ClaI (New England Biolabs), and subcloned into the pJ151-HDR-IRES-GFP vector. The CRISPR nickase strategy was used for targeted insertion (42335). For H1 LMX1A-GFP generation, the pair of nickases were electroporated into H1 hESCs with the pJ151-LMX1A-IRES-GFP vector using the Neon Transfection System (Invitrogen) following default parameters. The cells were selected using puromycin and tested for LMX1A-GFP activity. See Table S5 for the list of sequences for primers and gRNA used for H1 LMX1A-GFP generation.
HEK 293T cells were cultured in DMEM (Gibco) with 10% fetal bovine serum (Gibco) and were transfected at ∼60% confluency with 5 μg of a psPAX2 packaging plasmid and 2 μg of a VSV. G enveloping plasmid and 5 μg of the intended LentiCRISPRv2 gRNA construct in 250 μl of OptiMEM (Gibco) reduced serum media. A 3:1 ratio of polyethylenimine (Sigma-Aldrich) to DNA was prepared separately, diluted in OptiMEM and briefly vortexed and incubated for 20 min, then added dropwise to cell culture plates. Media were changed the following day, and 24 h later those media were harvested and spun down (2000 g for 2 min). Supernatant was filtered with a 0.45 μm filter and Lenti-X Concentrator (Takara) was added to media in a 1:3 volume ratio. Tubes were rotated for 1 h at 4°C then spun at 1500 g for 45 min at 4°C. The viral pellets were resuspended in 250 μl of DMEM/F12, aliquoted and kept at −80°C for long-term storage.
For the monolayer differentiation protocols, the cells were seeded and differentiated on Geltrex-coated round glass cover slips (Fisher Sci). At the timepoint stated, the cells were fixed with 4% paraformaldehyde (PFA) and stored at 4°C until ready for staining. For midbrain organoids, the organoids were fixed in 4% PFA overnight and washed extensively the next day before placing in a 30% sucrose (BioShop) solution in PBS at 4°C overnight, Subsequently, the organoids were then embedded in OCT compound (VWR) for cryosectioning. The frozen organoids were sectioned at 16 μm using a cryostat (Leica) and mounted on Superfrost plus microscope slides (ThermoFisher Scientific). For staining, the slides were permeabilized and blocked in PBS with 0.1% Triton X-100 (Sigma-Aldrich) and 5% donkey serum (Sigma-Aldrich) for 1 h at room temperature. Primary antibody was prepared in blocking solution and added to coverslips for overnight incubation at 4°C, followed by extensive washing. Secondary antibody was added in blocking solution and incubated for 1 h in the dark at room temperature with the cover slips. After washing, the coverslips were mounted onto slides (VWR) using Fluoromount mounting media (Sigma-Aldrich) with DAPI (Sigma-Aldrich). For rat brain imaging, rats were anesthetized with 5% isoflurane before perfusion and then intracardially perfused with 50 ml phosphate-buffered saline (PBS), followed by 50 ml 4% paraformaldehyde in PBS. The brains were removed and stored in 4% paraformaldehyde in PBS overnight at 4°C and then transferred to a 30% sucrose solution for cryoprotection for 48 h at 4°C. Tissue was embedded in the freezing media HistoPrep (Fisher Chemical) and cut in 40 µm coronal sections. Sections were incubated in permeabilizing solution containing 1.2% Triton-X 100 in PBS followed by a blocking solution containing 10% normal goat serum (Thermofisher) in PBS for 1 h to avoid non-specific binding. Sections were then incubated in primary antibody overnight and then briefly washed the next day then incubated secondary antibody in 2% NGS for 1 h and washed. Sections were mounted in Vectashield medium (Vector Laboratories). For the antibody list, see Table S6. All slides were then imaged on a Zeiss LSM 710 confocal microscope. Processing of images was performed with ImageJ.
The cells were harvested using TrypLE (Gibco) for single cell dissociation before staining. For the H1 hESC LMX1A-GFP reporter line, the cells were incubated with the viability dye eFluor 780 (Invitrogen) for 30 min and washed extensively before analysis. For FZD antibody staining, the differentiated cells were harvested and blocked for 1 h with 3% BSA on ice. The cells were then stained with the selective FZD IgG antibody (https://www.r-project.org/) using default parameters (https://satijalab.org/seurat/) in R software v4.1. Seurat objects were created with the cell-gene expression files generated from the CellRanger pipeline for each treatment. The Seurat objects generated from each timepoint (i.e. day 11 or day 30) were merged. Cells were filtered based on a 200 cut-off for minimum number of genes, 7500 for maximum of genes and less than 5% of mitochondrial expression. The gene expression of each cell was normalized using the global-scaling normalization method to normalize RNA expression measurements for each cell by the total expression, which was then multiplied by a scale factor of 10,000. Highly variable genes by cell were computed and the top 2000 features from each dataset were used for subsequent downstream clustering. An elbow plot was used to estimate the number of principle components required for UMAP embedding. This distance matrix was then reduced to low dimensionality using UMAP with the first 15 principal components dimensions and clusters identified using a resolution of 0.5. For differential gene expression between clusters, the FindMarkers function was used with a comparison of the indicated clusters. GSEA was performed with GSEA software (Broad Institute) using a ranked gene expression list based on Log2Fold change for day 11 cluster 0 versus clusters 1, 2 and 5. The GO:0045746 (Notch), GO:0000165 (MAPK), M5923 (PI3K_AKT) gene sets were used for GSEA extracted from MSigDB. For all subsequent analyses, meta, filtered and normalized data were exported from Seurat for integration in various R packages. The CellCycleScoring was performed using the list of signature genes from (https://github.com/carmonalab/UCell. The list of gene markers used for UCell signature scoring is listed in Table S3. The scRNA datasets were prepared for the ShinyApp using the ShinyCell package (https://andyydh.shinyapps.io/scRNA_VM_progenitor/.
HiDDEN clustering analysis was performed on the 3990 DA+ neurons from the combined CHIR and F5L6 populations (https://github.com/tudaga/LabelCorrection. Dimensionality reduction was performed using principal component analysis. HiDDEN Continuous Scores were derived as the fitted conditional probability of label 1 given the gene expression profile for each cell computed by training a logistic regression. The optimal number of principal components (P=55) to use in the logistic regression was determined using the Kolmogorov–Smirnov heuristic (Fig. S6I). GO term enrichment analysis of the 394 genes enriched in F5L6_L1 versus L0 (combined CHIR_L0 and F5L6_L0) was performed using the web tool PANTHER Overrepresentation Test (Released in 20221013) to identify significant GO biological process terms using Fisher's Exact test and a Bonferroni multiple testing correction with adjusted P< 0.05. Dot plots in Fig. S6J were produced in scanpy based on the HiDDEN-refined binary labels and the log-normalized scaled gene expression.
All statistical methods are explained in the figure legends. Data were analyzed using GraphPad Prism (version 8) software in at least three independent experiments. Unless stated otherwise, values are shown as mean±s.e.m and asterisks in figures indicate significance according to a two-tailed Student's t-test between two groups (*P≤0.05, **P≤0.01).
We thank Kin Chan at the Network Biology Collaborative Centre and Troy Keleta at Princess Margaret Genomic Center for next-generation sequencing service. The center for Pharmaceutical Oncology provided the support and instruments that were used in these experiments. We also thank the Temerty Faculty of Medicine flow cytometry facility for assisting us with cell sorting experiments. We would also like to thank all members of the Angers labs for helpful discussion throughout the course of this study, particularly Dr Graham Macleod for providing feedback on the manuscript.
Author contributions
Conceptualization: A.Y., S.A.; Methodology: A.Y., R.C., E.R., H.S., Q.P., L.L.B., J.J.A.; Validation: A.Y.; Formal analysis: A.Y., A.G., S.A.; Investigation: A.Y., R.C.; Data curation: A.G.; Writing - original draft: A.Y.; Writing - review & editing: R.C., S.A.; Visualization: A.Y., A.G.; Supervision: R.B., S.S.S., A.S., S.A.; Funding acquisition: S.A.
Funding
This work was supported by the University of Toronto Medicine by Design program (MBDC2-2019-03 to S.S.S. and S.A.) which receives funding from the Canada First Research Excellence Fund (to S.S.S. and S.A), and by the Canadian Institutes of Health Research PJT-407961 to A.S. and PJT-175160 to S.A.). Open access funding provided by the University of Toronto. Deposited in PMC for immediate release.
Data availability
The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (Edgar et al., 2002) under series accession number GSE241043.
This article has an associated ‘The people behind the papers’ interview with some of the authors.
The peer review history is available online at https://journals.biologists.com/dev/lookup/doi/10.1242/dev.202545.reviewer-comments.pdf
Competing interests
S.A., S.S.S., J.J.A. and L.L.B. hold shares in AntlerA Therapeutics and are inventors on patents for the antibodies described in the manuscript.
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