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Decoding spatiotemporal transcriptional dynamics and epithelial fibroblast crosstalk during gastroesophageal junction … – Nature.com

Single-cell map of epithelial lineage development at the GE-SCJ

The adult human esophageal mucosa is lined with stratified squamous epithelium that meets the columnar epithelium-lined stomach at the GE-SCJ (Fig.1a). Whereas in the mouse, the esophagus opens into the stomach that comprises two regions- a stratified squamous epithelium-lined fore-stomach similar to the esophagus and columnar epithelium-lined stomach (Fig.1a). To study the developmental process and the evolution of cellular features during GE-SCJ histogenesis, we carried out single-cell transcriptome analyses of the esophagus, GE-SCJ, and stomach tissue samples obtained from embryonic day 15 (E15), E19, newborn (pup), and adult mice. Although we expected tissue level changes during the different developmental stages of GE-SCJ, the nature of transcriptional shifts, regulatory mechanisms, and the intermediate cell types during the temporal development and GE-SCJ histogenesis is unknown. Towards this, scRNA-seq data offer a vital input source for unambiguously identifying an individual cell (or cell group) based on their transcriptional states. The uniform manifold approximation and projection (UMAP) distribution of the generated time course single-cell transcriptomes showed a clear separation of cells by developmental time at pre- and postnatal stages (Supplementary Fig.1a). We performed unsupervised clustering and annotated based on the expression of known lineage signatures and cell type markers. This analysis revealed the presence of squamous and columnar epithelial, stromal, endothelial, immune, and neural cell populations (Fig. 1b, Supplementary Fig.1b). UMAP sub-clustering of epithelial cells revealed transcriptionally distinct clusters separated based on squamous and columnar lineages and reflecting their developmental state (Fig.1c). Since esophageal epithelium at GE-SCJ is predisposed to replacement with non-resident metaplastic epithelium16,17, we first focused on understanding the temporal evolution and establishment of epithelial lineages at the GE-SCJ during development (Fig.1d). To identify precursor cells of squamous and columnar epithelial lineages at the GE-SCJ, pseudotime analysis using scRNA seq data was performed by reconstructing branching developmental trajectories using diffusion maps. This analysis revealed two different lineages branching out from the embryonic epithelial cells at the center from the E15 and E19 stages (Fig.1e).

a Schematic of human and mouse adult esophagus and stomach anatomy, including GE-SCJ. b UMAP of scRNA-seq data of esophagus, GE-SCJ, and stomach from embryonic day 15 (E15), E19, pup, and adult mice showing six distinct cellular clusters; dots represent single cells, colored by cell types. c UMAP of epithelial cells, color-coded by tissue type and time point. d UMAP of GE-SCJ epithelial, colored by time point. e Diffusion map (DM) illustrates the branching differentiation of GE-SCJ epithelial cells. f Heatmap of differentially expressed genes (DEGs) across subclusters, with cells ordered by developmental trajectory as in (e). g Normalized expression of selected markers, visualized by DM projection as in (e). h Violin plots show expression levels of specific genes across tissues and stages. i smRNA-ISH and immunostaining images of mouse GE-SCJ with Sox11 (white), KRT5 (green), KRT8 (red), and nuclei (blue). j Immunostained images of the mouse stomach, including distal esophagus with KRT5 (green), KRT7 (Red), P63 (white), and nuclei (blue). Magnified view of the boxed GE-SCJ region (Right panel). Sq, Co, PR, Es, Fs, and Hs indicate squamous epithelia, columnar epithelia, precursor cell region, esophagus, forestomach, and hind stomach. Images are representative of three biological replicates in (ij). k Dendrogram from URD trajectory analysis of GE-SCJ epithelial cells; each dot represents a single cell, colored by time point. Cells are ordered based on pseudotime values, starting from early at the top to late at the bottom of the tree. l, m UMAP of re-clustered GE-SCJ epithelial subpopulation positive for all selected embryonic markers (Vcan, Igf2, Sox11, and H19), colored by time point (l) and lineage type (m). np Joint gene-weighted density estimation of columnar (n), precursor (o), and squamous (p) epithelia. q Bar plot of epithelial types relative proportion at GE-SCJ by time point. r UMAP showing epithelial subclusters in combined GE-SCJ cells from E15 to adult, colored by cluster. s Sankey plot representing the contribution of epithelial cells from each time point to the combined GE-SCJ epithelial subclusters, as shown in (r).

Differential expression analysis across GE-SCJ epithelial cell clusters unraveled the gene expression signature associated with embryonic precursor epithelial cells (Sox11, Igf2, H19, Cldn6, Vcan, and Bex1)18,19,20,21,22,23,24 committing to either the squamous (Trp63, Col17a1, Krt5, Krt15, Krt13, Lgals7) or columnar (Muc5b, Furin, Pgc, Muc6, Agr2) epithelial lineages (Fig.1f, g, and Supplementary Data2). Next, we analyzed the absolute expression of embryonic precursor, squamous, and columnar epithelial marker genes in the GE-SCJ region across all time points (Fig.1h). We found that cells expressing embryonic precursor-associated gene signatures were lost in the postnatal stages (Fig.1h, i, Supplementary Fig.2a, b). However, expression of Krt7, previously described as an exclusive marker for the residual embryonic epithelial cell at adult GE-SCJ and implicated in BE development25,26 was observed to be expressed in cells across all the time points (Fig.1h). These observations were further clarified by immunohistochemistry (IHC) and/or single-molecule RNA in situ hybridization (smRNA-ISH) for KRT5, P63, and KRT7 (Fig.1j, Supplementary Fig.2cf). All the epithelial cells lining E13 mucosa express KRT7. However, these KRT7 cells in the esophagus and foregut region differentiate into P63+KRT5+ cells and show reduced KRT7 expression during squamous stratification. Eventually, KRT7high cells positioned above the P63+KRT5+ squamous epithelial cells in the esophagus and forestomach sloughed off during the E19 stage, thus visibly demarcating the KRT7low squamous and KRT7high columnar epithelial regions of the esophagus and stomach respectively in the adult stage (Fig.1j, Supplementary Fig.2cf). This data shows that in the adult GE-SCJ mucosa, the columnar and squamous epithelial cells express distinct gene signatures from embryonic epithelium, indicating lineage commitment of these epithelial cells. The tree diagram delineated the epithelial differentiation steps by ordering cells based on their pseudotime values, starting from the early embryonic cells that branch into late squamous (Sq3) and columnar epithelial cells (Gland base and pit) (Fig. 1k). To identify early differentiation events, we extracted the early embryonic cell population and performed re-clustering. This revealed the presence of three subclusters within them, showing higher expression of aforementioned lineage-specific markers for squamous, columnar, and precursor populations (Fig.1lp, Supplementary Fig.1c, d). The cell proportion graph further substantiates our findings that the precursor cell population was only present in the embryonic epithelial cells (at E15 and E19) and, to a very less extent, in the pup but not in the adult stage. (Fig.1q). Similarly, the precursor cell population was restricted to embryonic stages in the esophagus and stomach epithelia (Supplementary Fig.1eg). Next, to understand the overall GE-SCJ epithelial characteristics, we performed combined clustering of GE-SCJ cells from all time points, revealing nine subpopulations (Jn_1 9) together with the projected precursor cell population that were either shared or unique during different developmental stages (Fig.1r). Sankey analysis showed that the precursor cell population was majorly contributed by E15 epithelial cells. In contrast, the postnatal epithelial cells majorly contributed to Jn-36 and 8-9 clusters (Fig.1s).

Corroborating to scRNA seq data in Fig.1, we observed that the adult GE-SCJ comprises two epithelial lineages, namelysquamous and columnar, each characterized by lineage-specific gene expression patterns. Similar to P63+KRT5+ and KRT7high expression pattern (Fig.1j, Supplementary Fig.2cf), we observed that KRT8+ cells from the E13 stage differentiate to P63+KRT5+ squamous and KRT8high columnar epithelia during GE-SCJ development eventually defining the adult GE-SCJ (Fig.2ac, Supplementary Fig.3ac). Furthermore, the smRNA-ISH analysis confirmed that Krt5 and Krt8 mRNA are specifically expressed in the adult esophagus and stomach epithelial cells, respectively (Supplementary Fig.3d, e). Next, by inducing lineage tracing in Krt5-CreERT2; Rosa26-tdTomato and Krt8-CreERT2; Rosa26-tdTomato mice (Fig.2d), we confirmed that the Krt5 cells regenerate squamous epithelium of esophagus and Krt8 cells regenerate columnar epithelium of the stomach that meet at GE-SCJ (Fig.2e, f).

ac Tiled images of the entire stomach, including distal esophagus of E13, E16, and E19 mice (a); GE-SCJ of the adult mouse (b) and human (c) immunostained with KRT5 (green), KRT8 (Red), P63 (white), and nuclei (blue). A magnified view of the boxed GE-SCJ regions (right panel) (a). df Treatment scheme for lineage tracing of mice (d) and tiled images of GE-SCJ tissue sections from Krt5-CreERT2; Rosa26-tdTomato (e) or Krt8-CreERT2; Rosa26-tdTomato (f). Nuclei (blue). The white dotted line indicates the basal cells of squamous epithelia at GE-SCJ. g UMAP of esophagus and stomach epithelia (excluding GE-SCJ); cells color-coded by time point. h, i URD differentiation tree of the esophagus (h) and stomach (i) epithelial population; each dot represents a single cell, colored by cell type. Cells ordered based on pseudotime values starting from early (top) to late (bottom). j Circular dendrogram indicating the similarity between epithelial cell clusters as in (h, i) from both tissue types at different time points; Font color indicates time point and tissue type. k Heatmap showing top 20 DEG across esophagus and stomach epithelial stem cell compartments from the embryonic to adult time points; color bar denotes the z-scored mean expression range from high (deep pink) to low (blue). l Heatmap of 20 most variable transcription factors (TF) across epithelial stem cell compartments. The color bar depicts the scaled TF activity scores from high (deep pink) to low (blue). m Confocal images of the mouse GE-SCJ immunostained with CDH1 (green), GATA6 (red), SOX2 (white), and nuclei (blue). Sq, Co, Es, Fs, Hs indicate squamous epithelia, columnar epithelia, esophagus, forestomach, and hind stomach, respectively (ac, e, f, m). Images are representative of three biological replicates in (ac, e, f, m).

Next, we dissected the cell-type specification and subcellular differentiation within squamous and columnar lineage from the scRNA-seq data of E15, E19, pup, and adult esophagus and stomach samples. We clustered epithelial cells from the esophagus and stomach at individual time points separately (Supplementary Fig.3f, g). E15 and E19 esophagus contains early basalstem-like epithelial sub-clusters (Sq1, Sq2), which exhibited higher expression of embryonic developmental genes such as Sox11, Vcan, and Fras1. Whereas the actual higher-order differentiation of epithelial cells was observed in postnatal tissues starting from the pup stage (Sq1A, Sq1B, Sq2A, Sq2B, Sq2C, Sq3). Sq1 represented the basal cell population with a remarkably higher expression of Trp63, Krt5, and Col17a1. Sq2 was positive for parabasal markers like Jun and Fosb, while Sq3 was positive for differentiation markers such as Krt13, Lor, and Spink5 (Supplementary Figs.3f, 4a, c). In the case of the stomach, at E15, all the epithelial cells show high proliferation and expression of embryonic developmental markers. However, two subgroups of cells showed relatively low expression of proliferation (Mki67, Top2a) and developmental (Vcan) markers, indicating the onset of differentiation of these early epithelial cells into other cell types (Neck-like and Pit-like). The presence of epithelial cell types defining the stomach gland region was evident only from E19, which contains cells expressing Lgr5, Axin2, Chga (Base), Atp4a, Muc6 (Neck), Stmn1, Mki67 (Isthmus), Gkn2, Tff1 (Pit) genes (Supplementary Figs.3g, 4b, d). Cell type proportion analysis across both samples at pre- and postnatal stages showed that early embryonic columnar epithelial cells were present only in the E15 and E19 stomach samples. However, in the case of the esophagus, the basal squamous epithelium was shared at all the time points in opposition to differentiated cells that were present only during postnatal time points (Supplementary Fig.4e). Combined clustering of epithelial cells from both esophagus and stomach across all time points revealed that the clustering of cells was not only driven by cell type but was also influenced by tissue type and developmental stages (Fig.2g).

Pseudotime analysis of esophagus epithelial cells showed linear trajectory starting from E15, branched into two trajectories leading to differentiated states of i) E19 (Sq2) and pup (Sq2c) and ii) adult (Sq1-3) (Fig.2h). Whereas, in the stomach, we recovered a branching tree which clearly showed the ordering of cells from embryonic to adult time points with cells from base region confined separately from cells that belong to neck and pit regions (Fig.2i). Additionally, in the rightmost branch of the trajectory, a combination of cells mostly from E15, E19 and few from pup time points exhibited expression of early embryonic markers like Sox11, Vcan, while differentiated cells such as Chga and Muc5ac were found in the left trajectories mainly in pup and adult states (Supplementary Fig.4f-i). Since scRNA-seq data represents the cells transcriptome at a given time, it is inferred that the embryonic differentiated cells (neck-like and pit-like), which are distinct from the differentiated adult cells on the rightmost branch, could indicate transient states and may differentiate to the adult type or likely shed off during development. Dendrogram analysis of identified cell types within the esophagus and stomach from all time points also confirmed that squamous and columnar epithelial cells were transcriptionally dissimilar (Fig.2j). In the esophagus, basal and parabasal cells occupy separate subbranches, while highly differentiated cells (Sq2C-Pup and Sq3-Adult) appeared in a distinct subbranch, revealing transcriptional distinction between these cell types. Similarly, in the stomach, epithelial cells from the adult time point formed a separate branch, emphasizing the well-developed glandular units comprising complex cell types distinct from earlier developmental time points.

To understand the transcriptional difference and essential regulators underlying precursor cell population and stem cell compartment of the lineage-committed esophagus and stomach epithelia, we performed differential expression (DE) and transcription factors (TF) activity analysis (Fig.2k, l, and Supplementary Data3, 4). DE analysis showed some transcriptional similarity of precursor cell population with embryonic stem cell compartment. However, no similarity was observed with the postnatal stem cell compartment (Fig.2k, and Supplementary Data3). We computed TF activities based on the expression levels of their target genes. TF-target interactions were sourced from curated evidence with high confidence levels using DoRothEA27. This analysis revealed an overlap of cell cycle-related genes between the precursor cell population and the early-stage stem cell compartment, correlating to the higher proliferation. Columnar lineage stem cells of the stomach were enriched for the TF activities of Gata6, Foxa1/2, and Hnf4a28,29,30, which were also enriched but at a lower extent in the precursor cell population, suggesting the shared identity of columnar stem cells and precursor cells. Squamous lineage-defining Trp63, Sox2, and Klf531 genes are only expressed in the esophageal epithelial cells. SOX2 expression was confirmed to be high in the squamous epithelium, aligning with previous findings32, and GATA6 was highly expressed in the columnar lineage at the GE-SCJ (Fig.2m, Supplementary Fig.S4j). GATA6 expression was confined specifically to the lower part of the stomach gland, suggesting that it might play a role in columnar stem cell maintenance and differentiation that needs to be further elucidated. In line with this, other studies have shown that GATA6 regulates intestinal epithelial proliferation, lineage maturation, and BMP repression33,34,35. Further, TFs such as Nanog, Tead1, Prdm14, Pax536,37 activity were enriched in the early-stage squamous epithelium and specific cell states of columnar epithelia (Fig.2l, and Supplementary Data4). However, their mechanistic role in lineage commitment within the squamous and columnar epithelia is unclear and an avenue for future research. Thus, this study provides the temporal landscape of the TF activity of epithelial stem cells during GE-SCJ development.

To gain insights into the heterogeneity of the stromal fibroblast population, which shapes epithelial morphogenesis, we analyzed stromal cells from the pre- and postnatal esophagus, stomach, and GE-SCJ tissue regions. As a result, we identified a clear separation of stromal clusters according to pre- and postnatal developmental stages (Supplementary Fig.5a, b). Next, to elucidate the pivotal role of underlying fibroblasts in steering the development of distinct squamous and columnar epithelia, we focused on the esophagus and stomach fibroblast cells, excluding the GE-SCJ, as it is a blend of the esophagus and stomach stromal niche (Fig. 3a). Unsupervised clustering of combined-fibroblast (C-FB) population revealed 16 transcriptionally distinct cellular subsets segregated based on tissue region and time points (Fig.3b, Supplementary Fig.5c). Euclidean distance measurement showed that fibroblast subpopulations from the embryonic stage grouped together and are distinct from the postnatal stromal clusters. Thus, pre- and postnatal fibroblasts possess distinct transcriptional properties (Fig.3c). These subclusters were grouped into 4 major types based on the cells transcriptional state similarity (Fig.3d). Group-1 includes C-FB1, C-FB11, and C-FB15 consisting of cells from all the time points, represented by smooth muscle cells that highly expressed Acta2, Myh11, Tagln (Fig.3a, d, f, Supplementary Data5). Groups 2 and 3 expressed fibroblast marker genes (Col1a1, Col3a1, Dcn, Lum, Postn) segregated into embryonic and adult fibroblasts, respectively38. Group 4 type fibroblasts (C-FB7) expressed muscle cell phenotypic markers such as Acta1, Tnnt3, and Mb and formed a distinct cluster (Fig.3d, f Supplementary Data5). Validation of ACTA2 and POSTN proteins in mouse E19 and Adult GE-SCJ showed the presence of two distinct Group1 and Group 2-3 fibroblast populations (Fig.3e, Supplementary Fig.6a, b). Among Group 2 and 3 fibroblast clusters, C-FB2-4, 10, and 16 enriched for the collagen-related genes, suggesting their role in establishing mechanical structure during development. C-FB9 is highly enriched for the proliferation marker genes Mki67, Top2a, and Stmn1, suggesting a putative fibroblast precursor cell population in the embryonic stage. C-FB6 and C-FB8 derived from the postnatal tissue enriched for the Wingless-related integration site (WNT) inhibitor genes Dkk2 and Sfrp4, indicating their role in the WNT signal modulation. The C-FB12 cluster expressed Rgs5 and Fn1, previously characterized as pericyte-like cells39. C-FB13 exhibited strong expression of Bmp4, Ptch1 which mediates key signaling pathways like Bone Morphogenetic Proteins (BMP) and Sonic Hedgehog (SHH), indicating a potential role in the epithelial morphogenesis during development40,41 (Fig.3d, f, Supplementary Data5). We further identified the transcriptional signatures of fibroblasts specific to tissue regions (esophagus or stomach specific) and developmental stages with few markers shared over time for both esophagus and stomach (Fig.3a, f, Supplementary Fig.5d, f, Supplementary Data5). The Sankey analysis highlighted the shared (C-FB2, 5, 8, 9, 14, 15, 16) or mutually exclusive (C-FB1, 3, 4, 6 for esophagus and C-FB10, 11, 12, 13 for stomach) cluster contributions of different stromal cell sub-types across the tissue during development (Fig.3g, h). Similarly, we individually examined the distribution and heterogeneity of fibroblast types within the esophagus and stomach at all time points. We observed a clear separation of the fibroblast population between the pre- and postnatal stages, while some fibroblast states were shared across the developmental stages (Supplementary Fig.5gl).

a, b UMAP of combined fibroblast (C-FB) cell clusters from esophagus and stomach samples; colored by tissue type and time point (a) in shades of green and magenta, respectively, and cluster annotation (b). c, d Dendrograms highlighting the similarity between fibroblast cell clusters from esophageal and stomach tissue types at different time points (c) and at annotated cluster levels (d); font color denotes subclusters as in figures (a, b), respectively. e Tiled images of mouse esophagus, GE-SCJ, and stomach tissue sections from E19 and adult stages immunostained with CDH1 (green), POSTN (red), and ACTA2 (white) and nuclei (blue). Images are representative of three biological replicates. Sq, Co indicates squamous and columnar epithelia, respectively. f Heatmap of top 20 DEG across fibroblast subclusters as in (b) and subclusters were grouped as in (d); Color bar denotes the z-scored mean expression values ranging from high (deep pink) to low (blue). g, h Sankey plots highlighting the contribution of fibroblast cells from the esophagus (g) and stomach (h) samples at each time point to the subclusters, as shown in (b).

Our previous study14 shows that Wnt signaling between epithelia and stromal microenvironment plays a crucial role in dictating lineage specification. Here, we observed that Rspo3, a key WNT signaling agonist known for regulating stem cell regeneration42, was expressed by a subset of fibroblasts in both esophagus and stomach (Fig.4a, cf). Interestingly, the proximity of Rspo3 signals to the epithelial stem cell compartment of the esophagus and stomach differed. The average distance of the Rspo3 signals to the epithelia is greater in the esophagus than in the stomach (Fig.4df). On the contrary, Dkk2, a WNT inhibitory morphogen43,44, was strongly expressed in the fibroblasts and smooth muscle cells of the esophagus with relatively low expression in the stomach (Fig.4b, c, gi and Supplementary Data6). Further, expression of Kremen1, a receptor of DKK244, is observed only in the esophageal epithelial cells (Fig.4m), suggesting the establishment of the WNT inhibitory microenvironment in the esophagus. Further lineage tracing of canonical WNT signaling target gene Axin245 in mice confirmed that esophageal epithelial cells were negative for AXIN2 lineage. In contrast, the AXIN2+ cells labeled the columnar epithelium of the stomach gland (Fig.4n, o, Supplementary Fig.6c). This observation was further confirmed by smRNA-ISH for Lgr5 and Axin2 in adult mice (Supplementary Fig.6dg). Together, the data revealed that the fibroblast compartment evolves concordant to the temporal development of GE-SCJ from embryonic to adult stages. The distinct sub-cell types of fibroblasts underlying the esophagus and stomach epithelia have a unique spatial organization and secrete unique location-specific morphogens. We show that the spatially defined distinct WNT fibroblast microenvironment underlying the columnar and squamous epithelia that meet at GE-SCJ plays a vital role in determining the adult GE-SCJ borders.

a, b Feature plots showing normalized expression levels of markers Rspo3 (a) and Dkk2 (b) within fibroblast cells. c Trend plots depict the changes associated with mean expression levels of the selected markers over time, as in (a, b). Line color denotes genes, and point shapes represent tissue type. di smRNA-ISH images of the WNT pathway genes Rspo3 (d) and Dkk2 (g) in the mouse esophagus tissue (i), GE-SCJ (ii), and stomach glands (iii). Nuclei (blue). Quantification of Rspo3 (e) and Dkk2 (h) signal counts in epithelia (Ep), stroma (St), and myofibroblast (My) in the mouse GE-SCJ tissue regions and distance (m) from epithelia to Rspo3 (f) and Dkk2 (i) signal. Data are mean+/-SEM (e, f, and h, i). n=number of signal count and their distance to epithelia (f, i) from three non-overlapping 100m2 regions of esophagus and stomach tissues. jl Confocal images of adult mouse esophagus and stomach tissue sections immunostained for CDH1 (green), POSTN (red), and ACTA2 (red) and smRNA-ISH for Rspo3 (white), Dkk2 (white) and Sfrp4 (white) as indicated. m Violin plot showing the normalized gene expression values of Lrp6 and Kremen1 from embryonic to adult time points at different tissue regions. n Scheme for lineage tracing of mice expressing Axin2-CreERT2/Rosa26-tdTomato. o Tiled images of GE-SCJ sections from Axin2-CreERT2/Rosa26-tdTomato mice co-immunostained for KRT5 (green), AXIN2 lineage traced cells marked by Tdtomato (red), and nuclei (blue). Sq, Co indicates squamous and columnar epithelia, respectively. Images are representative of three biological replicates in (d, g, jl, o). For (e, f, and h, i), source data are provided as a Source Data file.

Based on the above-observed distribution of WNT signals in the fibroblasts (Fig.4ao, Supplementary Fig.6ce), we tested the role of WNT signaling in stemness and regeneration by establishing stomach and esophageal epithelial organoids. Mouse esophageal stem cells grew into mature squamous stratified esophageal epithelial organoids in the presence and absence of WNT3a and RSPO1 (W/R) (Fig.5a). However, they lost the stemness and growth capacity over a few passages in the presence of W/R (Fig.5a, b, e, f). Consistently, patient-derived esophageal cells fail to form organoids in the presence of W/R, while their absence supports the growth and differentiation into mature stratified epithelium (Fig.5c, d). This is in contrast to previous studies that showed the culture of esophageal organoids with either the Wnt agonist R-Spondin alone6 or in combination with a Wnt ligand46, suggesting that Wnt signaling is dispensable for the esophageal organoid formation.

ad Bright-field images of the mouse (a, b) and human (c, d) esophageal and stomach organoids grown in the presence or absence of WNT3A (W) and R-spondin1 (R). b, d Higher magnification of (a, c). e, f Percentage of organoid formation (e) and long-term passaging (f) from esophagus and stomach under indicated conditions and passages (P); data derived from two biological replicates (n=2). # indicates organoids can be passaged beyond the stated number. g, h Images of mouse esophageal and stomach organoid immunolabeled for KRT5 (green), KRT7 (Red), P63 (white), KRT8 (Red), nuclei (blue). i, j Organoid diameter measurement from mouse esophagus (i) and stomach (j) grown in indicated media. n=number of organoids measured. Data are representative of three biological replicates. Data are mean+/-SEM; statistical significance was calculated using a two-sided t-test, P-values as indicated. k, l Bright-field (k) and confocal images showing KRT5 (green), KRT8 (red), MUC5AC (white), and nuclei in blue (l). m, n smRNA-ISH images of Lgr5 (m) and Axin2 (n) in mouse esophagus (i) and stomach organoids with inset images (ii). Lgr5-highlighted in arrowhead (m-ii). oq Scheme for lineage tracing of mice (o). Organoids cultured from cells lineage traced for KRT5 (p) and KRT8 (q) in indicated media. r UMAP showing cellular subclusters of esophageal and stomach epithelial organoids. Cells colored by cluster (ST, stomach; ES, esophagus; Co, Columnar epithelia; Sq, squamous epithelia). s Pseudotime trajectories in esophagus epithelial subclusters. tv Dot plot depicting relative gene expression for stomach (t) and esophagus (u) epithelial subclusters for canonical and non-canonical WNT pathway (v). Circle size denotes percentage of cells expressing a gene; color represents the scaled mean expression level from high (red) to low (blue) (tv). w, x Images of human tissue (upper panel) and mouse esophagus organoids (lower panel), immunostained for KRT17 (yellow), JUN (red), KRT6 (red), CDH1 (green) and nuclei (blue). Images are representative of three biological replicates in (ad, g-h, kn, p-q, w-x). For (e, f, and i, j), source data is provided as a Source Data file.

In contrast to the esophagus, and in agreement with previous studies47,48, W/R conditioned media was essential for stomach columnar epithelial organoid growth (Fig.5af). Cultured organoids maintained in vivo epithelial lineage specificity and morphology of esophagus (P63+KRT5+) and stomach (KRT8high, KRT7high), respectively (Figs.2ac, 5g, h, Supplementary Fig.3a-c). A stem cell marker of the stomach, Lgr5, and WNT target genes Axin2 were absent in esophagus organoids (Fig.5m, n). Further, inhibition of endogenous WNT signaling by pan canonical and non-canonical WNT secretion inhibitor IWP2 did not influence the growth of esophageal organoids but reduced the stomach organoid growth and accelerated its differentiation with high expression of MUC5AC (Fig.5il).

Next, we asked if these distinct epithelial stem cell lineages possess the plasticity to transdifferentiate with altering WNT growth factors. For this, epithelial cells from the esophagus and stomach were isolated from induced Krt5-CreERT2;Rosa26-tdTomato and Krt8-CreERT2;Rosa26-tdTomato mice, and cultured as organoids in the presence or absence of W/R media (Fig.5oq). Irrespective of the presence or absence of W/R esophageal stratified organoids from Krt5-CreERT2;Rosa26-tdTomato mice were found to be labeled, whereas matched stomach columnar organoids were not (Fig.5p). Similarly, stomach columnar organoids from Krt8-Cre;Rosa26-tdTomato mice were found to be labeled, whereas matched esophageal stratified organoids were not labeled (Fig.5q). Thus, the adult GE-SCJ consists of two committed squamous and columnar epithelial stem cells that do not transdifferentiate with the change in the WNT microenvironment. Instead, spatial WNT signaling factors play a critical role in the differential proliferation of stratified and columnar epithelia, maintaining the homeostasis of the GE-SCJ.

Further, global transcriptomic and scRNA seq analysis of the esophageal and stomach organoids corroborated the single-cell transcriptional signatures of the in vivo epithelial tissue. Microarray analysis revealed that among 34393 unique probes, encompassing protein-coding genes and long non-coding RNAs, 8030 genes were differentially regulated between columnar and squamous epithelium (Supplementary Fig.7a, Supplementary Data7). Gene ontology terms associated with the differentially expressed genes between the esophagus and stomach organoids showed enrichment of distinct pathways specific to the epithelial types (Supplementary Fig.7b and Supplementary Data8). Pathways related to epidermal cell development, keratinocyte differentiation, transcription and translation, and regulation of cell-cell adhesion were highly enriched in the esophageal epithelial cells. In the stomach epithelial cells, metabolic and catabolic processes related to lipids, fatty acids, and ion transport were enriched. While WNT signaling was critical in regulating GE-SCJ homeostasis, our analysis revealed that columnar epithelial cells were enriched for the canonical WNT beta-catenin and non-canonical WNT/Ca2+ pathway genes. In contrast, squamous epithelial cells were enriched for the non-canonical WNT/planar cell polarity (PCP) pathway genes (Supplementary Fig.7c).

Further, scRNA seq analysis revealed the heterogeneity and subcellular composition of columnar and squamous epithelial cells of gastroesophageal organoids. We categorized cells from stomach (ST) organoids into two major clusters (ST-Co1, ST-Co2 and the squamous epithelial cells of esophageal (ES) organoids were segregated into five unique clusters (Sq1, Sq2A, Sq2B, Sq3A and Sq3B) (Fig.5r). The UMAP recapitulates the differentiation stages of the columnar stomach and stratified esophageal epithelial cells. The ST-Co1 subcluster was enriched for the expression of well-known stomach stem cell markers Lgr5, Aqp5, and Axin2 with high levels of Pgc, Muc6, Gkn3, and Atp4a expression, which are key markers of cells present in the neck and isthmus region. These cells also expressed high levels of proliferation markers, including Mki67, Pcna, Top2a, and Stmn1. The second subcluster, ST-Co2, comprises mostly pit cells of the stomach gland, which expressed high levels of Gkn1, Gkn2, and Tff1 (Fig.5r, t). The esophageal subcluster Sq1 expressed Col7a1, Timm9, Trp63, Stmn1, and Krt17, representing the stratified epitheliums basal cells. The Sq2A subcluster consists of transient proliferating cells expressing Mki67, Top2a, Pcna, Fau, Gstm1, Jun, and Upk3bl. The subcluster Sq2B was enriched for Atf3, Cav1, Ybx1, Cald1, and Sox4, while Sq3A and Sq3B subclusters exhibited differentiation-associated gene markers such as Rhov, Krt6a, Krt13, Anxa1, Tgm1, Spink5, Gsta5, Sprr3 and Elf5 (Fig.5r, u, Supplementary Fig.7dg). Similar to our bulk transcriptomic data (Supplementary Fig.7c), we further identified the distinct expression patterns of the canonical and non-canonical WNT signaling genes in subpopulations of the columnar and esophageal epithelium from the scRNA seq data (Fig.5v).

Since little is known about the esophageal epithelial differentiation trajectories in vitro, we performed a pseudo-temporal reconstruction of the lineage using slingshot49. We show two distinct trajectories, all originating from the basal stem cell compartment of Sq1, differentiating into distinct sub-lineages Sq2 and Sq3 (Fig.5s). Further, by immunostaining, we spatially located the cell types in scRNA seq data that express KRT17, JUN, and KRT6 in human and mouse tissue and organoids, revealing three major subtypes, KRT17+/JUN- basal stem cells KRT17+/JUN+ parabasal cells and KRT6+ differentiated cells (Fig.5wx). Thus, organoids reflect the in vivo epithelial heterogeneity and illustrate the differential impact of WNT signaling on gastroesophageal epithelial stem cell regeneration and differentiation dynamics.

Our approach by employing tissue and organoid models and transcriptome analyses at both global and single-cell levels indicated that the spatial signaling factors are crucial in dictating the squamocolumnar epithelial homeostasis in GE-SCJ. Hence, to gain insights into the pathways and uncover the molecular regulatory networks between epithelial and fibroblast cell populations during GE-SCJ development, we performed gene set enrichment analysis (GSEA) using scRNA-seq data. We identified key signaling pathways differentially enriched between tissue types and time points (Fig.6a, and Supplementary Data9). Pathways such as bile acid and fatty acid metabolism were enriched in the stomach epithelia. While MYC target genes were enriched in esophagus and stomach epithelia, they gradually decreased towards the adult stage, suggesting an overall reduction in cell proliferation as higher-order differentiation proceeded with development. Interestingly, stroma from both esophagus and stomach exhibited strong enrichment for PI3K- FGFR1 cascade, Platelet-Derived Growth Factor (PDGF) signaling, and myogenesis. The hallmark of inflammatory response was more upregulated in both adult tissue stromal regions, and the hallmark of complement was highly enriched in the esophagus stromal cells, suggesting the presence of activated fibroblast50.

a Heatmap of gene set enrichment scores of fibroblasts and epithelial cells of esophagus and stomach from embryonic to adult time points with specific pathways highlighted; column represents individual cells colored by tissue type and time point; colors in the scale bar denotes the z-scored enrichment values ranging from high (deep pink) to low (blue). b, c Heatmap comparing the overall (aggregated both incoming and outgoing) signaling patterns associated with both fibroblast and epithelial compartments in the esophagus (b) and stomach (c) between E19 and adult time points. The color bar denotes the relative signaling strength (row-scaled values) of a pathway across cell types and time points. The relative strength of a pathway is calculated by normalizing each row of values to fall within the range 0-1 and depicted as low (white) to high (dark brown). Colored bar plot on top depicts the total signaling strength of a particular cell type by summarizing all pathways in the heatmap. d Dot plot showing the expression levels of ligands, receptors, and modulators associated with key signaling pathways in both fibroblasts and the epithelial subpopulation of esophagus and stomach at E19 and adult stages. Dot size represents the percentage of cells expressing a particular gene; the color bar indicates the intensity of scaled mean expression levels ranging from high (red) to low (blue). Genes are color-coded based on the signaling pathways to which they belong.

However, the enrichment results did not reveal information regarding the directionality and temporal dynamics of these signaling pathways. Therefore, we scrutinized for alterations in signaling patterns and their strengths between embryonic and adult stages using comparative CellChat51 analysis. In order to mitigate the complexity of cellular interactions and their interpretation, we designated E19 and adult mice as representatives for the pre- and postnatal stages, respectively, and were used for the interaction study. We found that many pathways, such as Laminin and FN1, were enriched during both the pre- and postnatal stages of the esophagus, while pathways including MK, NCAM, and VCAM were more enriched in the prenatal esophagus; Transforming Growth Factor Beta (TGF-), Fibroblast Growth Factor (FGF), and Chemokine (C-X-C motif) Ligand (CXCL) were more enriched in the postnatal esophagus (Supplementary Fig.8a). Interestingly, in case of stomach, majority of the pathways showed more enrichment during the pre-natal phase (Supplementary Fig.8a).

Next, we identified the patterns for incoming, outgoing (Supplementary Fig.8b, c), and overall signaling associated with epithelial and fibroblast cells (Fig.6b, c). In our analysis, incoming or receiver signals refer to the communication received by a cell population through expressed receptors. Conversely, outgoing or sender signals pertain to the communication initiated by a cell population, typically through the expression of ligands. Our analysis indicated that fibroblasts predominantly served as the signaling senders during the epithelial-fibroblast interplay in the esophagus and stomach (Supplementary Fig.8b, c). For Instance, in the esophagus, the Notch pathway has consistently stronger incoming signals in the epithelium compared to fibroblasts at both E19 and adult stages. At the E19 stage, fibroblasts predominantly exhibit outgoing Notch signals, whereas in adult tissues, epithelial cells emerge as the primary source. This pattern indicates that epithelial cells function as receivers of Notch signals across both examined stages. In contrast, fibroblasts transition from being predominant senders at E19 to a less active signaling role in adults (Supplementary Fig.8b). This observation aligns with our earlier study, emphasizing the significance of basal squamous epithelial stem cells as the primary source of outgoing Notch signal and differentiated cells as the receivers contributing to stratification14.

Overall interactions for cell adhesion signaling pathways, including collagen, THBS, Laminin, and FN1, were higher in fibroblast cells of both pre-and postnatal stages, whereas NCAM, VCAM, and OCLN were found higher only in prenatal fibroblasts. Further, TGF- signaling was highly expressed in fibroblasts of the prenatal stomach, while in postnatal phase, it was more active in the esophagus. When compared between the esophagus and stomach, the signaling strength for BMP, non-canonical WNT (ncWNT), NOTCH, WNT, and FGF was retained at a similar level during esophagus development, whereas in the stomach, signaling was predominant at the early stage (Fig.6b, c). These results provide a comprehensive overview of the evolution of organ-specific epithelial-stromal signaling, which regulates several biological processes and homing of tissue-resident cells during the histogenesis of GE-SCJ52,53.

Next, we checked for the sources and targets of signaling involved in the development associated pathways such as WNT, BMP, TGF-, Insulin-like Growth Factor (IGF), FGF, NOTCH, SHH, and PDGF. We manually collected and curated key ligands (L), receptors (R), and positive and negative modulators (M) for each pathway (from publicly available literature together with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database) and assessed their mRNA expression level across all epithelial and stromal subclusters of E19 and adult esophagus and stomach samples. We used the individual time point-based subclustered fibroblasts (Supplementary Fig.8d, e) and epithelial cells of both the esophagus and stomach for analysis (Supplementary Fig.3f, g). This comprehensive analysis unraveled a detailed expression pattern of L-R-M across various epithelial and stromal subclusters, offering insights into the intricate network of epithelial-fibroblast communication during the GE-SCJ development(Fig.6d). BMP pathway genes were expressed relatively more in the fibroblasts than epithelial cells throughout development. Other pathway genes, such as Igf1, Mdk, and Ptn, were highly expressed in the fibroblasts of both esophagus and stomach during the prenatal stage. The distinct expression profiles of FGF ligands in fibroblasts, with Fgf7 highly expressed in the esophagus and Fgf10 in the stomach, suggest a regulatory role in the GE-SCJ. The expression patterns of Fgf7 and Fgf10 align with their requirement for esophageal13 and stomach48,54 epithelium, as evidenced by organoid studies6,32nonetheless, their precise contribution to GE-SCJ development remains to be elucidated. Hedgehog signaling genes Ihh and Shh were expressed in high levels in stomach epithelia during the prenatal stage, while receptors like Notch1, Sdc1, Fgfr2, and Fgfr3 were expressed in high levels in esophageal epithelial cells. WNT ligand genes Wnt4, Wnt5b, Wnt7b, and Wnt10a were strongly expressed only by squamous epithelia. In particular, Wnt4 was highly expressed among all esophageal epithelial subclusters, indicating its role in epithelial-stromal interaction, proliferation, and differentiation in the stratified epithelium55. WNT receptor Fzd6 plays a significant role in the PCP pathway during development and is an inhibitor of cWNT signaling specifically expressed at a higher level in the esophagus epithelial subclusters56,57. The known ncWNT ligand Wnt5b was briefly expressed in the early esophagus, while Wnt5a58 was highly expressed in the fibroblasts of the stomach. The Wnt inhibitors Dkk2 and Sfrp4 expressions were restricted to the fibroblasts of the adult esophagus (Fig.6d). Taken together, our data reveal differential pathway enrichment and alterations in the signaling patterns between squamous and columnar niches governing GE-SCJ development and homeostasis.

To better understand epithelial-fibroblast interactions, we analyzed signaling interactions based on ligand-receptor pairs between epithelia and fibroblasts at a subcluster level. This analysis retrieved unknown additional information on autocrine and paracrine signaling. We identified significant ligand-receptor pairs by combining differential expression analysis with cell-cell communication analysis. Our results revealed that pathways such as WNT, BMP, TGF-, Epidermal Growth Factor (EGF), FGF, and PDGF were among the significant ones. Overall, cell-cell interaction showed fibroblasts predominantly sent FGF and TGF- signals to the epithelia. In comparison, PDGF and EGF signals were sent predominantly from epithelial cells to fibroblasts. The BMP and WNT signals act in both autocrine and paracrine manner in both epithelia and fibroblasts. However, the type of ligands and receptors involved varied between the esophagus and stomach (Fig.7ac, Supplementary Fig.9ac). Further, we investigated the direction of signaling involving significant ligands identified from our cell-cell interactions (Fig.7ac, Supplementary Fig.9ac, left panel) together with ligands and receptor expression dynamics across developmental time points in both the stomach and esophagus (Fig.7ac, Supplementary Fig.9ac, right panel). Interestingly, Tgfb2 and Fgf7 expression levels increased over time in esophageal fibroblasts, whereas Pdgfa/b/c and Hbegf expression exhibited a declining trend over time in the epithelia of both tissues (Supplementary Data10).

ac Graphical abstract of tissue-specific signaling directions between epithelia and fibroblasts (left); trend plots showing the mean expression dynamics of key ligands and receptors over time (right); for the following signaling pathways of interest: BMP (a) TGF- (b) PDGF (c). Lines colored by gene with shapes representing the epithelial (circle) and fibroblast (triangle) cell population; arrows in graphical depictions show signaling direction and colored by signal origin: squamous epithelia (green), columnar epithelia (light pink) and fibroblast (brown). df Chord diagrams depicting inferred cell-cell communications mediated by multiple significant ligand-receptors between epithelia and fibroblast in esophagus and stomach at E19 and adult time points for BMP (d) TGF- (e) PDGF (f) pathways; in lower half of the circos plot, outer bars colored by signal sending cell groups; inner bars colored by proportion of receiving cell groups; edges colored by signal senders. g, h Confocal images of the adult mouse esophagus (g) and stomach (h) tissue sections immunostained for CDH1 (green), PDGFRA (red), and smRNA-ISH probed for Pdgfa (white), and nuclei (blue). Images are representative of three biological replicates. Yellow arrow indicates the direction of predicted interaction between epithelial and fibroblast cells for PDGF signaling.

Further, the inferred significant L-R pairs for BMP, TGF-, FGF, EGF, cWNT, ncWNT, and PDGF-mediated communications between epithelia and fibroblasts were visualized using a chord diagram (Fig.7df, Supplementary Fig.9df). FGF signaling takes place in both autocrine and paracrine manner, where signals are usually sent by the fibroblasts and directed towards epithelial and fibroblast cells in both the esophagus and stomach (Supplementary Fig.9d). In the case of EGF signaling, different ligands were expressed by the differentiated squamous epithelial cells and stomach epithelial cells (Supplementary Fig.9e). These ligands interact in both autocrine and paracrine settings by binding to either Egfr or Egfr-Erbb2 receptor pair, implying that epithelia are the signaling source and signals were directed either back to epithelia or towards fibroblasts in both esophagus and stomach. Our ligand-receptor analysis of WNT signaling revealed that esophageal cells express Wnt4, Wnt10a, Wnt7b, Wnt5a, and Wnt11 ligands (Supplementary Fig.9f) involved in either one or both canonical and non-canonical WNT pathways. Interestingly, most WNT signal senders were epithelial cells, and receivers were fibroblasts, while non-canonical Wnt5a and -Wnt11 signals were primarily restricted to senders and receivers within fibroblasts. On the other hand, in the stomach, Wnt4 and Wnt5a gene expression were observed, with senders and receivers being bi-directional between epithelial and fibroblast compartments (Supplementary Fig.9f). Further, we spatially validated one of the key L-R interaction predictions where the Pdgfa ligand is primarily sent by Sq1-2 of the esophagus and tuft/endocrine cell types of the stomach targeting different fibroblasts (Fig.7f). We confirmed the presence of Pdgfa sender cells (epithelia) and PDGFRA-expressing receiver cells (fibroblast) in the vicinity in both the esophagus and stomach, suggesting possible interaction (Fig.7g, h). In line with this, a previous study showed that PDGFA expressing intestinal epithelium signals with PDGFRA expressing stromal cells for proper villi formation during gastrointestinal development59. Together, our findings deciphered the direction of the communication network and the role each cell type plays during different developmental stages in the process of GE-SCJ histogenesis.

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Decoding spatiotemporal transcriptional dynamics and epithelial fibroblast crosstalk during gastroesophageal junction ... - Nature.com

Researchers find the "recipe" for growing new limbs – ZME Science

For as long as superheroes have been imagined, theres been a superhero who can regrow limbs. Other animals (like salamanders and sharks) do it, why couldnt we? Scientists have also tackled this question because, obviously, humans dont naturally regrow limbs. But before we move on to regrowing limbs ourselves, we need to understand how other species do it.

In a new study, researchers mapped the proteins that kick off limb creation in mice and chicks, finding that a cocktail of just three proteins performs the initial magic.

People in the field have known a lot of the proteins critical for limb formation, but we found that there are proteins we missed, said study co-first author ChangHee Lee, research fellow in genetics in the lab of Cliff Tabin at Harvard Medical School.

When the body produces stem cells, undifferentiated cells capable of self-renewal and differentiation into specialized cell types, its proteins that decide whether the stem cells will be limb-producing or not limb-producing. Lee and colleagues found that just three proteins (Prdm16, Zbtb16, and Lin28a) are sufficient to encourage stem cells to develop into limbs in mice and chicks. A fourth protein, Lin41, speeds the process up.

The role of these protein is not entirely surprising.

Prdm16 is a critical regulator in the development and function of brown adipose tissue. It plays a significant role in determining whether precursor cells become brown fat cells or muscle cells. This protein is also involved in the regulation of hematopoietic (blood cell) stem cell differentiation and may play roles in other tissue types, indicating its importance in cell fate decisions. Lin28a plays a central role in developmental timing and stem cell maintenance and promotes the pluripotency of embryonic stem cells. Meanwhile, Zbtb16 is involved in the regulation of development, differentiation, and apoptosis (programmed cell death). It is also a transcriptional repressor, meaning it can turn off the expression of certain genes.

Together, this combination of cells ensures that stem cells can grow into a new limb.

Weve found the proteins that imbue limbness to this subgroup of mesenchymal cells, said Lee. People didnt know how to make mesenchymal stem cells into limb progenitors before. Now we can do this and study early limb differentiation.

This finding essentially enables researchers to take mouse fibroblasts (the most common type of connective tissue) and direct them to become limb progenitors.

With this approach, the team was able to grow limb progenitor cells and lay them out in a 3D scaffold. Then, they optimized the stem cell growth condition until the cells started to develop towards a limb-like structure. This means the stem cells were able to survive, proliferate, and, critically, maintain their limb progenitor identity after extended culture, said co-senior author Cliff Tabin, also from Harvard Medical school.

The team also tested out several protocols for growing the cells and found what they believe to be the optimal one theyve also made the protocols available for free online.

We tested a lot of conditions to see what the cells like and what they dont like. We found they are particularly finicky about stiffness, said Lee. The only limitation weve found so far is that the cells grow so well that they fill up the containers we use, which is a good problem to have.

The next step also involves identifying what ingredients need to be added for the different types of tissues in limbs, like tendons, ligaments, and skin. They also want to investigate what directs further limb development (like the protein cocktail that directs finger or toe formation, for instance). Ultimately, the team wants to use this approach to regrow different body parts to treat injury or disease.

Its important to understand the basic properties of cells that have a therapeutic value, said Lee. Culturing and maintaining limb progenitor cells and directing them to more specific lineages is fundamentally important for the long-term goal of replenishing cells in the clinic.

The study was published in Developmental Cell.

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Researchers find the "recipe" for growing new limbs - ZME Science

New Stem Cell Research Offers First Glimpse of Early Human Development – SciTechDaily

Using a novel stem cell model, scientists have advanced our understanding of gastrulationa critical early stage of human developmentoffering new insights that could improve outcomes in pregnancy and the understanding of developmental disorders. The image above shows a blastoid, a stem cell model system that allows scientists to study the nuances of human gastrulation. Credit: Laboratory of Stem Cell Biology and Molecular Embryology at The Rockefeller University

Its one of lifes most defining momentsthat crucial step in embryonic development, when an indistinct ball of cells rearranges itself into the orderly three-layered structure that sets the stage for all to come. Known as gastrulation, this crucial process unfolds in the third week of human development. Gastrulation is the origin of our own individualization, the emergence of our axis, says Rockefellers Ali Brivanlou. It is the first moment that separates our heads from our behinds.

Observing the molecular underpinnings of this pivotal event would go a long way toward helping scientists prevent miscarriages and developmental disorders. But studying human gastrulation has proven both technologically difficult and ethically complicated, and thus current approaches have had limited success in expanding our understanding of early human development. Now Brivanlou and colleagues have demonstrated how a stem cell model system known as a blastoid can allow the study of the nuances of human gastrulation in the presence of pre-implantation extra-embryonic cell types. Their study, published in Stem Cell Reports, describes the scientific and clinical potential of this new platform.

Gastrulation was a tremendous black box. We had never seen ourselves at that stage, Brivanlou says. This moves us closer to understanding how we begin.

Prior to implantation, an embryo is a ball of about 250 cells organized as a blastocyst. This elusive ball of cells was difficult to study directly, so scientists developed blastoidsstem-cell-based blastocyst models. Blastoids can be cloned, experimentally manipulated, and programmed, allowing scientists to study identical blastoids over and over again.

The question was whether blastoids could gastrulate in vitro. Unlike a blastocyst in vivo, which rolls around in the uterus until it attaches to maternal tissue, blastoids were good at modeling the ball of cells from which life emerges, but it remained unclear whether this in vitro model could model later stages of human development. That is, until Brivanlou developed a platform to allow blastoids to attach in vitro, and thereby progress toward gastrulation.

We were then able to see epiblast symmetry breaking, marked by BRA expression, for the first time with the high molecular resolution, says Riccardo De Santis, a research associate in the Brivanlou lab and lead author on the study. This allowed us to start asking more detailed questions about the earliest moments of life.

With this unprecedented clarity, the team directly observed two key moments in gastrulation: the first epiblast symmetry-breaking event and the emergence of the molecular markers of the primitive streak and mesoderm upon in vitro attachment.

The primitive streak is a structure that marks the beginning of gastrulation and lays the foundation for the three primary layers of the embryo. One of those layers, the mesoderm, forms during gastrulation and gives rise to muscles, bones, and the circulatory system. The team discovered that, as early as seven days after attachment, they were already able to use molecular markers to detect the earliest signature of a nascent primitive streak and mesodermal cells.

To confirm their findings, the team also compared the blastoid results with data from in vitro attached human embryos and demonstrated that blastoids express the same genes in vitro that a regular embryo would at that stage in vivo, a strong demonstration of the power of blastoids as models for human embryonic development. Further highlighting the power of the labs in vitro attached blastoid system, the team then used it to demonstrate that pathways that regulate the rise of the primitive streak and mesoderm in vivo also regulate blastoids symmetry breaking in vitroall with nothing but stem-cell-derived blastoid models.

Along the way, the team also demonstrated that gastrulation in vitro can begin at day 12, earlier than once thought. This will change textbooks, Brivanlou says. Weve contributed to redefining the molecular signature and timing of the onset of gastrulation upon in vitro attachment.

The results demonstrate that blastoids, when combined with the Brivanlou labs unique attachment platform, are now capable of conveying insights into early human development that have long been inaccessible. De Santis envisions a future in which blastoid-based research leads to advancements in diagnosing and treating developmental disorders, or offers insights into potential causes of early miscarriages during gastrulation.

Many couples cant have babies because the embryo doesnt attach properly, and many miscarriages occur in the first few weeks of pregnancy, De Santis explains. We now have a model system that can help us understand the molecular mechanism that defines whether a pregnancy will be successful or not. In the near future, De Santis hopes to combine this method with machine learning to help predict pregnancy outcomes and the trajectories of developmental disorders by observing how model blastoids built with particular genetic makeups fare in vitro.

A better understanding of gastrulationand the ability to study it with a reliable model systemimpacts everything from survival of the fetus to autism to neurodegeneration.

Reference: The emergence of human gastrulation upon in vitro attachment by Riccardo De Santis, Eleni Rice, Gist Croft, Min Yang, Edwin A. Rosado-Olivieri and Ali H. Brivanlou, 14 December 2023, Stem Cell Reports. DOI: 10.1016/j.stemcr.2023.11.005

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Mesenchymal stem cell based therapies for uveitis: a systematic review of preclinical studies | Eye – Nature.com

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Mesenchymal stem cell based therapies for uveitis: a systematic review of preclinical studies | Eye - Nature.com

Pluripotent positives in allogeneic stem cell therapies – BioProcess Insider

Ricardo Baptista, former chief technology officer at cell therapy developer Alder Therapeutics, told delegates The use of pluripotent stem cells is gaining traction when it comes to allogeneic stem cell therapies.

Baptista said there are several reasons for this and listed the benefits of using pluripotent stem cells. He discussed how pluripotent cells can be cultured in all systems, including 2D, 3D, static and dynamic. Additionally, Baptista said the lines can be edited [which equates] to the concept of a universal cell line and theoretically an unlimited choice of cells.

In turn, this means the therapies can be used off-the-shelf and target diseases with point-of-care therapy. Moreover, it is cell banks-based and there is the possibility to generate universal cells and the potential to leverage processing technologies from biopharma.

Baptista added there is an increased number of doses per lot and the costs of goods [is] spread across multiple doses, meaning the therapies are more easily accessible to a wider patient population.

Currently all approved chimeric antigen receptor (CAR) T-cell therapies are autologous. Autologous products are made by taking, reengineering, and reintroducing a patients own cells. Autologous methods of treatment usually have a low risk of rejection but are not always suitable for all patients because of the limitations in the quality and availability of the individuals cells.

Allogeneic therapies, however, can use cells or tissues from different individuals. As these are not personalized therapies, one advantage over autologous is the relative ease to mass-produce such products and thus, increase patient access. While allogeneic treatments could potentially treat more people, it has not yet fully reached commercialization due to the risk of rejection and immunosuppressive and matching measures required.

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Pluripotent positives in allogeneic stem cell therapies - BioProcess Insider

Tr1X Announces FDA Clearance of First Investigational New Drug Application for TRX103, an Allogeneic Regulatory T … – BioSpace

SAN DIEGO, April 10, 2024 /PRNewswire/ --Tr1X, Inc., anautoimmune and inflammatory disease cell therapy company focused on the development of novel allogeneic regulatory T cell therapies (Allo-Tregs) and allogeneic regulatory T cells expressing Chimeric Antigen Receptors (Allo-CAR Tregs), today announced the U.S. Food and Drug Administration (FDA) accepted the company's Investigational New Drug (IND) application for TRX103 for the prevention of Graft versus Host Disease (GvHD) in patients undergoing HLA-mismatched hematopoietic stem cell transplantation (HSCT). The company plans to initiate a Phase 1 study of TRX103, an investigational allogeneic off-the-shelf Tr1 Treg therapy, for this indication in the second quarter of 2024. Additionally, the company is on track to submit an IND for TRX103 for patients with refractory Crohn's disease in the third quarter of 2024.

"The FDA's clearance of our IND for TRX103, the first ever allogeneic engineered Tr1 regulatory T cell product, is an important milestone that could quickly provide us with proof-of-concept data while we continue to develop TRX103 for multiple autoimmune and inflammatory diseases, including Crohn's disease," said Maria Grazia Roncarolo M.D., Co-Founder, President and Head of R&D at Tr1X. "Donor-derived autologous Tr1 cells have shown clinical promise in improving immune reconstitution and reducing GvHD but have limited potential due to lack of feasibility and high cost. TRX103, an off-the-shelf product with unique biological properties compared to other Treg and CAR-T cell therapies, has the potential to reduce inflammation, suppress pathogenic cells, and reset the immune system. TRX103 is currently produced cost effectively at scale in a fully closed end-to-end system using a process that yields billions of cells in a single campaign. This should enable Tr1X to develop further pipeline candidates that address even larger patient populations with equally unmet medical needs."

"Allogeneic stem cell transplantation is the only curative treatment formany advanced blood cancers and genetic and acquired diseases.However, there remains a burden of morbidity and mortalityrelated to GvHD and its complications, including severe infections," said Monzr M. Al Malki, M.D., lead investigator of the Phase 1 study, Associate Professor in the Department of Hematology & Hematopoietic Cell Transplantation, and Director of the Unrelated Donor, Haploidentical and Cord Blood Transplant Programs at City of Hope National Medical Center. "As a result, innovative treatments are urgently needed. We look forward to starting this first-in-human trial to evaluate the safety, tolerability and clinical activity of these allogeneic Tr1 Treg cells and their potential to benefit patients in this setting."

About TRX103 TRX103 is an investigational allogeneic off-the-shelf engineered T cell product generated from CD4+ cells sourced from healthy donors. These donor-derived CD4+ cells are engineered to produce cells that mimic the function of Tr1 regulatory T cells. Tr1X is developing TRX103 for the treatment of several immune and inflammatory disorders. Multiple preclinical models of disease have shown TRX103 to be tolerable and effective and to have the potential to reset immune systems to a healthy state. TRX103 has the potential to overcome major limitations of current cell therapies for autoimmune diseases, which include limited persistence and side effects including cytokine release syndrome (CRS) and neurotoxicity.

About Tr1X Tr1X is a privately held biotechnology company focused on engineering cures for immune and inflammatory diseases. Founded by industry veterans, including the scientists behind the discovery of Tr1 cells, the company's pipeline of off-the-shelf allogeneic cell therapies is being developed for the treatment of and potential cure of autoimmune diseases with high unmet medical need. The company is backed by leading investors, including The Column Group, NEVA SGR and Alexandria Venture Investments, and has received additional grant support from the California Institute for Regenerative Medicine (CIRM). For more information visit http://www.tr1x.bio.

Investor Contact: Tr1X Investor Relations investors@tr1x.bio

Media Contact: Julie Normart jnormart@realchemistry.com

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Stem cell treatment for spinal injury, and BRCA breakthrough | Podcasts – The Naked Scientists

Could an injection of stem cells grown from your own abdominal fat be the key to improving outcomes for people with spinal cord injuries? In an early stage trial in America, scientists have found that over two thirds of the small group of patients they treated showed improvements. They think that the stem cells are boosting the blood supply to the injured region of the spinal cord, and helping to control inflammation, which may lead to reduced scarring and better prospects for recovering some of the lost nerve connections. Mohamad Bydon is a neurosurgeon at the Mayo Clinic and led the new study

Mohamad - The historical teaching around spinal cord injury is you deliver surgery, you do physiotherapy, and that's basically it. And things haven't really advanced in a long, long time. So what we wanted to do was really impact the space and say, are there other treatments that we could add to augment the recovery, to aid the recovery, to improve the recovery?

Chris - And your intervention? What's the rationale behind what you're doing and how are you doing it?

Mohamad - So at a very high level, at a 30,000 foot view, the question becomes, what are the other things that we can add? And that's where we believe regenerative medicine will be a part of this paradigm. It's not going to be the only answer: you still need your surgery, you still need your physical therapy, there's other things like stimulation that are being discussed, but we believe regenerative therapy, specifically with stem cells, will be beneficial in helping to improve outcomes for patients.

Chris - So what stem cells? Where from and what do you do with them?

Mohamad - So stem cells are cells that can become a number of different things once they enter the body and they come from a number of different areas. Specifically in this study we used what are known as mesenchymal stem cells, adipose derived. Those words mean stem cells from your own fat, belly fat. I had a colleague who said to me recently, 'Who knew that belly fat could be so useful?' So, from your own belly fat, we remove that and expand the cells until we get to the right number of cells and then we proceed to reinject those cells once they're expanded and cultured into the spinal cord.

Chris - How many cells were you putting in once you'd grown them or expanded them and where exactly were they going? Were they going into the substance of the spinal cord or around it?

Mohamad - There were 100 million cells. Frankly, we need to work on dosing still, but a hundred million is the dose that we expand the cells to. Once we do that expansion, we proceed to inject it into the faecal sac. There's the substance of the spinal cord itself, and then there's a sac that surrounds the spinal cord - it's called the faecal sac or the dura mater - and that is a lining that surrounds the spinal cord. It also surrounds the brain. Inside that layer there's something called cerebrospinal fluid. So what we do is we put these cells inside the dura, into the fluid, and then the cells go to the area of highest injury and area of highest inflammation, which is the area of injury.

Chris - What do the cells look like? Are they still very much stem cells at this time when you're doing this? And then when they go to the areas of injury, is this only in people who've just had an injury or will they go to areas of injury that happened years ago?

Mohamad - Good question. The cells definitively are stem cells and there are certain markers and hallmark features that stem cells have. To your question on longevity, our current trial is in patients who've had their injury within a year. Many trials deliver therapy to patients who've had the injury right away; you had your injury yesterday, we're going to give you therapy today. This trial was not designed like that because some patients have natural improvement and so the earliest we injected any patient was at seven months. The latest that we injected any patient was at 22 months. Some of the patients that we injected out to 22 months had a very significant response. Now, we haven't done studies looking at longer, although now we're starting studies to look longer out. So what would it look like if we did patients after five years, ten years? What would that look like? Those are also things that we're evaluating and looking to treat.

Chris - Do you know for sure that the stem cells when you put them in actually go to the injury side or do they just go everywhere and some randomly crop up at the site of the injury? Have you actually followed them to see what happens to them and how long they persist for after you put them in?

Mohamad - We've done testing on this and we know that the cells go to the site of injury at the same time. The cells have an impact across the spinal cord and the brain and that's okay. The impact that we've seen has been positive or had no impact. So, we haven't seen it be negative. The cells themselves then work through a couple of different potential mechanisms once they get there. There's potentially a regenerative mechanism through the stem cells themselves regenerating that area, but the other potential mechanism is a vascular mechanism where the stem cells induce a more vascular area where scar tissue would normally be a very nonvascular area without blood vessels. Blood vessels are important because they deliver good nutrients, they take out bad nutrients, and so areas of injury tend to wall themselves off and lose their vasculature. These cells can be very helpful because they can reset the vasculature in those areas, allowing the areas to heal more properly.

Chris - For the patients, what were the outcomes like and in what ways did people improve in ways that you wouldn't have anticipated had they just been managed the way we normally, historically, have been managing spinal cord injuries?

Mohamad - What we looked at, in terms of safety, we found adverse events. Mostly, they were headaches or back pain that would improve over a few days. We never saw any significant or long-term side effects. On the effectiveness side, in terms of our secondary endpoint on effectiveness, what we found was that seven of the ten patients showed some improvement, three of them being very significant improvement, four of them being mild to moderate improvement, and the other three patients showed no improvement but did not get worse. Some patients who required a harness and multiple assistance to be able to bear weight and get up could now walk without that: they could walk on their own. Other patients had improvement in bowel and bladder function.

Chris - How do you know, though, that you didn't, just by chance, select people for this study who are that bit fitter? They're more likely to have a good outcome and, had they been just left to their own devices with the gold standard care they would otherwise have had, they would've ended up at the same endpoint?

Mohamad - This is a good question, and this is a question that we debated at length with the regulatory bodies. Most studies in this space treat patients right after the injury, in which case your question becomes very relevant. In our case, we waited. Most of the improvement after a spinal cord injury occurs within the first six months. Much less improvement occurs as you keep going over time, much, much less. The earliest we ever treated a patient was seven months and we had patients that we treated as late as 22 months and everybody had plateaued. Nobody was continuing their improvement. Remember, this is a phase 1 trial of ten patients. The definitive trial would be randomised controlled, which we're doing now, which is a phase 2 randomised controlled trial of best medical management versus our interventional therapy. But this is a signal and this is an important signal that will inform our future trials.

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Stem cell treatment for spinal injury, and BRCA breakthrough | Podcasts - The Naked Scientists

Human Neuron Model Paves the Way for New Alzheimer’s Therapies – Weill Cornell Medicine Newsroom

Weill Cornell Medicine scientists have developed an innovative human neuron model that robustly simulates the spread of tau protein aggregates in the braina process that drives cognitive decline in Alzheimer's disease and frontotemporal dementia. This new model has led to the identification of novel therapeutic targets that could potentially block tau spread.

The preclinical study, published April 5 in Cell, is a significant advancement in Alzheimer's disease research.

Dr. Li Gan. Credit: John Abbott

Currently no therapies can stop the spread of tau aggregates in the brains of patients with Alzheimers disease," said lead study author, Dr. Li Gan, director of the Helen and Robert Appel Alzheimers Disease Research Institute and the Burton P. and Judith B. Resnick Distinguished Professor in Neurodegenerative Diseases in theFeil Family Brain and Mind Research Instituteat Weill Cornell Medicine. Our human neuron model of tau spread overcomes the limitations of previous models and has unveiled potential targets for drug development that were previously unknown.

Human pluripotent stem cells can develop into any cell of the body and can be coaxed to become neurons to model brain diseases in a lab dish. However, it had been nearly impossible to model tau propagation in these young neurons, as tau propagation requires decades in aging brains.

Celeste Parra Bravo

Dr. Gans team used CRISPR technology to modify the genomes of human stem cells, prompting them to express forms of tau associated with diseased aging brains. "This model has been a game-changer, simulating tau spread in neurons within weeksa process that would typically take decades in the human brain," Dr. Gan said.

In their quest to halt tau propagation, Dr. Gan's team employed CRISPRi screening to disable 1,000 genes to ascertain their roles in tau spread. They discovered 500 genes that have a significant impact on tau abundance.

Dr. Shiaoching Gong

CRISPRi technology allowed us to use unbiased approaches to look for drug targets, not confined to what was previously reported by other scientists, said one of the lead study authors Celeste Parra Bravo, a neuroscience doctoral candidate in the Weill Cornell Graduate School of Medical Sciences working in the Gan lab.

One discovery includes the UFMylation cascade, a cellular process involving the attachment of a small protein named UFM1 to other proteins. This process's connection to tau spread was previously unknown. Post-mortem studies of brains from patients with Alzheimers disease found that UFMylation is altered, and the team also found in preclinical models that inhibition of the enzyme required for UFMylation blocks tau propagation in neurons.

We are particularly encouraged by the confirmation that inhibiting UFMylation blocked tau spread in both human neurons and mouse models, said paper co-author Dr. Shiaoching Gong, associate professor of research in neuroscience in the Appel Instituteat Weill Cornell Medicine.

Many Alzheimers disease treatments initially show promise in mouse models but do not succeed in clinical trials, Dr. Gan said. With the new human cell model, she is optimistic about the path ahead. "Our discoveries in human neurons open the door to developing new treatments that could truly make a difference for those suffering from this devastating disease."

Many Weill Cornell Medicine physicians and scientists maintain relationships and collaborate with external organizations to foster scientific innovation and provide expert guidance. The institution makes these disclosurespublic to ensure transparency. For this information, see profile forDr. Li Gan.

This research was supported in part by the National Institute of Neurological Disorders and Stroke, the National Institute on Aging and the National Institute of General Medical Sciences, all part of the National Institutes of Health, through grant numbers U54NS100717, R01AG072758, R01AG054214, R01AG074541, R25GM130494, and R01AG064239. Additional support was provided by the Rainwater Charitable Foundation and the JPB Foundation.

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Human Neuron Model Paves the Way for New Alzheimer's Therapies - Weill Cornell Medicine Newsroom

Newark teenager suffering from a rare form of cancer who is having treatment funded by the NHS matched with stem … – LincsOnline

A teenager given a terminal diagnosis just before Christmas has now been told 95% of the cancer gone after experimental treatment.

Dan Evans, 18, is now looking to Israel for his next stage of treatment.

A major community fundraiser was launched to pay for the treatment after the teenager was told he had exhausted all avenues available on the NHS and to go home and make memories.

But money was not needed when the NHS agreed to fund the combination of drugs which had proved successful in trials for Dan.

And he is showing a positive response to treatment, having been told 95% of his cancer is gone.

Defying doctors and his diagnosis, a small part of the cancer is still visible on Dans neck but it has low activity.

Its still early days, but Dan certainly is on the right path now and its certainly a different path to the one he was on just a few months ago, said his dad, Mark.

As one of the final stages and vital to his recovery and to stay in remission once he gets to that stage, Dan is in need of a stem cell transplant. That process is due to start on May 27.

The NHS went on a world wide search for a matching donor. In such a short time, four stem cell donors have been found in Israel, however, the family needs to await confirmation on availability.

Dan had previously done the Ancestry test to discover more about his family history and roots, which showed him to be 1% connected to Israel.

Mark added: I am still concerned but I just feel that it is going to be OK, he is going to be fine.

After everything he has gone through, it just has to be fine, there is no other option really, it has to be fine,

Because there is still a little bit of cancer there, we are always going to be a little nervous, it is only natural.

The day he is finally in remission will be wonderful, absolutely wonderful and I cant wait.

Dan, a former pupil at Sir William Robertson Academy in Welbourn, was diagnosed with stage four Primary Mediastinal B Cell Lymphoma (PMBCL) in December 2022.

After several failed treatments, the family was told there were no more viable treatments available on the NHS and was told to take him home and make memories.

Instead, through research, Dan, who lives in Newark with his family, found a combination of drugs that had proved successful in trial, but not approved on the NHS.

His family set up a GoFundMe page with a 100,000 target and raised nearly half of their target amount in a short time before Dan was given the treatment for free on the NHS through compassionate use.

Dan had the first dose of treatment on January 26 and ever since, what once was a terminal diagnosis, has been improving and defying the rules of life and medicine.

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Newark teenager suffering from a rare form of cancer who is having treatment funded by the NHS matched with stem ... - LincsOnline

Dynamic molecular network analysis of iPSC-Purkinje cells differentiation delineates roles of ISG15 in SCA1 at the … – Nature.com

iPSC culture and differentiation to pan-neurons

SCA1-iPSCs and normal iPSCs were differentiated to pan-neurons87. SCA1-iPSCs and normal iPSCs were cultured in TeSR-E8 medium (STEMCELL Technologies, BC, Canada) with 10M Y27632 (253-00513, Wako, Osaka, Japan). After 24h, medium was changed to Stem Fit (AK02N, Ajinomoto, Tokyo, Japan) containing 5M SB431542 (13031, Cayman Chemical, Ann Arbor, MI, USA), 5M CHIR99021(13122, Cayman Chemical, Ann Arbor, MI, USA), and 5M dorsomorphin (044-33751, Wako, Osaka, Japan). After 5 days, iPSCs were dissociated with TrypLE Select (12563-011, Thermo Fisher Scientific, MA, USA). Neurospheres were then cultured in KBM medium (16050100, KHOJIN BIO, Saitama, Japan) with 20ng/mL Human-FGF-basic (100-18B, Peprotech, London, UK), 10ng/mL Recombinant Human LIF (NU0013-1, Nacalai, Kyoto, Japan), 10M Y27632 (253-00513, Wako, Osaka, Japan), 3M CHIR99021 (13122, Cayman Chemical, Ann Arbor, MI, USA), and 2M SB431542 (13031, Cayman Chemical, Ann Arbor, MI, USA) for 10 days. Finally, neurospheres were dissociated and seeded onto chambers coated with poly-L-ornithine (P3655, Sigma-Aldrich, St. Louis, MO, USA) and laminin (23016015, Thermo Fisher Scientific, Waltham, MA, USA), and cultured in DMEM/F12 (D6421, Sigma-Aldrich, St. Louis, MO, USA) supplemented with B27 (17504044, Thermo Fisher Scientific, Waltham, MA, USA), Glutamax (35050061, Thermo Fisher Scientific, Waltham, MA, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA) for 14 days.

SCA1-iPSCs and normal iPSCs were differentiated to Purkinje cells88. To form EBs, iPSCs were dissociated with TrypLE Select (12563-011, Thermo Fisher Scientific, MA, USA), and 24,000 cells were aggregated by centrifugation at 200g for 2min in 96-well U-bottomed culture plates (650-180, Greiner, Kremsmnster, Austria) coated with Lipidure (CM5206, Nichiyu, Tokyo, Japan). Cells were cultured with gfCDM/insulin medium, 1:1 Iscoves modified Dulbeccos medium (12440053, Thermo Fisher Scientific, Waltham, MA, USA), and Hams F-12 (11765054, Thermo Fisher Scientific, Waltham, MA, USA) with 7g/mL insulin (I5500, Sigma-Aldrich, St. Louis, MO, USA); 1x chemically defined lipid concentrate (11905031, Thermo Fisher Scientific, Waltham, MA, USA); 15g/ml apo-transferrin (T4382, Sigma-Aldrich, St. Louis, MO, USA); 450M monothioglycerol (195-15791, Thermo Fisher Scientific, Waltham, MA, USA); 5mg/mL BSA (A7608, Sigma-Aldrich, St. Louis, MO, USA), Glutamax (35050061, Thermo Fisher Scientific, Waltham, MA, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA); 20M Y-27632 (253-00513, Wako, Osaka, Japan); and 10M SB431542 (13031, Cayman Chemical, Ann Arbor, MI, USA). After 2 days, 50ng/mL recombinant human FGF2 (233-FB-025, R&D systems, MN, USA) was added to culture medium. After 21 days, EBs were transferred to 10cm Petri dishes (1020-100, Iwaki, Shizuoka, Japan) coated with Lipidure (CM5206, Nichiyu, Tokyo, Japan) and cultured for 14 days in Neurobasal/N2 medium, Neurobasal (21103-049, Thermo Fisher Scientific, Waltham, MA, USA) with N2 supplement (17502048, Thermo Fisher Scientific, Waltham, MA, USA), Glutamax (35050061, Thermo Fisher Scientific, Waltham, MA, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA).

EBs were dissociated and cocultured with rhombic lip (RL) cells isolated from cerebellums of E14 Slc:ICR mice to induce differentiation into Purkinje cells. Briefly, RLs and EBs were dissociated with TrypLE Select (12563-011, Thermo Fisher Scientific, MA, USA) and cocultured in DMEM/F12 medium (11330032, Sigma-Aldrich, St. Louis, MO, USA) with 10% FBS, N2 supplement (17502048, Thermo Fisher Scientific, Waltham, MA, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA). A total of 1.0106 cells at cell ratio = 1:10 (EB: RL) with 80L medium were seeded on chambers coated with poly-L-lysine (P1524-25MG, Sigma-Aldrich, St. Louis, MO, USA) and laminin (23016015, Thermo Fisher Scientific, Waltham, MA, USA). After incubation for 6h, DMEM/F-12 supplemented with N2 (17502048, Thermo Fisher Scientific, Waltham, MA, USA), 100g/mL BSA (A7608, Sigma-Aldrich, St. Louis, MO, USA), 50ng/mL human BDNF (248-BDB-010/CF, R&D systems, MN, USA), 50ng/mL human NT3 (267-N3-005/CF, R&D systems, MN, USA), and penicillin/streptomycin (15140-122, Thermo Fisher Scientific, Waltham, MA, USA) medium was added and cultured for 10 days.

EB-derived differentiating cells and RL-derived cells were cultured in cell culture insert dishes (140640, Thermo Fisher, Waltham, MA, USA) in which the two types of cells could be separated to avoid RL contamination of RNA-seq samples. The cell culture insert dish was coated with poly-L-lysine (P1524-25MG, Sigma-Aldrich, St. Louis, MO, USA) and laminin (23016015, Thermo Fisher Scientific, Waltham, MA, USA), embryonic body-derived cells were then seeded on the lower well, and RL-derived cells were seeded on a polycarbonate insert with a 4m pore. The culture medium was the same as described above.

To quantify the cell growth rate of SCA1-iPSCs and normal iPSCs, 30,000 iPSCs were seeded per well on Day 0. After 2, 4, 6, or 8 days, cells were collected, dissociated by 0.5 x TrypLE Select (12563-011, Thermo Fisher Scientific, MA, USA) and counted by using Burker-Turk hemocytometer. To quantify the size of iPSC-derived EBs, images of EBs were taken by microscope (IX70, Olympus) on day 1, 7, 14 and 21, and the 2D areas of EBs reflecting their 3D sizes were measured by ImageJ software (version 1.50, NIH, MD, USA).

AAV-HMGB1-EGFP or AAV-EGFP were infected into differentiated pan-neurons (MOI 2000). Twelve days after AAV infection, cells were fixed with 1% paraformaldehyde in PBS for 30min. After blocking in PBS containing 10% FBS for 30min, cells were stained with the following primary and secondary antibodies: mouse anti-III-tubulin 1 (1:2000 for 16h at 4C, #T8660 Sigma-Aldrich, St. Louis, MO, USA), rabbit anti-PSD95 (1:1000 for 16h at 4C, 3409, Cell Signaling Technology, Danvers, MA, USA), donkey anti-mouse IgG Alexa 488-conjugated (1:600 for 1h at room temperature, #715-545-150, Jackson ImmunoResearch Laboratories, West Grove, PA, USA), and donkey anti-rabbit IgG Alexa488-conjugated (1:1000 for 1h at room temperature, A-21206, Thermo Fisher Scientific). The dendritic spine was assessed after acquisition of image by confocal microscopy (FV1200 laser scanning microscope, Olympus, Tokyo, Japan). We used x40 objective lens (UPLSAPO40X2 (NA:0.95)). The dendritic spine density was measured by ImageJ software (version 1.50, NIH, MD, USA). After calibration of scale information, dendrite (Tuj-1-immunostained image) were manually traced and calculate the dendrite length in observing image window. Next, the spine (PSD95-immunostained dots) was counted, and the dendritic spine density was defined as the number of spines in 1m length of a dendrite, calculated by dividing a spine number in one dendrite by the length of it.

Differentiated Purkinje cells were fixed with 1% paraformaldehyde in PBS for 30min. Cells were incubated with 10% FBS followed by incubation with primary and secondary antibodies as follows: mouse anti-Calbindin (1:2000 for 16h at 4C, C9848, Sigma-Aldrich, St. Louis, MO, USA) and donkey anti-mouse IgG Alexa 488-conjugated (1:1000 for 1h at room temperature, A-21202, Thermo Fisher Scientific).

Mice brain tissues at different timepoints (P0, P28, P91, and P392) were fixed with 4% paraformaldehyde in 0.1M phosphate buffer for 12h and embedded in paraffin. Sagittal sections were deparaffinized in xylene and rehydrated in ethanol. For antigen retrieval, sections were incubated in Tris-EDTA solution pH 9.0 (100mM Tris-base and 10mM EDTA) at 121C for 15min. Human brain paraffin sections of SCA1 patients or control sections were also processed. Sections were then incubated with 0.5% triton-X 100 in PBS for 20min to perform permeabilization. Next, sections were incubated with 10% FBS in PBS for 30min and were incubated with primary and secondary antibodies as follows: rabbit anti-ISG15 (1:100 for 16h at 4C, HPA004627, Sigma-Aldrich, St. Louis, MO, USA), mouse anti-Calbindin (1:2000 for 16h at 4C, C9848, Sigma-Aldrich, St. Louis, MO, USA), mouse anti-Atxn1 (1:100 for 16h at 4C, MABN37, Millipore, Burlington, MA, USA), mouse anti-Ub (1:100 for 16h at 4C, #3936, Cell Signaling technology, Danvers, MA, USA), donkey anti-mouse IgG Alexa 488-conjugated (1:600 for 1h, #715-545-150, Jackson ImmunoResearch Laboratories, West Grove, PA, USA), and donkey anti-rabbit IgG Cy3-conjugated (1:600 for 1h, #711-165-152, Jackson ImmunoResearch Laboratories, West Grove, PA, USA). Nuclei were stained with DAPI (0.2g/mL in PBS, D523, DOJINDO Laboratories, Kumamoto, Japan). Z-stacked images (0.5m interval x 5 slices) were acquired from cerebellar cortex (Lobule IV/V) using a confocal microscope (FV1200IXGP44, Olympus, Tokyo, Japan) and a super-resolution microscope (LSM980 with Airyscan 2, Zeiss, Oberkochen, Germany). Signal intensities were measured using ImageJ software.

Male mice brain tissues at different time points (P0, P28, P91, and P392) were homogenized using the BioMasher II (#893062, Nippi, Tokyo, Japan) with RIPA buffer (10mM Tris-HCl pH 7.5, 150mM NaCl, 1mM EDTA, 1% Triton-X 100, 0.1% SDS, 0.1% DOC, and 1:250 volume Protease Inhibitor Cocktail (#539134, Calbiochem, San Diego, CA, USA)). Homogenates were centrifuged at 12,000g for 10min, and supernatants were added to equal volumes of sample buffer (0.1M Tris-HCl pH 6.8, 4% SDS, 20% glycerol, 0.05% BPB, and 12% -mercaptoethanol) and boiled at 100C for 10min. Samples were subjected to SDS-PAGE and transferred onto PVDF membrane. After blocking the membranes with 5% skim milk in TBST (20mM Tris-HCl pH 7.5, 150mM NaCl, 0.05% Tween-20) for 1h, membranes were incubated with primary and secondary antibodies as follows: rabbit anti-ISG15 (1:1000 for 3h at room temperature, HPA004627, Sigma-Aldrich, St. Louis, MO, USA), mouse anti-GAPDH (1:3000 for 16h at 4C, MAB374, Merck, Darmstadt, Germany), mouse anti-Atxn1 (1:1000 for 3h at room temperature, MABN37, Millipore, Burlington, MA, USA), mouse anti-Ub (1:1000 for 16h at 4C, #3936, Cell Signaling Technology, Danvers, MA, USA), mouse anti-Myc (1:3000 for 1h at room temperature, M047-3, MBL, Aichi, Japan), rabbit anti-FLAG (1:3000 for 1h at room temperature, F7425, Sigma, St. Louis, MO, USA), sheep anti-mouse IgG HRP conjugated (1:3000 for 1h, NA931, Cytiva, Tokyo, Japan), and rabbit anti-IgG HRP conjugated (1:3000 for 1h, NA934, Cytiva, Tokyo, Japan). Proteins were detected using Amersham ECL select (RPN2235, Cytiva, Tokyo, Japan) on an Image-Quant luminescence image analyzer LAS500 (Cytiva, Tokyo, Japan). Signal intensities were measured using ImageJ software.

SCA1-iPSCs and normal iPSCs were collected and homogenized in 350L RNA RLT buffer (Qiagen)/0.01% 2-mercaptoethanol (Wako, Tokyo, Japan). Total RNA was purified with RNeasy mini kit (Qiagen). To eliminate genomic DNA contamination, on-column DNA digestion was conducted for each sample with DNase I (Qiagen). Prepared RNA samples were subjected to a HiSeq-based RNA-seq by TAKARA (700 million bp reads).

Gene expression profiles of each sample were evaluated by the number of short reads that were mapped onto gene coding sequences in the reference human genome assembly hg38. Differential expression genes were analyzed with DESeq234. Log2FC (Fold Change) between SCA1-derived and normal cells was calculated by DESeq2, and the difference of gene expression was determined at | Log2FC|>0.5.

To generate the pathological network based on PPI, UniProt accession numbers were added to genes identified in RNA-seq-based gene expression analysis. The pathological PPI network was constructed by connecting genes using the integrated database of the Genome Network Project (GNP) (https://cell-innovation.nig.ac.jp/GNP/index_e.html), which includes BIND, BioGrid (http://www.thebiogrid.org/), HPRD, IntAct (http://www.ebi.ac.uk/intact/site/index.jsf), and MINT. A database of GNP-collected information was created on the Supercomputer System available at the Human Genome Center of the University of Tokyo.

To create a static molecular network, statistically significantly changed molecules were connected at two neighboring time points based on interactions in the PPI database (an integrated database collected by GNP, which includes BIND, BioGrid (http://www.thebiogrid.org/), HPRD, IntAct (http://www.ebi.ac.uk/intact/site/index.jsf), and MINT), without considering their cause-result relationships. Each network starting from a changed molecule was expanded step-by-step from one-hop (directly linked) to six extra connections, and the degree of significance at each expansion step was evaluated by calculating the z-score of their ratio of changed nodes.

To gain insights into the dynamics of the pathological molecular network, a PPI-based chronological molecular network was constructed by connecting two proteins between the two neighboring time points using the integrated PPI database.

To estimate the impact of a significantly differentially expressed gene to the future time point, a gene in a certain time point was connected to a set of genes in the next time point based on the PPI database (an integrated database collected by GNP, which includes BIND, BioGrid (http://www.thebiogrid.org/), HPRD, IntAct (http://www.ebi.ac.uk/intact/site/index.jsf), and MINT) and defined as downstream genes. The magnitude of impact of a gene at the first time point (defined as r) was calculated by the ratio of downstream genes that were significantly changed in mRNA expression levels to all genes at the second time point. The selection of genes was performed based on comparison of impact of a specific gene (specific impact) and total impact at the second time point. The statistical significance of the impact was examined using two-tailed Fishers exact test with post-hoc Benjamini-Hochberg procedure (adjusted p-value<0.05, red dots). The statistical significance of mRNA expression change was examined using log2FC between SCA1 and normal cells (|Log2FC|> 0.5, blue dots). A digraph was created from a significantly differentially expressed gene at the original time point (iPSCs) to significant genes at the end point (Purkinje cells) via significant genes at the intermediate time point based on the impact analysis. The digraph predicts the original gene whose change at the initial time point leads to molecular changes at the final time point.

To select cytokine-relevant genes, Gene Ontology (GO) enrichment analysis was performed using clusterProfiler89 package in R. A list of genes included in a selected pathway was used as input of enrichGO function of clusterProfiler. From all the enrichment results, GO terms related to cytokine were selected to extract a list of cytokine-relevant genes. The GO terms related to cytokine were searched by keyword cytokine, and thereafter terms that were not related to cytokine such as cytokinesis were excluded.

A search for transcriptomic studies of SCA1 in Homo sapiens and Mus musculus in the NCBI Sequence Read Archive (SRA; https://www.ncbi.nlm.nih.gov/sra) was performed on August 7, 2023 using the keywords spinocerebellar ataxia type 1 and SCA1. Eight studies (PRJNA305316, PRJNA422988, PRJNA472147, PRJNA472754, PRJNA503578, PRJNA688073, PRJNA871289, and PRJNA903078) that include RNA-seq data and raw sequence information generated from cerebellar tissues of SCA1 mouse models were found, while no studies with human RNA-seq data were found.

Raw sequence data were downloaded from NCBI SRA using the SRA Toolkit (https://github.com/ncbi/sra-tools) and mapped to the M. musculus genome assembly GRCm38/mm10 using HISAT2. Gene expression was calculated using the featureCounts function from Subread v1.5.2. Gene expression differences between the SCA1 and control groups were tested using Welchs t-test. Gene expression changes with a p-value0.05 were considered significant.

RNA was isolated from iPSCs, EBs and Purkinje cells with TRIzol RNA Isolation Reagents (15596026, Thermo Fisher Scientific, MA, USA). Reverse transcription was performed by using the SuperScript VILO cDNA Synthesis kit (11754-250, Invitrogen, Carlsbad, CA, USA). Quantitative PCR analyses were performed with the 7500 Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) by using THUNDERBIRD SYBR qPCR Mix (QPS-201, TOYOBO, Osaka, Japan) and assessed by the standard curve method. The primer sequences were:

ISG15, forward primer: 5-CGCAGATCACCCAGAAGATCG-3 and reverse primer: 5- TTCGTCGCATTTGTCCACCA-3

UbE2L6, forward primer: 5-GTGGCGAAAGAGCTGGAGAG-3 and reverse primer: 5 -ACACTGTCTGCTGGTGGAGTTC- 3

ARIH1, forward primer: 5-CAGGAGGAGGATTACCGCTAC-3 and reverse primer: 5-CTCCCGGATACATTCCACCA-3

GAPDH, forward primer: 5-AGATCATCAGCAATGCCTCCTG-3 and reverse primer: 5-ATGGCATGGACTGTGGTCATG-3

PCR conditions for amplification were 40 cycles of 95C for 1min for enzyme activation, 95C for 15sec for denaturation, and 60C for 1min for extension. The expression levels of ISG15, UbE2L6 and ARIH1 were corrected by GAPDH.

Frozen mouse brains (male, P28) were lysed with TNE buffer (10mM Tris-HCL (pH 7.5), 150mM NaCl, 1mM EDTA, and 1% NP-40) and collected by centrifugation (15,000g10min). Aliquots (100g protein in cerebellar tissue lysate) were then incubated 1h with a 50% slurry of protein G-sepharose beads. After centrifugation (2000g3min), the supernatants were incubated with 1g rabbit anti-ISG15 antibody (aHPA004627, Sigma-Aldrich, St. Louis, MO, USA) overnight at 4C. Reactants were then incubated with Protein G-sepharose beads for 4h, washed with TNE buffer, and eluted by sample buffer. For double-precipitation, samples were incubated with 2g biotin-labeled mouse anti-Ub (#3936, Cell Signaling technology, Danvers, MA, USA) overnight at 4C, followed by incubation with streptavidin beads (TrueBlot(R) Streptavidin Magnetic Beads, S000-18-5, Rockland, Pottstown, PA, USA). The collected samples were further incubated with 2g mouse anti-Atxn1 (MABN37, Millipore, Burlington, MA, USA) overnight at 4C. Then, reactants were incubated with Protein G-sepharose beads for 4h, washed with TNE buffer, and eluted in sample buffer.

HeLa cells were seeded at a density of 4105 cells/well in a 6-well plate (3516, Corning, Glendale, AZ, USA) and transfected with 30 pmol human ISG15-siRNA (sc-43869, Santa Cruz Biotechnology, Dallas, TX, USA) or scrambled siRNA (SR30004, OriGene, Rockville, MD, USA) using 4L Lipofectamine RNAiMAX (13778-075, Thermo Fisher Scientific, Waltham, MA, USA). At 24h after siRNA transfection, 2.5g myc-Ataxin1-33Q, myc-Ataxin1-86Q, or FLAG-Ku70 plasmid was transfected using 5L Lipofectamine 2000 (11668-019, Thermo Fisher Scientific, Waltham, MA, USA). At 48h after siRNA transfection, 100nM Bafilomycin A1 (BVT-0252-C100, AdipoGen, Liestal, Basel-Landschaft, Switzerland) or 5M MG132 (139-18451 Wako, Osaka, Japan) was added to the culture medium in order to inhibit autophagy or proteasome-dependent protein degradation, respectively. At 49h, 100g/mL cycloheximide (033-20993, Wako, Osaka, Japan) was added to the culture medium in order to inhibit protein synthesis. The cells were collected at 0, 6, 12, and 24h after addition of cycloheximide, lysed with RIPA buffer (10mM Tris-HCl pH 7.5, 150mM NaCl, 1mM EDTA, 1% Triton-X 100, 0.1% SDS, 0.1% DOC, and 1:250 volume Protease Inhibitor Cocktail (#539134, Calbiochem, San Diego, CA, USA)), and centrifuged at 12,000g for 10min. The supernatants were mixed with equal volumes of sample buffer (0.1M Tris-HCl pH6.8, 4% SDS, 20% glycerol, 0.05% BPB, and 12% -mercaptoethanol), boiled at 100C for 10min, and subjected to SDS-PAGE.

Mutant Atxn1-KI mice (Sca1154Q/2Q mice) were generously gifted by Prof. Huda Y. Zoghbi (Baylor College of Medicine, TX, USA)39. The backcrossed strain with C57BL/6 mice were further crossed with C57BL/6 female mice more than 10 times in our laboratory. The number of CAG repeats was checked by fragment analysis using the following primers: forward (5CACCAGTGCAGTAGCCTCAG3, labeled with 6-carboxyfluorescein) and reverse (5AGCTCTGTGGAGAGCTGGAA3). Mice were maintained under suitable humidity (around 50%) at 22C with a 12h light-dark cycle. We have complied with all relevant ethical regulations for animal use.

Cerebellar specimens collected at autopsy from three SCA1 patients and three control patients without neurological disorders (lung cancer, leukemia, and cholangiocarcinoma) were used. The details of the SCA1 patients (51-year-old female, 54-year-old female, and 50-year-old male) were described previously90,91,92. Their CAG repeat expansion in the Atxn1 gene was confirmed by PCR and their numbers of CAG repeats were around 50, although the exact numbers were not determined by fragment analysis or Sanger sequencing. Human plasma samples were acquired from SCA1 patients with a PCR-based genetic diagnosis or control patients without neurological disorders. Essential information about the SCA1 patients and controls is shown in Fig.8D. Other clinical information is not linked with samples according to ethics regulations. All ethical regulations to human research participants were followed.

In total, 100 L of human plasma samples that had been diluted 2-fold with saline (OTSUKA normal saline 20mL, Otsuka Pharmaceutical Factory, Tokushima, Japan) was added to a 96-well plate precoated with an anti-ISG15 antibody (#CY-8085, CircuLex Human ISG15 ELISA Kit, MBL, Tokyo, Japan) and incubated for 16h at 4C. Plates were washed and subsequently incubated with a peroxidase-conjugated anti-ISG15 antibody (Atlas Antibodies, HPA004627-100UL, Bromma, Sweden) for 2h at room temperature. For detection, Substrate Reagent (#CY-8085, CircuLex Human ISG15 ELISA Kit, MBL, Tokyo, Japan) was added to each well. The reaction was terminated with Stop Solution (#CY-8085, CircuLex Human ISG15 ELISA Kit, MBL, Tokyo, Japan), and absorbance at 450nm was measured on a microplate reader (SPARK 10M, TECAN, Grodig, Austria). A standard curve was generated using 0, 1.5, 3, 6, and 12ng/mL Recombinant Human ISG15 (UL-601-500, R&D Systems, Minneapolis, MN, USA) diluted with Sample Diluent (326078738, HMGB1 ELISA Kit Exp, Shino-test, Tokyo, Japan).

We analyzed three iPSC lines derived from two SCA1 patients for RNA-seq-based gene expression analysis. For meta-analysis using SCA1 model mice, we collected RNA-seq data from 3 to 17 mice in each time point from NCBI SRA database. Statistical analyses for biological experiments were performed using Graphpad Prism 8. Biological data following a normal distribution are presented as the meanSEM, with Tukeys HSD test or Dunnetts test for multiple group comparisons or with Welchs t-test for two group comparisons. The distribution of observed data was depicted with box plots, with the data also plotted as dots. Box plots show the medians, quartiles, and whiskers, which represent data outside the 25th75th percentile range. To obtain each data, we performed biologically independent experiments. The number of samples was indicated in each figure and figure legends.

This study was performed in strict accordance with the recommendations of the Guide for the Care and Use of Laboratory Animals of the Japanese Government and National Institutes of Health. All experiments were approved by the Committees on Gene Recombination Experiments, Human Ethics, and Animal Experiments of the Tokyo Medical and Dental University (G2018-082C3, 2014-5-3, and A2021-211A). Human samples including post-mortem brains were provided with informed consent and their use was approved by the Committees on Human Ethics (O2020-002-03).

Further information on research design is available in theNature Portfolio Reporting Summary linked to this article.

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