Category Archives: Embryonic Stem Cells

New 3D embryonic stem cell culture system sheds light on gastrulation mechanism – MRC Laboratory of Molecular Biology

This site uses cookies. The LMB may use cookies to analyse how you use our website. We use external analysis systems which may set additional cookies to perform their analysis. These cookies (and any others in use) are detailed in our Privacy and Cookies Policy and are integral to our website. You can delete or disable these cookies in your web browser if you wish, but then our site may not work as it is designed. Ok

Follow this link:
New 3D embryonic stem cell culture system sheds light on gastrulation mechanism - MRC Laboratory of Molecular Biology

Bioluminescence imaging of Cyp1a1-luciferase reporter mice demonstrates prolonged activation of the aryl … – Nature.com

Rothhammer, V. & Quintana, F. J. The aryl hydrocarbon receptor: an environmental sensor integrating immune responses in health and disease. Nat. Rev. Immunol. 19, 184197 (2019).

Article CAS PubMed Google Scholar

Esser, C. & Rannug, A. The aryl hydrocarbon receptor in barrier organ physiology, immunology, and toxicology. Pharmacol. Rev. 67, 259279 (2015).

Article CAS PubMed Google Scholar

Burbach, K. M., Poland, A. & Bradfield, C. A. Cloning of the Ah-receptor cDNA reveals a distinctive ligand-activated transcription factor. Proc Natl Acad Sci USA 89, 81858189 (1992).

Article CAS PubMed PubMed Central Google Scholar

Ema, M. et al. cDNA cloning and structure of mouse putative Ah receptor. Biochem. Biophys. Res. Commun. 184, 246253 (1992).

Article CAS PubMed Google Scholar

Nebert, D. W. Aryl hydrocarbon receptor (AHR): pioneer member of the basic-helix/loop/helix per-Arnt-sim (bHLH/PAS) family of sensors of foreign and endogenous signals. Prog. Lipid Res. 67, 3857 (2017).

Article CAS PubMed PubMed Central Google Scholar

Bersten, D. C., Sullivan, A. E., Peet, D. J. & Whitelaw, M. L. bHLH-PAS proteins in cancer. Nat. Rev. Cancer 13, 827841 (2013).

Article CAS PubMed Google Scholar

Gutierrez-Vazquez, C. & Quintana, F. J. Regulation of the immune response by the aryl hydrocarbon receptor. Immunity 48, 1933 (2018).

Article CAS PubMed PubMed Central Google Scholar

Shinde, R. & McGaha, T. L. The aryl hydrocarbon receptor: connecting immunity to the microenvironment. Trends Immunol. 39, 10051020 (2018).

Article CAS PubMed PubMed Central Google Scholar

Murray, I. A. & Perdew, G. H. How Ah receptor ligand specificity became important in understanding its physiological function. Int. J. Mol. Sci. 21, 9614 (2020).

Article CAS PubMed PubMed Central Google Scholar

Agus, A., Planchais, J. & Sokol, H. Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host Microbe 23, 716724 (2018).

Article CAS PubMed Google Scholar

Chiaro, C. R., Patel, R. D., Marcus, C. B. & Perdew, G. H. Evidence for an aryl hydrocarbon receptor-mediated cytochrome p450 autoregulatory pathway. Mol. Pharmacol. 72, 13691379 (2007).

Article CAS PubMed Google Scholar

Rifkind, A. B. CYP1A in TCDD toxicity and in physiology-with particular reference to CYP dependent arachidonic acid metabolism and other endogenous substrates. Drug Metab. Rev. 38, 291335 (2006).

Article CAS PubMed Google Scholar

Wei, Y. D., Bergander, L., Rannug, U. & Rannug, A. Regulation of CYP1A1 transcription via the metabolism of the tryptophan-derived 6-formylindolo[3,2-b]carbazole. Arch Biochem. Biophys. 383, 99107 (2000).

Article CAS PubMed Google Scholar

Wei, Y. D., Helleberg, H., Rannug, U. & Rannug, A. Rapid and transient induction of CYP1A1 gene expression in human cells by the tryptophan photoproduct 6-formylindolo[3,2-b]carbazole. Chem. Biol. Interact 110, 3955 (1998).

Article CAS PubMed Google Scholar

Heath-Pagliuso, S. et al. Activation of the Ah receptor by tryptophan and tryptophan metabolites. Biochemistry 37, 1150811515 (1998).

Article CAS PubMed Google Scholar

Mescher, M. & Haarmann-Stemmann, T. Modulation of CYP1A1 metabolism: from adverse health effects to chemoprevention and therapeutic options. Pharmacol. Ther. 187, 7187 (2018).

Article CAS PubMed Google Scholar

Mimura, J., Ema, M., Sogawa, K. & Fujii-Kuriyama, Y. Identification of a novel mechanism of regulation of Ah (dioxin) receptor function. Genes Dev. 13, 2025 (1999).

Article CAS PubMed PubMed Central Google Scholar

MacPherson, L. et al. Aryl hydrocarbon receptor repressor and TiPARP (ARTD14) use similar, but also distinct mechanisms to repress aryl hydrocarbon receptor signaling. Int. J. Mol. Sci. 15, 79397957 (2014).

Article PubMed PubMed Central Google Scholar

Stockinger, B., Shah, K. & Wincent, E. AHR in the intestinal microenvironment: safeguarding barrier function. Nat. Rev. Gastroenterol. Hepatol. 18, 559570 (2021).

Article CAS PubMed PubMed Central Google Scholar

Ma, W. et al. Kynurenine produced by tryptophan 2,3-dioxygenase metabolism promotes glioma progression through an aryl hydrocarbon receptor-dependent signaling pathway. Cell Biol. Int. 46, 15771587 (2022).

Article CAS PubMed Google Scholar

Xiong, J. et al. Aryl hydrocarbon receptor mediates Jak2/STAT3 signaling for non-small cell lung cancer stem cell maintenance. Exp. Cell Res. 396, 112288 (2020).

Article CAS PubMed Google Scholar

Pan, Z. Y. et al. Activation and overexpression of the aryl hydrocarbon receptor contribute to cutaneous squamous cell carcinomas: an immunohistochemical study. Diagn Pathol. 13, 59 (2018).

Article PubMed PubMed Central Google Scholar

Mohamed, H. T. et al. Inflammatory breast cancer: activation of the aryl hydrocarbon receptor and its target CYP1B1 correlates closely with Wnt5a/b-beta-catenin signalling, the stem cell phenotype and disease progression. J. Adv. Res. 16, 7586 (2019).

Article CAS PubMed Google Scholar

Mian, C. et al. AHR over-expression in papillary thyroid carcinoma: clinical and molecular assessments in a series of Italian acromegalic patients with a long-term follow-up. PLoS One 9, e101560 (2014).

Article PubMed PubMed Central Google Scholar

To, K. K., Yu, L., Liu, S., Fu, J. & Cho, C. H. Constitutive AhR activation leads to concomitant ABCG2-mediated multidrug resistance in cisplatin-resistant esophageal carcinoma cells. Mol. Carcinog. 51, 449464 (2012).

Article CAS PubMed Google Scholar

Su, J. M., Lin, P., Wang, C. K. & Chang, H. Overexpression of cytochrome P450 1B1 in advanced non-small cell lung cancer: a potential therapeutic target. Anticancer Res. 29, 509515 (2009).

PubMed Google Scholar

Schiering, C. et al. Feedback control of AHR signalling regulates intestinal immunity. Nature 542, 242245 (2017).

Article CAS PubMed PubMed Central Google Scholar

Metidji, A. et al. The environmental sensor AHR protects from inflammatory damage by maintaining intestinal stem cell homeostasis and barrier integrity. Immunity 49, 353362 e355 (2018).

Article CAS PubMed PubMed Central Google Scholar

Murray, I. A., Patterson, A. D. & Perdew, G. H. Aryl hydrocarbon receptor ligands in cancer: friend and foe. Nat. Rev. Cancer 14, 801814 (2014).

Article CAS PubMed PubMed Central Google Scholar

Panda, S. K. et al. Repression of the aryl-hydrocarbon receptor prevents oxidative stress and ferroptosis of intestinal intraepithelial lymphocytes. Immunity 56, 797812 e794 (2023).

Article CAS PubMed Google Scholar

Ly, M. et al. Diminished AHR signaling drives human acute myeloid leukemia stem cell maintenance. Cancer Res. 79, 57995811 (2019).

Article CAS PubMed Google Scholar

Singh, K. P. et al. Loss of aryl hydrocarbon receptor promotes gene changes associated with premature hematopoietic stem cell exhaustion and development of a myeloproliferative disorder in aging mice. Stem Cells Dev. 23, 95106 (2014).

Article CAS PubMed Google Scholar

Singh, K. P., Wyman, A., Casado, F. L., Garrett, R. W. & Gasiewicz, T. A. Treatment of mice with the Ah receptor agonist and human carcinogen dioxin results in altered numbers and function of hematopoietic stem cells. Carcinogenesis 30, 1119 (2009).

Article CAS PubMed Google Scholar

Opitz, C. A. et al. An endogenous tumour-promoting ligand of the human aryl hydrocarbon receptor. Nature 478, 197203 (2011).

Article CAS PubMed Google Scholar

Rothhammer, V. et al. Type I interferons and microbial metabolites of tryptophan modulate astrocyte activity and central nervous system inflammation via the aryl hydrocarbon receptor. Nat. Med. 22, 586597 (2016).

Article CAS PubMed PubMed Central Google Scholar

Shinde, R. et al. Apoptotic cell-induced AhR activity is required for immunological tolerance and suppression of systemic lupus erythematosus in mice and humans. Nat. Immunol. 19, 571582 (2018).

Article CAS PubMed PubMed Central Google Scholar

Rosser, E. C. et al. Microbiota-derived metabolites suppress arthritis by amplifying Aryl-hydrocarbon receptor activation in regulatory B cells. Cell Metab. 31, 837851 e810 (2020).

Article CAS PubMed PubMed Central Google Scholar

Granados, J. C. et al. AHR is a master regulator of diverse pathways in endogenous metabolism. Sci. Rep. 12, 16625 (2022).

Article PubMed PubMed Central Google Scholar

Cannon, A. S. et al. AhR activation leads to attenuation of murine autoimmune hepatitis: single-cell RNA-Seq analysis reveals unique immune cell phenotypes and gene expression changes in the liver. Front. Immunol. 13, 899609 (2022).

Article CAS PubMed PubMed Central Google Scholar

Dean, J. W. et al. The aryl hydrocarbon receptor cell intrinsically promotes resident memory CD8(+) T cell differentiation and function. Cell Rep. 42, 111963 (2023).

Article CAS PubMed PubMed Central Google Scholar

Stinn, A., Furkert, J., Kaufmann, S. H. E., Moura-Alves, P. & Kolbe, M. Novel method for quantifying AhR-ligand binding affinities using microscale thermophoresis. Biosensors (Basel) 11, 60 (2021).

Article CAS PubMed Google Scholar

Finn, R. N. The physiology and toxicology of salmonid eggs and larvae in relation to water quality criteria. Aquat Toxicol. 81, 337354 (2007).

Article CAS PubMed Google Scholar

Noda, S. et al. Gene expression of detoxifying enzymes in AhR and Nrf2 compound null mutant mouse. Biochem. Biophys. Res. Commun. 303, 105111 (2003).

Article CAS PubMed Google Scholar

Hannon, S. L. & Ding, X. Assessing cytochrome P450 function using genetically engineered mouse models. Adv. Pharmacol 95, 253284 (2022).

Article CAS PubMed PubMed Central Google Scholar

Degrelle, S. A., Ferecatu, I. & Fournier, T. Novel fluorescent and secreted transcriptional reporters for quantifying activity of the xenobiotic sensor aryl hydrocarbon receptor (AHR). Environ. Int. 169, 107545 (2022).

Article CAS PubMed Google Scholar

Jones, S. N., Jones, P. G., Ibarguen, H., Caskey, C. T. & Craigen, W. J. Induction of the Cyp1a-1 dioxin-responsive enhancer in transgenic mice. Nucleic Acids Res. 19, 65476551 (1991).

Article CAS PubMed PubMed Central Google Scholar

Campbell, S. J., Carlotti, F., Hall, P. A., Clark, A. J. & Wolf, C. R. Regulation of the CYP1A1 promoter in transgenic mice: an exquisitely sensitive on-off system for cell specific gene regulation. J. Cell Sci. 109, 26192625 (1996).

Article CAS PubMed Google Scholar

Galijatovic, A. et al. The human CYP1A1 gene is regulated in a developmental and tissue-specific fashion in transgenic mice. J. Biol. Chem. 279, 2396923976 (2004).

Article CAS PubMed Google Scholar

Operana, T. N., Nguyen, N., Chen, S., Beaton, D. & Tukey, R. H. Human CYP1A1GFP expression in transgenic mice serves as a biomarker for environmental toxicant exposure. Toxicol Sci. 95, 98107 (2007).

Article CAS PubMed Google Scholar

Van de Pette, M. et al. Visualizing changes in Cdkn1c expression links early-life adversity to imprint Mis-regulation in adults. Cell Rep. 18, 10901099 (2017).

Article PubMed PubMed Central Google Scholar

Read more:
Bioluminescence imaging of Cyp1a1-luciferase reporter mice demonstrates prolonged activation of the aryl ... - Nature.com

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.

Continued here:
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.

Thanks for your feedback!

Read more from the original source:
Researchers find the "recipe" for growing new limbs - ZME Science

Paralyzed man who can walk again shows potential benefit of stem cell therapy – ABC News

This page either does not exist or is currently unavailable.

From here you can either hit the "back" button on your browser to return to the previous page, or visit the ABCNews.com Home Page. You can also search for something on our site below.

STATUS CODE: 500

Here is the original post:
Paralyzed man who can walk again shows potential benefit of stem cell therapy - ABC News

Two keys needed to crack three locks for better engineered blood vessels – Penn State University

UNIVERSITY PARK, Pa. Blood vessels engineered from stem cells could help solve several research and clinical problems, from potentially providing a more comprehensive platform to screen if drug candidates can cross from the blood stream into the brain to developing lab-grown vascular tissue to support heart transplants, according to Penn State researchers. Led by Xiaojun Lance Lian, associate professor of biomedical engineering and of biology, the team discovered the specific molecular signals that can efficiently mature nascent stem cells into the endothelial cells that comprise the vessels and regulate exchanges to and from the blood stream.

They published their findings today (March 21) in Stem Cell Reports. The team already holds a patent on foundational method developed 10 years ago and has filed a provisional application for the expanded technology described in this paper.

The researchers found they could achieve up to a 92% endothelial cell conversion rate by applying two proteins SOX17 and FGF2 to human pluripotent stem cells. This type of stem cell, which the researchers derived from a federally approved stem cell line, can differentiate into almost any other cell type if provided the right proteins or other biochemical signals. SOX17 and FGF2 engage three markers in stem cells, triggering a growth cascade that not only converts them to endothelial cells but also enables them to form tubular-like vessels in a dish.

The more efficient differentiation and lab-grown vessels could allow researchers to grow an artificial blood brain barrier to test neurological drugs under development, according to Lian. Other eventual clinical uses may include reestablishing vascular structures after heart damage.

Drugs designed to treat brain diseases need to pass through the blood brain barrier to be effective, Lian said. The blood brain barrier is a membrane packed with vessels and regulates what can pass from the blood into the brain. Our cells can form a tight layer in a dish, onto which we could add various chemicals and see how they pass through.

Next, Lian said, the team will collaborate with industry partners to advance the artificial blood brain barrier and begin testing various drugs. Getting to this point, however, required a decade of investigating the molecular mechanism underpinning how stem cells convert to endothelial cells.

In 2014, we published a protocol using a small molecule that could help the cells differentiate about 20% of the time, but weve now found that just one gene, SOX17, is sufficient for differentiating the about 80% of cells into endothelial cells, said Lian, associate professor of biomedical engineering and of biology at Penn State. That was completely unknown.

In their prior stem cell differentiation process, the low efficiency resulted in heterogenous cell populations, making them difficult to sort and to obtain enough for other research or clinical applications. Lian explained that the researchers knew some of the cells were endothelial cells, but they couldnt predict the other cell types.

To make more homogenous populations, the researchers examined the proteins at play during the process. They first noticed that cells expressed SOX17 during differentiation, so they removed the cells ability to express the protein and analyzed how its absence changed function.

Before knocking down SOX17 expression, about 20% of stem cells would become endothelial cells, Lian said After, differentiation dropped to about 5% at best. We found that SOX17 is required for this process. It was a lucky and surprising finding.

With the addition of SOX17, 80% of stem cells could differentiate. But the researchers wanted to do better, Lian said. The stem cells produce three markers, but SOX17 only triggers two of them to begin the differentiation process. The third marker, called CD31, doesnt work when only exposed to SOX17.

That was a problem for us. We spent two to three years figuring out why, Lian said, explaining that another protein, called FGF2 could induce the marker without affecting SOX17s influence on the other two markers. The combination results in up to 92% of the stem cells differentiating into endothelial cells a more than 350% increase in efficiency from the researchers original approach. Sometimes science is very difficult, but we do not give up.

With all three markers activated, the differentiated cells can form tubular-like vessels in a dish. They can also uptake proteins, like blood vessels in the body. The researchers tested this ability by inducing inflammation to see if the endothelial cells could detect the protein signal involved they could.

Our cells are indeed functional, Lian said. With SOX17 and FGF2, we can determine the fate of these stem cells to be precisely what we need.

Lian is also affiliated with the Materials Research Institute and the Huck Institutes of the Life Sciences at Penn State. Other collaborators on the study include Michael W. Ream, who is a graduate student in the Lian lab in the Department of Biomedical Engineering; Lauren N. Randolph, who earned her doctorate degree in biomedical engineering at Penn State and is now with the San Raffaele Telethon Institute for Gene Therapy in Italy; Yuqian Jian, who also earned her doctoral degree in biomedical engineering at Penn State and is now with the Departments of Pediatrics and of Genetics at Stanford University; and Yun Chang and Xiaoping Bao, both with Purdue Universitys Davidson School of Chemical Engineering.

The U.S. National Science Foundation and the National Institutes of Health funded this research.

More:
Two keys needed to crack three locks for better engineered blood vessels - Penn State University

Human stem-cell-based therapy for Parkinson’s disease proven safe PET – BioNews

A small clinical trial involving 12 patients with Parkinson's disease has reported no safety concerns with a newly developed human stem-cell-based therapy.

The therapy called TED A9 was delivered as a cell transplant injected directly into the brain of the participants as part of a Phase 1/2a clinical trial, which is principally concerned with assessing safety and dosing requirements.

The drug's developer, S.Biomedics, in Seoul, South Korea, claimed in a press release: 'According to Professor Jin-Woo Chang, [the principal investigator of the transplant conducted at Severance Hospital, Seoul,] none of the 12 Parkinson's disease participants had any side effects, complications, or unusual adverse reactions following the transplantation of TED-A9'.

The trial participants were aged between 50 and 75 years old, had been diagnosed with Parkinson's disease for more than five years, and had already motor complications such as freezing of gait or dyskinesia.

To ensure and monitor the safety of the treatment, an initial three patients were injected with a low dose (3.15 million cells) and monitored for three months, before another three patients were treated at high dose (6.3 million cells) and also monitored for three months.

No side effects, complications, or unusual adverse reactions were seen in either group during the three-month assessment period. Therefore, the clinical trial continued by adding three further patients to each of the low-dose and high-dose groups. Again, no safety concerns were seen.

Parkinson's symptoms are caused by the progressive loss of neurons that produce dopamine, a major chemical messenger in the brain. The TED-A9 therapy contains dopaminergic progenitor (precursor) cells, which had themselves been derived in the lab from embryonic stem cells.

The drug developers at S.Biomedics hope that the dopaminergic precursor cells in TED-A9 will treat Parkinson's disease by replacing the mature dopamine-producing nerve cells that are lost in patients.

Professor Dong-Wook Kim, a neurosurgeon and the principal developer of TED-A9, said: 'We have developed a fundamental therapeutic mechanism that directly replaces dopaminergic neurons lost in patients with Parkinson's disease. TED-A9 could represent a fundamental treatment that surpasses current therapies, which only temporarily alleviate the symptoms of Parkinson's disease,'.

The trial is expected to continue until February 2026, allowing safety of the therapy to be monitored for a total of five years. As part of the study, exploratory efficacy will also be examined for two years using clinical measures of motor symptoms and a patient questionnaire of daily life quality.

More Information is available at ClinicalTrials.gov.

More here:
Human stem-cell-based therapy for Parkinson's disease proven safe PET - BioNews

Unlocking the Secrets of Aging: Researchers Reveal Key to Intestinal Balance – SciTechDaily

University of Helsinki researchers discovered that the capacity of intestinal stem cells to maintain cellular balance in the gut diminishes with age, and identified a new mechanism linking nutrient adaptation of these stem cells to aging. This insight could lead to methods for preserving gut function in the elderly.

The ability of intestinal stem cells to preserve the cellular equilibrium in the gut diminishes with age. Scientists at the University of Helsinki have identified a novel interaction between the adaptation of intestinal stem cells to nutrients and the aging process. The finding may make a difference when seeking ways to maintain the functional capacity of the aging gut.

The cellular balance of the intestine is carefully regulated, and it is influenced, among other things, by nutrition: ample nutrition increases the total number of cells in the gut, whereas fasting decreases their number. The relative number of different types of cells also changes according to nutrient status.

The questions of how the nutrition status of the gut controls stem cell division and differentiation, and how the nutrient adaptation of stem cells changes as during aging have not been comprehensively answered. Nutrient adaptation refers to the way in which nutrients guide cell function.

On the left: Model organism fruit fly (Drosophila melanogaster), gastrointestinal tract highlighted in green. On the right: Microscope images of the fruit fly intestine where cell nuclei are stained (cyan). The intestine on the top is from well-fed animal, and the intestine below from an animal kept on a restricted diet. Credit: Jaakko Mattila

Researchers at the University of Helsinki identified a new regulatory mechanism that directs the differentiation of intestinal stem cells under a changing nutrient conditions. Cell signaling activated by nutrients increases the size of stem cells in the fruit fly intestine. The size of the stem cells, in turn, controls the cell type into which the stem cells differentiate. For stem cell function, flexible regulation of their size is essential.

In other words, the size of the cells dynamically increases or decreases, depending on the dietary conditions. Such flexibility enables stem cells to differentiate in accordance with the prevailing nutrient status. By utilizing intestine-wide cell imaging, the researchers found that the nutrient adaptation of stem cell size and the resulting differentiation vary in different regions of the gut.

Our observations demonstrate that the regulation of intestinal stem cells is much more region-specific than previously understood. This may be relevant to, for example, how we think about the pathogenetic mechanisms of intestinal diseases, says Jaakko Mattila, the corresponding author of the research article from the Faculty of Biological and Environmental Sciences, University of Helsinki.

The researchers also observed that the ability of intestinal stem cells to react to a changing nutrient status is greatly reduced in older animals. They also found that, in older animals, stem cells are in a state where they are constantly large in size, which restricts their ability to differentiate. With aging, flexible regulation of stem cell size was markedly better preserved in animals that had been kept under a diet regime that is known as intermittent fasting. In the past, intermittent fasting has been shown to prolong the lifespan of animals, and the results now obtained indicate that the improved preservation of stem cell function may underlie this prolongation.

According to the researchers, the mechanisms associated with the functioning, nutrient adaptation, and aging of human and fruit fly stem cells are fairly similar.

We believe that these findings have a broader significance towards understanding how to slow down the loss of tissue function caused by aging by controlling the nutrient adaptation of stem cells. However, more information is needed on the effect of the mechanism on human intestinal stem cells. Our work on the nutrient adaptation of stem cells continues, says Professor Ville Hietakangas from the Faculty of Biological and Environmental Sciences and the Institute of Biotechnology, University of Helsinki.

Reference: Stem cell mTOR signaling directs region-specific cell fate decisions during intestinal nutrient adaptation by Jaakko Mattila, Arto Viitanen, Gaia Fabris, Tetiana Strutynska, Jerome Korzelius and Ville Hietakangas, 9 February 2024, Science Advances. DOI: 10.1126/sciadv.adi2671

Here is the original post:
Unlocking the Secrets of Aging: Researchers Reveal Key to Intestinal Balance - SciTechDaily

Stem Cells. Let’s get things straight about what they are, and what they are not. Ethically and legally Sonoran News – Sonoran News

Once again, I receive so many emails regarding stem cells, and where they come from. This is the biggest topic of questions I receive each week. The first thing Id like to say is that in my office, Accurate Care Medical Wellness Center, only ethical and legal sources of stem cells are used. We also use cells and protocols that are the most effective for each condition a patient may have. I personally go to three stem cell conferences a year to stay current with all of the products and protocols that are available.

It seems that people have been told about what used to be done back when stem cell therapy started. Very little information is available to the public that explains the recent technology and sources of stem cells and their ability to restore function to otherwise dysfunctional systems of the body to promote healing. I will do my best to shed light on this subject, while clarifying what they are and what they are not.

Lets start with what stem cells we do not use in my office. Embryonic stem cells are derived from embryos that are typically created through in vitro fertilization (IVF) procedures for reproductive purposes. These unused embryos are typically donated for research purposes with the informed consent of the donors. In some cases, aborted embryos may be used. In 2001, then-President George W. Bush announced a policy limiting federal funding for research involving embryonic stem cells. This policy allowed federal funding only for research on embryonic stem cell lines that had already been established before August 9, 2001. In 2009, President Barack Obama issued an executive order that lifted the restrictions on federal funding for embryonic stem cell research. This allowed for the funding of research on new embryonic stem cell lines. We do not use embryonic stem cells for treatments in my office, as we do not use products that are illegal, unethical or controversial.

Amniotic stem cells are derived from the amniotic fluid and amniotic membrane surrounding the fetus during pregnancy, generally obtained through procedures like amniocentesis, the umbilical cord (in stem cell therapy for labs), or during a cesarean section childbirth. Unlike embryonic stem cells, which are derived from embryos, amniotic stem cells are obtained without harming the fetus and are ethically uncontroversial. We use MSCs or Mesenchymal Stem Cells, and extra cellular cells or exosomes in my office.

We use stem cells, now known as HCT or Human Cellular Tissue Therapy. This term is now used, as not all products used for treatments today contain stem cells, Some are extracellular products. Extracellular vesicles (EVs)derived from mesenchymal stem cells, (MSCs) play a critical role in the development of immune regulation and regeneration. These mimic the effects of stem cells and perform powerful functions. In my office, each patient, case and condition is unique, therefore different products and protocols are used for each and every patient. As work continues, researchers are actively developing engineered EVs that are even more effective.

Many patients tell me that theyve previously received the following types of stem cells when they received stem cell therapy that did not work prior to coming to my office.

Two of the popular forms of stem cell therapy theyve received are adipose (fat) and bone marrow derived cells. These two sources of stem cells can work well for younger patients. The challenge that arises with older patients, especially those over the age of 60, is that these cells come from an older body.

Many of the regenerative properties of the cells have been used up and overworked over the years. This leaves the patient with compromised cells that are not going to regrow the tissue in question to the level and expectations the patient and doctor are willing to see. The two forms of stem cell extraction from the patient are invasive, and can be quite painful, especially those from bone marrow. Using stem cells, and extracellular cells from the donated umbilical cord of live birth of a baby is much less invasive for the donor (baby) and the recipient (patient).

For any questions regarding regenerative medicine, and whether it may help you, please call my office for a complimentary consultation. I offer special discounts for my readers as well. We frequently offer evening lectures in my office to learn more about regenerative medicine. If you would like to be added to our list for future events, please call my office.

For questions regarding any of my articles, please email me at [emailprotected] Leisa-Marie Grgula. DC Chiropractic Physician Accurate Care Medical Wellness Center 18261 N. Pima Rd. Ste. #115 Scottsdale, AZ 85255 480-584-3955

View post:
Stem Cells. Let's get things straight about what they are, and what they are not. Ethically and legally Sonoran News - Sonoran News

Distinct pathways drive anterior hypoblast specification in the implanting human embryo – Nature.com

Ethics statement

Human embryo work was regulated by the Human Fertility and Embryology Authority under licence R0193. Approval was obtained from the Human Biology Research Ethics Committee at the University of Cambridge (reference HBREC.2021.26). All work is compliant with the 2021 International Society for Stem Cell Research (ISSCR) guidelines. Patients undergoing IVF at CARE Fertility, Bourn Hall Fertility Clinic, Herts & Essex Fertility Clinic, and Kings Fertility were given the option of continued storage, disposal or donation of embryos to research (including research project specific information) or training at the end of their treatment. Patients were offered counselling, received no financial benefit and could withdraw their participation at any time until the embryo had been used for research. Research consent for donated embryos was obtained from both gamete providers. Embryos were not cultured beyond day 14 post-fertilization or the appearance of the primitive streak. Human stem cell work was approved by the UK Stem Cell Bank Steering Committee (under approval SCSC21-38) and adheres to the regulations of the UK Code of Practice for the Use of Human Stem Cell Lines. Mice were kept in an animal house in individually ventilated housing on 12:12h lightdark cycle with ad libitum access to food and water. Ambient temperature was maintained at 2122C and humidity at 50%. Experiments with mice are regulated by the Animals (Scientific Procedures) Act 1986 Amendment Regulations 2012 and carried out following ethical review by the University of Cambridge Animal Welfare and Ethical Review Body. Experiments were approved by the Home Office under licences 70/8864 and PP3370287. CD1 wild-type males aged 645weeks and CD1 wild-type females aged 618weeks were used for this study. Animals were inspected daily, and those showing health concerns were culled by cervical dislocation.

Raw fastq files from human datasets26,27,36,45, cynomolgus monkey datasets28,35 and mouse datasets68,69,70,71 were obtained from public repositories with wget. All human datasets were aligned to the GRCh38 reference using kb-pythons kb ref function to generate a reference. For cynomolgus monkey, National Center for Biotechnology Information (NCBI) genome build 5.0 transcriptome fasta files were adjusted to Ensembl style and used in kb ref to generate a custom index. For the mouse, GRCm39 reference was used with kb ref to generate a custom index. All datasets were re-aligned using either kb-python or kallisto72,73, after data handling as below. Human datasets: 10x v2 data from Mol et al. were processed as previously described10. For Zhou et al.27, read1 files were trimmed using cutadapt74 for the reported adapter sequence. Trimmed reads were then aligned using the kb-python kb count function with custom specifications (-x 1,0,8:1,8,16:0,0,0) and the custom barcode whitelist available. Each pair of fastqs was processed individually into barcodegene matrices and concatenated. For Xiang et al.26, a batch file was generated with cell ID, read1 and read2 for each fastq pair listed. Kallistos pseudo quant command was then used to generate a cell IDgene matrix. For Blakely et al.36, reads were aligned using kallisto pseudo quant. For Petropoulos et al.45, single-end reads were processed with kallisto pseudo quant with a pre-made batch file as above with 43 base pair read length specified. Cynomolgus datasets: for Ma et al.28, read1 fastqs were trimmed using cutadapt for TSO and polyA tail as described in the original publication. Next, kb pythons kb count function was used with custom specifications (x 1,0,8:1,8,16:0,0,0). For Yang et al.35, reads were aligned using kb pythons kb count command with 10xv3 technology specified. For Nakamura et al.21, available count tables were used given the use of SOLiD sequencer limiting re-alignment program options. Mouse datasets: for Mohammed et al.70, kallisto pseudo quant with a generated batch file was used to generate a cell IDgene matrix. For Deng et al.69 and Cheng et al.68, single-end reads were aligned with kallisto pseudo quant. Finally, for Pijuan-Sala et al.71, each sample set of 33 fastq files was aligned with kb count, with 10xv1 technology specified. The resulting set of barcodegene matrices was then concatenated for downstream analysis.

Following re-alignment, any datasets not generated using unique molecular identifier counts were normalized using quminorm75. First, matrices were converted to transcripts per kilobase million (TPM), and then the TPM matrix ran through quminorm with a shape parameter up to a maximum of 2 that did not create not available/applicable (NA) values in the matrix. Then, each individual dataset was made into a Seurat object76. Each individual dataset was then merged into a species-specific Seurat object, with SCT batch correction applied across datasets. Clusters were identified on the basis of canonical marker expression. To perform module scoring, gene lists were obtained from rWikiPathways77. For the monkey and mouse, gene symbols were converted to human homologues using bioMart78. Seurats AddModuleScore function was used with WikiPathway gene lists of interests as input. For CellPhoneDB analysis41, human data were split on the basis of stage, and subset matrix and metadata for cell type were output as txt files. CellPhoneDB was then run with respective files and counts-data set to gene_name. Data visualization was performed using Seurats DimPlot, FeaturePlot and VlnPlot functions, Scillus (https://scillus.netlify.app) Plot_Measure function, pheatmap and CellPhoneDBs dotplot function.

The scripts used for analyses are available at ref. 79.

Human embryos were thawed and cultured as described previously10,24. Briefly, cryopreserved human blastocysts (day 5 or 6) were thawed using the Kitazato thaw kit (VT8202-2, Hunter Scientific) according to the manufacturers instructions. The day before thawing, TS solution was placed at 37C overnight. The next day, IVF straws were submerged in 1ml pre-warmed TS for 1min. Embryos were then transferred to DS for 3min, WS1 for 5min and WS2 for 1min. These steps were performed in reproplates (REPROPLATE, Hunter Scientific) using a STRIPPER micropipette (Origio). Embryos were incubated at 37C and 5% CO2 in normoxia and in pre-equilibrated human IVC1 supplemented with 50ngml1 insulin growth factor-1 (IGF1) (78078, STEMCELL Technologies) under mineral oil for 14h to allow for recovery. Following thaw, blastocysts were briefly treated with acidic Tyrodes solution (T1788, Sigma) to remove the zona pellucida and placed in pre-equilibrated human IVC1 in eight-well -slide tissue culture plates (80826, Ibidi) in approximately 400l volume per embryo per well. Half medium changes were done every 24h. For small-molecule experiments, human IVC1 was supplemented with either 2M A83-01 (72022, STEMCELL Technologies)80,81, 25ngml1 Activin-A (Qk001, QKINE)82,83,84, 200nM LDN (S2618, SelleckChem)85,86, 50ngml1 BMP6 (SRP3017, Sigma Aldrich)85,86, 20M DAPT (72082, STEMCELL Technologies)87,88,89,90, 10M Compound-E (ab142164, Abcam)91,92,93, 20M MK-0752 (S2660, Selleck Chemicals)94,95,96 or dimethyl sulfoxide (DMSO) for 48h. In all cases, these concentrations fall within a range of those used for either vertebrate embryos or complex human ES cell-derived models of the embryo. Within these ranges, a low-to-intermediate concentration was selected to avoid non-specific cytotoxic effects while still considering the higher concentration needed for embryo permeation compared with minimal 2D cell culture to achieve inhibitor action. Further, all small molecules and proteins were tested on human ES cells to validate the efficacy and test for cytotoxicity. For analysis, embryos were fixed in 4% paraformaldehyde for 20min at room temperature for downstream analysis.

Pregnant, time-staged mice were culled by cervical dislocation, and uteri were dissected and placed in M2 medium (pre-warmed if embryos were for in vitro culture, ice cold if for fixing). E3.5 blastocysts were flushed out of uteri of pregnant females and either fixed for immunofluorescence analysis or transferred to acidic Tyrodes solution for zona pellucida removal. Embryos were cultured for 48h in CMRL (11530037, Thermo Fisher Scientific) supplemented with 1 B27 (17504001, Thermo Fisher Scientific), 1 N2 (made in-house), 1 penicillinstreptomycin (15140122, Thermo Fisher Scientific), 1 GlutaMAX (35050-038, Thermo Fisher Scientific), 1 sodium pyruvate (11360039, Thermo Fisher Scientific), 1 essential amino acids (11130-036, Thermo Fisher Scientific), 1 non-essential amino acids (11140-035, Thermo Fisher Scientific) and 1.8mM glucose (G8644, Sigma) supplemented with 20% foetal bovine serum5,28. Embryos were incubated with 25ngml1 Activin-A, 200nM LDN, 50ngml1 BMP6, 20M DAPT or DMSO for 48h. For E4.5, E5.5 and E5.75 collections, embryos were dissected directly from the uteri and fixed for analysis. For E5.0 collection, embryos were dissected from the uteri, and Reicherts membrane was removed before culturing or 36h with relevant small molecules as described above.

Shef6 human ES cells (R-05-031, UK Stem Cell Bank) were routinely cultured on 1.6% v/v Matrigel (354230, Corning) in mTeSR1 medium (85850, STEMCELL Technologies) at 37C and 5% CO2. Cells were passaged every 35days with TrypLE Express Enzyme (12604-021, Thermo Fisher Scientific). The ROCK inhibitor Y-27632 (72304, STEMCELL Technologies) was added for 24h after passaging. Cells were routinely tested for mycoplasma contamination by polymerase chain reaction. To convert primed human ES cells to RSeT or PXGL naive conditions, cells were passaged onto mitomycin-C inactivated CF-1 MEFs (3103 cellscm2; GSC-6101G, Amsbio) in human ES cell medium containing Dulbeccos modified Eagle medium (DMEM)/F12 supplemented with 20% Knockout Serum Replacement (10828010, Thermo Fisher Scientific), 100M -mercaptoethanol (31350-010, Thermo Fisher Scientific), 1 GlutaMAX (35050061, Thermo Fisher Scientific), 1 non-essential amino acids, 1 penicillinstreptomycin and 10ngml1 FGF2 (University of Cambridge, Department of Biochemistry) and 10M ROCK inhibitor Y-27632 (72304, STEMCELL Technologies). For RSeT conversion, cells were switched to RSeT medium (05978, STEMCELL Technologies). Cells were maintained in RSeT and passaged as above every 46days. For PXGL conversion, previously described protocols were used97. Briefly, cells were cultured in hypoxia and medium was switched to chemically Resetting Media 1 (cRM-1), which consists of N2B27 supplemented with 1M PD0325901 (University of Cambridge, Stem Cell Institute), 10ngml1 human recombinant LIF (300-05, PeproTech) and 1mM valproic acid. N2B27 contains 1:1 DMEM/F12 and Neurobasal A (10888-0222, Thermo Fisher Scientific) supplemented with 0.5 B27 (10889-038, Thermo Fisher Scientific) and 0.5 N2 (made in-house), 100M -mercaptoethanol, 1 GlutaMAX and 1 penicillinstreptomycin. cRM-1 was changed every 48h for 4days. Subsequently, medium was changed to PXGLN2B27 supplemented with 1M PD0325901, 10ngml1 human recombinant LIF, 2M G6983 (2285, Tocris) and 2M XAV939 (X3004, Merck). PXGL cells were passaged every 46days using TrypLE (12604013, Thermo Fisher Scientific) for 3min, and 10M ROCK inhibitor Y-27632 and 1lcm2 Geltrex (A1413201, Thermo Fisher Scientific) were added at passage for 24h.

For small-molecule experiments, primed or PXGL human ES cells were plated into ibiTreat dishes at normal passage densities. Forty-eight hours after passage, medium was changed to N2B27 supplemented with 25ngml1 Activin-A, 2M A83-01, 50ngml1 BMP6, 200nM LDN or 20M DAPT. Plates were then fixed for 20min in 4% paraformaldehyde for downstream analysis. For 3D culture of primed human ES cells, 30,000 cells were resuspended in 200l of ice-cold Geltrex and the resulting mix was plated into a single well of an 8 -well ibiTreat dish. Geltrex was polymerized by placement at 37C for 10min. Two-hundred microlitres of mTeSR1 with ROCK inhibitor Y-27632 was added after polymerization. Twenty-four hours later, the medium was changed to N2B27 (10M DAPT). Medium was refreshed 24h later, and the plate was fixed in 4% paraformaldehyde for 30min after a total of 48h in experimental conditions. Conditioned medium experiments were performed as described previously48. Briefly, 80l of ice-cold Geltrex was added to an 8 -well ibiTreat dish to create a 100% Geltrex bed. This was polymerized at 37C for 4min. A total of 1103 cellscm2 primed human ES cells were then added onto this bed in DMEM/F12 and allowed to settle for 15min. After this, medium was carefully switched to conditioned medium (described below) with 5% Geltrex (v/v) and 10M ROCK inhibitor Y-27632. Conditioned medium with 5% Geltrex was refreshed daily for the next 2days, and the resulting spheroids were fixed after a total of 72h.

YSLC differentiation was carried out as published48. Briefly, Shef6 human ES cells cultured in RSeT medium for at least 2weeks were plated onto ibiTreat dishes at 1103 cellscm2 in RSeT medium with 10M Y-27632. Medium was changed the next day to ACL differentiation medium consisting of N2B27 supplemented with 5% v/v Knockout Serum Replacement, 100ngml1 Activin-A, 3M CHIR99021 (University of Cambridge Stem Cell Institute) and 10ngml1 human recombinant LIF. Medium was refreshed every 48h, and 2M A83-01, 200nM LDN or 20M DAPT was added to ACL medium for 48h from either day 2 to day 4, followed by fixation, or day 4 to day 6 followed by fixation. For conditioned medium experiments, at day 6 cells were washed three times with phosphate-buffered saline and then mTeSR Plus medium (100-0276; STEMCELL Technologies) was added for 24h. Medium was collected from YSLCs and passed through a 0.45-m filter (16555, Sartorious), and stored for up to 1week at 4C.

CD1 mouse ES cells (generous gift from Prof. Jennifer Nichols (Stem Cell Institute, University of Cambridge, UK)) were routinely cultured on gelatin-coated (G7765, Sigma Aldrich) dishes in N2B27 supplemented with 1m PD0325901, 3m CHIR99021 and 10ngml1 mouse Lif (University of Cambridge, Stem Cell Institute). Medium was changed every 48h, and cells were passaged every 35days using trypsinethylenediaminetetraacetic acid (25300062; Life Technologies). For experiments, cells were passaged as normal into ibiTreat dishes. The following day, medium was switched to either N2B27+2iLif, N2B27, or N2B27+200nM LDN. Medium was refreshed after 24h, and cells were fixed after 48h.

Embryos were fixed in 4% paraformaldehyde, permeabilized in 0.1M glycine with 0.3% Triton X-100 and placed in blocking buffer containing 1% bovine serum albumin and 10% foetal bovine serum. Primary antibodies were diluted in blocking buffer and added overnight at 4C. Fluorescently tagged secondary antibodies were added for 2h at room temperature. Primary antibodies used in this study are as follows: mouse monoclonal anti OCT3/4 (sc5279, Santa Cruz; 1:200 dilution), rat monoclonal anti SOX2 (14-19811-82, Thermo Fisher Scientific; 1:500 dilution), goat polyclonal anti NANOG (AF1997 R&D Systems; 1:500 dilution), rabbit monoclonal anti GATA6 (5851, Cell Signaling Technology; 1:2,000 dilution), goat polyclonal anti GATA6 (AF1700, R&D Systems; 1:200 dilution), mouse anti monoclonal Cdx2 (MU392-UC, Biogenex; 1:200 dilution), goat polyclonal anti CER1 (AF1075, R&D Systems; 1:250 dilution), rat monoclonal anti Cerebus1 (MAB1986, R&D Systems; 1:200 dilution), rabbit monoclonal anti Phospho-Smad1(Ser463/465)/Smad5(Ser463/465)/Smad9(Ser465/467) (13820T, Cell Signaling Technology; 1:200 dilution), rabbit monoclonal anti Smad2.3 (8685T, Cell Signaling Technology; 1:200 dilution), rabbit monoclonal anti-cleaved caspase 3 (9664, Cell Signaling Technology; 1:200 dilution), mouse monoclonal anti Podocalyxin (MAB1658, R&D Systems; 1:500 dilution), goat polyclonal anti Brachyury (AF2085, R&D Systems; 1:500 dilution), rat monoclonal anti GATA4 (14-9980-82, Thermo Fisher Scientific; 1:500 dilution), goat polyclonal anti AP2-gamma (AF5059, R&D Systems; 1:500 dilution), goat polyclonal anti Otx2 (AF1979, R&D Systems; 1:1,000 dilution) and Alexa Flour 594 Phalloidin (A12381, Thermo Fisher Scientific; 1:500 dilution).

Immunofluorescence images were captured on a Leica SP8 confocal and processed and analysed using Fiji (http://fiji.sc). Epiblast, hypoblast and CER1-positive cell numbers were manually counted using the multi-point cell counter plugin. Quantification of trophectoderm was performed using Imaris software (version 9.1.2) using the spots tool with manual curation. To quantify n/c SMAD2.3 in human and mouse embryos, the central three planes of individual cells were used to generate a three-plane z-stack. Individual 4,6-diamidino-2-phenylindole (DAPI)-positive nuclei were used to generate a nuclear mask using the Analyze Particles function on either the DAPI or lineage-associated transcription factor channel. The adjacent cytoplasmic area was drawn individually for each nucleus and the mean fluorescence of each region was measured, and the ratio computed. When embryos were stained with E-Cadherin, the membrane was delineated to allow for cytoplasmic region of interest determination. When embryos were stained with podocalyxin, the cytoplasmic region of interest was drawn to ensure delineation of a region captures suitable intra-cellular variation allowing for valid normalization. Measurements were computed on raw SMAD2.3 signal. To quantify pSMAD1.5.9 nuclear intensity, a nuclear mask generated on a central three-plane z-stack for each nucleus, and mean fluorescence values were measured. Within each three-plane z-stack, a background fluorescence taken adjacent to or within a cavity of the embryo was used for background normalization ((frac{text{mean nuclear intensity}}{1+text{mean background intensity}})). Background was normalized to (that is, to provide a comparable signal-to-noise ratio) rather than subtracted to account for the variability in laser penetration between experiments and z-planes. In stem cell experiments, nuclear masks were generated, mean fluorescence was measured, and all values were normalized to a control (DMSO) value of 1. To calculate the percentage of cleaved caspase-3-positive or CER1-positive cells, individual cells were manually counted using the cell counter plugin and presented as a percentage of all DAPI-negative or GATA6-positive cells. For 3D spheroid classification, the total number of structures was counted manually using the Cell Counter plugin, and each was assigned to a class of spheroid. For conditioned medium 3D spheroid quantifications, the central three planes of individual spheroids were used to generate a nuclear mask on the DAPI channel, and the mean nuclear pSMAD1.5.9 signal was quantified along with the signal of an acellular region for background normalization. To generate figures, images were processed by generating z-stacks of approximately five to ten planes to allow for visualization of embryo topology with cells on disparate planes followed by consistent adjustment of brightness and contrast.

No statistical method was used to pre-determine sample sizes. Sample sizes are similar to previous publications7,10,24. For characterization of normal development, embryos lacking any of the three lineages were excluded. Multiple SMAD fluorescence intensities were taken per lineage per embryo. All embryos were included in functional experiments and each cell type count is taken from individual embryos. All stem cell experiments were performed independently at least twice. Investigators were not blinded to group allocation during the experiment or analysis, as blinding would not have been possible due to medium preparation and changing requirements. Group allocation was not performed randomly; rather, based on visual assessment of embryos, investigators attempted to ensure balanced distributions of blastocysts/implanting embryos assessed as expanded with nice inner cell masses versus embryos that appeared delayed or with visible cell death across experimental groups. Statistical tests, except the Bayesian distribution model, were performed in Prism 9 (GraphPad), and where relevant, two-sided tests were used. Normality was tested with a ShapiroWilk test. Bayesian distribution modelling, which is suited to the small sample sizes used in human embryo studies, was used as a supplemental tool to assess how each small-molecule treatment affected the distribution of cell number. To do this, the brms R package was used98,99, with the assumption of a Poisson distribution and the Control counts set to inform the priors and be used as reference. Brms default Markov chain Monte Carlo settings were used. Coefficient credible intervals were either below 0.33, denoting a decrease in the distribution compared to control, or above 0.33, indicating an increase in the distribution compared to control. Credible intervals that bridged this range indicate no significant difference. All coefficients and credible intervals in addition to MannWhitney test P values are presented in Supplementary Table 8. For data presentation, box plots encompass the 25th to 75th percentile in the box, with the median marked by the central line, and the mean marked by a cross. The minimum and maximum are marked by the whiskers. For violin plots, the dashed line marks the median value and dotted lines mark the 25th and 75th percentiles. For summary plots (for example, Fig. 2h,i), the mean standard error of the mean is plotted.

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

See the original post here:
Distinct pathways drive anterior hypoblast specification in the implanting human embryo - Nature.com