In light of the Alabama court ruling, a look at the science of IVF : Short Wave – NPR

Blastocyst illustration. A blastocyst is a hollow ball of cells with a fluid centre formed after several divisions of a fertilised cell (zygote). The inner cell mass (purple) contains the cells that will form the embryo proper, the embryonic stem cells (ESCs). Kateryna Kon/Science Photo Library/Getty Images hide caption

Blastocyst illustration. A blastocyst is a hollow ball of cells with a fluid centre formed after several divisions of a fertilised cell (zygote). The inner cell mass (purple) contains the cells that will form the embryo proper, the embryonic stem cells (ESCs).

Since the first successful in vitro fertilization pregnancy and live birth in 1978, nearly half a million babies have been born using IVF in the United States. Since the first successful in vitro fertilization pregnancy and live birth in 1978, nearly half a million babies have been born using IVF in the United States. Reproductive endocrinologist Amanda Adeleye explains the science behind IVF, the barriers to accessing it and her concerns about fertility treatment in the post-Roe landscape.

For more on IVF success rates, check out the Society for Assisted Reproductive Technology's database.

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This episode was produced by Berly McCoy and Rebecca Ramirez. It was edited by Brit Hanson and Rebecca Ramirez. Brit checked the facts. The audio engineer was Josh Newell.

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In light of the Alabama court ruling, a look at the science of IVF : Short Wave - NPR

Runx1+ vascular smooth muscle cells are essential for hematopoietic stem and progenitor cell development in vivo – Nature.com

A subpopulation of subaortic mesenchyme in the AGM co-expresses NG2 and Runx1

We examined the expression of PC/vSMC markers in the dorsal aorta of E10.5 and E11 mouse embryos. Wholemount immunostaining and immunohistochemistry on frozen sections were performed using PC/vSMC markers NG2 or SMA with CD31, an endothelial and HSPC marker (TableS1). Imaging analysis showed that NG2+SMA+CD31- vSMCs surround NG2-SMA-CD31+ endothelial cells (Figs.1a, S1a, b), confirming previous reports27. Further to its expression in hematopoietic and hemogenic endothelial cells, Runx1 was also detected in the sub-aortic mesenchyme22,23. Therefore, we hypothesised that at least some of these cells also express NG2. We first confirmed that both intra-aortic hematopoietic cell clusters (IAHCs) (Fig.1b, stars) and hemogenic endothelial cells (Fig.1b, arrowheads) are Runx1+; we also identified a subpopulation of NG2+ PC/vSMCs, mainly located in the ventral aspect of the dorsal aorta, that also express Runx1 (Fig.1b, arrows). Other Runx1+ cells in the perivascular area do not express NG2 (Fig.1b). Finally, we confirmed our recent study28 that some cells around the notochord express NG2 in the trunk (Fig.S1a, circle). However, these peri-notochord cells do not express SMA, CD31 (Fig.S1a, circle) nor Runx1 (Fig.S1ef). To confirm the presence of NG2+Runx1+ cells in the E11 AGM, we used Runx1-IRES-GFP mouse embryos29. In these GFP knock-in mice, GFP intensity correlates with Runx1 expression level. Flow cytometric analysis showed the presence of a distinct population of NG2+Runx1(GFP)+ cells in the AGM (Fig.1c). These cells first appear at E10, in line with the presence of Runx1 in mesenchymal cells30 and importantly, their frequency peaks at E10.5 (Fig.1d). Together, these data show that in the AGM, a subset of the sub-aortic mesenchyme expresses both NG2 and Runx1 and that the highest frequency of these cells coincides with the onset of HSC generation at E10.5.

a Three-dimensional (3D) wholemount immunostaining with SMA, CD31 and NG2 of E10.5 (3138 somite pairs (sp)) WT dorsal aorta; b NG2 and Runx1 expression on single plane wholemount WT E10.5 sections. NG2+Runx1+vSMCs (arrows), hemogenic endothelial cells (arrowheads) and intra-aortic hematopoietic clusters (IAHCs, stars) (TableS1); c Representative example of flow cytometric analysis of NG2+Runx1(GFP)+ (green box) in E10.5 Runx1-IRES-GFP AGM and E10.5 WT control. d Percentages of NG2+Runx1(GFP)+ cells in E9 (21-25sp) body (n=6), E10/E10.5/E11 AGMs (n=8/7/7), N=5, Kruskal-Wallis and Dunns post-hoc test. e Representative examples of wholemount 3D-images showing SMA, CD31 and NG2 in E10.5 cKO dorsal aortae; f SMA, Runx1 and CD31 immunofluorescence of E11 WT and cKO transversal frozen sections; n=WT/cKO: 2/2, N=2. g cKit and CD31 wholemount 3D-images in E10.5 WT and cKO AGM; h Number of intra-aortic hematopoietic clusters (IAHCs) in E10.5 AGM; n=WT/KO: 5/4, N=4. Number of colony forming unit-culture (CFU-C) in i E10.5 (31-38sp) AGM; n=WT/HET/KO: 14/10/5 embryos; N=7 and j E11 (4352sp) AGM; n=WT/HET/KO: 22/8/19 embryos; N=11; one-way ANOVA and Tukeys post-hoc test (TableS2). k Percentages of donor cell chimerism 4-months post-transplantation of 6 E11 WT (NG2+/+;Runx1fl/+or NG2+/+;Runx1fl/fl), 7 HET (NG2-Cre;Runx1fl/+) and 6 cKO AGMs (NG2-Cre;Runx1fl/fl) into sub-lethally adult irradiated recipients (1xAGM cells transplanted/recipient; N=4). Each dot represents one recipient. Mice are reconstituted when 5% donor cells are found in the host peripheral blood (dashed line); one-tailed Z score test for two population proportions (TablesS3 and S4). For wholemount staining in a, b, e, g: WT/cKO (N=6/4): SMA (n=9/7), CD31 (n=10/7), cKit (n=3/2), NG2 (n=3/1) and WT Runx1 (n=4) in 3 distinct combinations (TableS1). D = dorsal, V = ventral. N = number of independent experiments; n = number of biological samples (embryos). All data are presented as mean valuesSEM. Source data for d, h, i, j and k are provided as a Source Data file.

Runx1 deletion in endothelial cells impairs HSC emergence in the AGM24,25,26. However, the effect of Runx1 deletion in PC/vSMCs on hematopoiesis in vivo is still unknown. To address this, we examined conditional knock-out (cKO) NG2-Cre;Runx1fl/fl mouse embryos. In previous studies, the NG2-Cre mouse strain revealed a role for pericytes in supporting both fetal liver and adult bone marrow HSC maintenance31,32. Our data shows that E10.5 and E11 cKO embryos do not exhibit visible vascular abnormalities. This was confirmed by the normal expression of CD31, SMA and NG2 (Figs.1e, f, S1c, d) in the AGM. In contrast, SMA+Runx1+ PC/vSMCs with low expression of Runx1 were reduced in the cKO dorsal aorta compared to WT littermate controls (Figs.1f, S1eg). CD31+Runx1+ endothelial cell number and frequency was also decreased (Fig.S1g). Furthermore, CD31+cKit+ IAHC numbers were significantly reduced by three-fold (p=0.02) (Fig.1g, h). Hematopoietic progenitor (HP) assays were performed to test if hematopoietic function was affected. All HP numbers were significantly reduced in cKO AGMs at both E10.5 (Fig.1i, TableS2) and E11 (Fig.1j, TableS2). To test whether definitive HSCs were also affected, we performed HSC assays in vivo. At 1- and 4-months post-transplantation of AGM cells into sub-lethally irradiated mice, chimerism and multilineage reconstitution were examined by flow cytometry in the peripheral blood. Compared to the WT littermate control group, in which 66.7% (4 out of 6) recipients were reconstituted, only 14.3% (1 in 7, p=0.025) and 16.7% (1 in 6, p=0.040) mice injected with heterozygous or homozygous cKO AGMs, respectively, were reconstituted over the long term (Fig.1k, TablesS34). These findings indicate that the absence of Runx1 in aortic NG2+ cells impairs HSC generation and/or maintenance and HP development in the AGM.

To test whether NG2+ cells contribute directly to hematopoietic lineages, we isolated NG2+ and NG2+Runx1(GFP)+cells from E11 WT and Runx1-IRES-GFP AGMs, respectively, and seeded them in methylcellulose. In parallel, NG2- or NG2-cKit+ cells were sorted as controls. HPs were exclusively found in the NG2- cell fractions. Neither NG2+ cells (Fig.S2a) nor NG2+Runx1(GFP)+cells (Fig.S2b) gave rise to hematopoietic cell colonies in vitro (TableS5). To further assess whether NG2+ cells are hematopoietic precursors, we crossed NG2-Cre mice with a knock-in reporter mouse line in which tdTomato is preceded by a transcriptional stop flanked by two loxP sites under the Rosa26 promoter. In these mice, NG2+ cells and their progeny are tdTomato+. E11 AGM-derived tdTomato+ and tdTomato- cells were sorted and seeded in methylcellulose. HPs were only found in the tdTomato- cell fraction (Fig.S2c, TableS5) reinforcing the observation that NG2+ cells and their progeny do not contribute to hematopoietic lineages at this stage. Flow cytometric analysis confirmed the presence of tdTomato in a subset of NG2+ cells in the E11 AGM (Fig.S2d), validating our mouse model, while no overlap was found between tdTomato and CD45, a hematopoietic cell marker (Fig.S2e). We next performed immunohistochemistry on NG2-Cre;tdTomatofl/+ frozen sections and confirmed the expression of tdTomato in a subset of SMA+ cells (Fig.S2f) in the E11 AGM. CD31+ cells did not express tdTomato (Fig.S2g). Further analysis revealed that cells expressing hematopoietic markers F4/80 and CD45 do not co-express NG2 nor SMA (Fig.S2h). Together, these data indicate that NG2+ cells do not contribute to the AGM HSPC pool and suggest that NG2+Runx1+ PC/vSMCs act as a supportive niche to maintain hematopoietic activity in the AGM.

In the early developing embryo, HSPCs reside in other intra-embryonic and extra-embryonic hematopoietic organs such as the head, fetal liver (FL), placenta and yolk-sac (YS). Flow cytometric analysis of these organs harvested from Runx1-IRES-GFP mouse embryos also confirmed the presence of NG2+Runx1(GFP)+ cells (Fig.S3a, b). We next performed in vitro HP functional assays with cells harvested from all organs and genotypes of NG2-Cre;Runx1fl at both E10 and E11 developmental stages. No significant differences were found when comparing the total CFU-C numbers between genotypes in most organs (Fig.S3c, d, TablesS67). A significant increase of total number of CFU-C was observed in E10 AGM in both heterozygous and cKO mouse embryos (Fig.S3c). When analyzed individually, a significant increase in the number of erythroid colonies was detected in the cKO compared to WT littermate (p=0.0149) (TableS6). Likewise, a 2.8-fold increase in the number of erythroid colonies was detected in the E11 cKO head compared to the WT littermate (p=0.01), while the total number of CFU-C in the E11 head remained unchanged (TableS7). Moreover, we found a significant decrease in both CFU-GM (p=0.016) and CFU-GEMM (p=0.039), between WT and cKO YS (TableS7), possibly due to the defect found in the E11 AGM.

To test whether HSC activity increases in the FL due to the possible migration of AGM HSCs, E11 FL cells from all genotypes were transplanted into sub-lethally irradiated recipient mice. Neither the donor chimerism nor the percentage of reconstituted mice by donor cells showed changes between the groups (Fig.S3e). Compared to NG2+/+;Runx1fl/+ WT littermates, in which 70% of recipients (7 out of 10) were reconstituted, mice injected with NG2-Cre:Runx1fl/+ heterozygous or NG2-Cre:Runx1fl/fl cKO E11 FL showed similar reconstitution over the long term, with 67% (2 out of 3, p=0.348) and 60% (3 out of 5, p=0.421) reconstituted mice, respectively (Fig.S3e, TablesS34). Since the deletion of Runx1 in NG2+ cells only affects HSPCs in the AGM, immunohistochemistry on WT embryonic head and placenta was performed to localise NG2+Runx1+ cells. The rare NG2+Runx1+ double positive cells identified did not seem to be perivascular (Fig.S3f, stars). In line with this observation, we found that Runx1 and SMA do not overlap when NG2 and SMA were expressed in PC/vSMCs (Fig.S3f, arrowheads). Instead, the head contains few NG2+SMA- that are F4/80+, suggesting that NG2+Runx1+ cells are macrophages (Fig.S3f, arrowhead). Overall, our data shows that the deletion of Runx1 in NG2+ cells only affects selective HSPC subsets in non-AGM hematopoietic organs in the E11 mouse embryo.

To better understand the role of Runx1 in the HSC-generating microenvironment, single-cell RNA-sequencing (scRNA-seq) on NG2+/+;Runx1fl/+ E11 AGM was performed. We used graph-based clustering and known marker distribution to define and investigate the gene expression profiles of various populations that reside in the E11 AGM and identified eight populations of interest (Fig.2a, b). The co-expression of Cspg4 (NG2) and Acta2 (SMA) in the PC/vSMC population was confirmed (Fig.2c). This population is also enriched in Rgs5, Pdgfrb and Pdgfra in line with our previous work28, and a subset of these cells express Runx1 (Fig.2c, d), confirming our imaging and flow cytometric analysis. The expression of Mcam (CD146 or S-ENDO1), a pericyte/vSMC precursor marker recently identified in a subset of NG2+ cells in the E11 AGM21 and upregulated in AGM hematopoiesis supportive stromal cell lines19, was detected in a subset of PC/vSMCs, partially overlapping with Runx1+ cells (Fig.2c, d). However, Mcam was mainly enriched in endothelial cells (ECs) and also in subpopulations of hemogenic endothelial cells, including those entering endothelial-to-hematopoietic transition (HEC/EHT), IAHCs and SNS cells (Fig.2d), confirming published work including ours28,33. Immunostainings with CD146 and CD31 on E11 WT AGM frozen sections further validated our sequencing analysis at the protein level: both CD31+ endothelial cells (Fig.2e, f, arrows) and SMA+ PC/vSMCs (Fig.2f, stars) are CD146+. Importantly, Pecam-1 (CD31) expression in PC/vSMCs was low/negative in our scRNA-seq data (Fig.2c, d), in line with our immunohistochemistry, confocal imaging, and our recent published work28. Other genes expressed by hematopoietic and hemogenic/endothelial cells such as Adgre1 (F4/80), Mrc1 (CD206), Cdh5 (VE CADHERIN), Tek (TIE-2), CD34, CD93, Pdgfb, Sox7, Sox17, Sox18, Gfi1b, and Itga2b (CD41) were not expressed in PC/vSMCs (Fig.2d). These genes were used to distinguish populations of macrophages (MPs), IAHCs, HEC/EHT, and ECs (Fig.2ad). Erythroid cellsand erythroid progenitors (Ery/EryP; Gypa/CD235+), SNS (Gata3+) and skeletal muscle progenitors (SkMP; MyoD1+, Cdh15+) were also identified (Fig.2ad). Kit was expressed in all IAHCs and in a subset of PC/vSMCs, while Kit expression in HEC/EHTs was low (Fig.2d). Altogether, these data show that we successfully captured multiple cell types that comprise the E11 AGM, including a population of Runx1+ PC/vSMCs which constitutes 19.7% of all NG2+Acta2+ PC/vSMCs cells. Furthermore, the transcriptome of Cspg4+Runx1+ non-hematopoietic non-endothelial PC/vSMCs was found to partially overlap with that of the Cspg4+Mcam+ PC/vSMC precursors previously described21.

a t-SNE plot highlighting eight populations of interest identified in the E11 WT AGM. Each dot represents one cell and colours represent cell clusters as indicated. The number of cells in each population is shown in brackets. MP (macrophages); Ery/EryP (erythroid/progenitors); IAHC (intra-aortic hematopoietic clusters); HEC/EHT (hemogenic endothelial cells including those that enter endothelial-to-hematopoietic transition); EC (endothelial cells); SNS (sympathetic nervous system); SkMP (skeletal muscle progenitors), PC/vSMC (pericytes/vascular smooth muscle cells, NG2+Acta2+). Other cells (OC) are coloured in grey. b t-SNE plot highlighting the eight populations identified after excluding all other (grey) cells. c Zoom into PC/vSMC cluster (black rectangle) further show the presence or the absence of selected genes that characterise this population and confirms the presence of Runx1 in a subset of cells. d Violin plots showing distribution of expression for selected genes that contributed to the identification of cell clusters. Immunohistochemistry on frozen E11 WT sections stained with eCD146/CD31/DAPI and fCD146/SMA/DAPI, n=2 samples tested, N=2 independent experiments. Arrows: vascular cells, asterisks: perivascular cells. DA: dorsal aorta, CV: cardinal veins, NC: notochord. Source data for e (first column,20X) is provided as a Source Data file.

Our scRNA-seq analysis revealed that not all Cspg4+Runx1+ cells in the E11 AGM express Acta2 (Fig.3a, b). We therefore investigated if Cspg4+Runx1+Acta2 cells are PC/vSMCs which had not yet acquired Acta2 expression. Differential expression analysis of Acta2+ versus Acta2 cells within the NG2+Runx1+ cell population in the WT AGM revealed that markers of sclerotome-derived vSMCs such as Sox9, Pax1, Pax9 and Col2a134 are among the highest upregulated genes in Cspg4+Runx1+Acta2 cells (Fig.3c). In contrast, Cspg4+Runx1+Acta2+ cells are enriched in genes that identify more mature pericytes such as Acta2, CD248, Mcam, Rgs5 or Pdgfrb (Fig.3c), some of which are potential Runx1 target genes (star). Pdgfra and Ptn genes were recently associated with Runx1+ subaortic (non-smooth muscle) mesenchymal cells with possible role in hematopoiesis in the E10.5 AGM of the mouse embryo35. Our scRNA-seq analysis show that, in E11 AGM, Pdgfra and Ptn are also expressed in Cspg4+Runx1+ cells with no significant difference between Acta2+ and Acta2 (Fig.3c). Further analysis showed that Gene Ontology (GO) biological processes significantly enriched in Cspg4+Runx1+Acta2+ cells include smooth muscle cell chemotaxis and migration, collagen-activated signalling pathway, neural crest cell differentiation and regulation of BMP signalling (Fig.3d), previously shown by our laboratory to control in vivo HSPC generation in the mouse AGM28. In Cspg4+Runx1+Acta2 cells, significantly enriched GO biological processes include mesenchymal stem cell differentiation and cartilage and bone development (Fig.3e), consistent with the sclerotome origin of these cells.

a t-SNE plots showing the distribution of Runx1 and Acta2 expression in NG2+Runx1+ cells in the WT E11 AGM after excluding all other (grey) cells found in the Fig.2a. b Zoom into NG2+Runx1+ cluster (black rectangle) shows the presence or the absence of Acta2. c Heatmap showing the expression of Cspg4 and Runx1 and 15 selected genes out of 25 top significantly upregulated genes in WT NG2+Runx1+Acta2+ cells (upper half) and NG2+Runx1+Acta2- cells (bottom half) at single cell level; *Runx1 potential target genes. Pdgfra and Ptn genes were next added to inform their expression in both populations. Barplot of fold enrichment for selected GO biological processes significantly overrepresented in genes significantly upregulated in both dWT NG2+Runx1+Acta2+ and eNG2+Runx1+Acta2- cells. f t-SNE of WT E11 AGM cells, overlaid with principal pseudotime curve inferred by Slingshot, predicting a lineage from NG2+Runx1+Acta2- cells to NG2+Runx1+Acta2+ cells. g WT NG2+Runx1+cells arranged in pseudotime (x-axis) based on the inferred curve. Y-axis represents log normalised gene expression.

Indeed, PC/vSMCs in the AGM have been shown to originate from the sclerotome and display markers of this compartment at least during the early phases of mural cell recruitment36. A recent study showed that the maturation of sclerotome-derived vSMCs in the mouse AGM depends on a transcriptional switch from a sclerotome signature with the repression of Pax1, Scx and Sox9, and activation of Acta2 and other vSMC genes34.

To test whether NG2+Runx1+Acta2 cells follow a maturation trajectory towards Cspg4+Runx1+Acta2+ vSMCs, we performed cell lineage inference with Slingshot, a trajectory inference method for scRNA-seq data that can incorporate knowledge of developmental markers. Having defined a cluster of Cspg4+Runx1+Acta2 cells as an origin, Slingshot infers a cell lineage and constructs a pseudotime curve representing that lineage (Fig.3f, arrow). Gene expression along pseudotime shows that sclerotome markers such as Sox9, Pax1 and Pax9 are gradually downregulated while markers of mural cells such as Acta2, Rgs5, Pdgfr, Cnn1, Mcam and CD248, are gradually upregulated in an inferred transition from Cspg4+Runx1+Acta2 to Cspg4+Runx1+Acta2+ cells (Fig.3g). Our scRNA-seq analysis shows that Cspg4+Runx1+ AGM cells display a sclerotome-derived vSMC transcriptomic profile.

We next explored the impact of Runx1 deletion in the hematopoietic niche and its possible effect on PC/vSMCs by performing scRNA-seq of NG2-Cre;Runx1fl/fl cKO E11 AGMs (Fig.4a, b). Cell populations were defined in a similar way to the WT AGM by using graph-based clustering and known marker distribution. This comparison revealed changes in the proportions of the different cell types between WT and cKO AGM, including a significant reduction in the proportion of cells associated with clusters 2 (Ery/EryP), 3 (IAHC), 6 (SNS), and 7 (SkMP) (Fig.4c).

a t-SNE plot showing eight populations of interest found in the E11 cKO AGM. Each dot represents one cell and colours represent cell clusters as indicated. MP(macrophages); Ery/EryP(erythroid/progenitors); IAHC (intra-aortic hematopoietic clusters); HEC/EHT (hemogenic endothelial cells including those that enterendothelial-to-hematopoietic transition),EC (endothelial cells); SNS (sympathetic nervous system); SkMP(skeletal muscle progenitors); PC/vSMC (pericytes/vascular smooth muscle cells, NG2+Acta2+). Other cells (OC)are coloured in grey. The number of cells in each cluster is shown in brackets. b t-SNE plot highlighting the eight populations identified after excluding all other (grey) cells. c Percentage of single live cells found in each E11 AGM sample (cell number/total cells) defined by scRNA-seq in WT (full bars) and cKO (empty bars) AGMs. Colours and numbers correspond to each population defined in a; chi-squared two-tailed test was used for comparison. d Barplot of fold enrichment for selected GO biological processes significantly overrepresented in genes significantly downregulated in cKO PC/vSMCs compared to their WT counterparts. Heatmap of ligand-receptor interactions inferred by NicheNet from e WT and f cKO E11 AGM cells. Colour represents the interaction potential score between the 10 top-ranked ligands expressed in ECs and their inferred targets expressed in PC/vSMCs. Ligands and receptors are ordered by hierarchical clustering. g Scatter plots of AUC vs log10(FDR) showing downregulated genes associated with selected GO terms in cKO PC/vSMCs. Red dots represent significantly downregulated genes (FDR<0.05); dashed line shows FDR = 0.05. Gene labels with red borders represent potential Runx1 target genes.

Changes in gene expression between WT and cKO Cspg4+Runx1+ cells were first investigated. We found that genes significantly downregulated in cKO Cspg4+Runx1+ cells were mainly associated with biological processes including translation, oxidative phosphorylation, cellular response to stress and mitochondria-related function (Fig.S4ac). As deletion of Runx1 may have also affected Runx1 PC/vSMCs, the transcriptome of all cKO Cspg4+Acta2+ PC/vSMCs with their WT counterpart was compared. Genes significantly downregulated in cKO Cspg4+Acta2+ PC/vSMCs had significant enrichment of biological processes including translation, smooth muscle cell differentiation, cytoskeleton, vasculogenesis and cell communication (Fig.4d). One pathway essential to vasculogenesis is PDGF-B/PDGFR; we therefore applied NicheNet on our WT scRNA-seq data to predict ligand-receptor interaction between ECs and PC/vSMCs, focusing on PDGF-B-related genes. The highest scoring predicted interaction was between Pdgfb, a growth factor released by ECs, and Nrp1 (Fig.4e), a receptor known to control the differentiation/recruitment of mesenchymal stem cells and the stimulation of smooth muscle cell migration37,38.

The interaction between Pdgfb and Pdgfrb was also amongst the highest scoring interactions in both WT (Fig.4e) and cKO (Fig.4f). Additional interactions involve Edn, Tgfb or Bmp pathways, previously associated with a role in AGM hematopoiesis39,40. Interestingly, in cKO ECs, Pdgfa, another gene from the PDGF family, was no longer in the top 10 ranking ligands (Fig.4f) possibly due to the downregulation of Pdgfra in cKO PC/vSMCs (Fig.4f). Other genes including Des, Angpt1, Gsk3b, Tcf21, Col1a1, Pcna, Ccnd3 and Mcm7, potential Runx1 downstream target genes41, were also significantly downregulated (Fig.4g, red boxes). The reduction inCol1a1 expression suggests changes in the gene profile of the extracellular matrix (ECM). Indeed, additional ECM related genes were significantly downregulated in the cKO PC/vSMCs, such as Sparcl1, Col3a1 and Col5a1 (Fig.4g). Collectively, these data show that the genetic programme of PC/vSMCs in cKO AGM is modified upon Runx1 deletion and this involves changes in molecules that constitute the ECM of the aortic wall.

Endothelial cells share the same basement membrane with PC/vSMCs42. This, coupled with the transcriptomic changes in the cKO PC/vSMCs described above, suggest that the genetic programme of the adjacent ECs may have also been altered by Runx1 deficiency in NG2+ cells. Although the number of endothelial cells in the NG2-Cre:Runx1fl/fl cKO did not significantly change (Fig.4a) and the formation of the dorsal aorta appeared to be unaffected (Fig.1e), we investigated transcriptomic changes in ECs that could affect their function in vivo. As before, we performed differential expression analysis, followed by overrepresentation analysis on genes significantly downregulated in cKO ECs (Fig.5a). Multiple GO biological processes were significantly overrepresented in these genes, with many related to EC development and angiogenesis; proliferation, migration and differentiation; response to hypoxia and fluid shear stress; as well as smooth muscle cell or mesenchymal cell development and hematopoiesis (Fig.5a). Interestingly, we found that Sox18 was the most downregulated gene in cKO ECs (Fig.5b). Col4a1, the most abundant extracellular matrix associated gene, known to co-localise with Sox18 in ECs in the mouse embryo43, was also found within the top 25 downregulated genes (Fig.5b). Sox18 and Col4a1 were the most downregulated genes associated with the blood vessel development GO term, while other gene expression including Cdh5, Pecam1, Sox17, Pdgfb, MCam and Notch were also affected.

a Barplot of fold enrichment for selected GO biological processes significantly overrepresented in genes significantly downregulated in cKO ECs compared to their WT counterparts. b Scatter plots of AUC vs log10(FDR) showing downregulated genes associated with selected GO terms including blood vessel development and mesenchymal cells and vSMCs in cKO ECs. Red dots represent significantly downregulated genes (FDR<0.05); dashed line shows FDR=0.05. Sox18 and Ctnnb1 expression in WT ECs in both scRNA-seq (c, EC zoom and t-SNE plots) and bulk RNA-seq post-sort (d, TPM). e Scatter plots of AUC vs log10(FDR) showing downregulated genes associated with selected GO terms including the basement membrane and extracellular matrix in cKO ECs. Red dots represent significantly downregulated genes (FDR<0.05); dashed line shows FDR=0.05. Selected genes that were altered in cKO ECs ine are shown in WT ECs in both scRNA-seq (f, ECand HEC/EHT zoom and t-SNE plots) and bulk RNA-seq post-sort (g, TPM). TPM: transcript per Million mapped reads values.

Genes associated with cell adhesion, regulation of smooth muscle cell proliferation and differentiation, along with mesenchyme development such as Sox18 and Ctnnb1 were also significantly downregulated in cKO EC (Fig.5b, arrow). We confirmed that both Sox18 and Ctnnb1 are expressed by ECs in our single cell datasets (Fig.5c) and next validated their expression in NG2-PDGFR-ckit-CD45-CD31+ Runx1- purified ECs from E11 Runx1-IRES-GFP AGMs (Figs.5d, S5a).

Some significantly downregulated genes associated with blood vessel development such as Loxl2, Hspg2, Col4a2, Col15a1 and Col18a1 (Fig. 5b)are also known to be associated to the ECM. Further analysis of endothelial extracellular matrix encoding genes previously described44 revealed that most of these genes were also significantly downregulated in cKO ECs (Fig.5e). The expression of these genes in WT ECs at single-cell level (Fig.5f) was confirmed post-sort at population-level (Fig.5g) with most genes being highly expressed in ECs only. One of them was Sparc (Fig.5e blue arrow, Fig.5g), a central ECM secreted Ca2+-binding glycoprotein that interacts with many other ECM proteins including Col1 and Col445,46. Among the SPARC family, Sparcl1 (Sparc-like 1), known to bind to Col147, was also found to be significantly downregulated in cKO ECs (Fig.5b, c). Together, these analyses show that Runx1 deficiency in NG2+cells leads to significant transcriptomic changes in endothelial cells including extracellular matrix related genes. We did not detect transcriptional changes in the NG2-Cre;Runx1fl/fl cKO HEC/EHT cell cluster, although this observation is inconclusive due to the low number of cells captured.

Transcriptomic changes in vascular and perivascular cells may have also affected IAHCs. As hematopoietic cells are highly heterogeneous and progenitors were significantly affected (Fig.1), we first explored WT IAHCs in more detail. Previous studies showed that IAHCs are composed of both Runx1+ and Runx1- cells28,48,49 and we were able to confirm this by flow cytometry in Runx1-IRES-GFP AGMs (Fig.S5a). We also confirmed the expression of Runx1 in HEC/EHT and its absence in ECs by flow cytometry in Runx1-IRES-GFP E11 AGMs (Fig.S5a), in line with published work50. To validate their cell identity, we next purified and sequenced 243 Runx1 (GFP)+ and 27 Runx1 (GFP)- IAHCs (NG2-PDGFR- CD31+ckit+) as well as 5822 EC (Runx1-) and 248 HEC/EHT (Runx1+) cells from NG2-PDGFR- ckit- CD45- CD31+ E11 Runx1-IRES-GFP AGMs (Fig.S5a) and performed bulk RNA sequencing (RNA-seq). The purity of the sort was first confirmed (Fig.S5b). While CD45 antibody was not used to isolate IAHCs (Fig.S5a), our bulk RNA-seq data (Fig.S5b) show that not all IAHC cells express Ptprc (CD45) in line with previous studies48,49,51, and seems to be present only when Runx1 is expressed. Next, the identity of all sorted cell populations based on the expression of genes known to be expressed in these cells15,35,48 was confirmed (Fig.S5c). Interestingly, the transcriptomic profile of Runx1 (GFP)+ and Runx1(GFP)- sorted IAHCs appears to be distinct. While CD34, Gata2, Lmo2, Etv6, and Eglf7 are expressed in both Runx1(GFP)+ and Runx1(GFP)- IAHCs at various levels, Adgrg1, Gfi1, Myb and CD44 are mainly found in Runx1(GFP)+ IAHCs (Fig.S5c). Instead, as they also express Tek, Kdr, Eng, Esam and Gata2 (Fig.S5c, d), Runx1(GFP)- IAHCs are at the transcription level, closer to type-1 pre-HSCs52,53 or to recently described CD31+ckithighGata2medium IAHCs that are Runx1-Ptprc-48 with possible (micro)-niche role54. Our analyses confirm the heterogeneity of Runx1(GFP)+/-CD31+C-KIT+IAHCs in the E11 AGM at both protein and transcriptomic levels, and indicate that most IAHC cells captured in our full/unsorted AGM scRNA-seq are Runx1-.

To explore transcriptomic changes between WT and cKO IAHCs, differential expression analysis followed by overrepresentation analysis on genes significantly downregulated in cKO IAHCs was carried out (Fig.6a). Several GO biological processes were significantly overrepresented in these downregulated genes, including ribosome assembly processes, regulation of translation, RNA transport and localisation, and others such as response to DNA damage, gene expression and cellular processes (Fig.6a). In line with this, we found that the top 25 significantly downregulated genes in cKO IAHCs were mostly ribosomal protein coding genes from both Rps and Rpl families. Other genes in the top 25 are known to be required for transcriptional or translational initiation such as Btf3, Pabpc1 and Bclaf1 (Fig.6b). Interestingly, one of the top significantly downregulated genes in the cKO was Sox18 (Fig.6b, arrow), previously reported to be expressed in both IAHCs and ECs in the mouse AGM55 and confirmed here by our WT scRNA-seq data (Fig.2d). Furthermore, Sox18 has been transiently detected during early hematopoiesis in a model of embryonic stem cell differentiation in vitro, controlling early HP proliferation and maturation56. In line with this, further GO analysis revealed that Sox18 is associated with cellular processes including cell maturation, cell differentiation and regulation of stem cell proliferation (Fig.6b). The latter two GO terms are also associated with other genes significantly downregulated in cKO IAHCs such as Hmgb2, encoding a chromatin-associated non-histone protein involved in transcription and chromatin remodelling (Fig.6b). This transcriptomic analysis shows that the deletion of Runx1 in NG2+ PC/vSMCs within the AGM niche significantly alters the genetic programme of IAHCs.

a Barplot of fold enrichment for selected GO biological processes significantly overrepresented in genes significantly downregulated in cKO HSPCs compared to WT HSPCs. b Scatter plot of AUC (representing strength of downregulation) vs log10(FDR), showing the top 25 significantly downregulated genes (red circles) in cKO HSPCs. Scatter plots of AUC vs log10(FDR) highlighting downregulated genes associated with Gene Ontology (GO) biological processes. Red dots found above the dashed line (corresponding to FDR=0.05) represent significantly downregulated genes (FDR<0.05).

Despite the decrease in HPs and HSCs in cKO AGM, NG2-Cre;Runx1fl/fl mice are born with no obvious defects and develop into adulthood. Because of this, we sought to explore the effect of Runx1 deletion in NG2+ PC/vSMC on adult HSPCs. The presence of these progenitors in the adult bone marrow (BM) of mutant mice was analyzed by flow cytometry and compared to WT mice. No significant differences were found in either Lin-Sca1+cKit+ (LSK) (Fig.7a, b) nor LSK CD150+CD48-(SLAM) cell frequencies (Fig.7c, d) between cKO mice and WT controls. We performed HP assays and found that the frequencies of hematopoietic cell colonies were similar in all mutants and WT littermates (Fig.7e, TableS8). To assess the capacity of these cells to reconstitute hematopoiesis in vivo, 5105 bone marrow cells harvested from all genotypes were transplanted into sub-lethally irradiated WT mice recipients. Compared to the control group in which 62.1% (18 out of 29) mice were reconstituted, mice injected with NG2-Cre;Runx1fl/+ or NG2-Cre;Runx1fl/fl BM cells showed a significant reduction in the long-term reconstitution potential, with only 27.3% (3 out of 11, p=0.024) and 20% (4 out of 20, p=0.002) of transplanted mice being reconstituted respectively (Fig.7f, TableS3). In addition, the percentage of donor chimerism was significantly reduced in the cKO group. On average, the donor chimerism with WT cells was 33% compared to the 16% and 9% observed when BM cells from NG2-Cre;Runx1fl/+heterozygous and NG2-Cre;Runx1fl/fl cKO (p=0.002) were injected respectively (Fig.7f, TableS4). The remaining HSCs in the mutant NG2-Cre;Runx1fl/+ and NG2-Cre;Runx1fl/fl adult BM are multilineage, showing similar contributions of donor cells to myeloid or lymphoid cell compartments (Fig.7g), and self-renew (Fig.7h). Interestingly, no NG2+Runx1(GFP)+ cells were detected in adult Runx1-IRES-GFP BM hematopoietic niches (Fig.7i), suggesting that they are exclusive to the embryo and that the BM hematopoietic defect found in adults is developmentally driven.

a, bRepresentative plots and percentages of Lin-Sca1+cKit+ (LSK) and c, dLSK CD150+CD48-(SLAM) bone marrow (BM) cells by flow cytometry of WT/ NG2+/+;Runx1fl/+,NG2+/+;Runx1fl/fl (n=9), HET NG2-Cre;Runx1fl/+ (n=4) and cKO NG2-Cre;Runx1fl/fl (n=4) adult mice is shown. e Colony-forming unit-culture (CFU-C) numbers per 104 adult BM cells; n=WT/HET/cKO: 13/7/8 mice. N=7 independent experiments. Data are meanSEM (TableS8). f Hematopoietic stem cell repopulating potential and donor chimerism of WT and mutant BM cells in vivo. 5105 BM donor WT, HET and cKO cells were injected into 29, 11 and 20 Ly5.1 HET recipients, respectively, with 18, 3 and 4 found to be reconstituted respectively (Table S3, p=0.024 (WT/HET) and p=0.002 (WT/cKO) by Z score test for 2 population proportions). Mice are reconstituted when 5% donor cells are found in the host peripheral blood; p=0.002 (WT/cKO) by Kruskal-Wallis and Dunns post-hoc test (TableS4). g Histograms showing the contribution of CD45.2+CD45.1- donor cells to myeloid cells (CD11b+Gr1+/-), B cells (CD19+) and T cells (CD4/8+) in all reconstituted host mice from (f). (n=WT/HET/cKO=18/3/4), p=0.019 (WT/HET) for B cells by one-way ANOVA and Tukeys post-hoc test. h BM cells from selected reconstituted primary recipients (found in f) were transplanted into multiple irradiated secondary recipients. Mice are reconstituted when 5% donor cells are found in the host peripheral blood (TableS34). i Representative flow cytometric analysis plot of NG2 in Runx1-IRES-GFP adult BM (n=6). All data are presented as Mean values+/-SEM. N=number of independent experiments; n = number of biological samples. Source data for b, d, e, f, g and h are provided as a Source Data file.

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U.S. Senators Introduce Bill to Protect Aborted Babies from Medical Experimentation – Daily Citizen

In late January, pro-life senators introduced legislation to protect the human remains of aborted babies from medical experimentation for research purposes.

The bill,S. 3713, is titledProtecting Life and Integrity in Research Act of 2024.

If passed, the measure would prohibit the federal government from funding, approving, or in any way supporting research on aborted babies.

The language of S. 3713 expressly forbids medical research organization to solicit or accept aborted fetal human remains as a research donation.

Senator Cindy Hyde-Smith, chair of theSenate Pro-Life Caucus, is the bills primary sponsor. Seventeen other senators join her ascosponsors.

The law regulating human fetal tissue research is complex. Over the years, federal policy has changed significantly based on the views of each presidential administration.

Currentfederal lawpermits research on fetal human tissue if human embryos are not intentionally created or destroyed for the explicit purpose of research.

According to those statutory stipulations, it is still lawful for taxpayer dollars to fund research on aborted babies if the aborting mothers consent.

Opponents of this practice contend that taxpayers money should not be used to promote unethical research on children.

In apress release, Senator Hyde-Smith called the harvesting and trafficking of aborted babies body parts heinous and unethical.

Proponents tout the possibility of discovering medical advancements by using human fetal tissue, but recentanalysisby the Charlotte Lozier Institute calls that assumption into question.

The reportconcludesthat medical research using ethically non-controversial adult and induced pluripotent stem cells continues to advance in the pursuit of cures and treatments, while embryonic stem cells have largely fallen by the wayside, proving that science does not need to kill in order to cure.

Since 2015, the National Institutes of Healths funding for human fetal tissue research has been as high as $115 million annually. Spending for 2024 isprojectedto be approximately $61 million.

Source: National Institutes of Health

Focus on the Family, asreportedby the Daily Citizen, believes human fetal tissue research requiring the destruction of human beings is a violation of the sanctity of life.

Every human, in every condition from the single cell stage of development to natural death, is made in Gods image and possesses inestimable worth. Our human nature not our size, level of development, environment or functional capacity gives us worth and dignity as human beings. Therefore, devaluing and destroying the life of a human embryo opens the door to the devaluing and destroying any human life.

According to supporters of the legislation, the bill is endorsed by Susan B. Anthony Pro-Life America, Americans United for Life, Catholic Vote, March for Life Action, U.S. Conference of Catholic Bishops Committee on Pro-Life Activities, Students for Life Action, and Concerned Women for America LAC.

A companion measure was introduced in the U.S. House of Representatives last year,H.R. 398, but no action has been taken to advance the legislation.

The Senate bill was referred to the Senate Committee on Health, Education, Labor, and Pensions, where it awaits further action. The Daily Citizen will keep you updated on its progress.

Image from Shutterstock.

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U.S. Senators Introduce Bill to Protect Aborted Babies from Medical Experimentation - Daily Citizen

Embryo Patrol. Artificial Embryos Are Not Human Babies | by Karen Marie Shelton | ILLUMINATION-Curated | Jan, 2024 – Medium

Artificial Embryos Are Not Human Babies Didactic Model of Human Embryonic Development Wagner Souza Esilva Wikimedia

Artificial embryos are not human. Theyre simply a cluster of cells. To be legally human, they must meet the definition of an in vitro fertilized human ovum.

As of the end of 2023, artificial embryos couldnt be successfully implanted into mammals or humans. They couldnt lead to pregnancies, and there is no plan for that to happen in the future.

Synthetic embryos utilize stem cells groundbreakingly, sidestepping the need for sperm or eggs. Ongoing breakthroughs might eventually aid research into genetic disorders and improve babies health, including reducing the risk of problem pregnancies and miscarriages.

Artificial embryos are not related to in vitro fertilization (IVF), which can lead to a human pregnancy.

The term is misleading. These structures arent really synthetic, nor are they exactly embryos. But theyre similar. They are tiny balls of cells arising from a sperm fertilizing an egg but created from stem cells grown in the lab.

Synthetic human embryos, or SHEEFs (synthetic human entities with embryo-like features), are created from very early (actually pre-embryonic) zygotic cells called stem cells.

The stem cells are called pluripotent because they have the potential to develop into almost every cell of the body.

The lab-created embryos are not connected to a beating heart or a brain. They do include cells that would typically go on to form a version of a placenta, yolk sac, and embryo itself.

The model embryos, which resemble human versions, recreate the earliest stages of human development. They could provide a crucial window into genetic disorders and the underlying biological causes of recurrent miscarriage.

Robin Lovell-Badge, headent head of stem cell biology and developmental genetics at Francis Crick Institute in London, reported project advancements. She explained weve cultivated embryos to a specific stage just beyond what is equivalent to 14 days of development for a natural embryo.

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Embryo Patrol. Artificial Embryos Are Not Human Babies | by Karen Marie Shelton | ILLUMINATION-Curated | Jan, 2024 - Medium

Clinical applications of stem cell-derived exosomes | Signal Transduction and Targeted Therapy – Nature.com

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Developing in-vivo chimeric lungs with pluripotent stem cells – Drug Target Review

Reverse-blastocyst complementation elucidates the conditions required to form lungs in rat-mouse chimeric models.

Researchers from the Nara Institute of Science and Technology (NAIST) have used the reverse-blastocyst complementation (rBC) method to understand the conditions required to form lungs in rat-mouse chimeric models. They also used the tetraploid-based organ complementation (TOC) method to create a rat-derived lung in their mouse model.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of global deaths. The pathogenesis of COPD is based on the innate and adaptive inflammatory immune response to the inhalation of toxic particles and gases. Although tobacco smoking is the primary cause of this inhalation injury, many other environmental and occupational exposures contribute to the pathology of COPD.1

Lung transplantation is the only viable treatment option, yet finding suitable lung donors is difficult. However, regenerative medicine is advancing the development of lungs from pluripotent stem cells (PSCs) using interspecies animal models. Current expectations for realisation of the promise of PSCs are at the highest they have ever been. However, there are many challenges that need to be addressed in order to bring PSC technology within the grasp of many more patients. Three particular challenges are: tumorigenicity, immunogenicity, and heterogeneity.2

PSCs and embryonic stem cells (ESCs) from one species can be injected into blastocytes, the cluster of diving cells made by a fertilised egg, in a biological technique named blastocyst complementation to create interspecies chimeric animals. This has enabled successful regeneration of the heart, pancreas and kidney in rat-mouse chimeras, but functional lung formation remains to be achieved successfully in vitro due to the complex three-dimensional (3D) structures and multiple cell types needed. This has warranted more research into the viable conditions required to generate PSC-derived organs.

Fibroblast growth factors (FGFs)are polypeptides with various biological activities bothin vivoandin vitro.3 The fibroblast growth factor 10 (Fgf10) and its interaction with the Fgf receptor isoform IIIb (Fgfr2b) in the lungs are essential for lung development. The rBC method in the new study involved injecting mutant ESCs which fail to show lung formation into wild-type (WT) embryos. This allowed for efficient detection of mutant PSCs in the recipient tissue, aiding the determination of the conditions necessary for successful lung formation in the organ-deficient animal.

The team, led by Dr Shunsuke Yuri and Dr Ayako Isotani, discovered that WT ESCs provide uniform contributions across target and non-target organs in the chimeras. This helped to ascertain that a particular number of WT or normal cells are required to overcome the lung development failure in Fgf10-deficient or Fgfr2b-deficient animals.

Having this understanding enabled them to produce rat-derived lungs in the Fgfr2b-deficient mouse embryos with the TOC method, without the requirement of producing a mutant mouse line. Dr Yuri said: Interestingly, we found that the rat epithelial cells conserved intrinsic species-specific timing in the interspecies model, resulting in an underdeveloped lung. Consequently, these lungs remained nonfunctional post-birth.

The studys findings identified the factors required for the successful generation of functional lungs in rat-mouse interspecies chimeras, as well as the issues to overcome. Dr Yuri concluded: We believe that our study makes an important contribution to the literature by presenting a faster and more efficient method of exploring blastocyst complementation.

These novel results can significantly advance the progress toward developingin-vivochimeric lungs for the purpose of transplantation, which could transform the practical application of regenerative medicine.

This study was published in Development.

1 Hogg JC, Timens W. The Pathology of Chronic Obstructive Pulmonary Disease. Annual Review of Pathology: Mechanisms of Disease. 2008 October 27 [2024 January 5]; 4:435-459. Available from: https://doi.org/10.1146/annurev.pathol.4.110807.092145

2 Yamanaka S. Pluripotent Stem Cell-Based Cell Therapy Promise and Challenges. Cell Stem Cell. 2020 October 1 [2024 January 5]; 27(4):523-31. Available from: https://doi.org/10.1016/j.stem.2020.09.014

3 Birnbaum D, Coulier F, Emoto H, Itoh N, Mattei MG, Tagashira S. Structure and Expression of Human Fibroblast Growth Factor-10*. Journal of Biological Chemistry. 1997 September [2024 January 2024]; 272(37):23191-4. Available from: https://doi.org/10.1074/jbc.272.37.23191

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No, Rep. Steve Scalise Didn’t Vote Against Stem Cell Research From Which He Is Now Benefiting – Yahoo News

A long-dormant medical controversy was revived last week following an announcement from House Majority Leader Steve Scalise. On January 5, his office released a statement indicating that he was undergoing a stem cell transplant as part of his previously announced treatment for multiple myeloma. Hearkening back to the stem cell controversies of the early 2000s, a number of posts emerged onlineincluding a viral Reddit thread and tweet with 54,000 likes and almost 10,000 retweetsaccusing the congressman of hypocrisy for receiving a treatment that he allegedly fought against.

The treatment Scalise is receiving has no relation to the embryonic stem cell research often opposed by pro-life Americans, however, and the congressman has never voted to restrict research into the form of treatment he is receiving.

Scalise first announced his diagnosis of multiple myelomaa rare blood cancerin late August 2023, telling reporters a month later that his body had responded well to his first round of treatment. The good news is the cancer has dropped dramatically because of the success of the chemotherapy, he said in September. The next step for Scalise, as mentioned above, is an autologous stem cell transplant. [Rep. Scalise] is currently undergoing the transplant process, marking a significant milestone in his battle against cancer, his offices January 5 statement read. Once the procedure is completed, he will be recovering under the supervision of his medical team and will work remotely until returning to Washington next month. Scalise is receiving treatment in his home state of Louisiana.

Because multiple myeloma attacks a patients bone marrowan essential tissue for the bodys production of blood cellsstem cell transplants are often used to help replace marrow damaged by the cancer with new and healthy marrow. In a typical autologous stem cell treatment, the kind which Scalise is receiving, a patients own hematopoietic stem cells are extracted and frozen multiple weeks before treatment. These cells used to be extracted from the bone marrow itself, but today most patients are given a growth factor that allows for stem cells to be taken directly from the blood. The patient is then given intensive chemotherapy, often in a single large dose, before receiving a transfusion of his or her own healthy stem cells. It then takes two to three weeks for the transfused stem cells to restore the functionality of the bone marrow, during which patients can be substantially immunocompromised because of their bodies inability to produce the white blood cells necessary for proper immune function.

Unlike embryonic stem cells, which are harvested from early stage human embryos and can take the form of any cell in the body, the hematopoietic stem cells used in the treatment of multiple myeloma are extracted from a patients own body or from a voluntary donor and can develop into only a limited range of blood cells. These are not the type of stem cells that are in an embryo that can become anything, Dr. Marc Braunstein, a hematology and stem cell transplant expert at NYU Langone Health, told The Dispatch Fact Check. These are slightly differentiated stem cells that are destined to become blood cells, but not anything else.

Traditional stem cell therapies are widely accepted and utilized in modern medical practice, unlike the embryonic stem cell research that reached a point of national controversy in the mid-2000s. We can debate the ethics of using embryonic stem cells, Braunstein said, but I think in this case were not talking about that at all. According to Braunstein, even patients who are practicing Jehovahs Witnessa religious group that typically rejects the use of blood transfusionsare often not opposed to autologous stem cell treatments. For those individuals who may be leery about the use of embryonic stem cells, I dont think they would be as concerned with the use of adult hematopoietic stem cells, Braunstein said.

Furthermore, Scalise has not taken any notable votes against stem cell researchembryonic or non-embryonic. Two notable bills intended to advance embryonic stem cell research, the Stem Cell Research Enhancement Act of 2005 and Stem Cell Research Enhancement Act of 2007, passed both the House and Senate, but both were vetoed by then President George W. Bush. These votes occurred prior to Scalise assuming office in May 2008, however, and very little legislative activity involving embryonic stem cell research has happened since.

In September 2020, Scalise co-signed a letter by Mississippi Sen. Roger Wicker calling for an end to taxpayer funded embryonic stem cell research at the National Institutes of Health, but the letter expressed no opposition to non-embryonic stem cell research or treatment. In fact, Scalise voted in favor of the Stem Cell Therapeutic and Research Reauthorization Act of 2010, Stem Cell Therapeutic and Research Reauthorization Act of 2015, and TRANSPLANT Act of 2021, all of which reauthorized a program intended to support patients in need of stem cell transplants.

Asked by The Dispatch Fact Check whether they believed allegations of hypocrisy were unfair, Scalises office declined to comment further, instead saying that the statement on his treatment spoke for itself.

If you have a claim you would like to see us fact check, please send us an email at factcheck@thedispatch.com. If you would like to suggest a correction to this piece or any other Dispatch article, please email corrections@thedispatch.com.

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No, Rep. Steve Scalise Didn't Vote Against Stem Cell Research From Which He Is Now Benefiting - Yahoo News

Embryonic-stem-cell-derived mesenchymal stem cells relieve experimental contact urticaria by regulating the functions … – Nature.com

Animals

All animal experimental procedures were approved by the Dong-A University Medical School Institutional Animal Care and Use Committee (Approval No. DIACUC-21-11) at 22.03.2021. Female BALB/c mice that were 78weeks old were purchased from Orient Bio Inc. (Gyeonggi-do, korea). The classification of experimental groups involves the random assignment of mice of the same age and within a 1g difference in body weight after the acclimatization process. The mice are then housed in a pathogen-free facility at Dong-A University (Busan, Korea). Mice were maintained at Dong-A University facility at 22C1C room temperature, 4060% humidity, on a 12h lightdark cycle (7a.m. to 7p.m.), and given food and water freely, according to institutional guidelines. All experiments were performed under inhalation anesthesia with isoflurane, and mice were euthanized by CO2 inhalation at the end of the experiment. This study adhered to the guidelines set forth by the laboratory animal ethics committee of Dong-A University and the ARRIVE guidelines. To ensure statistical significance, 5 or more mice per group were used, and all experimental protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of Dong-A University. Inhalational anesthesia using isoflurane was used to induce anesthesia when sacrificing all experimental animals.

Contact urticaria mouse model was induced according to a previously reported method31,32. Mice were initially sensitized by applying 100l of a TMA (trimellitic anhydride; 500mg/ml, Alfa Aesar, Ward Hill, MA, USA) in acetone/olive oil (4:1, v/v) on the shaved hind flank. This sensitization process is essential for inducing an immune response to TMA. Secondary sensitizations were performed on the hind flank to reinforce the immune response. On the 7th and 10th days after the first sensitization, mice sensitized 50l of a TMA solution (250mg/ml) in acetone/olive oil (4:1, v/v). On day 13 after the initial sensitization, contact urticaria (CU) was induced by challenging the ears with 25l of a TMA solution (100mg/ml) dissolved in acetone/olive oil (4:1, v/v). The disease symptoms were assessed by measuring ear thickness, itching, and lesions on the skin. Particularly, skin lesions were evaluated by determining the ratio of the affected area, indicating erythema and edema on a 3 cm2 area of the dorsal skin. The symptom evaluation of experimental animals was evaluated based on all animals without exclusion criteria. For histological analysis, H&E and mast cell staining were conducted. Cellular and molecular analysis involved the use of flow cytometry to assess immune cell activity in mouse lymphoid organs, along with genetic analysis of the lesions. Experimental animals of 10 mice per group were performed by blindly selecting 56 mice for efficient handling of animal-derived flow cytometric and genetic analysis. The corresponding author and one of the first authors (S.Y. Hyun) were aware of the group assignment, and the symptoms, flow cytometry, and other molecular analysis results were evaluated together by multiple blinded co-authors.

For the purposes of in vitro experiments, we used the nave CD3+ T cell isolation Kit (Miltenyi Biotec, Bergisch-Gladbach, Germany) to enrich nave CD3+ cells from the spleens of BALB/c mice (6weeks old). All steps were conducted strictly following the manufacturers protocol.

M-MSCs used in this study was provided Mirae Cell Bio (Seoul, Korea). M-MSCs differentiated from H9 hESCs30 were maintained in EGM2-MV medium (Lonza, San Diego, CA, USA) containing supplement Mix (promocell, Heidelberg, Germany) and 50 ug/ml Gentamicin (Gibco, NewYork, USA) in a humidified atmosphere containing 5% CO2 at 37, as previously described33. M-MSCs at less than ten passages were used for in vitro cell culture and in vivo animal experiments. Bone marrow-derived MSCs (BM-MSCs) were maintained in MSCBM medium (Lonza, Basel, Switzerland) containing supplement kit (Lonza) in a humidified incubator at 5% CO2 at 37. Bone marrow-derived mast cells (BMMCs) derived from BALB/c mice were cultured in RPMI 1640 medium containing 2mM L-glutamine, 0.1mM nonessential amino acids, antibiotics, 10% fetal bovine serum (FBS), and IL-3 (10ng/ml; PeproTech Inc., Rocky hill, NJ, USA). After 4weeks,>98% of the cells were verified as BMMCs, as previously described34. Mouse splenic T cells were presorted by CD3 mAb-microbeads (Miltenyi Biotec, Bergisch Gladbach, Germany) followed by the manufacturer's method. For T cell polarization, splenic nave CD3+ T cells were cultured onto a 24-well plate coated with 1g/ml of anti-CD3 (eBioscience, San Diego, CA, USA) in complete RPMI 1640 medium and supplemented with TH1 reagents [IL-2 (20ng/ml, PeproTech Inc.), IL-12 (20ng/ml, PeproTech Inc.), and anti-IL-4 (10g/ml, Bio X cell, West Lebanon, NH, USA)] or TH2 reagents [IL-2 (20ng/ml, PeproTech Inc.), IL-4 (100ng/ml, PeproTech Inc.), and anti-IFN- (10g/ml, Bio X cell)]. After 48h, the cells were co-cultured with M-MSCs for 24h under polarized conditions. To confirm the regulation of immune cells by M-MSCs in vitro, BMMCs (5.0105 cells/well), splenic T cells (5.0105 cells/well), or polarizing splenic T cells (5.0105 cells/well) were co-cultured with the indicated ratio of M-MSCs for 24h. Co-cultured splenic T cells were identified by flow cytometry analysis. The analysis of polarized T cells was assessed by distinguishing them using the gating strategy as depicted in Supplementary Fig.S1. The ratio of degranulation of BMMCs was analyzed by -hexosaminidase secretion.

After the primary sensitization of the contact urticaria model, M-MSCs were injected subcutaneously into the ear as a single administration on the 10th days or twice on the 10th and 12th days. A primary disease improvement evaluation was performed through single administration or two administrations, and an appropriate cell administration group for disease efficacy was selected. The administration of an equal amount of BM-MSCs and oral administration of cetirizine (50mg/kg) (Sigma-Aldrich, St. Louis, MO, USA) were used as positive controls. To deplete TGF-, BALB/c mice were intraperitoneally injected with 300g of anti-TGF- mAb (1D11.16.8, Bio X cell) or an isotype-matched control mAb (Bio X cell) twice on days 0 and 3 of M-MSCs administration.

Single-cell suspensions were isolated from the spleen, cervical lymph node (cLN), and ear. Ear tissues were isolated into single cells using the gentleMACS dissociator (Miltenyi Biotec) followed by the manufacturers method. For the detection of intracellular cytokines, the isolated cells were stimulated with PMA (50ng/ml; Sigma-Aldrich), ionomycin (500ng/ml; Sigma-Aldrich), and brefeldin A (3g/ml; eBioscience) for 4h before analysis and a fixation/permeabilization kit were from eBioscience. Before cell surface markers were stained, Fc receptors were blocked with anti-CD16 and anti-CD32 mAbs (2.4G2, BD Biosciences), and conjugated and dead cells were excluded by analysis on the basis of forward and side light scatter parameters and staining with a Zombie NIR Fixable Viability Kit (Biolegend, San Diego, CA, USA). The antibodies against proteins were as follows: Antibodies against CD3 (17A2) and CD8a (53-6.7) were obtained from BioLegend. Antibodies for CD4 (RM4-5), IFN- ((XMG1.2), IL-4 (11B11) were obtained from eBioscience. Antibodies for CD3 (17A2), CD45 (30-F11), and CD127 (A7R34) were obtained from BioLegend. The cells were then analyzed with a NovoCyte flow cytometer (Agilent) and FlowJo version 10 software (Tree Star, Ashland, OR, USA).

BMMCs (5.0105 cells/well) co-cultured with M-MSCs (0.5 to 2106 cells/well) for 24h were sensitized for 4h with Monoclonal dinitrophenol (DNP)-specific IgE (100ng/ml; Sigma). The IgE-primed BMMCs were then stimulated with 50ng/ml of DNP-human serum albumin (DNP-HSA, Sigma-Aldrich) in Tyrode-BSA buffer (20mM Hepes (pH 7.4), 135mM NaCl, 5mM KCl, 1.8mM CaCl2, 1mM MgCl2, 5.6mM glucose, and 0.1% BSA) for 15min in the presence or absence of the M-MSCs.

Degranulation was determined by measuring the release of the granule marker -hexosaminidase as previously described35. The degree of degranulation of BMMCs was expressed as the % of the activity of -hexosaminidase secreted out of the cells compared to the total activity of -hexosaminidase.

M-MSCs were co-cultured with splenic T cells or BMMCs for 24h and then effector cells were removed. M-MSCs were rinsed with PBS and left on ice for 5min to stop the reaction. Total RNA was extracted using AccuPrep Universal RNA Extraction Kit (Bioneer, Daejeon, Korea), and cDNA was synthesized using AccuPower CycleScript RT PreMix (Bioneer) according to the manufacturers instructions. The PCR reaction was amplified using AccuPower PCR PreMix (Bioneer) and PCR was performed at 95 for 2min, 95 for 20s, 58 for 40s, 72 for 30s, 72 for 5min for 30 cycles. Primers used as follow: human Hgf (forward 5-TCCATGATACCACACGAACACAGC-3, reverse 5-TGCACAGTACTCCCAGCGGGTGTG-3); human Ido1 (forward 5-TTTGCTAAAGGCGCTGTTGG-3, reverse 5-CCTTCATACACCAGACCGTCTGA-3); human Pdl1 (forward 5-TATGGTGGTGCCGACTACAA-3, reverse 5-TGCTTGTCCAGATGACTTCG-3); human Il10 (forward 5-AGACATCAGGGTGGCGACTCTAT-3, reverse 5-GGCTCCCTGGTTTCTCTTCCTAAG-3); human Pge2 (forward 5-ACCATCTACCCCTTCCTTT-3, reverse 5-CCGCTTCCCAGAGGATCT-3); human Tgfb (forward 5-GGGACTATCCACCTGCAAGA -3, reverse 5-CCTCTTGGCGTAGTAGTCG-3); human Gapdh (forward 5-ACCACAGTCCATGCCATCAC-3, reverse 5-TCCACCACCCTGTTGCTGTA-3). Snap-frozen disease-inducing mouse ear tissues were ground to powder. Total RNA isolation and PCR reaction were performed in the same manner as above. Real-time PCR was performed Thermal Cycler Dice Real Time System III TP950 (Takara, Shiga-ken, Japan). Primers used as follow: mouse Il4 (forward 5-ACAGGAGAAGGGACGCCAT-3, reverse 5-GAAGCCCTACAGACGAGCTCA-3); mouse Il6 (forward 5-GAGGATACCACTCCCAACAGACC-3, reverse 5-AAGTGCATCATCGTTGTTCATACA-3); mouse Ifng (forward 5-CAGCAACAGCAAGGCGAAAAAGG-3, reverse 5-TTTCCGCTTCCTGAGGCTGGAT-3); mouse Tnfa (forward 5-AGTGACAAGCCTGTAGCCCACGT -3, reverse 5-CCATCGGCTGGCACCACTAGTT-3); mouse Gapdh (forward 5-CATCACTGCCACCCAGAAGACTG-3, reverse 5-ATGCCAGTGAGCTTCCCGTTCAG-3);

After the induction of contact urticaria in mice, their ear tissues were fixed in 4% paraformaldehyde in phosphate-buffered saline for 24h and then embedded in paraffin. The tissues were dehydrated in a graded ethanol series (70 to 100%), rinsed three times with xylene for 3min each, and then embedded in paraffin. Sections of paraffin-embedded tissues, with a thickness of 6m, were prepared and stained with hematoxylin (Sigma-Aldrich) and eosin (Sigma-Aldrich) to compare and analyze the degree of cell invasion and epidermal thickness in the tissue. Additionally, sections of tissues with a thickness of 6m were stained with a 1% toluidine blue (Sigma-Aldrich) solution to assess the number of infiltrating mast cells and the degree of degranulation.

The in vitro experiment was repeated three independent times, and the animal experiment was based on five or more animals per group, and if the results of the first experiment were insufficient, the significance was evaluated within a total of 10 animals in the group. The data are presented as the meanstandard error (SEM) from three or more independent experiments for in vitro experiments. Statistical analysis was done by unpaired Student's t-test. One-way analysis of variance (ANOVA) with Tukey's post hoc test was performed for multiple comparisons. Statistical significance (*P<0.05 and **P<0.01) was determined with Prism version 7.0 (GraphPad, San Diego, CA).

This study was approved by the Institutional Animal Care and Use Committee (IACUC) of Dong-A University(DIACUC-21-11). All animal experiments were performed in accordance with the guidelines and regulationsof the institutional guidelines.

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Embryonic-stem-cell-derived mesenchymal stem cells relieve experimental contact urticaria by regulating the functions ... - Nature.com

Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid … – Nature.com

CNN architecture and dataset for estimating spatial gene expression patterns

Our model utilizes a CNN that takes a phase-contrast image as input and estimates a fluorescent image as output (Fig.1A). The typical input to a CNN is a two-dimensional (2D) image. This 2D image is passed through several convolution layers, each followed by a nonlinear activation function. The training parameters correspond to the weights of these convolution kernels and the biases. Our network has a U-Net-like architecture27, which is an encoder-decoder structure with skip connections. The embedded features from the encoder are passed through the decoder, which consists of upsampling and convolution layers to increase the resolution of the intermediate feature maps to obtain a fluorescent image as output.

In our model, the ResNet5028 was used as the backbone of the encoder. The size of the input image for the ResNet50 is (3times Htimes W). To use the pre-trained model of the ResNet50, gray-scale phase-contrast images were replicated in the axis of the channel to create three-channel images. At the first layer, a convolution with stride 2 is applied to the input image to generate features of size (64times frac{H}{2}times frac{W}{2}). The ResNet50 has 4 residual blocks and the size of the output features of these blocks are (256times frac{H}{4}times frac{W}{4}), (512times frac{H}{8}times frac{W}{8}), (1024times frac{H}{16}times frac{W}{16}), and (2048times frac{H}{32}times frac{W}{32}), respectively. These features are then concatenated to the decoder to exploit multi-scale information. The output of the decoder is a fluorescent image of size (1times frac{H}{2}times frac{W}{2}). Note that each convolution layer has a batch normalization (BN) layer and a rectified linear unit (ReLU) activation function, except for the final convolution layer, which has a sigmoid activation function to constrain the range of the output values between 0 and 1.

The network was optimized by minimizing the training loss computed on the output and corresponding ground-truth fluorescent images. The combination of mean squared error (MSE) and cosine similarity, which captures structural patterns from the entire image, was used as the training loss.

To train, validate, and test our model, we cultured retinal organoids derived from mouse ESCs using the SFEBq method10. In this culture, a GFP gene was knocked-in under the promoter of a master gene of retinal differentiation, Rax. Using this method, we obtained a dataset of a pair of phase-contrast image and fluorescent image of Rax during retinal differentiation (Fig.1B). Images were captured for 96 organoids at 4.5, 5, 6, 7, and 8days after the start of SFEBq, where each sample was captured as 14 Z-stack images. This resulted in a total of (96times 5times 14=6720) image pairs were obtained. These image pairs were divided into 5880, 420, and 420 samples for training, validation, and test, respectively. 84, 6, and 6 organoids were used for training, validation, and test, respectively; thus, each organoid does not appear in the different datasets. For data augmentation, we randomly flipped the input images vertically and horizontally during training. While the image resolution of both phase-contrast and fluorescent images is (960times 720), the (512times 512) regions where organoids appear were extracted.

To demonstrate our model, we applied it to 420 samples of the test data. As a result, the proposed model successfully estimated the spatial expression patterns of Rax from phase-contrast images during retinal organoid development (Fig.2). During development, multiple optic vesicles are formed through large and complicated deformations (Fig.2A). This process begins with a spherical embryonic body, with some portions of the tissue surface evaginating outward to form hemispherical vesicles, i.e., optic vesicles. Importantly, the resulting morphology of retinal organoids, especially optic vesicles, varies widely29. This process is known to be governed by the expression of the Rax gene (Fig.2B). That is, the Rax gene is gradually expressed in several parts of the tissue surface, so-called eye field, where cells differentiate from neuroepithelium into several types of retinal cells.

Estimated spatial Rax expression patterns during retinal organoid development. (A) Phase-contrast images from day 4.5 to day 8. (B) Captured fluorescent images of Rax as ground-truths. (C) Estimated fluorescent images with our model. (D) Error maps between captured and estimated images. The error metric was a squared error. The organoids in (AD) are identical. Scale bars indicate 200m.

Our model successfully recapitulated the above features of Rax expression (Fig.2C), i.e., the Rax intensity was relatively low and homogenous at days 4.5, 5, 6, and gradually increased around the evaginated tissue regions at days 7 and 8. Remarkably, the regions of high Rax expression were accurately estimated even in organoids with various morphologies. On the other hand, as the Rax intensity increases, especially around the evaginated tissue regions, the error of the estimated image from the ground-truth image increases with time (Fig.2D).

To quantitatively evaluate the accuracy of the estimation, we statistically analyzed the estimation results at each stage. To clarify whether the model can estimate Rax intensity in both samples with high and low Rax expression, each of the ground-truth and estimated fluorescence images was divided into two categories by the coefficient of variation of the foreground pixels in a fluorescent image at day 8 (Fig.3A). The samples in each group were labeled as positive and negative, respectively. For each of these categories, the mean and coefficient of variation of the pixel values were calculated (Fig.3BE). In calculating these values, the phase-contrast images were binarized to obtain foreground and background masks, and then computed using only the foreground pixels and normalized to those of the background pixels.

Statistical analysis of fluorescence at each developmental stage for positive and negative samples. (A) Histogram of coefficient of variation for foreground pixel values of fluorescent images at day 8. (B, C) Means of pixel values in positive and negative samples at each stage for ground-truth (green bars) and estimated fluorescent images (blue bars), respectively. (D, E) Coefficients of variation in positive samples at each stage for both ground-truth (green bars) and estimated fluorescent images (red bars), respectively. (F, G) Plots of ground-truth and estimated pixel values in positive and negative samples at each stage, respectively. Errors are 0% and 25% on the solid and dotted black lines, respectively. Error bars in (BE) indicate standard deviations.

Positive samples showed a gradual increase in mean and intensity over the days passed (Fig.3B). The negative sample, on the other hand, showed relatively low values from the beginning and did not change significantly over the days (Fig.3C). Similarly, the coefficients of variation increased in the positive samples but not in the negative samples (Fig.3D,E). These results indicate that the model successfully estimates the feature of the spatial Rax expression patterns during retinal organoid development, i.e., positive samples gradually increase Rax expressions and their heterogeneity, but negative samples do not. The intensity of the estimated images is relatively lower than the intensity of the ground-truth images in the positive samples and vice versa in the negative samples.

To clarify whether the model is capable to estimate intermediate values of the Rax expression, we analyzed the correlations between ground-truth and estimated values on foreground pixels at each stage, respectively (Fig.3F,G). The results show that in the positive sample (Fig.3F), the distribution of intensities is initially concentrated at low intensities and gradually expands to high intensities as the day progresses, with a wide distribution from low to high intensities. Similarly, in the negative sample, the luminance distribution is initially concentrated at low intensities, but does not expand as much as in the positive sample (Fig.3G). These results indicate that the model successfully estimated the plausible values across all pixel intensities, demonstrating the capability of our method to infer intermediate levels of gene expression. Notably, predicting Rax expression in the organoids at later stages, such as day 8 in our experiments, becomes more feasible for the model due to their characteristic morphologies. These distinct morphologies provide features that can be efficiently extracted by the convolution operators of the model.

To determine whether the estimated Rax expression patterns correspond to tissue morphologies, we quantified the spatial distribution of Rax intensity and the mean curvature along the tissue contour around each optic vesicle (Fig.4). For this analysis, four typical optic vesicles were selected from the positive samples and their curvature and Rax distribution were quantified. In this analysis, tissue contours were extracted and the radius of a circle passing through three points on the tissue contour was calculated as the inverse of the curvature. Moreover, the Rax intensity was measured as the average value along the depth direction from the tissue contour.

Correlation analysis of spatial Rax expression patterns and optic-vesicle morphologies. (A) Phase-contrast images. (B) Captured fluorescent images of Rax as ground-truths. (C) Estimated fluorescent images with our model. (D) Mean curvatures as a function of the distance along the organoid contour. (E) Captured and estimated fluorescent intensities of Rax along the organoid contour. The organoids in (AC) are identical and captured on day 8. The mean curvatures and fluorescence in (D, E) are for the regions indicated by the red line starting from the red dot in (A). Scale bars indicate 200m.

Optic vesicles are hemispherical, with positive curvature at the distal portion and negative curvature at the root (Fig.4A,D). The Rax intensity is continuously distributed around each vesicle, being highest at the distal part and gradually decreasing toward the root (Fig.4B,E). Furthermore, because the test images were taken with a conventional fluorescence microscope, structures above and below the focal plane are included in each image. Therefore, although some images have multiple overlapping vesicles (e.g., samples iii and iv), the model successfully estimated the Rax intensity of the overlapping regions as well.

MSE is commonly used as the training loss for training regression models. In addition to MSE, this model also uses cosine similarity, which can capture structural patterns from the entire image. To analyze the effect of cosine similarity on the estimation accuracy, we tested the model with different weights for both error metrics (Fig.5). The trained models were evaluated with MSE for each test dataset on different days (Fig.5A). The results demonstrated that cosine similarity improved the estimation accuracy at the early and intermediate stages, such as from day 4.5 to day 6. At these stages, the intensity in the differentiated region is weak, making it difficult for the network to capture structural patterns using MSE alone. Cosine similarity, on the other hand, enabled the network to learn the patterns from the weak intensity by calculating the correlation between the normalized ground-truth and the estimated images (Fig.5B). Therefore, our model has the capability to achieve the best estimate at different stages with appropriate weight balancing.

Effects of the balance of training loss on estimation accuracy. (A) Mean squared error at each stage with different hyperparameters, where bold and underlined numbers stand for the best and second best results on each day, respectively. (B) Examples of estimated fluorescent images at days 6 and 8 with different hyperparameters. The MSE of each estimated image is described in the upper left. The results with the lowest MSEs are surrounded by the red boxes. Scale bars indicate 200m.

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Machine learning-based estimation of spatial gene expression pattern during ESC-derived retinal organoid ... - Nature.com

Stem Cell Banking Market Size Revenue Hits $18.04 Billion by 2032 … – GlobeNewswire

Newark, Nov. 20, 2023 (GLOBE NEWSWIRE) -- The Brainy Insights estimates that the USD 7.93 Billion in 2022stem cell banking market will reach USD 18.04 Billion by 2032. As stem cell transplants become more viable therapeutic options, the demand for a reliable and secure source of stem cells has increased significantly. Stem cell banks are critical to the success of these treatments because they provide a secure and dependable means of storing and transferring stem cells for transplantation.

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Report Coverage Details

Key Insight of the Stem Cell Banking Market

Asia Pacific is anticipated to expand at the highest CAGR of 10.55% over the projection period.

Asia Pacific is expected to grow at the highest CAGR of 10.55% over the forecast period. It is due to increased public knowledge of stem cell's medical potential, as well as increased government spending in stem cell research and development. For many years, India has been at the forefront of medical advancements as one of the most popular foreign destinations for medical tourism. Furthermore, the development of novel treatments and procedures, as well as the higher success rate of stem cell treatment, are likely to drive expansion in the region's stem cell banking business.

The adult stem cells segment is expected to register the highest CAGR of 10.32% over the projected period in the stem cell banking market.

The adult stem cells segment is anticipated to grow at the highest CAGR of 10.32% in the stem cell banking market. The growing understanding of the variety and effectiveness of adult stem cell banking services is driving up demand. Adult stem cell preservation is being considered by patients, physicians, and researchers as a proactive strategy to future disease problems. This need promotes business competitiveness and innovation, resulting in enhanced storage systems and broader service offers.

Over the projected period, the sample preservation and storage segment is expected to register the highest CAGR of 10.73% in the stem cell banking market.

Over the forecasted period, the sample preservation and storage segment is anticipated to grow at the highest CAGR of 10.73% in the stem cell banking market. This vital service area includes cutting-edge cryopreservation processes, cutting-edge storage facilities, and stringent quality control systems. In this age of regenerative medicine, the efficiency of stem cell treatments is dependent on the quality and accessibility of preserved samples.

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Market Dynamics

Driver: A growing elderly population

An older population has a favourable impact on the market. This demographic shift is changing healthcare dynamics all around the world. As people age, they become more susceptible to degenerative diseases such as osteoarthritis, cardiovascular disease, and neurological disorders such as Alzheimer's and Parkinson's. Stem cells have immense promise for repairing damaged or ageing tissues, paving the way for new treatments and better quality of life for the elderly. This ageing population necessitates more modern healthcare treatments and represents a significant client base for stem cell banking services. Many people and families are aware of the option of keeping stem cells from themselves or loved ones, which can be taken from sources such as cord blood or adipose tissue. These stem cells can be used in future therapies to combat age-related health issues, offering comfort and hope.

Opportunity: Growing ethical issues over the use of embryonic stem cells

The market is being fueled by growing ethical concerns about the use of embryonic stem cells. Because embryonic stem cell research involves the killing of embryos, it has long been a subject of ethical debate, leading in moral and legislative constraints in a variety of domains. This has shifted the emphasis of stem cell research and therapeutic applications away from controversial sources and towards non-controversial sources such as adult stem cells and cord blood. As a result, it is becoming popular among individuals and institutions seeking the potential benefits of stem cell therapy without the ethical ambiguity of stem cell banking. Cord blood, in particular, has grown in prominence as a rich source of stem cells that is ethically sound. Families and healthcare practitioners recognise the value of keeping these cells as a form of biological insurance against future illnesses for the donor and potentially compatible family members.

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Some of the major players operating in the stem cell banking market are:

Cordlife Cryo-Save AG (A Group of Esperite) Stemcyte Smart Cells International Ltd. Cordvida CBR Systems, Inc. Lifecell Cryoviva India Cryo-Cell Viacord

Key Segments cover in the market:

By Product Type:

Human Embryonic Cells Adult Stem Cells IPS Cells

By Service Type:

Sample Analysis Sample Collection and Transportation Sample Preservation and Storage Sample Processing

By Region

North America (U.S., Canada, Mexico) Europe (Germany, France, U.K., Italy, Spain, Rest of Europe) Asia-Pacific (China, Japan, India, Rest of APAC) South America (Brazil and the Rest of South America) The Middle East and Africa (UAE, South Africa, Rest of MEA)

About the report:

The market is analyzed based on value (USD Billion). All the segments have been analyzed worldwide, regional, and country basis. The study includes the analysis of more than 30 countries for each part. The report analyzes driving factors, opportunities, restraints, and challenges for gaining critical insight into the market. The study includes porter's five forces model, attractiveness analysis, product analysis, supply, and demand analysis, competitor position grid analysis, distribution, and marketing channels analysis.

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Stem Cell Banking Market Size Revenue Hits $18.04 Billion by 2032 ... - GlobeNewswire