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Supp Table 5 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 5 Antibodies</p>
Supp Table 2 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 2 Gene sets for GSEA</p>
Supp Table 1 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 1 VEGFAC Top Differentially Expressed Genes by Cluster</p>
Supp Table 6 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 6 qRT PCR Probes</p>
Supp Table 5 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 5 Antibodies</p>
Supp Table 7 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 7 NGS Panel</p>
Supp Table 3 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 3 HD and MPN samples.</p>
Supp Table 3 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 3 HD and MPN samples.</p>
Supplementary Information from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Materials and Methods, Supplementary Figures S1-S12</p>
Supp Table 7 from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<p>Supplementary Table 7 NGS Panel</p>
Data from Human Bone Marrow Organoids for Disease Modeling, Discovery, and Validation of Therapeutic Targets in Hematologic Malignancies
<div>Abstract<p>A lack of models that recapitulate the complexity of human bone marrow has hampered mechanistic studies of normal and malignant hematopoiesis and the validation of novel therapies. Here, we describe a step-wise, directed-differentiation protocol in which organoids are generated from induced pluripotent stem cells committed to mesenchymal, endothelial, and hematopoietic lineages. These 3D structures capture key features of human bone marrow—stroma, lumen-forming sinusoids, and myeloid cells including proplatelet-forming megakaryocytes. The organoids supported the engraftment and survival of cells from patients with blood malignancies, including cancer types notoriously difficult to maintain <i>ex vivo</i>. Fibrosis of the organoid occurred following TGFβ stimulation and engraftment with myelofibrosis but not healthy donor–derived cells, validating this platform as a powerful tool for studies of malignant cells and their interactions within a human bone marrow–like milieu. This enabling technology is likely to accelerate the discovery and prioritization of novel targets for bone marrow disorders and blood cancers.</p>Significance:<p>We present a human bone marrow organoid that supports the growth of primary cells from patients with myeloid and lymphoid blood cancers. This model allows for mechanistic studies of blood cancers in the context of their microenvironment and provides a much-needed <i>ex vivo</i> tool for the prioritization of new therapeutics.</p><p><i><a href="https://aacrjournals.org/cancerdiscovery/article/doi/10.1158/2159-8290.CD-22-1303" target="_blank">See related commentary by Derecka and Crispino, p. 263</a>.</i></p><p><i><a href="https://aacrjournals.org/cancerdiscovery/article/doi/10.1158/2159-8290.CD-13-2-ITI" target="_blank">This article is highlighted in the In This Issue feature, p. 247</a></i></p></div>
Multi-organ single-cell RNA sequencing in mice reveals early hyperglycemia responses that converge on fibroblast dysregulation.
Diabetes causes a range of complications that can affect multiple organs. Hyperglycemia is an important driver of diabetes-associated complications, mediated by biological processes such as dysfunction of endothelial cells, fibrosis, and alterations in leukocyte number and function. Here, we dissected the transcriptional response of key cell types to hyperglycemia across multiple tissues using single-cell RNA sequencing (scRNA-seq) and identified conserved, as well as organ-specific, changes associated with diabetes complications. By studying an early time point of diabetes, we focus on biological processes involved in the initiation of the disease, before the later organ-specific manifestations had supervened. We used a mouse model of type 1 diabetes and performed scRNA-seq on cells isolated from the heart, kidney, liver, and spleen of streptozotocin-treated and control male mice after 8 weeks and assessed differences in cell abundance, gene expression, pathway activation, and cell signaling across organs and within organs. In response to hyperglycemia, endothelial cells, macrophages, and monocytes displayed organ-specific transcriptional responses, whereas fibroblasts showed similar responses across organs, exhibiting altered metabolic gene expression and increased myeloid-like fibroblasts. Furthermore, we found evidence of endothelial dysfunction in the kidney, and of endothelial-to-mesenchymal transition in streptozotocin-treated mouse organs. In summary, our study represents the first single-cell and multi-organ analysis of early dysfunction in type 1 diabetes-associated hyperglycemia, and our large-scale dataset (comprising 67 611 cells) will serve as a starting point, reference atlas, and resource for further investigating the events leading to early diabetic disease.
Adipokines and stroke: A systematic review and meta-analysis of disease risk and patient outcomes.
Obesity is reported to increase stroke risk, with adipocyte-derived cytokines or adipokines implicated as mediators. However, the relationship between adipokines and stroke is not well clarified. Thus, we aimed to evaluate the association of adipokines with stroke using fully adjusted risk estimates that incorporated body mass index in a meta-analysis. Data from 52 studies (62,428 patients) were pooled in a random-effects meta-analysis. Adiponectin was independently associated with a lower risk of pre-existing stroke (adjusted odds ratio: 0.64 [95% confidence interval: 0.46-0.88], p