TSPAN33

在一项新的研究中，来自美国辛辛那提儿童医院等研究机构的研究人员，通过不同寻常地应用博弈论和机器学习技术，发布了世界上关于来自不同捐献者的多种类型干细胞和早期祖细胞的最详细图谱。他们确定了造血干/祖细胞（hematopoietic stem and progenitor cell, HSPC）的80多个不同亚群。相关研究结果于2024年3月21日在线发表在Nature Immunology期刊上，论文标题为“An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors”。这些作者表示，“我们相信，我们对最早的HSPC、中间细胞状态和基质群体以及最丰富的终末状态的高度集中捕获策略，提供了迄今描述的骨髓HSPC区室的最深入视角。”对白血病的影响及其他这个复杂的研究项目涉及使用多种先进的基因组研究工具和计算方法，包括从不同种族和性别捐献者那里收集的 30 多万个骨髓细胞建立详细的基因组和蛋白质组图谱。除了细致的单细胞 RNA 测序工作和匹配的流式细胞术外，分析海量数据集还需要创新性地使用机器学习技术和博弈论来进行关键的统计分析。这些作者使用的一些研究工具，包括 scTriangulate 和 pyIninityFlow 这两个软件工具，它们都是辛辛那提儿童医院开发的。这些作者的主要发现包括：（1）将132种靶向HSPC和相关细胞的表面蛋白抗体混合在一起，他们的测试表明在分析人类骨髓细胞方面，这种新的抗体混合物优于商业产品；（2）通过对基因表达和表面蛋白进行新的整合，确定了 89 个细胞群体，这有助于揭示新的干/祖细胞群体；（3）分离新型细胞群体的策略和标志物：首先，利用两种新型表面标志物 C5L2 和 TSPAN33 来识别进入祖细胞状态和成为成熟中性粒细胞之间过渡状态的细胞，其次，利用 CD326 和 CD235a 对称为有核红细胞（erythroblasts）的人类红细胞形成进行独特分级；（4）用于开发预测急性髓性白血病（AML）等癌症的细胞起源和可能原因的新型在线计算工具。图片来自Nature Immunology, 2024, doi:10.1038/s41590-024-01782-4论文共同通讯作者Nathan Salomonis说，“我们的分析结合了先前多种图谱、表面标志物和基因的信息以及机器学习的新进展，揭示了以前隐藏的细胞状态，如最原始的干细胞。”这种新的干细胞图谱最直接的潜在应用之一是治疗中心如何监测接受白血病疗法的患者。论文共同通讯作者H. Leighton Grimes说，“比如，有些人会对维奈克拉（Venotoclax）与氮杂胞苷（2018年获批作为AML一线疗法的化疗药物）产生耐药性。在某些病例中，耐药性AML似乎是由‘单核细胞’形式的白血病干细胞（m-LSC）驱动的，而不是由更‘原始’形式的白血病干细胞（p-LSC）驱动的。有了这样的图谱，就更有可能确定AML中存在哪种类型的癌症干细胞，而这些信息可能为临床医生如何调整治疗方案提供参考。”这项新的研究报告称，在23种细胞状态中发现了69种抗体，这些抗体可能是对AML患者进行临床监测的潜在生物标志物。Grimes说，虽然这种新的干细胞图谱的开发是一个重要的开端，但要将有关血细胞形成的新数据整合到医疗护理中，还需要进行更多的研究和开发。这些作者预测，这种新的数字骨髓图谱将加速发现更多血液疾病的细胞和分子调节因子。为了支持这些努力，他们正在向研究界广泛提供他们的数据和工具。参考资料：Xuan Zhang et al. An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors. Nature Immunology, 2024, doi:10.1038/s41590-024-01782-4.Most detailed atlas to date of human blood stem cells could guide future leukemia carehttps://medicalxpress.com/news/2024-03-atlas-date-human-blood-stem.html本文仅用于学术分享，转载请注明出处。若有侵权，请联系微信：bioonSir 删除或修改！

Scientists at Cincinnati Children's lead deepest dive yet into the mechanics of blood cell development. They found new treasure. CINCINNATI , March 21, 2024 /PRNewswire/ -- Thanks to an unusual application of game theory and machine learning technology, a large team of scientists led by experts at Cincinnati Children's has published the world's most detailed "atlas" of the many types of stem cells and early progenitors involved in producing human blood from diverse donors. Continue Reading A large team of scientists led by experts at Cincinnati Children’s has published the world’s most detailed “atlas” of the many types of stem cells and early progenitors involved in producing human blood from diverse donors. The team has identified more than 80 distinct subsets of hematopoietic stem and progenitor cells (HSPCs) – early-stage cells that kick off production of mature red cells, white cells and other elements of our complex blood system. Details were published March 21, 2024, in Nature Immunology. "We believe our highly focused capture strategy of the earliest HSPCs, intermediate cell states plus stromal populations, and the most abundant end states provides the deepest view of bone marrow stem and progenitor compartments described to date," the co-authors state. The study was led by co-first authors Xuan Zhang, PhD, and Baobao Song, PhD, and co-corresponding authors Nathan Salomonis, PhD, and H. Leighton Grimes, PhD, all researchers with Cincinnati Children's. Overall, the study includes 26 co-authors from four academic medical centers and two biotech companies. Leukemia implications and more This complex research project involved using multiple advanced genomic research tools and computational methods to build detailed genomic and proteomic profiles of more than 300,000 bone marrow cells collected from a diverse mix of donors spanning race and sex. Cincinnati Children's is committed to promoting inclusivity and addressing population diversity in scientific studies as part of their ongoing mission. In addition to meticulous single-cell RNA sequencing work and matching flow cytometry, analyzing the massive data set required innovative use of machine learning technology and dipping into game theory to perform key statistical analyses. Some of the research tools the team used, including two software tools scTriangulate and pyIninityFlow, were developed at Cincinnati Children's. Among the team's key findings: Mixing a new "cocktail" of 132 antibodies that can target surface proteins on HSPCs and related cells. Testing demonstrated that the new mix outperforms commercial products for profiling human bone marrow cells. Identifying 89 clusters by a novel integration of gene expression and surface proteins helped reveal new stem/progenitor cell populations. Strategies and markers for isolating novel cell populations. First, two novel surface markers C5L2 and TSPAN33 to identify cells entering transitional states between progenitor status and becoming a mature neutrophil. Second, distinct staging of human red blood cell formation called erythroblasts with CD326 and CD235a. New online computational tools to predict the cellular origins and possible causes of cancers such as acute myeloid leukemia (AML). "Our analysis uncovers previously hidden cell states, such as the most primitive stem cells, by combining information from diverse prior atlases, surface markers and genes, and new advances in machine learning," Salomonis says. One of the most immediate potential applications of the new stem cell atlas centers on how treatment centers monitor patients receiving therapies for leukemia. "For example, some people develop resistance to Venotoclax with azacytidine (a chemotherapy that was approved in 2018 as a first line therapy for AML)," Grimes says. "In some cases, the therapy-resistant disease appears to be driven by a "monocytic" form of leukemia stem cell (m-LSC) rather than a more "primitive" form (p-LSC). With an atlas like this, it becomes more possible to determine which types of cancer stem cells are present in the AML, and that information could then inform how a clinician might adjust a treatment regimen." The new study reports finding 69 antibodies within 23 cell states that may represent attractive biomarkers for clinical monitoring of people with AML. While the development of the new stem cell atlas is an essential start, much more research and development will be needed to integrate the new data about blood cell formation into medical care, Grimes says. Making the tool available The co-authors predict that the new digital bone marrow atlas will accelerate the discovery of more cellular and molecular regulators of blood diseases. To support such efforts, the research team is making its data and tools widely available to the research community. While the Nature Immunology article has been made open access, full detail and access to this advanced digital resource, including data viewers, methods, raw data and code, are available at . About the study In addition to the first and corresponding authors, contributing co-authors from Cincinnati Children's include Guangyuan Li, Dan Schnell, Kairavee Thakkar, Michal Kouril, Kang Jin, Stuart Hay, and Sidharth Sen, all with the Division of Biomedical Informatics; plus Kyle Ferchen, Bailee Kain, David Bernardicius, Siyuan Ma, and Sierra Bennett, all with the Division of Immunobiology. Data acquisition for this study included support from the Research Flow Cytometry Core and the DNA Sequencing and Genotyping Core at Cincinnati Children's. Contributors also included scientists from the Yale School of Medicine, University of Pittsburgh, University of Colorado School of Medicine, San Diego-based BioLegend Inc., and Massachusetts based Curiox Biosystems, Inc. Funding sources for this work include several grants from the National Institutes of Health, especially an RC2 grant from the NIDDK, complemented by an NIDDK Cooperative Centers of Excellence in Hematology grant, a Specialized Center of Research (SCOR) grant from the Leukemia & Lymphoma Society, and the US Department of Veterans Affairs. SOURCE Cincinnati Children's Hospital Medical Center