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Stankiewicz LN, Rossi FMV, Zandstra PW. Rebuilding and rebooting immunity with stem cells. Cell Stem Cell 2024; 31:597-616. [PMID: 38593798 DOI: 10.1016/j.stem.2024.03.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 03/08/2024] [Accepted: 03/15/2024] [Indexed: 04/11/2024]
Abstract
Advances in modern medicine have enabled a rapid increase in lifespan and, consequently, have highlighted the immune system as a key driver of age-related disease. Immune regeneration therapies present exciting strategies to address age-related diseases by rebooting the host's primary lymphoid tissues or rebuilding the immune system directly via biomaterials or artificial tissue. Here, we identify important, unanswered questions regarding the safety and feasibility of these therapies. Further, we identify key design parameters that should be primary considerations guiding technology design, including timing of application, interaction with the host immune system, and functional characterization of the target patient population.
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Affiliation(s)
- Laura N Stankiewicz
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
| | - Fabio M V Rossi
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
| | - Peter W Zandstra
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; Michael Smith Laboratories, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
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3
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Hislop J, Song Q, Keshavarz F K, Alavi A, Schoenberger R, LeGraw R, Velazquez JJ, Mokhtari T, Taheri MN, Rytel M, Chuva de Sousa Lopes SM, Watkins S, Stolz D, Kiani S, Sozen B, Bar-Joseph Z, Ebrahimkhani MR. Modelling post-implantation human development to yolk sac blood emergence. Nature 2024; 626:367-376. [PMID: 38092041 PMCID: PMC10849971 DOI: 10.1038/s41586-023-06914-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 11/29/2023] [Indexed: 01/16/2024]
Abstract
Implantation of the human embryo begins a critical developmental stage that comprises profound events including axis formation, gastrulation and the emergence of haematopoietic system1,2. Our mechanistic knowledge of this window of human life remains limited due to restricted access to in vivo samples for both technical and ethical reasons3-5. Stem cell models of human embryo have emerged to help unlock the mysteries of this stage6-16. Here we present a genetically inducible stem cell-derived embryoid model of early post-implantation human embryogenesis that captures the reciprocal codevelopment of embryonic tissue and the extra-embryonic endoderm and mesoderm niche with early haematopoiesis. This model is produced from induced pluripotent stem cells and shows unanticipated self-organizing cellular programmes similar to those that occur in embryogenesis, including the formation of amniotic cavity and bilaminar disc morphologies as well as the generation of an anterior hypoblast pole and posterior domain. The extra-embryonic layer in these embryoids lacks trophoblast and shows advanced multilineage yolk sac tissue-like morphogenesis that harbours a process similar to distinct waves of haematopoiesis, including the emergence of erythroid-, megakaryocyte-, myeloid- and lymphoid-like cells. This model presents an easy-to-use, high-throughput, reproducible and scalable platform to probe multifaceted aspects of human development and blood formation at the early post-implantation stage. It will provide a tractable human-based model for drug testing and disease modelling.
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Affiliation(s)
- Joshua Hislop
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Qi Song
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Kamyar Keshavarz F
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Amir Alavi
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Rayna Schoenberger
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ryan LeGraw
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jeremy J Velazquez
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tahere Mokhtari
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mohammad Naser Taheri
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | - Matthew Rytel
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Simon Watkins
- Center for Biologic Imaging, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Cell Biology and Molecular Physiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Donna Stolz
- Center for Biologic Imaging, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Cell Biology and Molecular Physiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Samira Kiani
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Berna Sozen
- Department of Genetics, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Mo R Ebrahimkhani
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Pathology, Division of Experimental Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
- Pittsburgh Liver Research Center, University of Pittsburgh, Pittsburgh, PA, USA.
- McGowan Institute for Regenerative Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
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5
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Robbins DJ, Pavletich TS, Patil AT, Pahopos D, Lasarev M, Polaki US, Gahvari ZJ, Bresnick EH, Matson DR. Linking GATA2 to myeloid dysplasia and complex cytogenetics in adult myelodysplastic neoplasm and acute myeloid leukemia. Blood Adv 2024; 8:80-92. [PMID: 38029365 PMCID: PMC10787255 DOI: 10.1182/bloodadvances.2023011554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/07/2023] [Accepted: 11/27/2023] [Indexed: 12/01/2023] Open
Abstract
ABSTRACT GATA binding protein 2 (GATA2) is a conserved zinc finger transcription factor that regulates the emergence and maintenance of complex genetic programs driving development and function of hematopoietic stem and progenitor cells (HSPCs). Patients born with monoallelic GATA2 mutations develop myelodysplastic neoplasm (MDS) and acute myeloid leukemia (AML), whereas acquired GATA2 mutations are reported in 3% to 5% of sporadic AML cases. The mechanisms by which aberrant GATA2 activity promotes MDS and AML are incompletely understood. Efforts to understand GATA2 in basic biology and disease will be facilitated by the development of broadly efficacious antibodies recognizing physiologic levels of GATA2 in diverse tissue types and assays. Here, we purified a polyclonal anti-GATA2 antibody and generated multiple highly specific anti-GATA2 monoclonal antibodies, optimized them for immunohistochemistry on patient bone marrow bioosy samples, and analyzed GATA2 expression in adults with healthy bone marrow, MDS, and acute leukemia. In healthy bone marrow, GATA2 was detected in mast cells, subsets of CD34+ HSPCs, E-cadherin-positive erythroid progenitors, and megakaryocytes. In MDS, GATA2 expression correlates with bone marrow blast percentage, positively correlates with myeloid dysplasia and complex cytogenetics, and is a nonindependent negative predictor of overall survival. In acute leukemia, the percent of GATA2+ blasts closely associates with myeloid lineage, whereas a subset of lymphoblastic and undifferentiated leukemias with myeloid features also express GATA2. However, the percent of GATA2+ blasts in AML is highly variable. Elevated GATA2 expression in AML blasts correlates with peripheral neutropenia and complex AML cytogenetics but, unlike in MDS, does not predict survival.
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Affiliation(s)
- Daniel J. Robbins
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI
| | - Tatiana S. Pavletich
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI
| | - Apoorva T. Patil
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI
| | - Demetra Pahopos
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI
| | - Michael Lasarev
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI
| | - Usha S. Polaki
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI
| | | | - Emery H. Bresnick
- Department of Cell and Regenerative Biology, University of Wisconsin-Madison, Madison, WI
- Wisconsin Blood Cancer Research Institute, University of Wisconsin-Madison, Madison, WI
| | - Daniel R. Matson
- Department of Pathology and Laboratory Medicine, University of Wisconsin-Madison, Madison, WI
- Wisconsin Blood Cancer Research Institute, University of Wisconsin-Madison, Madison, WI
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