1
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Zhang X, Liang X, Wen Y, Wu F, Gao G, Zhang L, Gu Y, Zhang J, Zhou F, Li W, Tang L, Yang X, Zhao H, Zhou C, Hirsch FR. RAC1 inhibition ameliorates IBSP-induced bone metastasis in lung adenocarcinoma. Cell Rep 2024; 43:114528. [PMID: 39052477 DOI: 10.1016/j.celrep.2024.114528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 05/17/2024] [Accepted: 07/08/2024] [Indexed: 07/27/2024] Open
Abstract
Macrophage-to-osteoclast differentiation (osteoclastogenesis) plays an essential role in tumor osteolytic bone metastasis (BM), while its specific mechanisms remain largely uncertain in lung adenocarcinoma BM. In this study, we demonstrate that integrin-binding sialoprotein (IBSP), which is highly expressed in the cancer cells from bone metastatic and primary lesions of patients with lung adenocarcinoma, can facilitate BM and directly promote macrophage-to-osteoclast differentiation independent of RANKL/M-CSF. In vivo results further suggest that osteolytic BM in lung cancer specifically relies on IBSP-induced macrophage-to-osteoclast differentiation. Mechanistically, IBSP regulates the Rac family small GTPase 1 (Rac1)-NFAT signaling pathway and mediates the forward shift of macrophage-to-osteoclast differentiation, thereby leading to early osteolysis. Moreover, inhibition of Rac1 by EHT-1864 or azathioprine in mice models can remarkably alleviate IBSP-induced BM of lung cancer. Overall, our study suggests that tumor-secreted IBSP promotes BM by inducing macrophage-to-osteoclast differentiation, with potential as an early diagnostic maker for BM, and Rac1 can be the therapeutic target for IBSP-promoted BM in lung cancer.
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Affiliation(s)
- Xiaoshen Zhang
- School of Medicine, Tongji University, Shanghai 200433, China; Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Xijun Liang
- Clinical Cancer Institute, Center for Translational Medicine, Naval Medical University, Shanghai 200433, China
| | - Yaokai Wen
- School of Medicine, Tongji University, Shanghai 200433, China; Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Fengying Wu
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Guanghui Gao
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Lei Zhang
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Yifeng Gu
- Interventional Radiology Department, Shanghai Sixth People's Hospital affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Jianping Zhang
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai 200032, China; Shanghai Key Laboratory of Bioactive Small Molecules, Fudan University, Shanghai 2000325, China
| | - Fei Zhou
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Wei Li
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Liang Tang
- Central Laboratory, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Xiaojun Yang
- Central Laboratory, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China
| | - Hui Zhao
- Shanghai Sixth People's Hospital affiliated with Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China.
| | - Caicun Zhou
- Department of Medical Oncology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai 200433, China.
| | - Fred R Hirsch
- Center of Excellence for Thoracic Oncology, The Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1128, New York, NY 10029-6574, USA
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2
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Xiao H, Hu X, Li P, Deng J. Global burden and trends of leukemia attributable to high body mass index risk in adults over the past 30 years. Front Oncol 2024; 14:1404135. [PMID: 38962277 PMCID: PMC11219942 DOI: 10.3389/fonc.2024.1404135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Accepted: 06/04/2024] [Indexed: 07/05/2024] Open
Abstract
Background High BMI (Body Mass Index) is a significant factor impacting health, with a clear link to an increased risk of leukemia. Research on this topic is limited. Understanding the epidemiological trends of leukemia attributable to high BMI risk is crucial for disease prevention and patient support. Methods We obtained the data from the Global Burden of Disease Study, analyzing the ASR (age-standardized rates), including ASDR (age-standardized death rate) and age-standardized disability-adjusted life years (DALYs) rate, and estimated annual percentage change (EAPC) by gender, age, country, and region from 1990 to 2019. Results In 2019, deaths and DALYs have significantly increased to 21.73 thousand and 584.09 thousand. The global age-standardized death and DALYs rates have slightly increased over the past 30 years (EAPCs: 0.34 and 0.29). Among four common leukemia subtypes, only CML (Chronic Myeloid Leukemia) exhibited a significant decrease in ASDR and age-standardized DALYs rate, with EAPC of -1.74 and -1.52. AML (Acute Myeloid Leukemia) showed the most pronounced upward trend in ASDR, with an EAPC of 1.34. These trends vary by gender, age, region, and national economic status. Older people have been at a significantly greater risk. Females globally have borne a higher burden. While males have shown an increasing trend. The regions experiencing the greatest growth in ASR were South Asia. The countries with the largest increases were Equatorial Guinea. However, It is worth noting that there may be variations among specific subtypes of leukemia. Regions with high Socio-demographic Index (SDI) have had the highest ASR, while low-middle SDI regions have shown the greatest increase in these rates. All ASRs values have been positively correlated with SDI, but there has been a turning point in medium to high SDI regions. Conclusions Leukemia attributable to high BMI risk is gradually becoming a heavier burden globally. Different subtypes of leukemia have distinct temporal and regional patterns. This study's findings will provide information for analyzing the worldwide disease burden patterns and serve as a basis for disease prevention, developing suitable strategies for the modifiable risk factor.
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Affiliation(s)
| | | | | | - Jianchuan Deng
- Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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3
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Rathgeber AC, Ludwig LS, Penter L. Single-cell genomics-based immune and disease monitoring in blood malignancies. Clin Hematol Int 2024; 6:62-84. [PMID: 38884110 PMCID: PMC11180218 DOI: 10.46989/001c.117961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 12/25/2023] [Indexed: 06/18/2024] Open
Abstract
Achieving long-term disease control using therapeutic immunomodulation is a long-standing concept with a strong tradition in blood malignancies. Besides allogeneic hematopoietic stem cell transplantation that continues to provide potentially curative treatment for otherwise challenging diagnoses, recent years have seen impressive progress in immunotherapies for leukemias and lymphomas with immune checkpoint blockade, bispecific monoclonal antibodies, and CAR T cell therapies. Despite their success, non-response, relapse, and immune toxicities remain frequent, thus prioritizing the elucidation of the underlying mechanisms and identifying predictive biomarkers. The increasing availability of single-cell genomic tools now provides a system's immunology view to resolve the molecular and cellular mechanisms of immunotherapies at unprecedented resolution. Here, we review recent studies that leverage these technological advancements for tracking immune responses, the emergence of immune resistance, and toxicities. As single-cell immune monitoring tools evolve and become more accessible, we expect their wide adoption for routine clinical applications to catalyze more precise therapeutic steering of personal immune responses.
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Affiliation(s)
- Anja C Rathgeber
- Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine
- Department of Hematology, Oncology, and Tumorimmunology Charité - Universitätsmedizin Berlin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin
| | - Leif S Ludwig
- Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin
| | - Livius Penter
- Department of Hematology, Oncology, and Tumorimmunology Charité - Universitätsmedizin Berlin
- BIH Biomedical Innovation Academy Berlin Institute of Health at Charité - Universitätsmedizin Berlin
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4
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Moiani A, Letort G, Lizot S, Chalumeau A, Foray C, Felix T, Le Clerre D, Temburni-Blake S, Hong P, Leduc S, Pinard N, Marechal A, Seclen E, Boyne A, Mayer L, Hong R, Pulicani S, Galetto R, Gouble A, Cavazzana M, Juillerat A, Miccio A, Duclert A, Duchateau P, Valton J. Non-viral DNA delivery and TALEN editing correct the sickle cell mutation in hematopoietic stem cells. Nat Commun 2024; 15:4965. [PMID: 38862518 PMCID: PMC11166989 DOI: 10.1038/s41467-024-49353-3] [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: 08/08/2023] [Accepted: 06/03/2024] [Indexed: 06/13/2024] Open
Abstract
Sickle cell disease is a devastating blood disorder that originates from a single point mutation in the HBB gene coding for hemoglobin. Here, we develop a GMP-compatible TALEN-mediated gene editing process enabling efficient HBB correction via a DNA repair template while minimizing risks associated with HBB inactivation. Comparing viral versus non-viral DNA repair template delivery in hematopoietic stem and progenitor cells in vitro, both strategies achieve comparable HBB correction and result in over 50% expression of normal adult hemoglobin in red blood cells without inducing β-thalassemic phenotype. In an immunodeficient female mouse model, transplanted cells edited with the non-viral strategy exhibit higher engraftment and gene correction levels compared to those edited with the viral strategy. Transcriptomic analysis reveals that non-viral DNA repair template delivery mitigates P53-mediated toxicity and preserves high levels of long-term hematopoietic stem cells. This work paves the way for TALEN-based autologous gene therapy for sickle cell disease.
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Affiliation(s)
| | - Gil Letort
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Sabrina Lizot
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Anne Chalumeau
- Université Paris Cité, Imagine Institute, Laboratory of Chromatin and Gene Regulation During Development, INSERM UMR 1163, Paris, France
| | - Chloe Foray
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Tristan Felix
- Université Paris Cité, Imagine Institute, Laboratory of Chromatin and Gene Regulation During Development, INSERM UMR 1163, Paris, France
| | | | | | - Patrick Hong
- Cellectis Inc., 430 East 29th Street, New York, NY, USA
| | - Sophie Leduc
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Noemie Pinard
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Alan Marechal
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | | | - Alex Boyne
- Cellectis Inc., 430 East 29th Street, New York, NY, USA
| | - Louisa Mayer
- Cellectis Inc., 430 East 29th Street, New York, NY, USA
| | - Robert Hong
- Cellectis Inc., 430 East 29th Street, New York, NY, USA
| | | | - Roman Galetto
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Agnès Gouble
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France
| | - Marina Cavazzana
- Biotherapy Clinical Investigation Center, Necker Children's Hospital, Assistance Publique Hopitaux de Paris, Paris, France
- Human Lymphohematopoiesis Laboratory, Imagine Institute, INSERM UMR1163, Paris Cité University, Paris, France
- Biotherapy Department, Necker Children's Hospital, Assistance Publique Hopitaux de Paris, Paris, France
| | | | - Annarita Miccio
- Université Paris Cité, Imagine Institute, Laboratory of Chromatin and Gene Regulation During Development, INSERM UMR 1163, Paris, France
| | | | | | - Julien Valton
- Cellectis S.A., 8 Rue de la Croix Jarry, Paris, France.
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5
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Bandyopadhyay S, Duffy MP, Ahn KJ, Sussman JH, Pang M, Smith D, Duncan G, Zhang I, Huang J, Lin Y, Xiong B, Imtiaz T, Chen CH, Thadi A, Chen C, Xu J, Reichart M, Martinez Z, Diorio C, Chen C, Pillai V, Snaith O, Oldridge D, Bhattacharyya S, Maillard I, Carroll M, Nelson C, Qin L, Tan K. Mapping the cellular biogeography of human bone marrow niches using single-cell transcriptomics and proteomic imaging. Cell 2024; 187:3120-3140.e29. [PMID: 38714197 PMCID: PMC11162340 DOI: 10.1016/j.cell.2024.04.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 02/02/2024] [Accepted: 04/12/2024] [Indexed: 05/09/2024]
Abstract
Non-hematopoietic cells are essential contributors to hematopoiesis. However, heterogeneity and spatial organization of these cells in human bone marrow remain largely uncharacterized. We used single-cell RNA sequencing (scRNA-seq) to profile 29,325 non-hematopoietic cells and discovered nine transcriptionally distinct subtypes. We simultaneously profiled 53,417 hematopoietic cells and predicted their interactions with non-hematopoietic subsets. We employed co-detection by indexing (CODEX) to spatially profile over 1.2 million cells. We integrated scRNA-seq and CODEX data to link predicted cellular signaling with spatial proximity. Our analysis revealed a hyperoxygenated arterio-endosteal neighborhood for early myelopoiesis, and an adipocytic localization for early hematopoietic stem and progenitor cells (HSPCs). We used our CODEX atlas to annotate new images and uncovered mesenchymal stromal cell (MSC) expansion and spatial neighborhoods co-enriched for leukemic blasts and MSCs in acute myeloid leukemia (AML) patient samples. This spatially resolved, multiomic atlas of human bone marrow provides a reference for investigation of cellular interactions that drive hematopoiesis.
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Affiliation(s)
- Shovik Bandyopadhyay
- Cellular and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael P Duffy
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kyung Jin Ahn
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jonathan H Sussman
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Minxing Pang
- Applied Mathematics & Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA, USA
| | - David Smith
- Center for Single Cell Biology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Gwendolyn Duncan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Iris Zhang
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey Huang
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Yulieh Lin
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara Xiong
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tamjid Imtiaz
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA
| | - Chia-Hui Chen
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Anusha Thadi
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Changya Chen
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jason Xu
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Melissa Reichart
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Zachary Martinez
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Caroline Diorio
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chider Chen
- Department of Oral and Maxillofacial Surgery/Pharmacology, School of Dental Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vinodh Pillai
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Oraine Snaith
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Derek Oldridge
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Siddharth Bhattacharyya
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ivan Maillard
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Martin Carroll
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Charles Nelson
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ling Qin
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Kai Tan
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Center for Single Cell Biology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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6
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Arora JK, Matangkasombut P, Charoensawan V, Opasawatchai A. Single-cell RNA sequencing reveals the expansion of circulating tissue-homing B cell subsets in secondary acute dengue viral infection. Heliyon 2024; 10:e30314. [PMID: 38818157 PMCID: PMC11137366 DOI: 10.1016/j.heliyon.2024.e30314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 06/01/2024] Open
Abstract
The roles of antibodies secreted by subsets of B cells in dengue virus (DENV) infection have been extensively studied, yet, the contribution of tissue-homing B cells to antiviral immunity remains unclear. In this study, we performed a comprehensive analysis of B cell subpopulations in peripheral blood samples from DENV-infected patients using single-cell RNA-sequencing (scRNA-seq) datasets and flow cytometry. We showed that plasma cells (PCs) and plasmablasts (PBs) were the predominant B cell populations during the acute phase of secondary natural DENV infection, but not in convalescent phase nor in healthy controls. Interestingly, these cells expressed proliferation, adhesion, and tissue-homing genes, including SELPLG, a homing marker of the skin, the initial infected site of DENV. Flow cytometry analysis confirmed a significant upregulation of cell surface expression of a cutaneous lymphocyte-associated antigen (CLA) encoded by SELPLG in PCs and PBs, compared to naive and memory B cells from the same patients. The analysis of an independent single-cell B-cell receptor sequencing (scBCR-seq) dataset of DENV-infected patients revealed that the peripheral blood PCs and PBs exhibited the highest clonal expansion in secondary DENV infection compared to other B cell subsets. These clonally expanded cells also expressed the highest levels of tissue-homing genes, including SELPLG. In addition, by utilizing a public scRNA-seq dataset of SARS-CoV2 infection, we demonstrated the upregulation of several tissue-homing genes in PCs and PBs. Our study provides evidence for the potential roles of tissue-homing B cell subsets in the context of immune responses against viral infections in humans.
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Affiliation(s)
- Jantarika Kumar Arora
- Doctor of Philosophy Program in Biochemistry (International Program), Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Ponpan Matangkasombut
- Department of Microbiology, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Single-cell Omics and Systems Biology of Diseases Research Unit, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| | - Varodom Charoensawan
- Department of Biochemistry, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Single-cell Omics and Systems Biology of Diseases Research Unit, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom, 73170, Thailand
- Division of Medical Bioinformatics, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Genomics, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- School of Chemistry, Institute of Science, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
| | - Anunya Opasawatchai
- Single-cell Omics and Systems Biology of Diseases Research Unit, Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
- Integrative Computational BioScience (ICBS) Center, Mahidol University, Nakhon Pathom, 73170, Thailand
- Department of Oral Microbiology, Faculty of Dentistry, Mahidol University, Bangkok, 10400, Thailand
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7
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Su EY, Fread K, Goggin S, Zunder ER, Cahan P. Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells. Sci Data 2024; 11:559. [PMID: 38816402 PMCID: PMC11139855 DOI: 10.1038/s41597-024-03399-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/21/2024] [Indexed: 06/01/2024] Open
Abstract
Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.
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Affiliation(s)
- Emily Y Su
- Institute for Cell Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kristen Fread
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Sarah Goggin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Eli R Zunder
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
| | - Patrick Cahan
- Institute for Cell Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Molecular Biology and Genetics, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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8
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Ediriwickrema A, Nakauchi Y, Fan AC, Köhnke T, Hu X, Luca BA, Kim Y, Ramakrishnan S, Nakamoto M, Karigane D, Linde MH, Azizi A, Newman AM, Gentles AJ, Majeti R. A single cell framework identifies functionally and molecularly distinct multipotent progenitors in adult human hematopoiesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.07.592983. [PMID: 38766031 PMCID: PMC11100686 DOI: 10.1101/2024.05.07.592983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Hematopoietic multipotent progenitors (MPPs) regulate blood cell production to appropriately meet the biological demands of the human body. Human MPPs remain ill-defined whereas mouse MPPs have been well characterized with distinct immunophenotypes and lineage potencies. Using multiomic single cell analyses and complementary functional assays, we identified new human MPPs and oligopotent progenitor populations within Lin-CD34+CD38dim/lo adult bone marrow with distinct biomolecular and functional properties. These populations were prospectively isolated based on expression of CD69, CLL1, and CD2 in addition to classical markers like CD90 and CD45RA. We show that within the canonical Lin-CD34+CD38dim/loCD90CD45RA-MPP population, there is a CD69+ MPP with long-term engraftment and multilineage differentiation potential, a CLL1+ myeloid-biased MPP, and a CLL1-CD69-erythroid-biased MPP. We also show that the canonical Lin-CD34+CD38dim/loCD90-CD45RA+ LMPP population can be separated into a CD2+ LMPP with lymphoid and myeloid potential, a CD2-LMPP with high lymphoid potential, and a CLL1+ GMP with minimal lymphoid potential. We used these new HSPC profiles to study human and mouse bone marrow cells and observe limited cell type specific homology between humans and mice and cell type specific changes associated with aging. By identifying and functionally characterizing new adult MPP sub-populations, we provide an updated reference and framework for future studies in human hematopoiesis.
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9
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Winter S, Götze KS, Hecker JS, Metzeler KH, Guezguez B, Woods K, Medyouf H, Schäffer A, Schmitz M, Wehner R, Glauche I, Roeder I, Rauner M, Hofbauer LC, Platzbecker U. Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche. Leukemia 2024; 38:936-946. [PMID: 38514772 PMCID: PMC11073997 DOI: 10.1038/s41375-024-02226-6] [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: 11/16/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024]
Abstract
Clonal hematopoiesis (CH) defines a premalignant state predominantly found in older persons that increases the risk of developing hematologic malignancies and age-related inflammatory diseases. However, the risk for malignant transformation or non-malignant disorders is variable and difficult to predict, and defining the clinical relevance of specific candidate driver mutations in individual carriers has proved to be challenging. In addition to the cell-intrinsic mechanisms, mutant cells rely on and alter cell-extrinsic factors from the bone marrow (BM) niche, which complicates the prediction of a mutant cell's fate in a shifting pre-malignant microenvironment. Therefore, identifying the insidious and potentially broad impact of driver mutations on supportive niches and immune function in CH aims to understand the subtle differences that enable driver mutations to yield different clinical outcomes. Here, we review the changes in the aging BM niche and the emerging evidence supporting the concept that CH can progressively alter components of the local BM microenvironment. These alterations may have profound implications for the functionality of the osteo-hematopoietic niche and overall bone health, consequently fostering a conducive environment for the continued development and progression of CH. We also provide an overview of the latest technology developments to study the spatiotemporal dependencies in the CH BM niche, ideally in the context of longitudinal studies following CH over time. Finally, we discuss aspects of CH carrier management in clinical practice, based on work from our group and others.
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Affiliation(s)
- Susann Winter
- Department of Internal Medicine I, University Hospital Carl Gustav Carus, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Katharina S Götze
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medicine III, Technical University of Munich (TUM), School of Medicine and Health, Munich, Germany
- German MDS Study Group (D-MDS), Leipzig, Germany
| | - Judith S Hecker
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Medicine III, Technical University of Munich (TUM), School of Medicine and Health, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich (TUM), Munich, Germany
| | - Klaus H Metzeler
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Hematology, Cellular Therapy, Hemostaseology and Infectious Disease, University of Leipzig Medical Center, Leipzig, Germany
| | - Borhane Guezguez
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Hematology and Oncology, University Medical Center Mainz, Mainz, Germany
| | - Kevin Woods
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Department of Hematology and Oncology, University Medical Center Mainz, Mainz, Germany
| | - Hind Medyouf
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
- Frankfurt Cancer Institute, Frankfurt am Main, Germany
| | - Alexander Schäffer
- Institute for Tumor Biology and Experimental Therapy, Georg-Speyer-Haus, Frankfurt am Main, Germany
| | - Marc Schmitz
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Immunology, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - Rebekka Wehner
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Institute of Immunology, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
| | - Ingmar Glauche
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Ingo Roeder
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), Dresden, Germany: German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Medicine and University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany; Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Dresden, Germany
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Martina Rauner
- Division of Endocrinology, Diabetes and Bone Diseases, Department of Medicine III, and Center for Healthy Aging, University Medical Center, TU Dresden, Dresden, Germany
| | - Lorenz C Hofbauer
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Division of Endocrinology, Diabetes and Bone Diseases, Department of Medicine III, and Center for Healthy Aging, University Medical Center, TU Dresden, Dresden, Germany.
| | - Uwe Platzbecker
- German Cancer Consortium (DKTK), CHOICE Consortium, Partner Sites Dresden/Munich/Frankfurt/Mainz, and German Cancer Research Center (DKFZ), Heidelberg, Germany.
- German MDS Study Group (D-MDS), Leipzig, Germany.
- Department of Hematology, Cellular Therapy, Hemostaseology and Infectious Disease, University of Leipzig Medical Center, Leipzig, Germany.
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10
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Putri GH, Howitt G, Marsh-Wakefield F, Ashhurst TM, Phipson B. SuperCellCyto: enabling efficient analysis of large scale cytometry datasets. Genome Biol 2024; 25:89. [PMID: 38589921 PMCID: PMC11003185 DOI: 10.1186/s13059-024-03229-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 03/27/2024] [Indexed: 04/10/2024] Open
Abstract
Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).
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Affiliation(s)
- Givanna H Putri
- The Walter and Eliza Hall Institute of Medical Research and The Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia.
| | - George Howitt
- Peter MacCallum Cancer Centre and The Sir Peter MacCallum, Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
| | - Felix Marsh-Wakefield
- Centenary Institute of Cancer Medicine and Cell Biology, The University of Sydney, Sydney, NSW, Australia
| | - Thomas M Ashhurst
- Sydney Cytometry Core Research Facility and School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Belinda Phipson
- The Walter and Eliza Hall Institute of Medical Research and The Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia.
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11
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Schäfer A, D'Almeida SM, Dorier J, Guex N, Villard J, Garcia M. Comparative assessment of cytometry by time-of-flight and full spectral flow cytometry based on a 33-color antibody panel. J Immunol Methods 2024; 527:113641. [PMID: 38365120 DOI: 10.1016/j.jim.2024.113641] [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: 12/22/2023] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
Mass cytometry and full spectrum flow cytometry have recently emerged as new promising single cell proteomic analysis tools that can be exploited to decipher the extensive diversity of immune cell repertoires and their implication in human diseases. In this study, we evaluated the performance of mass cytometry against full spectrum flow cytometry using an identical 33-color antibody panel on four healthy individuals. Our data revealed an overall high concordance in the quantification of major immune cell populations between the two platforms using a semi-automated clustering approach. We further showed a strong correlation of cluster assignment when comparing manual and automated clustering. Both comparisons revealed minor disagreements in the quantification and assignment of rare cell subpopulations. Our study showed that both single cell proteomic technologies generate highly overlapping results and substantiate that the choice of technology is not a primary factor for successful biological assessment of cell profiles but must be considered in a broader design framework of clinical studies.
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Affiliation(s)
- Antonia Schäfer
- Transplantation Immunology Unit and National Reference Laboratory for Histocompatibility, Geneva University Hospitals, Geneva, Switzerland
| | - Sènan Mickael D'Almeida
- Flow Cytometry Core Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Julien Dorier
- Bioinformatics Competence Center, University of Lausanne, Lausanne, Switzerland; Bioinformatics Competence Center, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Nicolas Guex
- Bioinformatics Competence Center, University of Lausanne, Lausanne, Switzerland; Bioinformatics Competence Center, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Jean Villard
- Transplantation Immunology Unit and National Reference Laboratory for Histocompatibility, Geneva University Hospitals, Geneva, Switzerland.
| | - Miguel Garcia
- Flow Cytometry Core Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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12
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Bernt KM. Mapping human hematopoiesis. Nat Immunol 2024; 25:590-591. [PMID: 38514889 DOI: 10.1038/s41590-024-01793-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Affiliation(s)
- Kathrin M Bernt
- Division of Pediatric Oncology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania and Abramson Cancer Center, Philadelphia, PA, USA.
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13
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Zhang X, Song B, Carlino MJ, Li G, Ferchen K, Chen M, Thompson EN, Kain BN, Schnell D, Thakkar K, Kouril M, Jin K, Hay SB, Sen S, Bernardicius D, Ma S, Bennett SN, Croteau J, Salvatori O, Lye MH, Gillen AE, Jordan CT, Singh H, Krause DS, Salomonis N, Grimes HL. An immunophenotype-coupled transcriptomic atlas of human hematopoietic progenitors. Nat Immunol 2024; 25:703-715. [PMID: 38514887 PMCID: PMC11003869 DOI: 10.1038/s41590-024-01782-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
Analysis of the human hematopoietic progenitor compartment is being transformed by single-cell multimodal approaches. Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) enables coupled surface protein and transcriptome profiling, thereby revealing genomic programs underlying progenitor states. To perform CITE-seq systematically on primary human bone marrow cells, we used titrations with 266 CITE-seq antibodies (antibody-derived tags) and machine learning to optimize a panel of 132 antibodies. Multimodal analysis resolved >80 stem, progenitor, immune, stromal and transitional cells defined by distinctive surface markers and transcriptomes. This dataset enables flow cytometry solutions for in silico-predicted cell states and identifies dozens of cell surface markers consistently detected across donors spanning race and sex. Finally, aligning annotations from this atlas, we nominate normal marrow equivalents for acute myeloid leukemia stem cell populations that differ in clinical response. This atlas serves as an advanced digital resource for hematopoietic progenitor analyses in human health and disease.
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Affiliation(s)
- Xuan Zhang
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Baobao Song
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Immunology Graduate Program, University of Cincinnati, Cincinnati, OH, USA
| | - Maximillian J Carlino
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Guangyuan Li
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kyle Ferchen
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Mi Chen
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
| | - Evrett N Thompson
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
- Department of Cell Biology, Yale School of Medicine, New Haven, CT, USA
| | - Bailee N Kain
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Dan Schnell
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kairavee Thakkar
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Michal Kouril
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Kang Jin
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Stuart B Hay
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sidharth Sen
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - David Bernardicius
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Siyuan Ma
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Sierra N Bennett
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | | | | | - Austin E Gillen
- Division of Hematology, University of Colorado School of Medicine, Aurora, CO, USA
- Rocky Mountain Regional VA Medical Center, Aurora, CO, USA
| | - Craig T Jordan
- Division of Hematology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Harinder Singh
- Departments of Immunology and Computational and Systems Biology, Center for Systems Immunology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Diane S Krause
- Yale Stem Cell Center, Yale School of Medicine, New Haven, CT, USA
- Department of Laboratory Medicine, Yale University, New Haven, CT, USA
- Department of Cell Biology, Yale School of Medicine, New Haven, CT, USA
| | - Nathan Salomonis
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
| | - H Leighton Grimes
- Division of Immunobiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
- Department of Pediatrics, University of Cincinnati, Cincinnati, OH, USA.
- Division of Experimental Hematology and Cancer Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
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14
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Lai A, Liu W, Wei H, Wang Y, Lin D, Zhou C, Liu B, Gu R, Li Y, Wei S, Gong B, Liu K, Gong X, Liu Y, Zhang G, Zhang J, Mi Y, Wang J, Qiu S. The RTK-RAS signaling pathway is enriched in patients with rare acute myeloid leukemia harboring t(16;21)(p11;q22)/ FUS::ERG. BLOOD SCIENCE 2024; 6:e00188. [PMID: 38742238 PMCID: PMC11090622 DOI: 10.1097/bs9.0000000000000188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 04/01/2024] [Indexed: 05/16/2024] Open
Abstract
Acute myeloid leukemia (AML) with t(16;21)(p11;q22)/FUS::ERG is a rare AML subtype associated with poor prognosis. However, its clinical and molecular features remain poorly defined. We determined the clinicopathological, genomic, and transcriptomic characteristics and outcomes of patients with AML harboring FUS::ERG at our center. Thirty-six AML patients harboring FUS::ERG were identified, with an incidence rate of 0.3%. These patients were characterized by high lactate dehydrogenase levels (median: 838.5 U/L), elevated bone marrow blast counts (median: 71.5%), and a CD56-positive immunophenotype (94.3%). Notably, we found that RTK-RAS GTPase (RAS) pathway genes, including NRAS (33%) and PTPN11 (24%), were frequently mutated in this subtype. Transcriptome analysis revealed enrichment of the phosphatidylinositol-3-kinase-Akt (PI3K-Akt), mitogen-activated protein kinase (MAPK), and RAS signaling pathways and upregulation of BCL2, the target of venetoclax, in FUS::ERG AML compared to RUNX1::RUNX1T1 AML, a more common AML subtype with good prognosis. The median event-free survival in patients with FUS::ERG AML was 11.9 (95% confidence interval [CI]: 9.0-not available [NA]) months and the median overall survival was 18.2 (95% CI: 12.4-NA) months. Allogeneic hematopoietic stem cell transplantation failed to improve outcomes. Overall, the high incidence of RTK-RAS pathway mutations and high expression of BCL2 may indicate promising therapeutic targets in this high-risk AML subset.
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Affiliation(s)
- Anli Lai
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Wenbing Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Hui Wei
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
- Tianjin Key Laboratory of Cell Therapy for Blood Diseases, Tianjin 300020, China
| | - Ying Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
- Tianjin Key Laboratory of Cell Therapy for Blood Diseases, Tianjin 300020, China
| | - Dong Lin
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Chunlin Zhou
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Bingcheng Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Runxia Gu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Yan Li
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Shuning Wei
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Benfa Gong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Kaiqi Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Xiaoyuan Gong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Yuntao Liu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Guangji Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Junping Zhang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
| | - Yingchang Mi
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
- Tianjin Key Laboratory of Cell Therapy for Blood Diseases, Tianjin 300020, China
| | - Jianxiang Wang
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
- Tianjin Key Laboratory of Cell Therapy for Blood Diseases, Tianjin 300020, China
| | - Shaowei Qiu
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
- Tianjin Institutes of Health Science, Tianjin 301600, China
- Tianjin Key Laboratory of Cell Therapy for Blood Diseases, Tianjin 300020, China
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15
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Bandyopadhyay S, Duffy M, Ahn KJ, Pang M, Smith D, Duncan G, Sussman J, Zhang I, Huang J, Lin Y, Xiong B, Imtiaz T, Chen CH, Thadi A, Chen C, Xu J, Reichart M, Pillai V, Snaith O, Oldridge D, Bhattacharyya S, Maillard I, Carroll M, Nelson C, Qin L, Tan K. Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.14.585083. [PMID: 38559168 PMCID: PMC10979999 DOI: 10.1101/2024.03.14.585083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The bone marrow is the organ responsible for blood production. Diverse non-hematopoietic cells contribute essentially to hematopoiesis. However, these cells and their spatial organization remain largely uncharacterized as they have been technically challenging to study in humans. Here, we used fresh femoral head samples and performed single-cell RNA sequencing (scRNA-Seq) to profile 29,325 enriched non-hematopoietic bone marrow cells and discover nine transcriptionally distinct subtypes. We next employed CO-detection by inDEXing (CODEX) multiplexed imaging of 18 individuals, including both healthy and acute myeloid leukemia (AML) samples, to spatially profile over one million single cells with a novel 53-antibody panel. We discovered a relatively hyperoxygenated arterio-endosteal niche for early myelopoiesis, and an adipocytic, but not endosteal or perivascular, niche for early hematopoietic stem and progenitor cells. We used our atlas to predict cell type labels in new bone marrow images and used these predictions to uncover mesenchymal stromal cell (MSC) expansion and leukemic blast/MSC-enriched spatial neighborhoods in AML patient samples. Our work represents the first comprehensive, spatially-resolved multiomic atlas of human bone marrow and will serve as a reference for future investigation of cellular interactions that drive hematopoiesis.
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Affiliation(s)
- Shovik Bandyopadhyay
- Cellular and Molecular Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Michael Duffy
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kyung Jin Ahn
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Minxing Pang
- Applied Mathematics & Computational Science Graduate Group, University of Pennsylvania, Philadelphia, PA
| | - David Smith
- Center for Single Cell Biology, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Gwendolyn Duncan
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Jonathan Sussman
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Iris Zhang
- Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA
| | - Jeffrey Huang
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Yulieh Lin
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Barbara Xiong
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Tamjid Imtiaz
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
| | - Chia-Hui Chen
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Anusha Thadi
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Changya Chen
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA
| | - Jason Xu
- Medical Scientist Training Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Melissa Reichart
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Vinodh Pillai
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Oraine Snaith
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Derek Oldridge
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Siddharth Bhattacharyya
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ivan Maillard
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Martin Carroll
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Charles Nelson
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ling Qin
- Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kai Tan
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, PA
- Center for Single Cell Biology, Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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16
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Li X, Zhang W, Wang Y, Li C, Wu Y, Shang Y, Lin H, Li Y, Wang Y, Zeng X, Cen Z, Lai X, Luo Y, Qian P, Huang H. Monocytes in allo-HSCT with aged donors secrete IL-1/IL-6/TNF to increase the risk of GVHD and damage the aged HSCs. iScience 2024; 27:109126. [PMID: 38405615 PMCID: PMC10884477 DOI: 10.1016/j.isci.2024.109126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 01/12/2024] [Accepted: 01/31/2024] [Indexed: 02/27/2024] Open
Abstract
Aging is considered a critical factor of poor prognosis in allogenic hemopoietic stem cell transplantation (allo-HSCT). To elucidate the underlying mechanisms, we comprehensively reintegrated our clinical data from patients after allo-HSCT and public single-cell transcriptomic profile from post-allo-HSCT and healthy individuals, demonstrating that old donors were more prone to acute GVHD (aGVHD) with pronounced inflammation accumulation and worse overall survival (OS). We also found the presence of inflammation-related CXCL2+ HSC subpopulation during aging with significantly enriched pro-inflammatory pathways. Shifting attention to the HSC microenvironment, we deciphered that IL-1/IL-6 and TRAIL (i.e., TNFSF10) ligand‒receptor pair serves as the crucial bridge between CD14/CD16 monocytes and hematopoietic stem/progenitor cells (HSPCs). The profound upregulation of these signaling pathways during aging finally causes HSC dysfunction and lineage-biased differentiation. Our findings provide the theoretical basis for achieving tailored GVHD management and enhancing allo-HSCT regimens efficacy for aged donors.
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Affiliation(s)
- Xia Li
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, 1369 West Wenyi Road, Hangzhou 311121, Zhejiang, China
- Institute of Hematology, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Zhejiang Province Engineering Laboratory for Stem Cell and Immunity Therapy, Hangzhou 310058, Zhejiang, China
| | - Wanying Zhang
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yanan Wang
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chentao Li
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yibo Wu
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, 1369 West Wenyi Road, Hangzhou 311121, Zhejiang, China
- Institute of Hematology, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Zhejiang Province Engineering Laboratory for Stem Cell and Immunity Therapy, Hangzhou 310058, Zhejiang, China
| | - Yifei Shang
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haikun Lin
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yufei Li
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yufei Wang
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiangjun Zeng
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, 1369 West Wenyi Road, Hangzhou 311121, Zhejiang, China
- Institute of Hematology, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Zhejiang Province Engineering Laboratory for Stem Cell and Immunity Therapy, Hangzhou 310058, Zhejiang, China
| | - Zenan Cen
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, 1369 West Wenyi Road, Hangzhou 311121, Zhejiang, China
- Institute of Hematology, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Zhejiang Province Engineering Laboratory for Stem Cell and Immunity Therapy, Hangzhou 310058, Zhejiang, China
| | - Xiaoyu Lai
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, 1369 West Wenyi Road, Hangzhou 311121, Zhejiang, China
- Institute of Hematology, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Zhejiang Province Engineering Laboratory for Stem Cell and Immunity Therapy, Hangzhou 310058, Zhejiang, China
| | - Yi Luo
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, 1369 West Wenyi Road, Hangzhou 311121, Zhejiang, China
- Institute of Hematology, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Zhejiang Province Engineering Laboratory for Stem Cell and Immunity Therapy, Hangzhou 310058, Zhejiang, China
| | - Pengxu Qian
- Liangzhu Laboratory, 1369 West Wenyi Road, Hangzhou 311121, Zhejiang, China
- Institute of Hematology, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Zhejiang Province Engineering Laboratory for Stem Cell and Immunity Therapy, Hangzhou 310058, Zhejiang, China
- Center for Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - He Huang
- Bone Marrow Transplantation Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, 1369 West Wenyi Road, Hangzhou 311121, Zhejiang, China
- Institute of Hematology, Zhejiang University, Hangzhou 310058, Zhejiang, China
- Zhejiang Province Engineering Laboratory for Stem Cell and Immunity Therapy, Hangzhou 310058, Zhejiang, China
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Busarello E, Biancon G, Lauria F, Ibnat Z, Ramirez C, Tomè G, Aass KR, VanOudenhove J, Standal T, Viero G, Halene S, Tebaldi T. Interpreting single-cell messages in normal and aberrant hematopoiesis with the Cell Marker Accordion. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584053. [PMID: 38559181 PMCID: PMC10979856 DOI: 10.1101/2024.03.08.584053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell technologies offer a unique opportunity to explore cellular heterogeneity in hematopoiesis, reveal malignant hematopoietic cells with clinically significant features and measure gene signatures linked to pathological pathways. However, reliable identification of cell types is a crucial bottleneck in single-cell analysis. Available databases contain dissimilar nomenclature and non-concurrent marker sets, leading to inconsistent annotations and poor interpretability. Furthermore, current tools focus mostly on physiological cell types, lacking extensive applicability in disease. We developed the Cell Marker Accordion, a user-friendly platform for the automatic annotation and biological interpretation of single-cell populations based on consistency weighted markers. We validated our approach on peripheral blood and bone marrow single-cell datasets, using surface markers and expert-based annotation as the ground truth. In all cases, we significantly improved the accuracy in identifying cell types with respect to any single source database. Moreover, the Cell Marker Accordion can identify disease-critical cells and pathological processes, extracting potential biomarkers in a wide variety of contexts in human and murine single-cell datasets. It characterizes leukemia stem cell subtypes, including therapy-resistant cells in acute myeloid leukemia patients; it identifies malignant plasma cells in multiple myeloma samples; it dissects cell type alterations in splicing factor-mutant cells from myelodysplastic syndrome patients; it discovers activation of innate immunity pathways in bone marrow from mice treated with METTL3 inhibitors. The breadth of these applications elevates the Cell Marker Accordion as a flexible, faithful and standardized tool to annotate and interpret hematopoietic populations in single-cell datasets focused on the study of hematopoietic development and disease.
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Affiliation(s)
- Emma Busarello
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Giulia Biancon
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Fabio Lauria
- Institute of Biophysics, CNR Unit at Trento, Italy
| | - Zuhairia Ibnat
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Christian Ramirez
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
| | - Gabriele Tomè
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
- Institute of Biophysics, CNR Unit at Trento, Italy
| | - Kristin R Aass
- Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Jennifer VanOudenhove
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Therese Standal
- Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | | | - Stephanie Halene
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
| | - Toma Tebaldi
- Laboratory of RNA and Disease Data Science, Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy
- Section of Hematology, Department of Internal Medicine, Yale Comprehensive Cancer Center, Yale University School of Medicine, New Haven, CT, USA
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18
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Cao XH, Fan ZY, Chang YJ, Xu LP, Zhang XH, Huang XJ, Zhao XY. Prediction model for EBV infection following HLA haploidentical matched hematopoietic stem cell transplantation. J Transl Med 2024; 22:244. [PMID: 38448996 PMCID: PMC10916301 DOI: 10.1186/s12967-024-05042-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 02/24/2024] [Indexed: 03/08/2024] Open
Abstract
AIMS Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is an effective treatment for hematological malignancies. However, viral infections, particularly EBV infection, frequently occur following allo-HSCT and can result in multi-tissue and organ damage. Due to the lack of effective antiviral drugs, these infections can even progress to post-transplant lymphoproliferative disorders (PTLD), thereby impacting the prognosis. In light of this, our objective is to develop a prediction model for EBV infection following allo-HSCT. METHODS A total of 466 patients who underwent haploidentical hematopoietic stem cell transplantation (haplo-HSCT) between September 2019 and December 2020 were included in this study. The patients were divided into a development cohort and a validation cohort based on the timing of their transplantation. Our aim was to develop and validate a grading scale using these cohorts to predict the risk of EBV infection within the first year after haplo-HSCT. Additionally, single-cell RNA sequencing (sc-RNAseq) data from the bone marrow of healthy donors were utilized to assess the impact of age on immune cells and viral infection. RESULTS In the multivariate logistic regression model, four predictors were retained: donor age, female-to-male transplant, graft MNC (mononuclear cell) dose, and CD8 dose. Based on these predictors, an EBV reactivation predicting score system was constructed. The scoring system demonstrated good calibration in both the derivation and validation cohorts, as confirmed by the Hosmer-Lemeshow test (p > 0.05). The scoring system also exhibited favorable discriminative ability, as indicated by the C statistics of 0.72 in the derivation cohort and 0.60 in the validation cohort. Furthermore, the clinical efficacy of the scoring system was evaluated using Kaplan-Meier curves based on risk ratings. The results showed significant differences in EBV reactivation rates between different risk groups, with p-values less than 0.001 in both the derivation and validation cohorts, indicating robust clinical utility. The analysis of sc-RNAseq data from the bone marrow of healthy donors revealed that older age had a profound impact on the quantity and quality of immune subsets. Functional enrichment analysis highlighted that older age was associated with a higher risk of infection. Specifically, CD8 + T cells from older individuals showed enrichment in the pathway of "viral carcinogenesis", while older CD14 + monocytes exhibited enrichment in the pathway of "regulation of viral entry into host cell." These findings suggest that older age may contribute to an increased susceptibility to viral infections, as evidenced by the altered immune profiles observed in the sc-RNAseq data. CONCLUSION Overall, these results demonstrate the development and validation of an effective scoring system for predicting EBV reactivation after haplo-HSCT, and provide insights into the impact of age on immune subsets and viral infection susceptibility based on sc-RNAseq analysis of healthy donors' bone marrow.
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Affiliation(s)
- Xun-Hong Cao
- Peking University People's Hospital, National Clinical Research Center for Hematologic Disease, Peking University Institute of Hematology, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China
| | - Ze-Ying Fan
- Peking University People's Hospital, National Clinical Research Center for Hematologic Disease, Peking University Institute of Hematology, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China
| | - Ying-Jun Chang
- Peking University People's Hospital, National Clinical Research Center for Hematologic Disease, Peking University Institute of Hematology, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China
| | - Lan-Ping Xu
- Peking University People's Hospital, National Clinical Research Center for Hematologic Disease, Peking University Institute of Hematology, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China
| | - Xiao-Hui Zhang
- Peking University People's Hospital, National Clinical Research Center for Hematologic Disease, Peking University Institute of Hematology, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China
| | - Xiao-Jun Huang
- Peking University People's Hospital, National Clinical Research Center for Hematologic Disease, Peking University Institute of Hematology, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China
| | - Xiang-Yu Zhao
- Peking University People's Hospital, National Clinical Research Center for Hematologic Disease, Peking University Institute of Hematology, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing, China.
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19
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Jin W, Dai Y, Chen L, Zhu H, Dong F, Zhu H, Meng G, Li J, Chen S, Chen Z, Fang H, Wang K. Cellular hierarchy insights reveal leukemic stem-like cells and early death risk in acute promyelocytic leukemia. Nat Commun 2024; 15:1423. [PMID: 38365836 PMCID: PMC10873341 DOI: 10.1038/s41467-024-45737-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 02/02/2024] [Indexed: 02/18/2024] Open
Abstract
Acute promyelocytic leukemia (APL) represents a paradigm for targeted differentiation therapy, with a minority of patients experiencing treatment failure and even early death. We here report a comprehensive single-cell analysis of 16 APL patients, uncovering cellular compositions and their impact on all-trans retinoic acid (ATRA) response in vivo and early death. We unveil a cellular differentiation hierarchy within APL blasts, rooted in leukemic stem-like cells. The oncogenic PML/RARα fusion protein exerts branch-specific regulation in the APL trajectory, including stem-like cells. APL cohort analysis establishes an association of leukemic stemness with elevated white blood cell counts and FLT3-ITD mutations. Furthermore, we construct an APL-specific stemness score, which proves effective in assessing early death risk. Finally, we show that ATRA induces differentiation of primitive blasts and patients with early death exhibit distinct stemness-associated transcriptional programs. Our work provides a thorough survey of APL cellular hierarchies, offering insights into cellular dynamics during targeted therapy.
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Affiliation(s)
- Wen Jin
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- Sino-French Research Center for Life Sciences and Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Yuting Dai
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Li Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Honghu Zhu
- Department of Hematology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, China
| | - Fangyi Dong
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Hongming Zhu
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Guoyu Meng
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Junmin Li
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Saijuan Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zhu Chen
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Sino-French Research Center for Life Sciences and Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Kankan Wang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- Sino-French Research Center for Life Sciences and Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
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20
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Mei S, Alchahin AM, Tsea I, Kfoury Y, Hirz T, Jeffries NE, Zhao T, Xu Y, Zhang H, Sarkar H, Wu S, Subtelny AO, Johnsen JI, Zhang Y, Salari K, Wu CL, Randolph MA, Scadden DT, Dahl DM, Shin J, Kharchenko PV, Saylor PJ, Sykes DB, Baryawno N. Single-cell analysis of immune and stroma cell remodeling in clear cell renal cell carcinoma primary tumors and bone metastatic lesions. Genome Med 2024; 16:1. [PMID: 38281962 PMCID: PMC10823713 DOI: 10.1186/s13073-023-01272-6] [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: 10/10/2022] [Accepted: 12/11/2023] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Despite therapeutic advances, once a cancer has metastasized to the bone, it represents a highly morbid and lethal disease. One third of patients with advanced clear cell renal cell carcinoma (ccRCC) present with bone metastasis at the time of diagnosis. However, the bone metastatic niche in humans, including the immune and stromal microenvironments, has not been well-defined, hindering progress towards identification of therapeutic targets. METHODS We collected fresh patient samples and performed single-cell transcriptomic profiling of solid metastatic tissue (Bone Met), liquid bone marrow at the vertebral level of spinal cord compression (Involved), and liquid bone marrow from a different vertebral body distant from the tumor site but within the surgical field (Distal), as well as bone marrow from patients undergoing hip replacement surgery (Benign). In addition, we incorporated single-cell data from primary ccRCC tumors (ccRCC Primary) for comparative analysis. RESULTS The bone marrow of metastatic patients is immune-suppressive, featuring increased, exhausted CD8 + cytotoxic T cells, T regulatory cells, and tumor-associated macrophages (TAM) with distinct transcriptional states in metastatic lesions. Bone marrow stroma from tumor samples demonstrated a tumor-associated mesenchymal stromal cell population (TA-MSC) that appears to be supportive of epithelial-to mesenchymal transition (EMT), bone remodeling, and a cancer-associated fibroblast (CAFs) phenotype. This stromal subset is associated with poor progression-free and overall survival and also markedly upregulates bone remodeling through the dysregulation of RANK/RANKL/OPG signaling activity in bone cells, ultimately leading to bone resorption. CONCLUSIONS These results provide a comprehensive analysis of the bone marrow niche in the setting of human metastatic cancer and highlight potential therapeutic targets for both cell populations and communication channels.
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Affiliation(s)
- Shenglin Mei
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
| | - Adele M Alchahin
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Ioanna Tsea
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Youmna Kfoury
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Taghreed Hirz
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Nathan Elias Jeffries
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Ting Zhao
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Yanxin Xu
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
| | - Hanyu Zhang
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Hirak Sarkar
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Shulin Wu
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Alexander O Subtelny
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - John Inge Johnsen
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 17176, Stockholm, Sweden
| | - Yida Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Keyan Salari
- Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Chin-Lee Wu
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Mark A Randolph
- Division of Plastic and Reconstructive Surgery, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - David T Scadden
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
| | - Douglas M Dahl
- Department of Urology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - John Shin
- Department of Neurosurgery, Harvard Medical School, Boston, MA, 02115, USA.
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA.
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA.
- Present: Altos Labs, San Diego, CA, 92121, USA.
| | - Philip J Saylor
- Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, MA, 02114, USA.
| | - David B Sykes
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA.
| | - Ninib Baryawno
- Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, 17176, Stockholm, Sweden.
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21
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Yu K, Deuitch N, Merguerian M, Cunningham L, Davis J, Bresciani E, Diemer J, Andrews E, Young A, Donovan F, Sood R, Craft K, Chong S, Chandrasekharappa S, Mullikin J, Liu PP. Genomic landscape of patients with germline RUNX1 variants and familial platelet disorder with myeloid malignancy. Blood Adv 2024; 8:497-511. [PMID: 38019014 PMCID: PMC10837196 DOI: 10.1182/bloodadvances.2023011165] [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: 07/10/2023] [Revised: 11/07/2023] [Accepted: 11/07/2023] [Indexed: 11/30/2023] Open
Abstract
ABSTRACT Familial platelet disorder with associated myeloid malignancies (FPDMM) is caused by germline RUNX1 mutations and characterized by thrombocytopenia and increased risk of hematologic malignancies. We recently launched a longitudinal natural history study for patients with FPDMM. Among 27 families with research genomic data by the end of 2021, 26 different germline RUNX1 variants were detected. Besides missense mutations enriched in Runt homology domain and loss-of-function mutations distributed throughout the gene, splice-region mutations and large deletions were detected in 6 and 7 families, respectively. In 25 of 51 (49%) patients without hematologic malignancy, somatic mutations were detected in at least 1 of the clonal hematopoiesis of indeterminate potential (CHIP) genes or acute myeloid leukemia (AML) driver genes. BCOR was the most frequently mutated gene (in 9 patients), and multiple BCOR mutations were identified in 4 patients. Mutations in 6 other CHIP- or AML-driver genes (TET2, DNMT3A, KRAS, LRP1B, IDH1, and KMT2C) were also found in ≥2 patients without hematologic malignancy. Moreover, 3 unrelated patients (1 with myeloid malignancy) carried somatic mutations in NFE2, which regulates erythroid and megakaryocytic differentiation. Sequential sequencing data from 19 patients demonstrated dynamic changes of somatic mutations over time, and stable clones were more frequently found in older adult patients. In summary, there are diverse types of germline RUNX1 mutations and high frequency of somatic mutations related to clonal hematopoiesis in patients with FPDMM. Monitoring changes in somatic mutations and clinical manifestations prospectively may reveal mechanisms for malignant progression and inform clinical management. This trial was registered at www.clinicaltrials.gov as #NCT03854318.
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Affiliation(s)
- Kai Yu
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Natalie Deuitch
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Matthew Merguerian
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
- Department of Pediatrics, Johns Hopkins University School of Medicine, Balltimore, MD
| | - Lea Cunningham
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
- Immune Deficiency Cellular Therapy Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Joie Davis
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Erica Bresciani
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Jamie Diemer
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Elizabeth Andrews
- Immune Deficiency Cellular Therapy Program, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Alice Young
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Frank Donovan
- Genomics Core, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Raman Sood
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Kathleen Craft
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Shawn Chong
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Settara Chandrasekharappa
- Genomics Core, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Jim Mullikin
- NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
| | - Paul P. Liu
- Oncogenesis and Development Section, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD
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22
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Yu D, Ayyala R, Sadek SH, Chittampalli L, Farooq H, Jung J, Nahid AA, Boldirev G, Jung M, Park S, Nguyen A, Zelikovsky A, Mancuso N, Joo JWJ, Thompson RF, Alachkar H, Mangul S. A rigorous benchmarking of alignment-based HLA typing algorithms for RNA-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.22.541750. [PMID: 38293199 PMCID: PMC10827116 DOI: 10.1101/2023.05.22.541750] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Accurate identification of human leukocyte antigen (HLA) alleles is essential for various clinical and research applications, such as transplant matching and drug sensitivities. Recent advances in RNA-seq technology have made it possible to impute HLA types from sequencing data, spurring the development of a large number of computational HLA typing tools. However, the relative performance of these tools is unknown, limiting the ability for clinical and biomedical research to make informed choices regarding which tools to use. Here we report the study design of a comprehensive benchmarking of the performance of 12 HLA callers across 682 RNA-seq samples from 8 datasets with molecularly defined gold standard at 5 loci, HLA-A, -B, -C, -DRB1, and -DQB1. For each HLA typing tool, we will comprehensively assess their accuracy, compare default with optimized parameters, and examine for discrepancies in accuracy at the allele and loci levels. We will also evaluate the computational expense of each HLA caller measured in terms of CPU time and RAM. We also plan to evaluate the influence of read length over the HLA region on accuracy for each tool. Most notably, we will examine the performance of HLA callers across European and African groups, to determine discrepancies in accuracy associated with ancestry. We hypothesize that RNA-Seq HLA callers are capable of returning high-quality results, but the tools that offer a good balance between accuracy and computational expensiveness for all ancestry groups are yet to be developed. We believe that our study will provide clinicians and researchers with clear guidance to inform their selection of an appropriate HLA caller.
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Affiliation(s)
- Dottie Yu
- Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, 1975 Zonal Ave, Los Angeles, CA 90033, USA
| | - Ram Ayyala
- Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, 1975 Zonal Ave, Los Angeles, CA 90033, USA
| | - Sarah Hany Sadek
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA
- Department of Biology, and Department of Computer Science, California State University, Fullerton, Fullerton, CA 92831
| | - Likhitha Chittampalli
- Department of Computer Science, Viterbi School of Engineering University of Southern California, Los Angeles, CA, USA
| | - Hafsa Farooq
- Department of Computer Science, Georgia State University Atlanta, GA 30303 USA
| | - Junghyun Jung
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA, USA
- Center for Genetic Epidemiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Abdullah Al Nahid
- Department of Biochemistry and Molecular Biology, Shahjalal University of Science and Technology, Sylhet 3114, Bangladesh
| | - Grigore Boldirev
- Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, GA, 30303, USA
| | - Mina Jung
- Department of Quantitative and Computational Biology, Dornsife College of Letters, Arts and Sciences, University of Southern California, 1975 Zonal Ave, Los Angeles, CA 90033, US
| | - Sungmin Park
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea
| | - Austin Nguyen
- Computational Biologist, Immune Monitoring & Cancer Omics Oregon Health & Science University, Biomedical Engineering, 3181 S.W. Sam Jackson Park Road Portland, OR 97239-3098
| | - Alex Zelikovsky
- Department of Computer Science, College of Arts and Sciences, Georgia State University, Atlanta, GA, 30303, USA
| | - Nicholas Mancuso
- Assistant Professor of Population and Public Health Sciences, Keck School of Medicina, University of Southern California, 1845 N. Soto Street, USA
| | - Jong Wha J Joo
- Department of Computer Science and Engineering, Dongguk University-Seoul, Seoul, 04620, South Korea
- Division of AI Software Convergence, Dongguk University-Seoul, Seoul, 04620, South Korea
| | - Reid F Thompson
- Assistant Professor of Radiation Medicine, School of Medicine, OHSU, Portland, OR 97239
- Assistant Professor of Biomedical Engineering, School of Medicine, OHSU, Portland, OR 97239
- Staff Physician, VA Portland Healthcare System, Portland OR 97239
| | - Houda Alachkar
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, CA, USA
| | - Serghei Mangul
- Department of Clinical Pharmacy, USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, 1540 Alcazar Street, Los Angeles, CA 90033, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles
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23
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Ma F, Wang S, Xu L, Huang W, Shi G, Sun Z, Cai W, Wu Z, Huang Y, Meng J, Sun Y, Fang M, Cheng M, Ji Y, Hu T, Zhang Y, Gu B, Zhang J, Song S, Sun Y, Yan W. Single-cell profiling of the microenvironment in human bone metastatic renal cell carcinoma. Commun Biol 2024; 7:91. [PMID: 38216635 PMCID: PMC10786927 DOI: 10.1038/s42003-024-05772-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Accepted: 01/03/2024] [Indexed: 01/14/2024] Open
Abstract
Bone metastasis is of common occurrence in renal cell carcinoma with poor prognosis, but no optimal treatment approach has been established for bone metastatic renal cell carcinoma. To explore the potential therapeutic targets for bone metastatic renal cell carcinoma, we profile single cell transcriptomes of 6 primary renal cell carcinoma and 9 bone metastatic renal cell carcinoma. We also include scRNA-seq data of early-stage renal cell carcinoma, late-stage renal cell carcinoma, normal kidneys and healthy bone marrow samples in the study to better understand the bone metastasis niche. The molecular properties and dynamic changes of major cell lineages in bone metastatic environment of renal cell carcinoma are characterized. Bone metastatic renal cell carcinoma is associated with multifaceted immune deficiency together with cancer-associated fibroblasts, specifically appearance of macrophages exhibiting malignant and pro-angiogenic features. We also reveal the dominance of immune inhibitory T cells in the bone metastatic renal cell carcinoma which can be partially restored by the treatment. Trajectory analysis showes that myeloid-derived suppressor cells are progenitors of macrophages in the bone metastatic renal cell carcinoma while monocytes are their progenitors in primary tumors and healthy bone marrows. Additionally, the infiltration of immune inhibitory CD47+ T cells is observed in bone metastatic tumors, which may be a result of reduced phagocytosis by SIRPA-expressing macrophages in the bone microenvironment. Together, our results provide a systematic view of various cell types in bone metastatic renal cell carcinoma and suggest avenues for therapeutic solutions.
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Affiliation(s)
- Fen Ma
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, 201203, Shanghai, China
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, China
| | - Shuoer Wang
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Lun Xu
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Wending Huang
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Guohai Shi
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
- Department of Urology, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
| | - Zhengwang Sun
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Weiluo Cai
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Zhiqiang Wu
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Yiming Huang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, China
| | - Juan Meng
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, China
| | - Yining Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, China
| | - Meng Fang
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Mo Cheng
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Yingzheng Ji
- Department of Orthopedic, Naval Medical Center of PLA, Second Military Medical University, 338 Huaihai West Road, Shanghai, China
| | - Tu Hu
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Yunkui Zhang
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
- Department of Anesthesiology, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
| | - Bingxin Gu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China
| | - Jiwei Zhang
- Shanghai Key Laboratory of Compound Chinese Medicines, The MOE Key Laboratory for Standardization of Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, 201203, Shanghai, China.
| | - Shaoli Song
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China.
| | - Yidi Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, 320 Yueyang Road, Shanghai, China.
| | - Wangjun Yan
- Department of Musculoskeletal Surgery, Fudan University Shanghai Cancer Center, 270 Dong'an Road, Shanghai, China.
- Department of Oncology, Shanghai Medical College, Fudan University, 138 Medical College Road, Shanghai, China.
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24
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Liu Y, Niu H, Ren J, Wang Z, Yan L, Xing L, Shao Z, Fu R, Cai Z, Wang H. Single-cell RNA sequencing reveals abnormal transcriptome signature of erythroid progenitors in pure red cell aplasia. Genes Dis 2024; 11:49-52. [PMID: 37588205 PMCID: PMC10425790 DOI: 10.1016/j.gendis.2023.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 03/01/2023] [Accepted: 03/02/2023] [Indexed: 08/18/2023] Open
Affiliation(s)
- Yumei Liu
- Department of Hematology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Haiyue Niu
- Department of Hematology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jie Ren
- Department of Hematology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zhiqin Wang
- School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Li Yan
- Department of Hematology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Limin Xing
- Department of Hematology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zonghong Shao
- Department of Hematology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Rong Fu
- Department of Hematology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zhigang Cai
- School of Basic Medical Sciences, Tianjin Medical University, Tianjin 300070, China
| | - Huaquan Wang
- Department of Hematology, Tianjin Medical University General Hospital, Tianjin 300052, China
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25
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Zeng AGX, Iacobucci I, Shah S, Mitchell A, Wong G, Bansal S, Gao Q, Kim H, Kennedy JA, Minden MD, Haferlach T, Mullighan CG, Dick JE. Precise single-cell transcriptomic mapping of normal and leukemic cell states reveals unconventional lineage priming in acute myeloid leukemia. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.26.573390. [PMID: 38234771 PMCID: PMC10793439 DOI: 10.1101/2023.12.26.573390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Initial classification of acute leukemia involves the assignment of blasts to cell states within the hematopoietic hierarchy based on morphological and immunophenotypic features. Yet, these traditional classification approaches lack precision, especially at the level of immature blasts. Single-cell RNA-sequencing (scRNA-seq) enables precise determination of cell state using thousands of markers, thus providing an opportunity to re-examine present-day classification schemes of acute leukemia. Here, we developed a detailed reference map of human bone marrow hematopoiesis from 263,519 single-cell transcriptomes spanning 55 cellular states. Cell state annotations were benchmarked against purified cell populations, and in-depth characterization of gene expression programs underlying hematopoietic differentiation was undertaken. Projection of single-cell transcriptomes from 175 samples spanning acute myeloid leukemia (AML), mixed phenotype acute leukemia (MPAL), and acute erythroid leukemia (AEL) revealed 11 subtypes involving distinct stages of hematopoietic differentiation. These included AML subtypes with notable lymphoid or erythroid lineage priming, challenging traditional diagnostic boundaries between AML, MPAL, and AEL. Quantification of lineage priming in bulk patient cohorts revealed specific genetic alterations associated with this unconventional lineage priming. Integration of transcriptional and genetic information at the single-cell level revealed how genetic subclones can induce lineage restriction, differentiation blocks, or expansion of mature myeloid cells. Furthermore, we demonstrate that distinct cellular hierarchies can co-exist within individual patients, providing insight into AML evolution in response to varying selection pressures. Together, precise mapping of hematopoietic cell states can serve as a foundation for refining disease classification in acute leukemia and understanding response or resistance to emerging therapies.
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Affiliation(s)
- Andy G X Zeng
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto; Toronto, ON, Canada
| | - Ilaria Iacobucci
- Department of Pathology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Sayyam Shah
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
| | - Amanda Mitchell
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
| | - Gordon Wong
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto; Toronto, ON, Canada
| | - Suraj Bansal
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
| | - Qingsong Gao
- Department of Pathology, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Hyerin Kim
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto; Toronto, ON, Canada
| | - James A Kennedy
- Division of Medical Oncology and Hematology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada
| | - Mark D Minden
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- Division of Medical Oncology and Hematology, University Health Network, Toronto, ON, Canada
| | | | - Charles G Mullighan
- Department of Pathology, St Jude Children's Research Hospital, Memphis, TN, USA
- Center of Excellence for Leukemia Studies, St. Jude Children's Research Hospital, Memphis, TN
| | - John E Dick
- Princess Margaret Cancer Centre, University Health Network; Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto; Toronto, ON, Canada
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26
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Iacobucci I, Zeng AGX, Gao Q, Garcia-Prat L, Baviskar P, Shah S, Murison A, Voisin V, Chan-Seng-Yue M, Cheng C, Qu C, Bailey C, Lear M, Witkowski MT, Zhou X, Peraza AZ, Gangwani K, Advani AS, Luger SM, Litzow MR, Rowe JM, Paietta EM, Stock W, Dick JE, Mullighan CG. SINGLE CELL DISSECTION OF DEVELOPMENTAL ORIGINS AND TRANSCRIPTIONAL HETEROGENEITY IN B-CELL ACUTE LYMPHOBLASTIC LEUKEMIA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.04.569954. [PMID: 38106088 PMCID: PMC10723356 DOI: 10.1101/2023.12.04.569954] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Sequencing of bulk tumor populations has improved genetic classification and risk assessment of B-ALL, but does not directly examine intratumor heterogeneity or infer leukemia cellular origins. We profiled 89 B-ALL samples by single-cell RNA-seq (scRNA-seq) and compared them to a reference map of normal human B-cell development established using both functional and molecular assays. Intra-sample heterogeneity was driven by cell cycle, metabolism, differentiation, and inflammation transcriptional programs. By inference of B lineage developmental state composition, nearly all samples possessed a high abundance of pro-B cells, with variation between samples mainly driven by sub-populations. However, ZNF384- r and DUX4- r B-ALL showed composition enrichment of hematopoietic stem cells, BCR::ABL1 and KMT2A -r ALL of Early Lymphoid progenitors, MEF2D -r and TCF3::PBX1 of Pre-B cells. Enrichment of Early Lymphoid progenitors correlated with high-risk clinical features. Understanding variation in transcriptional programs and developmental states of B-ALL by scRNA-seq refines existing clinical and genomic classifications and improves prediction of treatment outcome.
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27
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Shree A, Pavan MK, Zafar H. scDREAMER for atlas-level integration of single-cell datasets using deep generative model paired with adversarial classifier. Nat Commun 2023; 14:7781. [PMID: 38012145 PMCID: PMC10682386 DOI: 10.1038/s41467-023-43590-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 11/14/2023] [Indexed: 11/29/2023] Open
Abstract
Integration of heterogeneous single-cell sequencing datasets generated across multiple tissue locations, time, and conditions is essential for a comprehensive understanding of the cellular states and expression programs underlying complex biological systems. Here, we present scDREAMER ( https://github.com/Zafar-Lab/scDREAMER ), a data-integration framework that employs deep generative models and adversarial training for both unsupervised and supervised (scDREAMER-Sup) integration of multiple batches. Using six real benchmarking datasets, we demonstrate that scDREAMER can overcome critical challenges including skewed cell type distribution among batches, nested batch-effects, large number of batches and conservation of development trajectory across batches. Our experiments also show that scDREAMER and scDREAMER-Sup outperform state-of-the-art unsupervised and supervised integration methods respectively in batch-correction and conservation of biological variation. Using a 1 million cells dataset, we demonstrate that scDREAMER is scalable and can perform atlas-level cross-species (e.g., human and mouse) integration while being faster than other deep-learning-based methods.
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Affiliation(s)
- Ajita Shree
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Musale Krushna Pavan
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
| | - Hamim Zafar
- Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India.
- Department of Biological Sciences and Bioengineering, Indian Institute of Technology Kanpur, Kanpur, India.
- Mehta Family Centre for Engineering in Medicine, Indian Institute of Technology Kanpur, Kanpur, India.
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28
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Campanile M, Bettinelli L, Cerutti C, Spinetti G. Bone marrow vasculature advanced in vitro models for cancer and cardiovascular research. Front Cardiovasc Med 2023; 10:1261849. [PMID: 37915743 PMCID: PMC10616801 DOI: 10.3389/fcvm.2023.1261849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 09/12/2023] [Indexed: 11/03/2023] Open
Abstract
Cardiometabolic diseases and cancer are among the most common diseases worldwide and are a serious concern to the healthcare system. These conditions, apparently distant, share common molecular and cellular determinants, that can represent targets for preventive and therapeutic approaches. The bone marrow plays an important role in this context as it is the main source of cells involved in cardiovascular regeneration, and one of the main sites of liquid and solid tumor metastasis, both characterized by the cellular trafficking across the bone marrow vasculature. The bone marrow vasculature has been widely studied in animal models, however, it is clear the need for human-specific in vitro models, that resemble the bone vasculature lined by endothelial cells to study the molecular mechanisms governing cell trafficking. In this review, we summarized the current knowledge on in vitro models of bone marrow vasculature developed for cardiovascular and cancer research.
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Affiliation(s)
- Marzia Campanile
- Laboratory of Cardiovascular Research, IRCCS MultiMedica, Milan, Italy
| | - Leonardo Bettinelli
- Laboratory of Cardiovascular Research, IRCCS MultiMedica, Milan, Italy
- Department of Experimental Oncology, IRCCS-IEO, European Institute of Oncology, Milan, Italy
| | - Camilla Cerutti
- Department of Experimental Oncology, IRCCS-IEO, European Institute of Oncology, Milan, Italy
| | - Gaia Spinetti
- Laboratory of Cardiovascular Research, IRCCS MultiMedica, Milan, Italy
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29
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Todisco G, Creignou M, Bernard E, Björklund AC, Moura PL, Tesi B, Mortera-Blanco T, Sander B, Jansson M, Walldin G, Barbosa I, Reinsbach SE, Hofman IJ, Nilsson C, Yoshizato T, Dimitriou M, Chang D, Olafsdottir S, Venckute Larsson S, Tobiasson M, Malcovati L, Woll P, Jacobsen SEW, Papaemmanuil E, Hellström-Lindberg E. Integrated Genomic and Transcriptomic Analysis Improves Disease Classification and Risk Stratification of MDS with Ring Sideroblasts. Clin Cancer Res 2023; 29:4256-4267. [PMID: 37498312 PMCID: PMC10570683 DOI: 10.1158/1078-0432.ccr-23-0538] [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: 02/20/2023] [Revised: 05/12/2023] [Accepted: 07/25/2023] [Indexed: 07/28/2023]
Abstract
PURPOSE Ring sideroblasts (RS) define the low-risk myelodysplastic neoplasm (MDS) subgroup with RS but may also reflect erythroid dysplasia in higher risk myeloid neoplasm. The benign behavior of MDS with RS (MDSRS+) is limited to SF3B1-mutated cases without additional high-risk genetic events, but one third of MDSRS+ carry no SF3B1 mutation, suggesting that different molecular mechanisms may underlie RS formation. We integrated genomic and transcriptomic analyses to evaluate whether transcriptome profiles may improve current risk stratification. EXPERIMENTAL DESIGN We studied a prospective cohort of MDSRS+ patients irrespective of World Health Organization (WHO) class with regard to somatic mutations, copy-number alterations, and bone marrow CD34+ cell transcriptomes to assess whether transcriptome profiles add to prognostication and provide input on disease classification. RESULTS SF3B1, SRSF2, or TP53 multihit mutations were found in 89% of MDSRS+ cases, and each mutation category was associated with distinct clinical outcome, gene expression, and alternative splicing profiles. Unsupervised clustering analysis identified three clusters with distinct hemopoietic stem and progenitor (HSPC) composition, which only partially overlapped with mutation groups. IPSS-M and the transcriptome-defined proportion of megakaryocyte/erythroid progenitors (MEP) independently predicted survival in multivariable analysis. CONCLUSIONS These results provide essential input on the molecular basis of SF3B1-unmutated MDSRS+ and propose HSPC quantification as a prognostic marker in myeloid neoplasms with RS.
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Affiliation(s)
- Gabriele Todisco
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
- Unit of Precision Hematology Oncology, IRCCS S. Matteo Hospital Foundation, Pavia, Italy
| | - Maria Creignou
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Phase I Unit, Center for Clinical Cancer Studies, Karolinska University Hospital, Stockholm, Sweden
| | - Elsa Bernard
- Computational Oncology Service, Department of Epidemiology & Biostatistics and Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ann-Charlotte Björklund
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Pedro Luis Moura
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Bianca Tesi
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Laboratory, Karolinska University Hospital, Stockholm, Sweden
| | - Teresa Mortera-Blanco
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Birgitta Sander
- Division of Pathology, Department of Laboratory Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Monika Jansson
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Medical Unit Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Gunilla Walldin
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Medical Unit Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Indira Barbosa
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Susanne E. Reinsbach
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, Sweden
| | - Isabel Juliana Hofman
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Christer Nilsson
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Medical Unit Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Tetsuichi Yoshizato
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Marios Dimitriou
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - David Chang
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Svannildur Olafsdottir
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Sigita Venckute Larsson
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Magnus Tobiasson
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Medical Unit Hematology, Karolinska University Hospital, Stockholm, Sweden
| | - Luca Malcovati
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
- Unit of Precision Hematology Oncology, IRCCS S. Matteo Hospital Foundation, Pavia, Italy
| | - Petter Woll
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Sten Eirik W. Jacobsen
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
- MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | - Elli Papaemmanuil
- Computational Oncology Service, Department of Epidemiology & Biostatistics and Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Eva Hellström-Lindberg
- Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska Institutet, Stockholm, Sweden
- Medical Unit Hematology, Karolinska University Hospital, Stockholm, Sweden
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30
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Parry EM, Lemvigh CK, Deng S, Dangle N, Ruthen N, Knisbacher BA, Broséus J, Hergalant S, Guièze R, Li S, Zhang W, Johnson C, Long JM, Yin S, Werner L, Anandappa A, Purroy N, Gohil S, Oliveira G, Bachireddy P, Shukla SA, Huang T, Khoury JD, Thakral B, Dickinson M, Tam C, Livak KJ, Getz G, Neuberg D, Feugier P, Kharchenko P, Wierda W, Olsen LR, Jain N, Wu CJ. ZNF683 marks a CD8 + T cell population associated with anti-tumor immunity following anti-PD-1 therapy for Richter syndrome. Cancer Cell 2023; 41:1803-1816.e8. [PMID: 37738974 PMCID: PMC10618915 DOI: 10.1016/j.ccell.2023.08.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 05/30/2023] [Accepted: 08/30/2023] [Indexed: 09/24/2023]
Abstract
Unlike many other hematologic malignancies, Richter syndrome (RS), an aggressive B cell lymphoma originating from indolent chronic lymphocytic leukemia, is responsive to PD-1 blockade. To discover the determinants of response, we analyze single-cell transcriptome data generated from 17 bone marrow samples longitudinally collected from 6 patients with RS. Response is associated with intermediate exhausted CD8 effector/effector memory T cells marked by high expression of the transcription factor ZNF683, determined to be evolving from stem-like memory cells and divergent from terminally exhausted cells. This signature overlaps with that of tumor-infiltrating populations from anti-PD-1 responsive solid tumors. ZNF683 is found to directly target key T cell genes (TCF7, LMO2, CD69) and impact pathways of T cell cytotoxicity and activation. Analysis of pre-treatment peripheral blood from 10 independent patients with RS treated with anti-PD-1, as well as patients with solid tumors treated with anti-PD-1, supports an association of ZNF683high T cells with response.
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Affiliation(s)
- Erin M Parry
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Camilla K Lemvigh
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Health Technology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Stephanie Deng
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Nathan Dangle
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Neil Ruthen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | | | - Julien Broséus
- Inserm UMRS1256 Nutrition-Génétique et Exposition Aux Risques Environnementaux (N-GERE), Université de Lorraine, 54000 Nancy, France; Université de Lorraine, CHRU-Nancy, Service d'hématologie Biologique, Pôle Laboratoires, 54000 Nancy, France
| | - Sébastien Hergalant
- Inserm UMRS1256 Nutrition-Génétique et Exposition Aux Risques Environnementaux (N-GERE), Université de Lorraine, 54000 Nancy, France
| | - Romain Guièze
- CHU Clermont-Ferrand, 63000 Clermont-Ferrand, France; EA 7453 (CHELTER), Université Clermont Auvergne, 63001 Clermont-Ferrand, France
| | - Shuqiang Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Wandi Zhang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Connor Johnson
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Jaclyn M Long
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Evergrande Center for Immunologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Division of Gastroenterology, Hepatology, and Nutrition, Boston Children's Hospital, Boston, MA 02115, USA
| | - Shanye Yin
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Lillian Werner
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Annabelle Anandappa
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Noelia Purroy
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Satyen Gohil
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Giacomo Oliveira
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Pavan Bachireddy
- Department of Hematopoietic Biology and Malignancy, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Sachet A Shukla
- Department of Hematopoietic Biology and Malignancy, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Teddy Huang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Joseph D Khoury
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Beenu Thakral
- Department of Hematopathology, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Michael Dickinson
- Peter MacCallum Cancer Centre, Royal Melbourne Hospital, Melbourne, VIC, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Constantine Tam
- Alfred Health, Melbourne, VIC, Australia; Monash University, Melbourne, VIC, Australia
| | - Kenneth J Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Gad Getz
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Donna Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Pierre Feugier
- Inserm UMRS1256 Nutrition-Génétique et Exposition Aux Risques Environnementaux (N-GERE), Université de Lorraine, 54000 Nancy, France; Université de Lorraine, CHRU Nancy, service d'hématologie clinique, Nancy, France
| | - Peter Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02215, USA
| | - William Wierda
- Department of Leukemia, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Lars Rønn Olsen
- Department of Health Technology, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - Nitin Jain
- Department of Leukemia, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Harvard Medical School, Boston, MA 02115, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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31
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Zyla J, Papiez A, Zhao J, Qu R, Li X, Kluger Y, Polanska J, Hatzis C, Pusztai L, Marczyk M. Evaluation of zero counts to better understand the discrepancies between bulk and single-cell RNA-Seq platforms. Comput Struct Biotechnol J 2023; 21:4663-4674. [PMID: 37841335 PMCID: PMC10568495 DOI: 10.1016/j.csbj.2023.09.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 09/26/2023] [Accepted: 09/27/2023] [Indexed: 10/17/2023] Open
Abstract
Recent advances in sample preparation and sequencing technology have made it possible to profile the transcriptomes of individual cells using single-cell RNA sequencing (scRNA-Seq). Compared to bulk RNA-Seq data, single-cell data often contain a higher percentage of zero reads, mainly due to lower sequencing depth per cell, which affects mostly measurements of low-expression genes. However, discrepancies between platforms are observed regardless of expression level. Using four paired datasets with multiple samples each, we investigated technical and biological factors that can contribute to this expression shift. Using two separate machine learning models we found that, in addition to expression level, RNA integrity, gene or UTR3 length, and the number of transcripts potentially also influence the occurrence of zeros. These findings could enable the development of novel analytical methods for cross-platform expression shift correction. We also identified genes and biological pathways in our diverse datasets that consistently showed differences when assessed at the single cell versus bulk level to assist in interpreting analysis across transcriptomic platforms. At the gene level, 25 genes (0.12%) were found in all datasets as discordant, but at the pathway level, 7 pathways (2.02%) showed shared enrichment in discordant genes.
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Affiliation(s)
- Joanna Zyla
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice 44-100, Poland
| | - Anna Papiez
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice 44-100, Poland
| | - Jun Zhao
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA
- Department of Pathology, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Rihao Qu
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA
- Department of Pathology, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Xiaotong Li
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Yuval Kluger
- Computational Biology and Bioinformatics Program, Yale University, New Haven, CT 06510, USA
- Department of Pathology, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
- Applied Mathematics Program, Yale University, New Haven, CT, USA
| | - Joanna Polanska
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice 44-100, Poland
| | - Christos Hatzis
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Lajos Pusztai
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
| | - Michal Marczyk
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice 44-100, Poland
- Breast Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT 06520, USA
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32
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Athaya T, Ripan RC, Li X, Hu H. Multimodal deep learning approaches for single-cell multi-omics data integration. Brief Bioinform 2023; 24:bbad313. [PMID: 37651607 PMCID: PMC10516349 DOI: 10.1093/bib/bbad313] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 06/23/2023] [Accepted: 07/18/2023] [Indexed: 09/02/2023] Open
Abstract
Integrating single-cell multi-omics data is a challenging task that has led to new insights into complex cellular systems. Various computational methods have been proposed to effectively integrate these rapidly accumulating datasets, including deep learning. However, despite the proven success of deep learning in integrating multi-omics data and its better performance over classical computational methods, there has been no systematic study of its application to single-cell multi-omics data integration. To fill this gap, we conducted a literature review to explore the use of multimodal deep learning techniques in single-cell multi-omics data integration, taking into account recent studies from multiple perspectives. Specifically, we first summarized different modalities found in single-cell multi-omics data. We then reviewed current deep learning techniques for processing multimodal data and categorized deep learning-based integration methods for single-cell multi-omics data according to data modality, deep learning architecture, fusion strategy, key tasks and downstream analysis. Finally, we provided insights into using these deep learning models to integrate multi-omics data and better understand single-cell biological mechanisms.
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Affiliation(s)
- Tasbiraha Athaya
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Rony Chowdhury Ripan
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
| | - Xiaoman Li
- Burnett School of Biomedical Science, College of Medicine, University of Central Florida, Orlando, Florida, United States of America
| | - Haiyan Hu
- Department of Computer Science, University of Central Florida, Orlando, Florida, United States of America
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33
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Michielsen L, Lotfollahi M, Strobl D, Sikkema L, Reinders MT, Theis F, Mahfouz A. Single-cell reference mapping to construct and extend cell-type hierarchies. NAR Genom Bioinform 2023; 5:lqad070. [PMID: 37502708 PMCID: PMC10370450 DOI: 10.1093/nargab/lqad070] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 07/10/2023] [Indexed: 07/29/2023] Open
Abstract
Single-cell genomics is now producing an ever-increasing amount of datasets that, when integrated, could provide large-scale reference atlases of tissue in health and disease. Such large-scale atlases increase the scale and generalizability of analyses and enable combining knowledge generated by individual studies. Specifically, individual studies often differ regarding cell annotation terminology and depth, with different groups specializing in different cell type compartments, often using distinct terminology. Understanding how these distinct sets of annotations are related and complement each other would mark a major step towards a consensus-based cell-type annotation reflecting the latest knowledge in the field. Whereas recent computational techniques, referred to as 'reference mapping' methods, facilitate the usage and expansion of existing reference atlases by mapping new datasets (i.e. queries) onto an atlas; a systematic approach towards harmonizing dataset-specific cell-type terminology and annotation depth is still lacking. Here, we present 'treeArches', a framework to automatically build and extend reference atlases while enriching them with an updatable hierarchy of cell-type annotations across different datasets. We demonstrate various use cases for treeArches, from automatically resolving relations between reference and query cell types to identifying unseen cell types absent in the reference, such as disease-associated cell states. We envision treeArches enabling data-driven construction of consensus atlas-level cell-type hierarchies and facilitating efficient usage of reference atlases.
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Affiliation(s)
| | | | - Daniel Strobl
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, TUM School of Medicine, Technical University of Munich, 81675 Munich, Germany
| | - Lisa Sikkema
- Institute of Computational Biology, Helmholtz Zentrum München, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany
| | - Marcel J T Reinders
- Department of Human Genetics, Leiden University Medical Center, 2333ZC Leiden, The Netherlands
- Leiden Computational Biology Center, Leiden University Medical Center, 2333ZC Leiden, The Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, 2628XE Delft, The Netherlands
| | - Fabian J Theis
- To whom correspondence should be addressed. Tel: +49 89 3187 43260;
| | - Ahmed Mahfouz
- Correspondence may also be addressed to Ahmed Mahfouz. Tel: +31 71 52 69513;
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34
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Lewis JE, Hergott CB. The Immunophenotypic Profile of Healthy Human Bone Marrow. Clin Lab Med 2023; 43:323-332. [PMID: 37481314 DOI: 10.1016/j.cll.2023.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Flow cytometry enables multiparametric characterization of hematopoietic cell immunophenotype. Deviations from normal immunophenotypic patterns comprise a cardinal feature of many hematopoietic neoplasms, underscoring the ongoing essentiality of flow cytometry as a diagnostic tool. However, understanding of aberrant hematopoiesis requires an equal understanding of normal hematopoiesis as a comparator. In this review, we outline key features of healthy adult hematopoiesis and lineage specification as illuminated by flow cytometry and provide diagrams illustrating what a diagnostician may observe in flow cytometric plots. These features provide a profile of baseline hematopoiesis, to which clinical samples with suspected neoplasia may be compared.
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Affiliation(s)
- Joshua E Lewis
- Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA
| | - Christopher B Hergott
- Department of Pathology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA 02115, USA; Harvard Medical School, 25 Shattuck St, Boston, MA 02115, USA.
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35
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Abstract
Organismal aging exhibits wide-ranging hallmarks in divergent cell types across tissues, organs, and systems. The advancement of single-cell technologies and generation of rich datasets have afforded the scientific community the opportunity to decode these hallmarks of aging at an unprecedented scope and resolution. In this review, we describe the technological advancements and bioinformatic methodologies enabling data interpretation at the cellular level. Then, we outline the application of such technologies for decoding aging hallmarks and potential intervention targets and summarize common themes and context-specific molecular features in representative organ systems across the body. Finally, we provide a brief summary of available databases relevant for aging research and present an outlook on the opportunities in this emerging field.
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Affiliation(s)
- Shuai Ma
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China; ,
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Xu Chi
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China;
| | - Yusheng Cai
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China; ,
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
| | - Zhejun Ji
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Si Wang
- Advanced Innovation Center for Human Brain Protection and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China;
- Aging Translational Medicine Center, International Center for Aging and Cancer, Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Ren
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China;
- University of Chinese Academy of Sciences, Beijing, China
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China; ,
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, China
- Advanced Innovation Center for Human Brain Protection and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital, Capital Medical University, Beijing, China;
- University of Chinese Academy of Sciences, Beijing, China
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36
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Otto D, Jordan C, Dury B, Dien C, Setty M. Quantifying Cell-State Densities in Single-Cell Phenotypic Landscapes using Mellon. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.09.548272. [PMID: 37502954 PMCID: PMC10369887 DOI: 10.1101/2023.07.09.548272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Cell-state density characterizes the distribution of cells along phenotypic landscapes and is crucial for unraveling the mechanisms that drive cellular differentiation, regeneration, and disease. Here, we present Mellon, a novel computational algorithm for high-resolution estimation of cell-state densities from single-cell data. We demonstrate Mellon's efficacy by dissecting the density landscape of various differentiating systems, revealing a consistent pattern of high-density regions corresponding to major cell types intertwined with low-density, rare transitory states. Utilizing hematopoietic stem cell fate specification to B-cells as a case study, we present evidence implicating enhancer priming and the activation of master regulators in the emergence of these transitory states. Mellon offers the flexibility to perform temporal interpolation of time-series data, providing a detailed view of cell-state dynamics during the inherently continuous developmental processes. Scalable and adaptable, Mellon facilitates density estimation across various single-cell data modalities, scaling linearly with the number of cells. Our work underscores the importance of cell-state density in understanding the differentiation processes, and the potential of Mellon to provide new insights into the regulatory mechanisms guiding cellular fate decisions.
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Affiliation(s)
- Dominik Otto
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | - Cailin Jordan
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
- Molecular and Cellular Biology Program, University of Washington, Seattle WA
| | - Brennan Dury
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | - Christine Dien
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | - Manu Setty
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
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37
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Fetahu IS, Esser-Skala W, Dnyansagar R, Sindelar S, Rifatbegovic F, Bileck A, Skos L, Bozsaky E, Lazic D, Shaw L, Tötzl M, Tarlungeanu D, Bernkopf M, Rados M, Weninger W, Tomazou EM, Bock C, Gerner C, Ladenstein R, Farlik M, Fortelny N, Taschner-Mandl S. Single-cell transcriptomics and epigenomics unravel the role of monocytes in neuroblastoma bone marrow metastasis. Nat Commun 2023; 14:3620. [PMID: 37365178 DOI: 10.1038/s41467-023-39210-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 05/29/2023] [Indexed: 06/28/2023] Open
Abstract
Metastasis is the major cause of cancer-related deaths. Neuroblastoma (NB), a childhood tumor has been molecularly defined at the primary cancer site, however, the bone marrow (BM) as the metastatic niche of NB is poorly characterized. Here we perform single-cell transcriptomic and epigenomic profiling of BM aspirates from 11 subjects spanning three major NB subtypes and compare these to five age-matched and metastasis-free BM, followed by in-depth single cell analyses of tissue diversity and cell-cell interactions, as well as functional validation. We show that cellular plasticity of NB tumor cells is conserved upon metastasis and tumor cell type composition is NB subtype-dependent. NB cells signal to the BM microenvironment, rewiring via macrophage mgration inhibitory factor and midkine signaling specifically monocytes, which exhibit M1 and M2 features, are marked by activation of pro- and anti-inflammatory programs, and express tumor-promoting factors, reminiscent of tumor-associated macrophages. The interactions and pathways characterized in our study provide the basis for therapeutic approaches that target tumor-to-microenvironment interactions.
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Affiliation(s)
- Irfete S Fetahu
- St. Anna Children's Cancer Research Institute, Vienna, Austria.
| | - Wolfgang Esser-Skala
- Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
| | - Rohit Dnyansagar
- Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
| | - Samuel Sindelar
- Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria
| | | | - Andrea Bileck
- University of Vienna, Department of Analytical Chemistry, Faculty of Chemistry, Vienna, Austria
- Joint Metabolomics Facility, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Lukas Skos
- University of Vienna, Department of Analytical Chemistry, Faculty of Chemistry, Vienna, Austria
| | - Eva Bozsaky
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Daria Lazic
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Lisa Shaw
- Medical University of Vienna, Department of Dermatology, Vienna, Austria
| | - Marcus Tötzl
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | | | - Marie Bernkopf
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Magdalena Rados
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Wolfgang Weninger
- Medical University of Vienna, Department of Dermatology, Vienna, Austria
| | - Eleni M Tomazou
- St. Anna Children's Cancer Research Institute, Vienna, Austria
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria
- Medical University of Vienna, Institute of Artificial Intelligence, Center for Medical Data Science, Vienna, Austria
| | - Christopher Gerner
- University of Vienna, Department of Analytical Chemistry, Faculty of Chemistry, Vienna, Austria
- Joint Metabolomics Facility, University of Vienna and Medical University of Vienna, Vienna, Austria
| | - Ruth Ladenstein
- St. Anna Children's Hospital and St. Anna Children's Cancer Research Institute, Department of Studies and Statistics for Integrated Research and Projects, Vienna, Austria
- Medical University of Vienna, Department of Pediatrics, Vienna, Austria
| | - Matthias Farlik
- Medical University of Vienna, Department of Dermatology, Vienna, Austria
| | - Nikolaus Fortelny
- Department of Biosciences and Medical Biology, University of Salzburg, Salzburg, Austria.
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38
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Ennis S, Conforte A, O’Reilly E, Takanlu JS, Cichocka T, Dhami SP, Nicholson P, Krebs P, Ó Broin P, Szegezdi E. Cell-cell interactome of the hematopoietic niche and its changes in acute myeloid leukemia. iScience 2023; 26:106943. [PMID: 37332612 PMCID: PMC10275994 DOI: 10.1016/j.isci.2023.106943] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 03/22/2023] [Accepted: 05/19/2023] [Indexed: 06/20/2023] Open
Abstract
The bone marrow (BM) is a complex microenvironment, coordinating the production of billions of blood cells every day. Despite its essential role and its relevance to hematopoietic diseases, this environment remains poorly characterized. Here we present a high-resolution characterization of the niche in health and acute myeloid leukemia (AML) by establishing a single-cell gene expression database of 339,381 BM cells. We found significant changes in cell type proportions and gene expression in AML, indicating that the entire niche is disrupted. We then predicted interactions between hematopoietic stem and progenitor cells (HSPCs) and other BM cell types, revealing a remarkable expansion of predicted interactions in AML that promote HSPC-cell adhesion, immunosuppression, and cytokine signaling. In particular, predicted interactions involving transforming growth factor β1 (TGFB1) become widespread, and we show that this can drive AML cell quiescence in vitro. Our results highlight potential mechanisms of enhanced AML-HSPC competitiveness and a skewed microenvironment, fostering AML growth.
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Affiliation(s)
- Sarah Ennis
- The SFI Centre for Research Training in Genomics Data Science, Galway, Ireland
- Discipline of Bioinformatics, School of Mathematical & Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Alessandra Conforte
- Apoptosis Research Centre, School of Biological & Chemical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Eimear O’Reilly
- Apoptosis Research Centre, School of Biological & Chemical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Javid Sabour Takanlu
- Apoptosis Research Centre, School of Biological & Chemical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Tatiana Cichocka
- Apoptosis Research Centre, School of Biological & Chemical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Sukhraj Pal Dhami
- Apoptosis Research Centre, School of Biological & Chemical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Pamela Nicholson
- Next Generation Sequencing Platform, University of Bern, Bern, Switzerland
| | - Philippe Krebs
- Institute of Tissue Medicine and Pathology, University of Bern, Bern, Switzerland
| | - Pilib Ó Broin
- The SFI Centre for Research Training in Genomics Data Science, Galway, Ireland
- Discipline of Bioinformatics, School of Mathematical & Statistical Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Eva Szegezdi
- The SFI Centre for Research Training in Genomics Data Science, Galway, Ireland
- Apoptosis Research Centre, School of Biological & Chemical Sciences, University of Galway, H91 TK33 Galway, Ireland
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Stuart T, Hao S, Zhang B, Mekerishvili L, Landau DA, Maniatis S, Satija R, Raimondi I. Nanobody-tethered transposition enables multifactorial chromatin profiling at single-cell resolution. Nat Biotechnol 2023; 41:806-812. [PMID: 36536150 PMCID: PMC10272075 DOI: 10.1038/s41587-022-01588-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 10/24/2022] [Indexed: 12/24/2022]
Abstract
Chromatin states are functionally defined by a complex combination of histone modifications, transcription factor binding, DNA accessibility and other factors. Current methods for defining chromatin states cannot measure more than one aspect in a single experiment at single-cell resolution. Here we introduce nanobody-tethered transposition followed by sequencing (NTT-seq), an assay capable of measuring the genome-wide presence of up to three histone modifications and protein-DNA binding sites at single-cell resolution. NTT-seq uses recombinant Tn5 transposase fused to a set of secondary nanobodies (nb). Each nb-Tn5 fusion protein specifically binds to different immunoglobulin-G antibodies, enabling a mixture of primary antibodies binding different epitopes to be used in a single experiment. We apply bulk-cell and single-cell NTT-seq to generate high-resolution multimodal maps of chromatin states in cell culture and in human immune cells. We also extend NTT-seq to enable simultaneous profiling of cell surface protein expression and multimodal chromatin states to study cells of the immune system.
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Affiliation(s)
- Tim Stuart
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Stephanie Hao
- Technology Innovation Lab, New York Genome Center, New York, NY, USA
| | - Bingjie Zhang
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Levan Mekerishvili
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Dan A Landau
- New York Genome Center, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Silas Maniatis
- Technology Innovation Lab, New York Genome Center, New York, NY, USA
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Ivan Raimondi
- Technology Innovation Lab, New York Genome Center, New York, NY, USA.
- Weill Cornell Medicine, New York, NY, USA.
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40
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Xu J, Zhang A, Liu F, Chen L, Zhang X. CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data. Brief Bioinform 2023:7169137. [PMID: 37200157 DOI: 10.1093/bib/bbad195] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 04/03/2023] [Accepted: 04/30/2023] [Indexed: 05/20/2023] Open
Abstract
Single-cell omics technologies have made it possible to analyze the individual cells within a biological sample, providing a more detailed understanding of biological systems. Accurately determining the cell type of each cell is a crucial goal in single-cell RNA-seq (scRNA-seq) analysis. Apart from overcoming the batch effects arising from various factors, single-cell annotation methods also face the challenge of effectively processing large-scale datasets. With the availability of an increase in the scRNA-seq datasets, integrating multiple datasets and addressing batch effects originating from diverse sources are also challenges in cell-type annotation. In this work, to overcome the challenges, we developed a supervised method called CIForm based on the Transformer for cell-type annotation of large-scale scRNA-seq data. To assess the effectiveness and robustness of CIForm, we have compared it with some leading tools on benchmark datasets. Through the systematic comparisons under various cell-type annotation scenarios, we exhibit that the effectiveness of CIForm is particularly pronounced in cell-type annotation. The source code and data are available at https://github.com/zhanglab-wbgcas/CIForm.
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Affiliation(s)
- Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Aidi Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Fang Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Liang Chen
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, China
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41
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Zhang N, Wu J, Wang Q, Liang Y, Li X, Chen G, Ma L, Liu X, Zhou F. Global burden of hematologic malignancies and evolution patterns over the past 30 years. Blood Cancer J 2023; 13:82. [PMID: 37193689 DOI: 10.1038/s41408-023-00853-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Revised: 04/26/2023] [Accepted: 05/03/2023] [Indexed: 05/18/2023] Open
Abstract
Hematologic malignancies are among the most common cancers, and understanding their incidence and death is crucial for targeting prevention, clinical practice improvement, and research resources appropriately. Here, we investigated detailed information on hematological malignancies for the period 1990-2019 from the Global Burden of Disease study. The age-standardized incidence rate (ASIR), the age-standardized death rate (ASDR), and the corresponding estimated annual percentage changes (EAPC) were calculated to assess temporal trends in 204 countries and territories over the past 30 years. Globally, incident cases of hematologic malignancies have been increasing since 1990, reaching 1343.85 thousand in 2019, but the ASDR for all types of hematologic malignancies has been declining. The ASDR for leukemia, multiple myeloma, non-Hodgkin lymphoma, and Hodgkin lymphoma were 4.26, 1.42, 3.19, and 0.34 per 100,000 population in 2019, respectively, with Hodgkin lymphoma showing the most significant decline. However, the trend varies by gender, age, region, and the country's economic situation. The burden of hematologic malignancies is generally higher in men, and this gender gap decreases after peaking at a given age. The regions with the largest increasing trend in the ASIR of leukemia, multiple myeloma, non-Hodgkin lymphoma, and Hodgkin lymphoma were Central Europe, Eastern Europe, East Asia, and Caribbean, respectively. In addition, the proportion of deaths attributed to high body-mass index continued to rise across regions, especially in regions with high socio-demographic indices (SDI). Meanwhile, the burden of leukemia from occupational exposure to benzene and formaldehyde was more widespread in areas with low SDI. Thus, hematologic malignancies remain the leading cause of the global tumor burden, with growing absolute numbers but sharp among several age-standardized measures over the past three decades. The results of the study will inform analysis of trends in the global burden of disease for specific hematologic malignancies and develop appropriate policies for these modifiable risks.
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Affiliation(s)
- Nan Zhang
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Jinxian Wu
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Qian Wang
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yuxing Liang
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Xinqi Li
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Guopeng Chen
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Linlu Ma
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Xiaoyan Liu
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
- School of Nursing, Wuhan University, Wuhan, Hubei, China.
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42
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Xu Y, Kramann R, McCord RP, Hayat S. MASI enables fast model-free standardization and integration of single-cell transcriptomics data. Commun Biol 2023; 6:465. [PMID: 37117305 PMCID: PMC10144903 DOI: 10.1038/s42003-023-04820-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 04/06/2023] [Indexed: 04/30/2023] Open
Abstract
Single-cell transcriptomics datasets from the same anatomical sites generated by different research labs are becoming increasingly common. However, fast and computationally inexpensive tools for standardization of cell-type annotation and data integration are still needed in order to increase research inclusivity. To standardize cell-type annotation and integrate single-cell transcriptomics datasets, we have built a fast model-free integration method, named MASI (Marker-Assisted Standardization and Integration). We benchmark MASI with other well-established methods and demonstrate that MASI outperforms other methods, in terms of integration, annotation, and speed. To harness knowledge from single-cell atlases, we demonstrate three case studies that cover integration across biological conditions, surveyed participants, and research groups, respectively. Finally, we show MASI can annotate approximately one million cells on a personal laptop, making large-scale single-cell data integration more accessible. We envision that MASI can serve as a cheap computational alternative for the single-cell research community.
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Affiliation(s)
- Yang Xu
- UT-ORNL Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN, 37996, USA
- Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany
| | - Rachel Patton McCord
- Department of Biochemistry and Cellular and Molecular Biology, University of Tennessee, Knoxville, TN, 37996, USA.
| | - Sikander Hayat
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University, Aachen, Germany.
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43
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Su X, Wang L, Ma N, Yang X, Liu C, Yang F, Li J, Yi X, Xing Y. Immune heterogeneity in cardiovascular diseases from a single-cell perspective. Front Cardiovasc Med 2023; 10:1057870. [PMID: 37180791 PMCID: PMC10167030 DOI: 10.3389/fcvm.2023.1057870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 04/10/2023] [Indexed: 05/16/2023] Open
Abstract
A variety of immune cell subsets occupy different niches in the cardiovascular system, causing changes in the structure and function of the heart and vascular system, and driving the progress of cardiovascular diseases (CVDs). The immune cells infiltrating the injury site are highly diverse and integrate into a broad dynamic immune network that controls the dynamic changes of CVDs. Due to technical limitations, the effects and molecular mechanisms of these dynamic immune networks on CVDs have not been fully revealed. With recent advances in single-cell technologies such as single-cell RNA sequencing, systematic interrogation of the immune cell subsets is feasible and will provide insights into the way we understand the integrative behavior of immune populations. We no longer lightly ignore the role of individual cells, especially certain highly heterogeneous or rare subpopulations. We summarize the phenotypic diversity of immune cell subsets and their significance in three CVDs of atherosclerosis, myocardial ischemia and heart failure. We believe that such a review could enhance our understanding of how immune heterogeneity drives the progression of CVDs, help to elucidate the regulatory roles of immune cell subsets in disease, and thus guide the development of new immunotherapies.
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Affiliation(s)
- Xin Su
- China Academy of Chinese Medical Sciences, Guang’anmen Hospital, Beijing, China
| | - Li Wang
- Department of Breast Surgery, Xingtai People’s Hospital, Xingtai, China
| | - Ning Ma
- Department of Breast Surgery, Dezhou Second People’s Hospital, Dezhou, China
| | - Xinyu Yang
- Fangshan Hospital Beijing University of Chinese Medicine, Beijing, China
| | - Can Liu
- China Academy of Chinese Medical Sciences, Guang’anmen Hospital, Beijing, China
| | - Fan Yang
- China Academy of Chinese Medical Sciences, Guang’anmen Hospital, Beijing, China
| | - Jun Li
- China Academy of Chinese Medical Sciences, Guang’anmen Hospital, Beijing, China
| | - Xin Yi
- Department of Cardiology, Beijing Huimin Hospital, Beijing, China
| | - Yanwei Xing
- China Academy of Chinese Medical Sciences, Guang’anmen Hospital, Beijing, China
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44
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Solomon BD, Zheng H, Dillon LW, Goldman J, Hourigan CS, Heath J, Khatri P. Prediction of HLA genotypes from single-cell transcriptome data. Front Immunol 2023; 14:1146826. [PMID: 37180102 PMCID: PMC10167300 DOI: 10.3389/fimmu.2023.1146826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/04/2023] [Indexed: 05/15/2023] Open
Abstract
The human leukocyte antigen (HLA) locus plays a central role in adaptive immune function and has significant clinical implications for tissue transplant compatibility and allelic disease associations. Studies using bulk-cell RNA sequencing have demonstrated that HLA transcription may be regulated in an allele-specific manner and single-cell RNA sequencing (scRNA-seq) has the potential to better characterize these expression patterns. However, quantification of allele-specific expression (ASE) for HLA loci requires sample-specific reference genotyping due to extensive polymorphism. While genotype prediction from bulk RNA sequencing is well described, the feasibility of predicting HLA genotypes directly from single-cell data is unknown. Here we evaluate and expand upon several computational HLA genotyping tools by comparing predictions from human single-cell data to gold-standard, molecular genotyping. The highest 2-field accuracy averaged across all loci was 76% by arcasHLA and increased to 86% using a composite model of multiple genotyping tools. We also developed a highly accurate model (AUC 0.93) for predicting HLA-DRB345 copy number in order to improve genotyping accuracy of the HLA-DRB locus. Genotyping accuracy improved with read depth and was reproducible at repeat sampling. Using a metanalytic approach, we also show that HLA genotypes from PHLAT and OptiType can generate ASE ratios that are highly correlated (R2 = 0.8 and 0.94, respectively) with those derived from gold-standard genotyping.
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Affiliation(s)
| | - Hong Zheng
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA, United States
- Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA, United States
| | - Laura W. Dillon
- Laboratory of Myeloid Malignancies, National Heart Lung and Blood Institute, Bethesda, MD, United States
| | - Jason D. Goldman
- Swedish Center for Research and Innovation, Swedish Medical Center, Seattle, WA, United States
- Providence St. Joseph Health, Renton, WA, United States
- Division of Allergy & Infectious Diseases, University of Washington, Seattle, WA, United States
| | - Christopher S. Hourigan
- Laboratory of Myeloid Malignancies, National Heart Lung and Blood Institute, Bethesda, MD, United States
| | - James R. Heath
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Purvesh Khatri
- Institute for Immunity, Transplantation and Infection, School of Medicine, Stanford University, Stanford, CA, United States
- Center for Biomedical Informatics Research, Department of Medicine, School of Medicine, Stanford University, Stanford, CA, United States
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45
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Penter L, Liu Y, Wolff JO, Yang L, Taing L, Jhaveri A, Southard J, Patel M, Cullen NM, Pfaff KL, Cieri N, Oliveira G, Kim-Schulze S, Ranasinghe S, Leonard R, Robertson T, Morgan EA, Chen HX, Song MH, Thurin M, Li S, Rodig SJ, Cibulskis C, Gabriel S, Bachireddy P, Ritz J, Streicher H, Neuberg DS, Hodi FS, Davids MS, Gnjatic S, Livak KJ, Altreuter J, Michor F, Soiffer RJ, Garcia JS, Wu CJ. Mechanisms of response and resistance to combined decitabine and ipilimumab for advanced myeloid disease. Blood 2023; 141:1817-1830. [PMID: 36706355 PMCID: PMC10122106 DOI: 10.1182/blood.2022018246] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 01/10/2023] [Accepted: 01/11/2023] [Indexed: 01/29/2023] Open
Abstract
The challenge of eradicating leukemia in patients with acute myelogenous leukemia (AML) after initial cytoreduction has motivated modern efforts to combine synergistic active modalities including immunotherapy. Recently, the ETCTN/CTEP 10026 study tested the combination of the DNA methyltransferase inhibitor decitabine together with the immune checkpoint inhibitor ipilimumab for AML/myelodysplastic syndrome (MDS) either after allogeneic hematopoietic stem cell transplantation (HSCT) or in the HSCT-naïve setting. Integrative transcriptome-based analysis of 304 961 individual marrow-infiltrating cells for 18 of 48 subjects treated on study revealed the strong association of response with a high baseline ratio of T to AML cells. Clinical responses were predominantly driven by decitabine-induced cytoreduction. Evidence of immune activation was only apparent after ipilimumab exposure, which altered CD4+ T-cell gene expression, in line with ongoing T-cell differentiation and increased frequency of marrow-infiltrating regulatory T cells. For post-HSCT samples, relapse could be attributed to insufficient clearing of malignant clones in progenitor cell populations. In contrast to AML/MDS bone marrow, the transcriptomes of leukemia cutis samples from patients with durable remission after ipilimumab monotherapy showed evidence of increased infiltration with antigen-experienced resident memory T cells and higher expression of CTLA-4 and FOXP3. Altogether, activity of combined decitabine and ipilimumab is impacted by cellular expression states within the microenvironmental niche of leukemic cells. The inadequate elimination of leukemic progenitors mandates urgent development of novel approaches for targeting these cell populations to generate long-lasting responses. This trial was registered at www.clinicaltrials.gov as #NCT02890329.
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Affiliation(s)
- Livius Penter
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
- Department of Hematology, Oncology, and Tumorimmunology, Campus Virchow Klinikum, Berlin, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Yang Liu
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | | | - Lin Yang
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | - Len Taing
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Aashna Jhaveri
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | - Jackson Southard
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA
| | - Manishkumar Patel
- Human Immune Monitoring Center at the Icahn School of Medicine at Mount Sinai, New York, NY
| | - Nicole M. Cullen
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Kathleen L. Pfaff
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Nicoletta Cieri
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
| | - Giacomo Oliveira
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
| | - Seunghee Kim-Schulze
- Human Immune Monitoring Center at the Icahn School of Medicine at Mount Sinai, New York, NY
| | | | - Rebecca Leonard
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Taylor Robertson
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Elizabeth A. Morgan
- Harvard Medical School, Boston, MA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA
| | - Helen X. Chen
- Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
| | - Minkyung H. Song
- Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
| | - Magdalena Thurin
- Cancer Diagnosis Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
| | - Shuqiang Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA
| | - Scott J. Rodig
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA
| | - Carrie Cibulskis
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
| | - Stacey Gabriel
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
| | | | - Jerome Ritz
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Howard Streicher
- Cancer Therapy Evaluation Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD
| | - Donna S. Neuberg
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | - F. Stephen Hodi
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Matthew S. Davids
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Sacha Gnjatic
- Human Immune Monitoring Center at the Icahn School of Medicine at Mount Sinai, New York, NY
| | - Kenneth J. Livak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Translational Immunogenomics Lab, Dana-Farber Cancer Institute, Boston, MA
| | | | - Franziska Michor
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA
| | - Robert J. Soiffer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Jacqueline S. Garcia
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Catherine J. Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
- Harvard Medical School, Boston, MA
- Department of Medicine, Brigham and Women’s Hospital, Boston, MA
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Crowell HL, Morillo Leonardo SX, Soneson C, Robinson MD. The shaky foundations of simulating single-cell RNA sequencing data. Genome Biol 2023; 24:62. [PMID: 36991470 PMCID: PMC10061781 DOI: 10.1186/s13059-023-02904-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/20/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND With the emergence of hundreds of single-cell RNA-sequencing (scRNA-seq) datasets, the number of computational tools to analyze aspects of the generated data has grown rapidly. As a result, there is a recurring need to demonstrate whether newly developed methods are truly performant-on their own as well as in comparison to existing tools. Benchmark studies aim to consolidate the space of available methods for a given task and often use simulated data that provide a ground truth for evaluations, thus demanding a high quality standard results credible and transferable to real data. RESULTS Here, we evaluated methods for synthetic scRNA-seq data generation in their ability to mimic experimental data. Besides comparing gene- and cell-level quality control summaries in both one- and two-dimensional settings, we further quantified these at the batch- and cluster-level. Secondly, we investigate the effect of simulators on clustering and batch correction method comparisons, and, thirdly, which and to what extent quality control summaries can capture reference-simulation similarity. CONCLUSIONS Our results suggest that most simulators are unable to accommodate complex designs without introducing artificial effects, they yield over-optimistic performance of integration and potentially unreliable ranking of clustering methods, and it is generally unknown which summaries are important to ensure effective simulation-based method comparisons.
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Affiliation(s)
- Helena L Crowell
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | | | - Charlotte Soneson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
- Current address: Friedrich Miescher Institute for Biomedical Research and SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland.
- SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland.
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47
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Lee NYS, Li M, Ang KS, Chen J. Establishing a human bone marrow single cell reference atlas to study ageing and diseases. Front Immunol 2023; 14:1127879. [PMID: 37006302 PMCID: PMC10050687 DOI: 10.3389/fimmu.2023.1127879] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/20/2023] [Indexed: 03/17/2023] Open
Abstract
IntroductionAgeing in the human bone marrow is associated with immune function decline that results in the elderly being vulnerable to illnesses. A comprehensive healthy bone marrow consensus atlas can serve as a reference to study the immunological changes associated with ageing, and to identify and study abnormal cell states.MethodsWe collected publicly available single cell transcriptomic data of 145 healthy samples encompassing a wide spectrum of ages ranging from 2 to 84 years old to construct our human bone marrow atlas. The final atlas has 673,750 cells and 54 annotated cell types.ResultsWe first characterised the changes in cell population sizes with respect to age and the corresponding changes in gene expression and pathways. Overall, we found significant age-associated changes in the lymphoid lineage cells. The naïve CD8+ T cell population showed significant shrinkage with ageing while the effector/memory CD4+ T cells increased in proportion. We also found an age-correlated decline in the common lymphoid progenitor population, in line with the commonly observed myeloid skew in haematopoiesis among the elderly. We then employed our cell type-specific ageing gene signatures to develop a machine learning model that predicts the biological age of bone marrow samples, which we then applied to healthy individuals and those with blood diseases. Finally, we demonstrated how to identify abnormal cell states by mapping disease samples onto the atlas. We accurately identified abnormal plasma cells and erythroblasts in multiple myeloma samples, and abnormal cells in acute myeloid leukaemia samples.DiscussionThe bone marrow is the site of haematopoiesis, a highly important bodily process. We believe that our healthy bone marrow atlas is a valuable reference for studying bone marrow processes and bone marrow-related diseases. It can be mined for novel discoveries, as well as serve as a reference scaffold for mapping samples to identify and investigate abnormal cells.
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Affiliation(s)
- Nicole Yee Shin Lee
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Mengwei Li
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Kok Siong Ang
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Jinmiao Chen
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Immunology Translational Research Program, Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), Singapore, Singapore
- *Correspondence: Jinmiao Chen,
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48
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Aubin RG, Montelongo J, Hu R, Camara PG. Clustering-independent estimation of cell abundances in bulk tissues using single-cell RNA-seq data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.06.527318. [PMID: 36798206 PMCID: PMC9934539 DOI: 10.1101/2023.02.06.527318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Single-cell RNA-sequencing has transformed the study of biological tissues by enabling transcriptomic characterizations of their constituent cell states. Computational methods for gene expression deconvolution use this information to infer the cell composition of related tissues profiled at the bulk level. However, current deconvolution methods are restricted to discrete cell types and have limited power to make inferences about continuous cellular processes like cell differentiation or immune cell activation. We present ConDecon, a clustering-independent method for inferring the likelihood for each cell in a single-cell dataset to be present in a bulk tissue. ConDecon represents an improvement in functionality and accuracy with respect to current deconvolution methods. Using ConDecon, we discover the implication of neurodegenerative microglial inflammatory pathways in the mesenchymal transformation of ependymoma, recapitulate spatial patterns of cell differentiation during zebrafish embryogenesis, and make temporal inferences from bulk ATAC-seq data. Overall, ConDecon significantly enhances our understanding of dynamic cellular processes within bulk tissue samples.
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Affiliation(s)
- Rachael G Aubin
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
| | - Javier Montelongo
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
| | - Robert Hu
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
| | - Pablo G Camara
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3700 Hamilton Walk, Philadelphia, PA 19104
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49
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Haeusner S, Jauković A, Kupczyk E, Herrmann M. Review: cellularity in bone marrow autografts for bone and fracture healing. Am J Physiol Cell Physiol 2023; 324:C517-C531. [PMID: 36622067 DOI: 10.1152/ajpcell.00482.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The use of autografts, as primary cell and tissue source, is the current gold standard approach to treat critical size bone defects and nonunion defects. The unique mixture of the autografts, containing bony compartments and bone marrow (BM), delivers promising results. Although BM mesenchymal stromal cells (BM-MSCs) still represent a major target for various healing approaches in current preclinical research and respective clinical trials, their occurrence in the human BM is typically low. In vitro expansion of this cell type is regulatory challenging as well as time and cost intensive. Compared with marginal percentages of resident BM-MSCs in BM, BM mononuclear cells (BM-MNCs) contained in BM aspirates, concentrates, and bone autografts represent a readily available abundant cell source, applicable within hours during surgical procedures without the need for time-consuming and regulatory challenging cell expansion. This benefit is one reason why autografting has become a clinical standard procedure. However, the exact anatomy and cellularity of BM-MNCs in humans, which is strongly correlated to their unique mode of action and wide application range remains to be elucidated. The aim of this review was to present an overview of the current knowledge on these specific cell types found in human BM, emphasize the contribution of BM-MNCs in bone healing, highlight donor site dependence, and discuss limitations in the current isolation and subsequent characterization procedures. Hereby, the most recent and relevant examples of human BM-MNC cell characterization, flow cytometric analyses, and findings are summarized, with a strong focus on bone therapy.
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Affiliation(s)
- S Haeusner
- IZKF Group Tissue Regeneration in Musculoskeletal Diseases, University Hospital of Wuerzburg, Wuerzburg, Germany.,Bernhard-Heine-Center for Locomotion Research, University of Wuerzburg, Wuerzburg, Germany
| | - A Jauković
- Group for Hematology and Stem Cells, Institute for Medical Research, University of Belgrade, Belgrade, Serbia
| | - E Kupczyk
- Department of Trauma, Hand, Plastic and Reconstructive Surgery, University Hospital of Wuerzburg, Wuerzburg, Germany
| | - M Herrmann
- IZKF Group Tissue Regeneration in Musculoskeletal Diseases, University Hospital of Wuerzburg, Wuerzburg, Germany.,Bernhard-Heine-Center for Locomotion Research, University of Wuerzburg, Wuerzburg, Germany
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50
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Lotfollahi M, Rybakov S, Hrovatin K, Hediyeh-Zadeh S, Talavera-López C, Misharin AV, Theis FJ. Biologically informed deep learning to query gene programs in single-cell atlases. Nat Cell Biol 2023; 25:337-350. [PMID: 36732632 PMCID: PMC9928587 DOI: 10.1038/s41556-022-01072-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 12/08/2022] [Indexed: 02/04/2023]
Abstract
The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.
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Affiliation(s)
- Mohammad Lotfollahi
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Sergei Rybakov
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Karin Hrovatin
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Soroor Hediyeh-Zadeh
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Bioinformatics Division, WEHI, Melbourne, Victoria, Australia
| | - Carlos Talavera-López
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Division of Infectious Diseases and Tropical Medicine, Ludwig-Maximilian-Universität Klinikum, Munich, Germany
| | - Alexander V Misharin
- Division of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Wellcome Sanger Institute, Cambridge, UK.
- Department of Mathematics, Technical University of Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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