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Gupta SD, Sachs Z. Novel single-cell technologies in acute myeloid leukemia research. Transl Res 2017; 189:123-135. [PMID: 28802867 PMCID: PMC6584944 DOI: 10.1016/j.trsl.2017.07.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 07/18/2017] [Accepted: 07/20/2017] [Indexed: 12/29/2022]
Abstract
Acute myeloid leukemia (AML) is a lethal malignancy because patients who initially respond to chemotherapy eventually relapse with treatment refractory disease. Relapse is caused by leukemia stem cells (LSCs) that reestablish the disease through self-renewal. Self-renewal is the ability of a stem cell to produce copies of itself and give rise to progeny cells. Therefore, therapeutic strategies eradicating LSCs are essential to prevent relapse and achieve long-term remission in AML. AML is a heterogeneous disease both at phenotypic and genotypic levels, and this heterogeneity extends to LSCs. Classical studies in AML have aimed at characterization of the bulk tumor population, thereby masking cellular heterogeneity. Single-cell approaches provide a novel opportunity to elucidate molecular mechanisms in heterogeneous diseases such as AML. In recent years, major advancements in single-cell measurement systems have revolutionized our understanding of the pathophysiology of AML and enabled the characterization of LSCs. Identifying the molecular mechanisms critical to AML LSCs will aid in the development of targeted therapeutic strategies to combat this deadly disease.
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Affiliation(s)
- Soumyasri Das Gupta
- Division of Hematology, Oncology, and Transplantation, Department Medicine, University of Minnesota, Minneapolis, Minn
| | - Zohar Sachs
- Division of Hematology, Oncology, and Transplantation, Department Medicine, University of Minnesota, Minneapolis, Minn; Masonic Cancer Center, University of Minnesota, Minneapolis, Minn.
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Sachs K, Itani S, Fitzgerald J, Schoeberl B, Nolan GP, Tomlin CJ. Single timepoint models of dynamic systems. Interface Focus 2014; 3:20130019. [PMID: 24511382 DOI: 10.1098/rsfs.2013.0019] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Many interesting studies aimed at elucidating the connectivity structure of biomolecular pathways make use of abundance measurements, and employ statistical and information theoretic approaches to assess connectivities. These studies often do not address the effects of the dynamics of the underlying biological system, yet dynamics give rise to impactful issues such as timepoint selection and its effect on structure recovery. In this work, we study conditions for reliable retrieval of the connectivity structure of a dynamic system, and the impact of dynamics on structure-learning efforts. We encounter an unexpected problem not previously described in elucidating connectivity structure from dynamic systems, show how this confounds structure learning of the system and discuss possible approaches to overcome the confounding effect. Finally, we test our hypotheses on an accurate dynamic model of the IGF signalling pathway. We use two structure-learning methods at four time points to contrast the performance and robustness of those methods in terms of recovering correct connectivity.
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Affiliation(s)
- K Sachs
- Department of Microbiology and Immunology , Stanford University School of Medicine , Stanford, CA , USA
| | - S Itani
- Department of Electrical Engineering and Computer Sciences , University of California at Berkeley , Berkeley, CA , USA
| | | | - B Schoeberl
- Merrimack Pharmaceuticals , Cambridge, MA , USA
| | - G P Nolan
- Department of Microbiology and Immunology , Stanford University School of Medicine , Stanford, CA , USA
| | - C J Tomlin
- Department of Electrical Engineering and Computer Sciences , University of California at Berkeley , Berkeley, CA , USA
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A deep profiler's guide to cytometry. Trends Immunol 2012; 33:323-32. [PMID: 22476049 DOI: 10.1016/j.it.2012.02.010] [Citation(s) in RCA: 497] [Impact Index Per Article: 41.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2012] [Revised: 02/21/2012] [Accepted: 02/27/2012] [Indexed: 01/11/2023]
Abstract
In recent years, advances in technology have provided us with tools to quantify the expression of multiple genes in individual cells. The ability to measure simultaneously multiple genes in the same cell is necessary to resolve the great diversity of cell subsets, as well as to define their function in the host. Fluorescence-based flow cytometry is the benchmark for this; with it, we can quantify 18 proteins per cell, at >10 000 cells/s. Mass cytometry is a new technology that promises to extend these capabilities significantly. Immunophenotyping by mass spectrometry provides the ability to measure >36 proteins at a rate of 1000 cells/s. We review these cytometric technologies, capable of high-content, high-throughput single-cell assays.
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McGuire MF, Iyengar MS, Mercer DW. Computational approaches for translational clinical research in disease progression. J Investig Med 2012; 59:893-903. [PMID: 21712727 DOI: 10.2310/jim.0b013e318224d8cc] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Today, there is an ever-increasing amount of biological and clinical data available that could be used to enhance a systems-based understanding of disease progression through innovative computational analysis. In this article, we review a selection of published research regarding computational methods, primarily from systems biology, which support translational research from the molecular level to the bedside, with a focus on applications in trauma and critical care. Trauma is the leading cause of mortality in Americans younger than 45 years, and its rapid progression offers both opportunities and challenges for computational analysis of trends in molecular patterns associated with outcomes and therapeutic interventions.This review presents methods and domain-specific examples that may inspire the development of new algorithms and computational methods that use both molecular and clinical data for diagnosis, prognosis, and therapy in disease progression.
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Affiliation(s)
- Mary F McGuire
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, TX 77030, USA.
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McGuire MF, Iyengar MS, Mercer DW. Computational approaches for translational clinical research in disease progression. J Investig Med 2011; 59. [PMID: 21712727 PMCID: PMC3196807 DOI: 10.231/jim.0b013e318224d8cc] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Today, there is an ever-increasing amount of biological and clinical data available that could be used to enhance a systems-based understanding of disease progression through innovative computational analysis. In this article, we review a selection of published research regarding computational methods, primarily from systems biology, which support translational research from the molecular level to the bedside, with a focus on applications in trauma and critical care. Trauma is the leading cause of mortality in Americans younger than 45 years, and its rapid progression offers both opportunities and challenges for computational analysis of trends in molecular patterns associated with outcomes and therapeutic interventions.This review presents methods and domain-specific examples that may inspire the development of new algorithms and computational methods that use both molecular and clinical data for diagnosis, prognosis, and therapy in disease progression.
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Affiliation(s)
- Mary F. McGuire
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston TX USA,Contact: Mary F. McGuire, School of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin, #600, Houston, TX 77030 USA, , 1-832-364-6734
| | - M. Sriram Iyengar
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston TX USA
| | - David W. Mercer
- Department of Surgery, University of Nebraska Medical Center, Omaha NE USA
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Zhu J, Chen Y, Leonardson AS, Wang K, Lamb JR, Emilsson V, Schadt EE. Characterizing dynamic changes in the human blood transcriptional network. PLoS Comput Biol 2010; 6:e1000671. [PMID: 20168994 PMCID: PMC2820517 DOI: 10.1371/journal.pcbi.1000671] [Citation(s) in RCA: 53] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2009] [Accepted: 01/07/2010] [Indexed: 11/29/2022] Open
Abstract
Gene expression data generated systematically in a given system over multiple time points provides a source of perturbation that can be leveraged to infer causal relationships among genes explaining network changes. Previously, we showed that food intake has a large impact on blood gene expression patterns and that these responses, either in terms of gene expression level or gene-gene connectivity, are strongly associated with metabolic diseases. In this study, we explored which genes drive the changes of gene expression patterns in response to time and food intake. We applied the Granger causality test and the dynamic Bayesian network to gene expression data generated from blood samples collected at multiple time points during the course of a day. The simulation result shows that combining many short time series together is as powerful to infer Granger causality as using a single long time series. Using the Granger causality test, we identified genes that were supported as the most likely causal candidates for the coordinated temporal changes in the network. These results show that PER1 is a key regulator of the blood transcriptional network, in which multiple biological processes are under circadian rhythm regulation. The fasted and fed dynamic Bayesian networks showed that over 72% of dynamic connections are self links. Finally, we show that different processes such as inflammation and lipid metabolism, which are disconnected in the static network, become dynamically linked in response to food intake, which would suggest that increasing nutritional load leads to coordinate regulation of these biological processes. In conclusion, our results suggest that food intake has a profound impact on the dynamic co-regulation of multiple biological processes, such as metabolism, immune response, apoptosis and circadian rhythm. The results could have broader implications for the design of studies of disease association and drug response in clinical trials. Peripheral blood is the most readily accessible human tissue for clinical studies and experimental research more generally. Large-scale molecular profiling technologies have enabled measurements of mRNA expression on the scale of whole genomes. Understanding the relationships between human blood gene expression profiles and clinical traits is extremely useful for inferring causal factors for human disease and for studying drug response. Biological pathways and the complex behaviors they induce are not static, but change dynamically in response to external factors such as intake/uptake of nutrients and administration of drugs. We employed a randomized, two-arm cross-over design to assess the effects of fasting and feeding on the dynamic changes of blood transcriptional network. Our work has convincingly shown that feeding or increasing nutritional load affects the human circadian rhythm system which connects to other biological processes including metabolic and immune responses. We believe this is a first step towards a more comprehensive population-based study that seeks to connect changes in the blood transcriptome to drug response, and to disease and biology more generally.
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Affiliation(s)
- Jun Zhu
- Department of Genetics, Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co., Inc., Seattle, Washington, United States of America
- * E-mail: (JZ); (EES)
| | - Yanqing Chen
- Department of Genetics, Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co., Inc., Seattle, Washington, United States of America
| | - Amy S. Leonardson
- Department of Genetics, Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co., Inc., Seattle, Washington, United States of America
| | - Kai Wang
- Department of Genetics, Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co., Inc., Seattle, Washington, United States of America
| | - John R. Lamb
- Department of Genetics, Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co., Inc., Seattle, Washington, United States of America
| | - Valur Emilsson
- Molecular Profiling and Research Informatics Department, Merck Research Laboratories, Rahway, New Jersey, United States of America
| | - Eric E. Schadt
- Department of Genetics, Rosetta Inpharmatics, LLC, a wholly owned subsidiary of Merck & Co., Inc., Seattle, Washington, United States of America
- * E-mail: (JZ); (EES)
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