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Ghanegolmohammadi F, Eslami M, Ohya Y. Systematic data analysis pipeline for quantitative morphological cell phenotyping. Comput Struct Biotechnol J 2024; 23:2949-2962. [PMID: 39104709 PMCID: PMC11298594 DOI: 10.1016/j.csbj.2024.07.012] [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: 05/06/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/07/2024] Open
Abstract
Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.
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Affiliation(s)
- Farzan Ghanegolmohammadi
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab, Schepen’s Eye Research Institute of Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, USA
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
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2
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Sun X, Wu LF, Altschuler SJ, Hata AN. Targeting therapy-persistent residual disease. NATURE CANCER 2024; 5:1298-1304. [PMID: 39289594 DOI: 10.1038/s43018-024-00819-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 07/26/2024] [Indexed: 09/19/2024]
Abstract
Disease relapse driven by acquired drug resistance limits the effectiveness of most systemic anti-cancer agents. Targeting persistent cancer cells in residual disease before relapse has emerged as a potential strategy for enhancing the efficacy and the durability of current therapies. However, barriers remain to implementing persister-directed approaches in the clinic. This Perspective discusses current preclinical and clinical complexities and outlines key steps toward the development of clinical strategies that target therapy-persistent residual disease.
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Affiliation(s)
- Xiaoxiao Sun
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Lani F Wu
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
| | - Steven J Altschuler
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA.
| | - Aaron N Hata
- Massachusetts General Hospital Cancer Center, Boston, MA, USA.
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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3
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Yu M, Li YJ, Yang YN, Xue CD, Xin GY, Liu B, Qin KR. A microfluidic array enabling generation of identical biochemical stimulating signals to trapped biological cells for single-cell dynamics. Talanta 2024; 267:125172. [PMID: 37699267 DOI: 10.1016/j.talanta.2023.125172] [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: 03/28/2023] [Revised: 08/24/2023] [Accepted: 09/05/2023] [Indexed: 09/14/2023]
Abstract
Microfluidic-based analyses of single-cell dynamics in response to dynamic biochemical signals are emerging as pivotal approaches for investigating the effects of extracellular microenvironmental biochemical factors on cellular structure, function, and behavior. However, current devices often fail to consistently apply identical dynamic biochemical signals to trapped cells. In this study, we introduce a novel radially distributed single-cell trapping microfluidic array, designed to quantitatively and consistently apply identical biochemical stimulating signals to each trapped cell. Numerical simulations were employed to optimize microchannel geometry, enhancing trapping efficiency while minimizing signal distortion. Experimental validation demonstrated the trapping success rate and the single-cell trapping efficiency exceeding 99% and 85%, respectively. The microarray's capability to deliver identical dynamic biochemical stimulating signals, with various waveforms, to each unit was confirmed through fluorescein transport tests. Furthermore, we examined the intracellular calcium dynamics of U-2 OS human osteosarcoma cells in response to dynamic ATP signals, observing both single-peak calcium responses and calcium oscillations, which were modelled by a second-order system with a natural frequency of 1.6 mHz. Overall, our proposed microfluidic array offers a robust and valuable framework for advancing the understanding of single-cell dynamics.
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Affiliation(s)
- Miao Yu
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, No. 2, Linggong Rd., Dalian, 116024, China
| | - Yong-Jiang Li
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, No. 2, Linggong Rd., Dalian, 116024, China.
| | - Yu-Nong Yang
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, No. 2, Linggong Rd., Dalian, 116024, China
| | - Chun-Dong Xue
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, No. 2, Linggong Rd., Dalian, 116024, China
| | - Gui-Yang Xin
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, No. 2, Linggong Rd., Dalian, 116024, China
| | - Bo Liu
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, No. 2, Linggong Rd., Dalian, 116024, China
| | - Kai-Rong Qin
- School of Biomedical Engineering, Faculty of Medicine, Dalian University of Technology, No. 2, Linggong Rd., Dalian, 116024, China.
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4
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Küchenhoff L, Lukas P, Metz-Zumaran C, Rothhaar P, Ruggieri A, Lohmann V, Höfer T, Stanifer ML, Boulant S, Talemi SR, Graw F. Extended methods for spatial cell classification with DBSCAN-CellX. Sci Rep 2023; 13:18868. [PMID: 37914751 PMCID: PMC10620226 DOI: 10.1038/s41598-023-45190-4] [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: 03/28/2023] [Accepted: 10/17/2023] [Indexed: 11/03/2023] Open
Abstract
Local cell densities and positioning within cellular monolayers and stratified epithelia have important implications for cell interactions and the functionality of various biological processes. To analyze the relationship between cell localization and tissue physiology, density-based clustering algorithms, such as DBSCAN, allow for a detailed characterization of the spatial distribution and positioning of individual cells. However, these methods rely on predefined parameters that influence the outcome of the analysis. With varying cell densities in cell cultures or tissues impacting cell sizes and, thus, cellular proximities, these parameters need to be carefully chosen. In addition, standard DBSCAN approaches generally come short in appropriately identifying individual cell positions. We therefore developed three extensions to the standard DBSCAN-algorithm that provide: (i) an automated parameter identification to reliably identify cell clusters, (ii) an improved identification of cluster edges; and (iii) an improved characterization of the relative positioning of cells within clusters. We apply our novel methods, which are provided as a user-friendly OpenSource-software package (DBSCAN-CellX), to cellular monolayers of different cell lines. Thereby, we show the importance of the developed extensions for the appropriate analysis of cell culture experiments to determine the relationship between cell localization and tissue physiology.
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Affiliation(s)
- Leonie Küchenhoff
- BioQuant-Center for Quantitative Biology, Heidelberg University, 69120, Heidelberg, Germany
| | - Pascal Lukas
- BioQuant-Center for Quantitative Biology, Heidelberg University, 69120, Heidelberg, Germany
| | - Camila Metz-Zumaran
- Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Paul Rothhaar
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Alessia Ruggieri
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Volker Lohmann
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Thomas Höfer
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Megan L Stanifer
- Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Steeve Boulant
- Department of Molecular Genetics and Microbiology, College of Medicine, University of Florida, Gainesville, FL, USA
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
| | - Soheil Rastgou Talemi
- Department of Infectious Diseases, Virology, Center for Integrative Infectious Disease Research (CIID), Heidelberg University, 69120, Heidelberg, Germany
- Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Frederik Graw
- BioQuant-Center for Quantitative Biology, Heidelberg University, 69120, Heidelberg, Germany.
- Interdisciplinary Center for Scientific Computing, Heidelberg University, 69120, Heidelberg, Germany.
- Department of Medicine 5, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 12, 91054, Erlangen, Germany.
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5
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Stossi F, Singh PK, Safari K, Marini M, Labate D, Mancini MA. High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery. Biochem Pharmacol 2023; 216:115770. [PMID: 37660829 DOI: 10.1016/j.bcp.2023.115770] [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: 06/01/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 09/05/2023]
Abstract
Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods. Here, we will summarize some of the efforts in this vast and growing field, focusing on phenotypic screens (e.g., Cell Painting), single cell analytics and quality control, with particular attention to environmental toxicology and drug screening. We will discuss advantages and limitations of high throughput assays with various end points and levels of complexity.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA.
| | - Pankaj K Singh
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Kazem Safari
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
| | - Michela Marini
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Demetrio Labate
- GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Department of Mathematics, University of Houston, Houston, TX, USA
| | - Michael A Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA; GCC Center for Advanced Microscopy and Image Informatics, Houston, TX, USA; Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, TX, USA
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6
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Reimann A, Kull T, Wang W, Dettinger P, Loeffler D, Schroeder T. Embryonic stem cell ERK, AKT, plus STAT3 response dynamics combinatorics are heterogeneous but NANOG state independent. Stem Cell Reports 2023:S2213-6711(23)00142-X. [PMID: 37207650 DOI: 10.1016/j.stemcr.2023.04.008] [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: 08/16/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/21/2023] Open
Abstract
Signaling is central in cell fate regulation, and relevant information is encoded in its activity over time (i.e., dynamics). However, simultaneous dynamics quantification of several pathways in single mammalian stem cells has not yet been accomplished. Here we generate mouse embryonic stem cell (ESC) lines simultaneously expressing fluorescent reporters for ERK, AKT, and STAT3 signaling activity, which all control pluripotency. We quantify their single-cell dynamics combinations in response to different self-renewal stimuli and find striking heterogeneity for all pathways, some dependent on cell cycle but not pluripotency states, even in ESC populations currently assumed to be highly homogeneous. Pathways are mostly independently regulated, but some context-dependent correlations exist. These quantifications reveal surprising single-cell heterogeneity in the important cell fate control layer of signaling dynamics combinations and raise fundamental questions about the role of signaling in (stem) cell fate control.
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Affiliation(s)
- Andreas Reimann
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | - Tobias Kull
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | - Weijia Wang
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | - Philip Dettinger
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | - Dirk Loeffler
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland
| | - Timm Schroeder
- Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
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7
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Howard GR, Jost TA, Yankeelov TE, Brock A. Quantification of long-term doxorubicin response dynamics in breast cancer cell lines to direct treatment schedules. PLoS Comput Biol 2022; 18:e1009104. [PMID: 35358172 PMCID: PMC9004764 DOI: 10.1371/journal.pcbi.1009104] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 04/12/2022] [Accepted: 02/07/2022] [Indexed: 01/05/2023] Open
Abstract
While acquired chemoresistance is recognized as a key challenge to treating many types of cancer, the dynamics with which drug sensitivity changes after exposure are poorly characterized. Most chemotherapeutic regimens call for repeated dosing at regular intervals, and if drug sensitivity changes on a similar time scale then the treatment interval could be optimized to improve treatment performance. Theoretical work suggests that such optimal schedules exist, but experimental confirmation has been obstructed by the difficulty of deconvolving the simultaneous processes of death, adaptation, and regrowth taking place in cancer cell populations. Here we present a method of optimizing drug schedules in vitro through iterative application of experimentally calibrated models, and demonstrate its ability to characterize dynamic changes in sensitivity to the chemotherapeutic doxorubicin in three breast cancer cell lines subjected to treatment schedules varying in concentration, interval between pulse treatments, and number of sequential pulse treatments. Cell populations are monitored longitudinally through automated imaging for 600–800 hours, and this data is used to calibrate a family of cancer growth models, each consisting of a system of ordinary differential equations, derived from the bi-exponential model which characterizes resistant and sensitive subpopulations. We identify a model incorporating both a period of growth arrest in surviving cells and a delay in the death of chemosensitive cells which outperforms the original bi-exponential growth model in Akaike Information Criterion based model selection, and use the calibrated model to quantify the performance of each drug schedule. We find that the inter-treatment interval is a key variable in determining the performance of sequential dosing schedules and identify an optimal retreatment time for each cell line which extends regrowth time by 40%-239%, demonstrating that the time scale of changes in chemosensitivity following doxorubicin exposure allows optimization of drug scheduling by varying this inter-treatment interval. Acquired chemoresistance is a common cause of treatment failure in cancer. The scheduling of a multi-dose course of chemotherapeutic treatment may influence the dynamics of acquired chemoresistance, and drug schedule optimization may increase the duration of effectiveness of a particular chemotherapeutic agent for a particular patient. Here we present a method for experimentally optimizing an in vitro drug schedule through iterative rounds of experimentation and computational analysis, and demonstrate the method’s ability to improve the performance of doxorubicin treatment in three breast carcinoma cell lines. Specifically, we find that the interval between drug exposures can be optimized while holding drug concentration and number of treatments constant, suggesting that this may be a key variable to explore in future drug schedule optimization efforts. We further use this method’s model calibration and selection process to extract information about the underlying biology of the doxorubicin response, and find that the incorporation of delays on both cell death and regrowth are necessary for accurate parameterization of cell growth data.
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Affiliation(s)
- Grant R. Howard
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Tyler A. Jost
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
| | - Thomas E. Yankeelov
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Diagnostic Medicine, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Oncology, The University of Texas at Austin, Austin, Texas, United States of America
- Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, Texas, United States of America
- Livestrong Cancer Institutes, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Amy Brock
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, United States of America
- Department of Cell and Molecular Biology, The University of Texas at Austin, Austin, Texas, United States of America
- * E-mail:
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8
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Stossi F, Singh PK, Mistry RM, Johnson HL, Dandekar RD, Mancini MG, Szafran AT, Rao AU, Mancini MA. Quality Control for Single Cell Imaging Analytics Using Endocrine Disruptor-Induced Changes in Estrogen Receptor Expression. ENVIRONMENTAL HEALTH PERSPECTIVES 2022; 130:27008. [PMID: 35167326 PMCID: PMC8846386 DOI: 10.1289/ehp9297] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 01/16/2022] [Accepted: 01/20/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Diverse toxicants and mixtures that affect hormone responsive cells [endocrine disrupting chemicals (EDCs)] are highly pervasive in the environment and are directly linked to human disease. They often target the nuclear receptor family of transcription factors modulating their levels and activity. Many high-throughput assays have been developed to query such toxicants; however, single-cell analysis of EDC effects on endogenous receptors has been missing, in part due to the lack of quality control metrics to reproducibly measure cell-to-cell variability in responses. OBJECTIVE We began by developing single-cell imaging and informatic workflows to query whether the single cell distribution of the estrogen receptor-α (ER), used as a model system, can be used to measure effects of EDCs in a sensitive and reproducible manner. METHODS We used high-throughput microscopy, coupled with image analytics to measure changes in single cell ER nuclear levels on treatment with ∼100 toxicants, over a large number of biological and technical replicates. RESULTS We developed a two-tiered quality control pipeline for single cell analysis and tested it against a large set of biological replicates, and toxicants from the EPA and Agency for Toxic Substances and Disease Registry lists. We also identified a subset of potentially novel EDCs that were active only on the endogenous ER level and activity as measured by single molecule RNA fluorescence in situ hybridization (RNA FISH). DISCUSSION We demonstrated that the distribution of ER levels per cell, and the changes upon chemical challenges were remarkably stable features; and importantly, these features could be used for quality control and identification of endocrine disruptor toxicants with high sensitivity. When coupled with orthogonal assays, ER single cell distribution is a valuable resource for high-throughput screening of environmental toxicants. https://doi.org/10.1289/EHP9297.
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Affiliation(s)
- Fabio Stossi
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA
- Integrated Microscopy Core, Baylor College of Medicine, Houston, Texas, USA
- GCC Center for Advanced Microscopy and Image Informatics, Houston, Texas, USA
| | - Pankaj K. Singh
- GCC Center for Advanced Microscopy and Image Informatics, Houston, Texas, USA
- Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, Texas, USA
| | - Ragini M. Mistry
- GCC Center for Advanced Microscopy and Image Informatics, Houston, Texas, USA
| | - Hannah L. Johnson
- Integrated Microscopy Core, Baylor College of Medicine, Houston, Texas, USA
- GCC Center for Advanced Microscopy and Image Informatics, Houston, Texas, USA
| | | | - Maureen G. Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA
| | - Adam T. Szafran
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA
| | - Arvind U. Rao
- GCC Center for Advanced Microscopy and Image Informatics, Houston, Texas, USA
- Department of Computational Medicine and Bioinformatics, Biostatistics, Biomedical Engineering & Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan, USA
| | - Michael A. Mancini
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA
- Department of Pharmacology and Chemical Biology, Baylor College of Medicine, Houston, Texas, USA
- Integrated Microscopy Core, Baylor College of Medicine, Houston, Texas, USA
- Dan L. Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, Texas, USA
- GCC Center for Advanced Microscopy and Image Informatics, Houston, Texas, USA
- Center for Translational Cancer Research, Institute of Biosciences and Technology, Texas A&M University, Houston, Texas, USA
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9
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Yi E, Gujar AD, Guthrie M, Kim H, Zhao D, Johnson KC, Amin SB, Costa ML, Yu Q, Das S, Jillette N, Clow PA, Cheng AW, Verhaak RGW. Live-Cell Imaging Shows Uneven Segregation of Extrachromosomal DNA Elements and Transcriptionally Active Extrachromosomal DNA Hubs in Cancer. Cancer Discov 2022; 12:468-483. [PMID: 34819316 PMCID: PMC8831456 DOI: 10.1158/2159-8290.cd-21-1376] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 10/27/2021] [Accepted: 11/01/2021] [Indexed: 11/16/2022]
Abstract
Oncogenic extrachromosomal DNA elements (ecDNA) play an important role in tumor evolution, but our understanding of ecDNA biology is limited. We determined the distribution of single-cell ecDNA copy number across patient tissues and cell line models and observed how cell-to-cell ecDNA frequency varies greatly. The exceptional intratumoral heterogeneity of ecDNA suggested ecDNA-specific replication and propagation mechanisms. To evaluate the transfer of ecDNA genetic material from parental to offspring cells during mitosis, we established the CRISPR-based ecTag method. ecTag leverages ecDNA-specific breakpoint sequences to tag ecDNA with fluorescent markers in living cells. Applying ecTag during mitosis revealed disjointed ecDNA inheritance patterns, enabling rapid ecDNA accumulation in individual cells. After mitosis, ecDNAs clustered into ecDNA hubs, and ecDNA hubs colocalized with RNA polymerase II, promoting transcription of cargo oncogenes. Our observations provide direct evidence for uneven segregation of ecDNA and shed new light on mechanisms through which ecDNAs contribute to oncogenesis. SIGNIFICANCE: ecDNAs are vehicles for oncogene amplification. The circular nature of ecDNA affords unique properties, such as mobility and ecDNA-specific replication and segregation behavior. We uncovered fundamental ecDNA properties by tracking ecDNAs in live cells, highlighting uneven and random segregation and ecDNA hubs that drive cargo gene transcription.See related commentary by Henssen, p. 293.This article is highlighted in the In This Issue feature, p. 275.
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Affiliation(s)
- Eunhee Yi
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Amit D Gujar
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Molly Guthrie
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Hoon Kim
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
- Department of Biopharmaceutical Convergence, Department of Pharmacy, Sungkyunkwan University, Suwon-si, Gyeong gi-do, Korea
| | - Dacheng Zhao
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Kevin C Johnson
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Samirkumar B Amin
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Megan L Costa
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Qianru Yu
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Sunit Das
- Arthur and Sonia Labatt Brain Tumour Research Centre, Hospital for SickKids, University of Toronto, Toronto, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Division of Neurosurgery, Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, Canada
| | | | - Patricia A Clow
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut
| | - Albert W Cheng
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut.
- Department of Genetics and Genome Sciences, University of Connecticut Health Center, Farmington, Connecticut
- Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut
| | - Roel G W Verhaak
- The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut.
- Department of Neurosurgery, Amsterdam UMC, Amsterdam, the Netherlands
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10
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Wang L, Wang X, Wang T, Zhuang Y, Wang G. Multi-omics analysis defines 5-fluorouracil drug resistance in 3D HeLa carcinoma cell model. BIORESOUR BIOPROCESS 2021; 8:135. [PMID: 38650282 PMCID: PMC10991626 DOI: 10.1186/s40643-021-00486-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 12/09/2021] [Indexed: 11/10/2022] Open
Abstract
Cervical cancer is a serious health problem in women around the globe. However, the use of clinical drug is seriously dampened by the development of drug resistance. Efficient in vitro tumor model is essential to improve the efficiency of drug screening and the accuracy of clinical application. Multicellular tumor spheroids (MTSs) can in a way recapitulates tumor traits in vivo, thereby representing a powerful transitional model between 2D monolayer culture and xenograft. In this study, based on the liquid overlay method, a protocol for rapid generation of the MTSs with uniform size and high reproducibility in a high-throughput manner was established. As expected, the cytotoxicity results showed that there was enhanced 5-fluorouracil (5-FU) resistance of HeLa carcinoma cells in 3D MTSs than 2D monolayer culture with a resistance index of 5.72. In order to obtain a holistic view of the molecular mechanisms that drive 5-FU resistance in 3D HeLa carcinoma cells, a multi-omics study was applied to discover hidden biological regularities. It was observed that in the 3D MTSs mitochondrial function-related proteins and the metabolites of the tricarboxylic acid cycle (TCA cycle) were significantly decreased, and the cellular metabolism was shifted towards glycolysis. The differences in the protein synthesis, processing, and transportation between 2D monolayer cultures and 3D MTSs were significant, mainly in the heat shock protein family, with the up-regulation of protein folding function in endoplasmic reticulum (ER), which promoted the maintenance of ER homeostasis in the 3D MTSs. In addition, at the transcript and protein level, the expression of extracellular matrix (ECM) proteins (e.g., laminin and collagen) were up-regulated in the 3D MTSs, which enhanced the physical barrier of drug penetration. Summarizing, this study formulates a rapid, scalable and reproducible in vitro model of 3D MTS for drug screening purposes, and the findings establish a critical role of glycolytic metabolism, ER hemostasis and ECM proteins expression profiling in tumor chemoresistance of HeLa carcinoma cells towards 5-FU.
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Affiliation(s)
- Lin Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Xueting Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Tong Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
| | - Yingping Zhuang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China
- Qingdao Innovation Institute of East China University of Science and Technology, Shanghai, People's Republic of China
| | - Guan Wang
- State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, People's Republic of China.
- Qingdao Innovation Institute of East China University of Science and Technology, Shanghai, People's Republic of China.
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11
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Jones-Tabah J, Martin RD, Tanny JC, Clarke PBS, Hébert TE. High-Content Single-Cell Förster Resonance Energy Transfer Imaging of Cultured Striatal Neurons Reveals Novel Cross-Talk in the Regulation of Nuclear Signaling by Protein Kinase A and Extracellular Signal-Regulated Kinase 1/2. Mol Pharmacol 2021; 100:526-539. [PMID: 34503973 DOI: 10.1124/molpharm.121.000290] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 09/07/2021] [Indexed: 11/22/2022] Open
Abstract
Genetically encoded biosensors can be used to track signaling events in living cells by measuring changes in fluorescence emitted by one or more fluorescent proteins. Here, we describe the use of genetically encoded biosensors based on Förster resonance energy transfer (FRET), combined with high-content microscopy, to image dynamic signaling events simultaneously in thousands of neurons in response to drug treatments. We first applied this approach to examine intercellular variation in signaling responses among cultured striatal neurons stimulated with multiple drugs. Using high-content FRET imaging and immunofluorescence, we identified neuronal subpopulations with unique responses to pharmacological manipulation and used nuclear morphology to identify medium spiny neurons within these heterogeneous striatal cultures. Focusing on protein kinase A (PKA) and extracellular signal-regulated kinase 1/2 (ERK1/2) signaling in the cytoplasm and nucleus, we noted pronounced intercellular differences among putative medium spiny neurons, in both the magnitude and kinetics of signaling responses to drug application. Importantly, a conventional "bulk" analysis that pooled all cells in culture yielded a different rank order of drug potency than that revealed by single-cell analysis. Using a single-cell analytical approach, we dissected the relative contributions of PKA and ERK1/2 signaling in striatal neurons and unexpectedly identified a novel role for ERK1/2 in promoting nuclear activation of PKA in striatal neurons. This finding adds a new dimension of signaling crosstalk between PKA and ERK1/2 with relevance to dopamine D1 receptor signaling in striatal neurons. In conclusion, high-content single-cell imaging can complement and extend traditional population-level analyses and provides a novel vantage point from which to study cellular signaling. SIGNIFICANCE STATEMENT: High-content imaging revealed substantial intercellular variation in the magnitude and pattern of intracellular signaling events driven by receptor stimulation. Since individual neurons within the same population can respond differently to a given agonist, interpreting measures of intracellular signaling derived from the averaged response of entire neuronal populations may not always reflect what happened at the single-cell level. This study uses this approach to identify a new form of cross-talk between PKA and ERK1/2 signaling in the nucleus of striatal neurons.
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Affiliation(s)
- Jace Jones-Tabah
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
| | - Ryan D Martin
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
| | - Jason C Tanny
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
| | - Paul B S Clarke
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
| | - Terence E Hébert
- Department of Pharmacology and Therapeutics, McGill University, Montréal, Québec, Canada
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12
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A Microfluidic Array Device for Single Cell Capture and Intracellular Ca2+ Response Analysis Induced by Dynamic Biochemical Stimulus. Biosci Rep 2021; 41:229251. [PMID: 34269374 PMCID: PMC8319492 DOI: 10.1042/bsr20210719] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/29/2021] [Accepted: 07/13/2021] [Indexed: 01/09/2023] Open
Abstract
A microfluidic array was constructed for trapping single cell and loading identical dynamic biochemical stimulation for gain a better understanding of Ca2+ signalling in single cells by applying extracellular dynamic biochemical stimulus. This microfluidic array consists of multiple radially aligned flow channels with equal intersection angles, which was designed by a combination of stagnation point flow and physical barrier. Numerical simulation results and trajectory analysis shown the effectiveness of this single cell trapping device. Fluorescent experiment results demonstrated the effects of flow rate and frequency of dynamic stimulus on the profiles of biochemical concentration which exposed on captured cells. In this array chip, the captured single cells in each trapping channels were able to receive identical extracellular dynamic biochemical stimuli which being transmitted from the entrance at the middle of the microfluidic array. Besides, after loading dynamic Adenosine Triphosphate (ATP) stimulation on captured cells by this device, consistent average intracellular Ca2+ dynamics phase and cellular heterogeneity were observed in captured single K562 cells. Furthermore, this device is able to be used for investigating cellular respond in single cells to temporally varying environments by modulating the stimulation signal in terms of concentration, pattern, and duration of exposure.
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13
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Mendenhall AR, Martin GM, Kaeberlein M, Anderson RM. Cell-to-cell variation in gene expression and the aging process. GeroScience 2021; 43:181-196. [PMID: 33595768 PMCID: PMC8050212 DOI: 10.1007/s11357-021-00339-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 02/04/2021] [Indexed: 12/11/2022] Open
Abstract
There is tremendous variation in biological traits, and much of it is not accounted for by variation in DNA sequence, including human diseases and lifespan. Emerging evidence points to differences in the execution of the genetic program as a key source of variation, be it stochastic variation or programmed variation. Here we discuss variation in gene expression as an intrinsic property and how it could contribute to variation in traits, including the rate of aging. The review is divided into sections describing the historical context and evidence to date for nongenetic variation, the different approaches that may be used to detect nongenetic variation, and recent findings showing that the amount of variation in gene expression can be both genetically programmed and epigenetically controlled. Finally, we present evidence that changes in cell-to-cell variation in gene expression emerge as part of the aging process and may be linked to disease vulnerability as a function of age. These emerging concepts are likely to be important across the spectrum of biomedical research and may well underpin what we understand as biological aging.
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Affiliation(s)
- Alexander R Mendenhall
- Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA, USA.
- Nathan Shock Center for Excellence in the Basic Biology of Aging, School of Medicine, University of Washington, Seattle, WA, USA.
| | - George M Martin
- Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA, USA
- Nathan Shock Center for Excellence in the Basic Biology of Aging, School of Medicine, University of Washington, Seattle, WA, USA
| | - Matt Kaeberlein
- Department of Laboratory Medicine and Pathology, School of Medicine, University of Washington, Seattle, WA, USA
- Nathan Shock Center for Excellence in the Basic Biology of Aging, School of Medicine, University of Washington, Seattle, WA, USA
| | - Rozalyn M Anderson
- Department of Medicine, School of Medicine and Public Health, University of Wisconsin and Geriatric Research Education and Clinical Center, William S Middleton Memorial Veterans Hospital, Madison, WI, USA.
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14
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Pavlatovská B, Machálková M, Brisudová P, Pruška A, Štěpka K, Michálek J, Nečasová T, Beneš P, Šmarda J, Preisler J, Kozubek M, Navrátilová J. Lactic Acidosis Interferes With Toxicity of Perifosine to Colorectal Cancer Spheroids: Multimodal Imaging Analysis. Front Oncol 2020; 10:581365. [PMID: 33344237 PMCID: PMC7746961 DOI: 10.3389/fonc.2020.581365] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 10/20/2020] [Indexed: 11/13/2022] Open
Abstract
Colorectal cancer (CRC) is a disease with constantly increasing incidence and high mortality. The treatment efficacy could be curtailed by drug resistance resulting from poor drug penetration into tumor tissue and the tumor-specific microenvironment, such as hypoxia and acidosis. Furthermore, CRC tumors can be exposed to different pH depending on the position in the intestinal tract. CRC tumors often share upregulation of the Akt signaling pathway. In this study, we investigated the role of external pH in control of cytotoxicity of perifosine, the Akt signaling pathway inhibitor, to CRC cells using 2D and 3D tumor models. In 3D settings, we employed an innovative strategy for simultaneous detection of spatial drug distribution and biological markers of proliferation/apoptosis using a combination of mass spectrometry imaging and immunohistochemistry. In 3D conditions, low and heterogeneous penetration of perifosine into the inner parts of the spheroids was observed. The depth of penetration depended on the treatment duration but not on the external pH. However, pH alteration in the tumor microenvironment affected the distribution of proliferation- and apoptosis-specific markers in the perifosine-treated spheroid. Accurate co-registration of perifosine distribution and biological response in the same spheroid section revealed dynamic changes in apoptotic and proliferative markers occurring not only in the perifosine-exposed cells, but also in the perifosine-free regions. Cytotoxicity of perifosine to both 2D and 3D cultures decreased in an acidic environment below pH 6.7. External pH affects cytotoxicity of the other Akt inhibitor, MK-2206, in a similar way. Our innovative approach for accurate determination of drug efficiency in 3D tumor tissue revealed that cytotoxicity of Akt inhibitors to CRC cells is strongly dependent on pH of the tumor microenvironment. Therefore, the effect of pH should be considered during the design and pre-clinical/clinical testing of the Akt-targeted cancer therapy.
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Affiliation(s)
- Barbora Pavlatovská
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czechia
| | - Markéta Machálková
- Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czechia
| | - Petra Brisudová
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czechia
| | - Adam Pruška
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Karel Štěpka
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czechia
| | - Jan Michálek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czechia
| | - Tereza Nečasová
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czechia
| | - Petr Beneš
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czechia.,Center for Biological and Cellular Engineering, International Clinical Research Center, St. Anne's University Hospital, Brno, Czechia
| | - Jan Šmarda
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czechia
| | - Jan Preisler
- Department of Chemistry, Faculty of Science, Masaryk University, Brno, Czechia
| | - Michal Kozubek
- Centre for Biomedical Image Analysis, Faculty of Informatics, Masaryk University, Brno, Czechia
| | - Jarmila Navrátilová
- Department of Experimental Biology, Faculty of Science, Masaryk University, Brno, Czechia.,Center for Biological and Cellular Engineering, International Clinical Research Center, St. Anne's University Hospital, Brno, Czechia
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15
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Abstract
Biological systems are dynamic and display heterogeneity at all levels. Ubiquitous heterogeneity, here called for poikilosis, is an integral and important property of organisms and in molecules, systems and processes within them. Traditionally, heterogeneity in biology and experiments has been considered as unwanted noise, here poikilosis is shown to be the normal state. Acceptable variation ranges are called as lagom. Non-lagom, variations that are too extensive, have negative effects, which influence interconnected levels and once the variation is large enough cause a disease and can lead even to death. Poikilosis has numerous applications and consequences e.g. for how to design, analyze and report experiments, how to develop and apply prediction and modelling methods, and in diagnosis and treatment of diseases. Poikilosis-aware new and practical definitions are provided for life, death, senescence, disease, and lagom. Poikilosis is the first new unifying theory in biology since evolution and should be considered in every scientific study.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, 22184, Sweden
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16
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Abstract
Biological systems are dynamic and display heterogeneity at all levels. Ubiquitous heterogeneity, here called for poikilosis, is an integral and important property of organisms and in molecules, systems and processes within them. Traditionally, heterogeneity in biology and experiments has been considered as unwanted noise, here poikilosis is shown to be the normal state. Acceptable variation ranges are called as lagom. Non-lagom, variations that are too extensive, have negative effects, which influence interconnected levels and once the variation is large enough cause a disease and can lead even to death. Poikilosis has numerous applications and consequences e.g. for how to design, analyze and report experiments, how to develop and apply prediction and modelling methods, and in diagnosis and treatment of diseases. Poikilosis-aware new and practical definitions are provided for life, death, senescence, disease, and lagom. Poikilosis is the first new unifying theory in biology since evolution and should be considered in every scientific study.
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Affiliation(s)
- Mauno Vihinen
- Department of Experimental Medical Science, Lund University, Lund, 22184, Sweden
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17
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Ando T, Kato R, Honda H. Identification of an early cell fate regulator by detecting dynamics in transcriptional heterogeneity and co-regulation during astrocyte differentiation. NPJ Syst Biol Appl 2019; 5:18. [PMID: 31098297 PMCID: PMC6506553 DOI: 10.1038/s41540-019-0095-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Accepted: 04/16/2019] [Indexed: 01/19/2023] Open
Abstract
There are an increasing number of reports that characterize the temporal behavior of gene expression at the single-cell level during cell differentiation. Despite accumulation of data describing the heterogeneity of biological responses, the dynamics of gene expression heterogeneity and its regulation during the differentiation process have not been studied systematically. To understand transcriptional heterogeneity during astrocyte differentiation, we analyzed single-cell transcriptional data from cells representing the different stages of astrocyte differentiation. When we compared the transcriptional variability of co-expressed genes between the undifferentiated and differentiated states, we found that there was significant increase in transcriptional variability in the undifferentiated state. The genes showing large changes in both "variability" and "correlation" between neural stem cells (NSCs) and astrocytes were found to be functionally involved in astrocyte differentiation. We determined that these genes are potentially regulated by Ascl1, a previously known oscillatory gene in NSCs. Pharmacological blockade of Ntsr2, which is transcriptionally co-regulated with Ascl1, showed that Ntsr2 may play an important role in the differentiation from NSCs to astrocytes. This study shows the importance of characterizing transcriptional heterogeneity and rearrangement of the co-regulation network between different cell states. It also highlights the potential for identifying novel regulators of cell differentiation that will further increase our understanding of the molecular mechanisms underlying the differentiation process.
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Affiliation(s)
- Tatsuya Ando
- Department of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Aichi Japan
| | - Ryuji Kato
- Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya University, Nagoya, Aichi Japan
- Division of Micro-Nano Mechatronics, Institute of Nano-Life-Systems, Institutes of Innovation for Future Society, Nagoya University, Furocho, Chikusa-ku, Nagoya, 464-8602 Japan
| | - Hiroyuki Honda
- Department of Biomolecular Engineering, Graduate School of Engineering, Nagoya University, Nagoya, Aichi Japan
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18
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Campoy EM, Branham MT, Mayorga LS, Roqué M. Intratumor heterogeneity index of breast carcinomas based on DNA methylation profiles. BMC Cancer 2019; 19:328. [PMID: 30953488 PMCID: PMC6451266 DOI: 10.1186/s12885-019-5550-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 03/28/2019] [Indexed: 01/02/2023] Open
Abstract
Background Cancer cells evolve and constitute heterogeneous populations that fluctuate in space and time and are subjected to selection generating intratumor heterogeneity. This phenomenon is determined by the acquisition of genetic/epigenetic alterations and their selection over time which has clinical implications on drug resistance. Methods DNA extracted from different tumor cell populations (breast carcinomas, cancer cell lines and cellular clones) were analyzed by MS-MLPA. Methylation profiles were used to generate a heterogeneity index to quantify the magnitude of epigenetic heterogeneity in these populations. Cellular clones were obtained from single cells derived of MDA-MB 231 cancer cell lines applying serial limiting dilution method and morphology was analyzed by optical microscopy and flow cytometry. Clones characteristics were examined through cellular proliferation, migration capacity and apoptosis. Heterogeneity index was also calculated from beta values derived from methylation profiles of TCGA tumors. Results The study of methylation profiles of 23 fresh breast carcinomas revealed heterogeneous allele populations in these tumor pieces. With the purpose to measure the magnitude of epigenetic heterogeneity, we developed an heterogeneity index based on methylation information and observed that all tumors present their own heterogeneity level. Applying the index calculation in pure cancer cell populations such as cancer cell lines (MDA-MB 231, MCF-7, T47D, HeLa and K-562), we also observed epigenetic heterogeneity. In addition, we detected that clones obtained from the MDA-MB 231 cancer cell line generated their own new heterogeneity over time. Using TCGA tumors, we determined that the heterogeneity index correlated with prognostic and predictive factors like tumor size (p = 0.0088), number of affected axillary nodes (p = 0.007), estrogen receptor expression (p < 0.0001) and HER2 positivity (p = 0.0007). When we analyzed molecular subtypes we found that they presented different heterogeneity levels. Interestingly, we also observed that all mentioned tumor cell populations shared a similar Heterogeneity index (HI) mean. Conclusions Our results show that each tumor presents a unique epigenetic heterogeneity level, which is associated with prognostic and predictive factors. We also observe that breast tumor subtypes differ in terms of epigenetic heterogeneity, which could serve as a new contribution to understand the different prognosis of these groups.
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Affiliation(s)
- Emanuel M Campoy
- IHEM-CONICET, Av del libertador, 80, Mendoza, Argentina. .,Facultad de Ciencias Médicas, Av del Libertador 80, Universidad Nacional de Cuyo, Mendoza, Argentina.
| | | | - Luis S Mayorga
- IHEM-CONICET, Av del libertador, 80, Mendoza, Argentina.,Facultad de Ciencias Médicas, Av del Libertador 80, Universidad Nacional de Cuyo, Mendoza, Argentina
| | - María Roqué
- IHEM-CONICET, Av del libertador, 80, Mendoza, Argentina.,Facultad de Ciencias Exactas y Naturales, Padre Jorge Contreras 1300, Universidad Nacional de Cuyo, Mendoza, Argentina
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19
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Li X, Lee AP. High-throughput microfluidic single-cell trapping arrays for biomolecular and imaging analysis. Methods Cell Biol 2018; 148:35-50. [PMID: 30473073 PMCID: PMC6644722 DOI: 10.1016/bs.mcb.2018.09.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Single-cell analysis is of critical importance in revealing population heterogeneity, identifying minority sub-populations of interest, as well as discovering unique characteristics of individual cells. Microfluidic platforms work at the scale comparable to cell diameter and is suitable for single-cell manipulation. Here we present a microfluidic trapping array which is able to rapidly and deterministically trap single-cells in highly-packed microwells. This chapter first describes the design and fabrication protocols of the trapping array, and then presents its two representative applications: single-cell mRNA probing when integrated with a dielectrophoretic nanotweezer (DENT), and live-cell real-time imaging when combined with fluorescence lifetime imaging microscopy (FLIM). As the single-cell trapping efficiency is determined by the channel design instead of the flow rate, this trapping array can be coupled with different microfluidic sample processing units with different flow rates for various single-cell analyses.
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Affiliation(s)
- Xuan Li
- Department of Biomedical Engineering, University of California, Irvine, CA, United States
| | - Abraham P Lee
- Department of Biomedical Engineering, University of California, Irvine, CA, United States; Department of Mechanical & Aerospace Engineering, University of California, Irvine, CA, United States.
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20
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Singh M, Warita K, Warita T, Faeder JR, Lee REC, Sant S, Oltvai ZN. Shift from stochastic to spatially-ordered expression of serine-glycine synthesis enzymes in 3D microtumors. Sci Rep 2018; 8:9388. [PMID: 29925909 PMCID: PMC6010463 DOI: 10.1038/s41598-018-27266-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Accepted: 05/25/2018] [Indexed: 12/15/2022] Open
Abstract
Cell-to-cell differences in protein expression in normal tissues and tumors are a common phenomenon, but the underlying principles that govern this heterogeneity are largely unknown. Here, we show that in monolayer cancer cell-line cultures, the expression of the five metabolic enzymes of serine-glycine synthesis (SGS), including its rate-limiting enzyme, phosphoglycerate dehydrogenase (PHGDH), displays stochastic cell-to-cell variation. By contrast, in cancer cell line-derived three-dimensional (3D) microtumors PHGDH expression is restricted to the outermost part of the microtumors' outer proliferative cell layer, while the four other SGS enzymes display near uniform expression throughout the microtumor. A mathematical model suggests that metabolic stress in the microtumor core activates factors that restrict PHGDH expression. Thus, intracellular enzyme expression in growing cell ecosystems can shift to spatially ordered patterns in 3D structured environments due to emergent cell-cell communication, with potential implications for the design of effective anti-metabolic cancer therapies.
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Affiliation(s)
- Manjulata Singh
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Katsuhiko Warita
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Tomoko Warita
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - James R Faeder
- Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Robin E C Lee
- Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
| | - Shilpa Sant
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
- Department of Bioengineering, Swanson School of Engineering, McGowan Institute for Regenerative Medicine, and UPMC-Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Zoltán N Oltvai
- Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
- Department of Computational & Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA.
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21
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A hybrid mathematical modeling approach of the metabolic fate of a fluorescent sphingolipid analogue to predict cancer chemosensitivity. Comput Biol Med 2018; 97:8-20. [DOI: 10.1016/j.compbiomed.2018.04.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 04/10/2018] [Accepted: 04/12/2018] [Indexed: 01/25/2023]
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22
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Jolly MK, Kulkarni P, Weninger K, Orban J, Levine H. Phenotypic Plasticity, Bet-Hedging, and Androgen Independence in Prostate Cancer: Role of Non-Genetic Heterogeneity. Front Oncol 2018; 8:50. [PMID: 29560343 PMCID: PMC5845637 DOI: 10.3389/fonc.2018.00050] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2017] [Accepted: 02/19/2018] [Indexed: 12/21/2022] Open
Abstract
It is well known that genetic mutations can drive drug resistance and lead to tumor relapse. Here, we focus on alternate mechanisms-those without mutations, such as phenotypic plasticity and stochastic cell-to-cell variability that can also evade drug attacks by giving rise to drug-tolerant persisters. The phenomenon of persistence has been well-studied in bacteria and has also recently garnered attention in cancer. We draw a parallel between bacterial persistence and resistance against androgen deprivation therapy in prostate cancer (PCa), the primary standard care for metastatic disease. We illustrate how phenotypic plasticity and consequent mutation-independent or non-genetic heterogeneity possibly driven by protein conformational dynamics can stochastically give rise to androgen independence in PCa, and suggest that dynamic phenotypic plasticity should be considered in devising therapeutic dosing strategies designed to treat and manage PCa.
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Affiliation(s)
- Mohit Kumar Jolly
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
| | - Prakash Kulkarni
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, United States
| | - Keith Weninger
- Department of Physics, North Carolina State University, Raleigh, NC, United States
| | - John Orban
- Institute for Bioscience and Biotechnology Research, University of Maryland, Rockville, MD, United States
- Department of Chemistry and Biochemistry, University of Maryland, College Park, College Park, United States
| | - Herbert Levine
- Center for Theoretical Biological Physics, Rice University, Houston, TX, United States
- Department of Bioengineering, Rice University, Houston, TX, United States
- Department of Physics and Astronomy, Rice University, Houston, TX, United States
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23
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Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies. Methods Mol Biol 2018. [PMID: 29476461 DOI: 10.1007/978-1-4939-7680-5_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12-26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.
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24
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Ramon Y Cajal S, Castellvi J, Hümmer S, Peg V, Pelletier J, Sonenberg N. Beyond molecular tumor heterogeneity: protein synthesis takes control. Oncogene 2018; 37:2490-2501. [PMID: 29463861 PMCID: PMC5945578 DOI: 10.1038/s41388-018-0152-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2017] [Revised: 12/15/2017] [Accepted: 01/02/2018] [Indexed: 01/04/2023]
Abstract
One of the daunting challenges facing modern medicine lies in the understanding and treatment of tumor heterogeneity. Most tumors show intra-tumor heterogeneity at both genomic and proteomic levels, with marked impacts on the responses of therapeutic targets. Therapeutic target-related gene expression pathways are affected by hypoxia and cellular stress. However, the finding that targets such as eukaryotic initiation factor (eIF) 4E (and its phosphorylated form, p-eIF4E) are generally homogenously expressed throughout tumors, regardless of the presence of hypoxia or other cellular stress conditions, opens the exciting possibility that malignancies could be treated with therapies that combine targeting of eIF4E phosphorylation with immune checkpoint inhibitors or chemotherapy.
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Affiliation(s)
- Santiago Ramon Y Cajal
- Translational Molecular Pathology, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron 119-129, 08035, Barcelona, Spain. .,Pathology Department, Vall d'Hebron Hospital, 08035, Barcelona, Spain. .,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Madrid, Spain.
| | - Josep Castellvi
- Translational Molecular Pathology, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron 119-129, 08035, Barcelona, Spain.,Pathology Department, Vall d'Hebron Hospital, 08035, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Madrid, Spain
| | - Stefan Hümmer
- Translational Molecular Pathology, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron 119-129, 08035, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Madrid, Spain
| | - Vicente Peg
- Translational Molecular Pathology, Vall d'Hebron Research Institute (VHIR), Universitat Autònoma de Barcelona, Passeig Vall d'Hebron 119-129, 08035, Barcelona, Spain.,Pathology Department, Vall d'Hebron Hospital, 08035, Barcelona, Spain.,Spanish Biomedical Research Network Centre in Oncology (CIBERONC), Madrid, Spain
| | - Jerry Pelletier
- Department of Biochemistry and Goodman Cancer Research Center, McGill University, Montreal, QC, Canada
| | - Nahum Sonenberg
- Department of Biochemistry and Goodman Cancer Research Center, McGill University, Montreal, QC, Canada
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25
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Hussain S, Le Guezennec X, Yi W, Dong H, Chia J, Yiping K, Khoon LK, Bard F. Digging deep into Golgi phenotypic diversity with unsupervised machine learning. Mol Biol Cell 2017; 28:3686-3698. [PMID: 29021342 PMCID: PMC5706995 DOI: 10.1091/mbc.e17-06-0379] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2017] [Revised: 09/08/2017] [Accepted: 10/04/2017] [Indexed: 11/24/2022] Open
Abstract
Structural alterations of the Golgi apparatus may lead to phenotypes that human vision cannot easily discriminate. In this work, we present a high-content analysis framework including an unsupervised clustering step to automatically uncover Golgi phenotypic diversity. We use this deep phenotyping to quantitatively compare the effects of gene depletion. The synthesis of glycans and the sorting of proteins are critical functions of the Golgi apparatus and depend on its highly complex and compartmentalized architecture. High-content image analysis coupled to RNA interference screening offers opportunities to explore this organelle organization and the gene network underlying it. To date, image-based Golgi screens have based on a single parameter or supervised analysis with predefined Golgi structural classes. Here, we report the use of multiparametric data extracted from a single marker and a computational unsupervised analysis framework to explore Golgi phenotypic diversity more extensively. In contrast with the three visually definable phenotypes, our framework reproducibly identified 10 Golgi phenotypes. They were used to quantify and stratify phenotypic similarities among genetic perturbations. The derived phenotypic network partially overlaps previously reported protein–protein interactions as well as suggesting novel functional interactions. Our workflow suggests the existence of multiple stable Golgi organizational states and provides a proof of concept for the classification of drugs and genes using fine-grained phenotypic information.
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Affiliation(s)
| | | | - Wang Yi
- Institute of High Performance Computing, Singapore 138673
| | - Huang Dong
- Institute of High Performance Computing, Singapore 138673
| | - Joanne Chia
- Institute of Molecular and Cell Biology, Singapore 138673
| | - Ke Yiping
- School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798
| | - Lee Kee Khoon
- Institute of High Performance Computing, Singapore 138673
| | - Frédéric Bard
- Institute of Molecular and Cell Biology, Singapore 138673
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26
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Fal K, Liu M, Duisembekova A, Refahi Y, Haswell ES, Hamant O. Phyllotactic regularity requires the Paf1 complex in Arabidopsis. Development 2017; 144:4428-4436. [PMID: 28982682 PMCID: PMC5769633 DOI: 10.1242/dev.154369] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 09/25/2017] [Indexed: 12/19/2022]
Abstract
In plants, aerial organs are initiated at stereotyped intervals, both spatially (every 137° in a pattern called phyllotaxis) and temporally (at prescribed time intervals called plastochrons). To investigate the molecular basis of such regularity, mutants with altered architecture have been isolated. However, most of them only exhibit plastochron defects and/or produce a new, albeit equally reproducible, phyllotactic pattern. This leaves open the question of a molecular control of phyllotaxis regularity. Here, we show that phyllotaxis regularity depends on the function of VIP proteins, components of the RNA polymerase II-associated factor 1 complex (Paf1c). Divergence angles between successive organs along the stem exhibited increased variance in vip3-1 and vip3-2 compared with the wild type, in two different growth conditions. Similar results were obtained with the weak vip3-6 allele and in vip6, a mutant for another Paf1c subunit. Mathematical analysis confirmed that these defects could not be explained solely by plastochron defects. Instead, increased variance in phyllotaxis in vip3 was observed at the meristem and related to defects in spatial patterns of auxin activity. Thus, the regularity of spatial, auxin-dependent, patterning at the meristem requires Paf1c.
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Affiliation(s)
- Kateryna Fal
- Laboratoire Reproduction et Développement des Plantes, Univ Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRA, F-69342, Lyon, France
| | - Mengying Liu
- Laboratoire Reproduction et Développement des Plantes, Univ Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRA, F-69342, Lyon, France
| | - Assem Duisembekova
- Laboratoire Reproduction et Développement des Plantes, Univ Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRA, F-69342, Lyon, France
| | - Yassin Refahi
- Sainsbury Laboratory, University of Cambridge, Bateman Street, Cambridge CB2 1LR, UK
| | - Elizabeth S Haswell
- Department of Biology, Mailbox 1137, Washington University in Saint Louis, Saint Louis, MO 63130, USA
| | - Olivier Hamant
- Laboratoire Reproduction et Développement des Plantes, Univ Lyon, ENS de Lyon, UCB Lyon 1, CNRS, INRA, F-69342, Lyon, France
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27
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Data-analysis strategies for image-based cell profiling. Nat Methods 2017; 14:849-863. [PMID: 28858338 PMCID: PMC6871000 DOI: 10.1038/nmeth.4397] [Citation(s) in RCA: 402] [Impact Index Per Article: 57.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2016] [Accepted: 07/28/2017] [Indexed: 12/16/2022]
Abstract
Image-based cell profiling is a high-throughput strategy for the quantification of phenotypic differences among a variety of cell populations. It paves the way to studying biological systems on a large scale by using chemical and genetic perturbations. The general workflow for this technology involves image acquisition with high-throughput microscopy systems and subsequent image processing and analysis. Here, we introduce the steps required to create high-quality image-based (i.e., morphological) profiles from a collection of microscopy images. We recommend techniques that have proven useful in each stage of the data analysis process, on the basis of the experience of 20 laboratories worldwide that are refining their image-based cell-profiling methodologies in pursuit of biological discovery. The recommended techniques cover alternatives that may suit various biological goals, experimental designs, and laboratories' preferences.
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28
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Deb D, Rajaram S, Larsen JE, Dospoy PD, Marullo R, Li LS, Avila K, Xue F, Cerchietti L, Minna JD, Altschuler SJ, Wu LF. Combination Therapy Targeting BCL6 and Phospho-STAT3 Defeats Intratumor Heterogeneity in a Subset of Non-Small Cell Lung Cancers. Cancer Res 2017; 77:3070-3081. [PMID: 28377453 DOI: 10.1158/0008-5472.can-15-3052] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2015] [Revised: 01/31/2017] [Accepted: 03/20/2017] [Indexed: 02/07/2023]
Abstract
Oncogene-specific changes in cellular signaling have been widely observed in lung cancer. Here, we investigated how these alterations could affect signaling heterogeneity and suggest novel therapeutic strategies. We compared signaling changes across six human bronchial epithelial cell (HBEC) strains that were systematically transformed with various combinations of TP53, KRAS, and MYC-oncogenic alterations commonly found in non-small cell lung cancer (NSCLC). We interrogated at single-cell resolution how these alterations could affect classic readouts (β-CATENIN, SMAD2/3, phospho-STAT3, P65, FOXO1, and phospho-ERK1/2) of key pathways commonly affected in NSCLC. All three oncogenic alterations were required concurrently to observe significant signaling changes, and significant heterogeneity arose in this condition. Unexpectedly, we found two mutually exclusive altered subpopulations: one with STAT3 upregulation and another with SMAD2/3 downregulation. Treatment with a STAT3 inhibitor eliminated the upregulated STAT3 subpopulation, but left a large surviving subpopulation with downregulated SMAD2/3. A bioinformatics search identified BCL6, a gene downstream of SMAD2/3, as a novel pharmacologically accessible target of our transformed HBECs. Combination treatment with STAT3 and BCL6 inhibitors across a panel of NSCLC cell lines and in xenografted tumors significantly reduced tumor cell growth. We conclude that BCL6 is a new therapeutic target in NSCLC and combination therapy that targets multiple vulnerabilities (STAT3 and BCL6) downstream of common oncogenes, and tumor suppressors may provide a potent way to defeat intratumor heterogeneity. Cancer Res; 77(11); 3070-81. ©2017 AACR.
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Affiliation(s)
- Dhruba Deb
- Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Satwik Rajaram
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California
| | - Jill E Larsen
- Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Patrick D Dospoy
- Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Rossella Marullo
- Division of Hematology and Medical Oncology, Weill Cornell Medical College and New York Presbyterian Hospital, New York, New York
| | - Long Shan Li
- Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Kimberley Avila
- Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Fengtian Xue
- Departments of Pharmaceutical Sciences and Anesthesiology, University of Maryland, Baltimore, Maryland
| | - Leandro Cerchietti
- Division of Hematology and Medical Oncology, Weill Cornell Medical College and New York Presbyterian Hospital, New York, New York
| | - John D Minna
- Hamon Center for Therapeutic Oncology Research, University of Texas Southwestern Medical Center, Dallas, Texas. .,Departments of Pharmacology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Steven J Altschuler
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California.
| | - Lani F Wu
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California.
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29
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Masuzzo P, Huyck L, Simiczyjew A, Ampe C, Martens L, Van Troys M. An end-to-end software solution for the analysis of high-throughput single-cell migration data. Sci Rep 2017; 7:42383. [PMID: 28205527 PMCID: PMC5304333 DOI: 10.1038/srep42383] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Accepted: 01/10/2017] [Indexed: 12/12/2022] Open
Abstract
The systematic study of single-cell migration requires the availability of software for assisting data inspection, quality control and analysis. This is especially important for high-throughput experiments, where multiple biological conditions are tested in parallel. Although the field of cell migration can count on different computational tools for cell segmentation and tracking, downstream data visualization, parameter extraction and statistical analysis are still left to the user and are currently not possible within a single tool. This article presents a completely new module for the open-source, cross-platform CellMissy software for cell migration data management. This module is the first tool to focus specifically on single-cell migration data downstream of image processing. It allows fast comparison across all tested conditions, providing automated data visualization, assisted data filtering and quality control, extraction of various commonly used cell migration parameters, and non-parametric statistical analysis. Importantly, the module enables parameters computation both at the trajectory- and at the step-level. Moreover, this single-cell analysis module is complemented by a new data import module that accommodates multiwell plate data obtained from high-throughput experiments, and is easily extensible through a plugin architecture. In conclusion, the end-to-end software solution presented here tackles a key bioinformatics challenge in the cell migration field, assisting researchers in their high-throughput data processing.
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Affiliation(s)
- Paola Masuzzo
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lynn Huyck
- Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Aleksandra Simiczyjew
- Department of Cell Pathology, Faculty of Biotechnology, University of Wroclaw, Wroclaw, Poland
| | - Christophe Ampe
- Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium.,Department of Biochemistry, Ghent University, Ghent, Belgium
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30
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Gough A, Stern AM, Maier J, Lezon T, Shun TY, Chennubhotla C, Schurdak ME, Haney SA, Taylor DL. Biologically Relevant Heterogeneity: Metrics and Practical Insights. SLAS DISCOVERY 2017; 22:213-237. [PMID: 28231035 DOI: 10.1177/2472555216682725] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Heterogeneity is a fundamental property of biological systems at all scales that must be addressed in a wide range of biomedical applications, including basic biomedical research, drug discovery, diagnostics, and the implementation of precision medicine. There are a number of published approaches to characterizing heterogeneity in cells in vitro and in tissue sections. However, there are no generally accepted approaches for the detection and quantitation of heterogeneity that can be applied in a relatively high-throughput workflow. This review and perspective emphasizes the experimental methods that capture multiplexed cell-level data, as well as the need for standard metrics of the spatial, temporal, and population components of heterogeneity. A recommendation is made for the adoption of a set of three heterogeneity indices that can be implemented in any high-throughput workflow to optimize the decision-making process. In addition, a pairwise mutual information method is suggested as an approach to characterizing the spatial features of heterogeneity, especially in tissue-based imaging. Furthermore, metrics for temporal heterogeneity are in the early stages of development. Example studies indicate that the analysis of functional phenotypic heterogeneity can be exploited to guide decisions in the interpretation of biomedical experiments, drug discovery, diagnostics, and the design of optimal therapeutic strategies for individual patients.
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Affiliation(s)
- Albert Gough
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Andrew M Stern
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - John Maier
- 3 Department of Family Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Timothy Lezon
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Tong-Ying Shun
- 2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Chakra Chennubhotla
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Mark E Schurdak
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Steven A Haney
- 5 Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA
| | - D Lansing Taylor
- 1 Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA.,2 University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.,4 University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
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31
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Patsch K, Chiu CL, Engeln M, Agus DB, Mallick P, Mumenthaler SM, Ruderman D. Single cell dynamic phenotyping. Sci Rep 2016; 6:34785. [PMID: 27708391 PMCID: PMC5052535 DOI: 10.1038/srep34785] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2015] [Accepted: 09/19/2016] [Indexed: 12/25/2022] Open
Abstract
Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.
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Affiliation(s)
- Katherin Patsch
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA
| | - Chi-Li Chiu
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA
| | - Mark Engeln
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA
| | - David B Agus
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA
| | - Parag Mallick
- Department of Radiology, Canary Center at Stanford for Cancer Early Detection, Stanford University, Palo Alto, CA, USA
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA
| | - Daniel Ruderman
- Lawrence J. Ellison Institute for Transformative Medicine, University of Southern California, Los Angeles, California, USA
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32
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Lombard-Banek C, Moody SA, Nemes P. High-Sensitivity Mass Spectrometry for Probing Gene Translation in Single Embryonic Cells in the Early Frog ( Xenopus) Embryo. Front Cell Dev Biol 2016; 4:100. [PMID: 27761436 PMCID: PMC5050209 DOI: 10.3389/fcell.2016.00100] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Accepted: 08/29/2016] [Indexed: 01/01/2023] Open
Abstract
Direct measurement of protein expression with single-cell resolution promises to deepen the understanding of the basic molecular processes during normal and impaired development. High-resolution mass spectrometry provides detailed coverage of the proteomic composition of large numbers of cells. Here we discuss recent mass spectrometry developments based on single-cell capillary electrophoresis that extend discovery proteomics to sufficient sensitivity to enable the measurement of proteins in single cells. The single-cell mass spectrometry system is used to detect a large number of proteins in single embryonic cells in the 16-cell embryo of the South African clawed frog (Xenopus laevis) that give rise to distinct tissue types. Single-cell measurements of protein expression provide complementary information on gene transcription during early development of the vertebrate embryo, raising a potential to understand how differential gene expression coordinates normal cell heterogeneity during development.
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Affiliation(s)
| | - Sally A Moody
- Department of Anatomy and Regenerative Biology, The George Washington University Washington, DC, USA
| | - Peter Nemes
- Department of Chemistry, The George Washington University Washington, DC, USA
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33
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Louveaux M, Rochette S, Beauzamy L, Boudaoud A, Hamant O. The impact of mechanical compression on cortical microtubules in Arabidopsis: a quantitative pipeline. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2016; 88:328-342. [PMID: 27482848 PMCID: PMC5113706 DOI: 10.1111/tpj.13290] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2016] [Revised: 07/25/2016] [Accepted: 08/01/2016] [Indexed: 05/18/2023]
Abstract
Exogenous mechanical perturbations on living tissues are commonly used to investigate whether cell effectors can respond to mechanical cues. However, in most of these experiments, the applied mechanical stress and/or the biological response are described only qualitatively. We developed a quantitative pipeline based on microindentation and image analysis to investigate the impact of a controlled and prolonged compression on microtubule behaviour in the Arabidopsis shoot apical meristem, using microtubule fluorescent marker lines. We found that a compressive stress, in the order of magnitude of turgor pressure, induced apparent microtubule bundling. Importantly, that response could be reversed several hours after the release of compression. Next, we tested the contribution of microtubule severing to compression-induced bundling: microtubule bundling seemed less pronounced in the katanin mutant, in which microtubule severing is dramatically reduced. Conversely, some microtubule bundles could still be observed 16 h after the release of compression in the spiral2 mutant, in which severing rate is instead increased. To quantify the impact of mechanical stress on anisotropy and orientation of microtubule arrays, we used the nematic tensor based FibrilTool ImageJ/Fiji plugin. To assess the degree of apparent bundling of the network, we developed several methods, some of which were borrowed from geostatistics. The final microtubule bundling response could notably be related to tissue growth velocity that was recorded by the indenter during compression. Because both input and output are quantified, this pipeline is an initial step towards correlating more precisely the cytoskeleton response to mechanical stress in living tissues.
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Affiliation(s)
- Marion Louveaux
- Laboratoire Reproduction et Développement des PlantesUniversité de LyonENS de LyonUCB Lyon 1CNRSINRAF‐69342LyonFrance
- Laboratoire Joliot‐CurieCNRSENS de LyonUCB Lyon 1Université de Lyon46 allée d'Italie69364Lyon Cedex 07France
| | - Sébastien Rochette
- Département Dynamiques de l'Environnement CôtierLaboratoire d’Écologie Benthique Côtière (LEBCO)IfremerCS 1007029280PlouzanéFrance
| | - Léna Beauzamy
- Laboratoire Reproduction et Développement des PlantesUniversité de LyonENS de LyonUCB Lyon 1CNRSINRAF‐69342LyonFrance
- Laboratoire Joliot‐CurieCNRSENS de LyonUCB Lyon 1Université de Lyon46 allée d'Italie69364Lyon Cedex 07France
| | - Arezki Boudaoud
- Laboratoire Reproduction et Développement des PlantesUniversité de LyonENS de LyonUCB Lyon 1CNRSINRAF‐69342LyonFrance
- Laboratoire Joliot‐CurieCNRSENS de LyonUCB Lyon 1Université de Lyon46 allée d'Italie69364Lyon Cedex 07France
| | - Olivier Hamant
- Laboratoire Reproduction et Développement des PlantesUniversité de LyonENS de LyonUCB Lyon 1CNRSINRAF‐69342LyonFrance
- Laboratoire Joliot‐CurieCNRSENS de LyonUCB Lyon 1Université de Lyon46 allée d'Italie69364Lyon Cedex 07France
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34
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Handly LN, Yao J, Wollman R. Signal Transduction at the Single-Cell Level: Approaches to Study the Dynamic Nature of Signaling Networks. J Mol Biol 2016; 428:3669-82. [PMID: 27430597 PMCID: PMC5023475 DOI: 10.1016/j.jmb.2016.07.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Revised: 07/07/2016] [Accepted: 07/11/2016] [Indexed: 12/16/2022]
Abstract
Signal transduction, or how cells interpret and react to external events, is a fundamental aspect of cellular function. Traditional study of signal transduction pathways involves mapping cellular signaling pathways at the population level. However, population-averaged readouts do not adequately illuminate the complex dynamics and heterogeneous responses found at the single-cell level. Recent technological advances that observe cellular response, computationally model signaling pathways, and experimentally manipulate cells now enable studying signal transduction at the single-cell level. These studies will enable deeper insights into the dynamic nature of signaling networks.
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Affiliation(s)
- L Naomi Handly
- Departments of Chemistry and Biochemistry, Integrative Biology and Physiology, and Institute for Quantitative and Computational Biosciences (QCB), UCLA, Los Angeles, CA 90095, USA
| | - Jason Yao
- Departments of Chemistry and Biochemistry, Integrative Biology and Physiology, and Institute for Quantitative and Computational Biosciences (QCB), UCLA, Los Angeles, CA 90095, USA
| | - Roy Wollman
- Departments of Chemistry and Biochemistry, Integrative Biology and Physiology, and Institute for Quantitative and Computational Biosciences (QCB), UCLA, Los Angeles, CA 90095, USA.
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35
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Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects. PLoS Genet 2016; 12:e1006213. [PMID: 27479122 PMCID: PMC4968810 DOI: 10.1371/journal.pgen.1006213] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 07/02/2016] [Indexed: 01/11/2023] Open
Abstract
Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.
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36
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Lombard-Banek C, Reddy S, Moody SA, Nemes P. Label-free Quantification of Proteins in Single Embryonic Cells with Neural Fate in the Cleavage-Stage Frog (Xenopus laevis) Embryo using Capillary Electrophoresis Electrospray Ionization High-Resolution Mass Spectrometry (CE-ESI-HRMS). Mol Cell Proteomics 2016; 15:2756-68. [PMID: 27317400 PMCID: PMC4974349 DOI: 10.1074/mcp.m115.057760] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 06/16/2016] [Indexed: 12/12/2022] Open
Abstract
Quantification of protein expression in single cells promises to advance a systems-level understanding of normal development. Using a bottom-up proteomic workflow and multiplexing quantification by tandem mass tags, we recently demonstrated relative quantification between single embryonic cells (blastomeres) in the frog (Xenopus laevis) embryo. In this study, we minimize derivatization steps to enhance analytical sensitivity and use label-free quantification (LFQ) for single Xenopus cells. The technology builds on a custom-designed capillary electrophoresis microflow-electrospray ionization high-resolution mass spectrometry platform and LFQ by MaxLFQ (MaxQuant). By judiciously tailoring performance to peptide separation, ionization, and data-dependent acquisition, we demonstrate an ∼75-amol (∼11 nm) lower limit of detection and quantification for proteins in complex cell digests. The platform enabled the identification of 438 nonredundant protein groups by measuring 16 ng of protein digest, or <0.2% of the total protein contained in a blastomere in the 16-cell embryo. LFQ intensity was validated as a quantitative proxy for protein abundance. Correlation analysis was performed to compare protein quantities between the embryo and n = 3 different single D11 blastomeres, which are fated to develop into the nervous system. A total of 335 nonredundant protein groups were quantified in union between the single D11 cells spanning a 4 log-order concentration range. LFQ and correlation analysis detected expected proteomic differences between the whole embryo and blastomeres, and also found translational differences between individual D11 cells. LFQ on single cells raises exciting possibilities to study gene expression in other cells and models to help better understand cell processes on a systems biology level.
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Affiliation(s)
| | - Sushma Reddy
- From the ‡Department of Chemistry and ¶Thomas Jefferson High School for Science and Technology, Alexandria, Virginia
| | - Sally A Moody
- §Department of Anatomy and Regenerative Biology, The George Washington University, Washington, DC
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37
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Single-cell genome-wide studies give new insight into nongenetic cell-to-cell variability in animals. Histochem Cell Biol 2016; 146:239-54. [DOI: 10.1007/s00418-016-1466-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/06/2016] [Indexed: 01/21/2023]
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38
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Stern AM, Schurdak ME, Bahar I, Berg JM, Taylor DL. A Perspective on Implementing a Quantitative Systems Pharmacology Platform for Drug Discovery and the Advancement of Personalized Medicine. JOURNAL OF BIOMOLECULAR SCREENING 2016; 21:521-34. [PMID: 26962875 PMCID: PMC4917453 DOI: 10.1177/1087057116635818] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Drug candidates exhibiting well-defined pharmacokinetic and pharmacodynamic profiles that are otherwise safe often fail to demonstrate proof-of-concept in phase II and III trials. Innovation in drug discovery and development has been identified as a critical need for improving the efficiency of drug discovery, especially through collaborations between academia, government agencies, and industry. To address the innovation challenge, we describe a comprehensive, unbiased, integrated, and iterative quantitative systems pharmacology (QSP)-driven drug discovery and development strategy and platform that we have implemented at the University of Pittsburgh Drug Discovery Institute. Intrinsic to QSP is its integrated use of multiscale experimental and computational methods to identify mechanisms of disease progression and to test predicted therapeutic strategies likely to achieve clinical validation for appropriate subpopulations of patients. The QSP platform can address biological heterogeneity and anticipate the evolution of resistance mechanisms, which are major challenges for drug development. The implementation of this platform is dedicated to gaining an understanding of mechanism(s) of disease progression to enable the identification of novel therapeutic strategies as well as repurposing drugs. The QSP platform will help promote the paradigm shift from reactive population-based medicine to proactive personalized medicine by focusing on the patient as the starting and the end point.
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Affiliation(s)
- Andrew M. Stern
- Department of Computational and Systems Biology, Pittsburgh, PA, USA
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
| | - Mark E. Schurdak
- Department of Computational and Systems Biology, Pittsburgh, PA, USA
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- The University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Ivet Bahar
- Department of Computational and Systems Biology, Pittsburgh, PA, USA
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- The University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
| | - Jeremy M. Berg
- Department of Computational and Systems Biology, Pittsburgh, PA, USA
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- University of Pittsburgh Institute for Personalized Medicine, Pittsburgh, PA, USA
| | - D. Lansing Taylor
- Department of Computational and Systems Biology, Pittsburgh, PA, USA
- University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA
- The University of Pittsburgh Cancer Institute, Pittsburgh, PA, USA
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39
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McCarroll MN, Gendelev L, Keiser MJ, Kokel D. Leveraging Large-scale Behavioral Profiling in Zebrafish to Explore Neuroactive Polypharmacology. ACS Chem Biol 2016; 11:842-9. [PMID: 26845413 DOI: 10.1021/acschembio.5b00800] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Many psychiatric drugs modulate the nervous system through multitarget mechanisms. However, systematic identification of multitarget compounds has been difficult using traditional in vitro screening assays. New approaches to phenotypic profiling in zebrafish can help researchers identify novel compounds with complex polypharmacology. For example, large-scale behavior-based chemical screens can rapidly identify large numbers of structurally diverse and phenotype-related compounds. Once these compounds have been identified, a systems-level analysis of their structures may help to identify statistically enriched target pathways. Together, systematic behavioral profiling and multitarget predictions may help researchers identify new behavior-modifying pathways and CNS therapeutics.
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Affiliation(s)
- Matthew N. McCarroll
- University of California San Francisco, Institute of Neurodegenerative
Diseases, 675 Nelson Rising
Lane, San Francisco, California 94143, United States
| | - Leo Gendelev
- University of California San Francisco, Institute of Neurodegenerative
Diseases, 675 Nelson Rising
Lane, San Francisco, California 94143, United States
| | - Michael J. Keiser
- University of California San Francisco, Institute of Neurodegenerative
Diseases, 675 Nelson Rising
Lane, San Francisco, California 94143, United States
| | - David Kokel
- University of California San Francisco, Institute of Neurodegenerative
Diseases, 675 Nelson Rising
Lane, San Francisco, California 94143, United States
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40
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Abstract
Data visualization is a fundamental aspect of science. In the context of microscopy-based studies, visualization typically involves presentation of the images themselves. However, data visualization is challenging when microscopy experiments entail imaging of millions of cells, and complex cellular phenotypes are quantified in a high-content manner. Most well-established visualization tools are inappropriate for displaying high-content data, which has driven the development of new visualization methodology. In this review, we discuss how data has been visualized in both classical and high-content microscopy studies; as well as the advantages, and disadvantages, of different visualization methods.
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Affiliation(s)
- Heba Z Sailem
- a Department of Engineering Science , University of Oxford , Oxford , UK
| | - Sam Cooper
- b Department of Computational Systems Medicine , Imperial College, South Kensington Campus , London , UK , and.,c Division of Cancer Biology , Chester Beatty Laboratories, Institute of Cancer Research , London , UK
| | - Chris Bakal
- c Division of Cancer Biology , Chester Beatty Laboratories, Institute of Cancer Research , London , UK
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41
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Dueck H, Eberwine J, Kim J. Variation is function: Are single cell differences functionally important?: Testing the hypothesis that single cell variation is required for aggregate function. Bioessays 2015; 38:172-80. [PMID: 26625861 PMCID: PMC4738397 DOI: 10.1002/bies.201500124] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
There is a growing appreciation of the extent of transcriptome variation across individual cells of the same cell type. While expression variation may be a byproduct of, for example, dynamic or homeostatic processes, here we consider whether single-cell molecular variation per se might be crucial for population-level function. Under this hypothesis, molecular variation indicates a diversity of hidden functional capacities within an ensemble of identical cells, and this functional diversity facilitates collective behavior that would be inaccessible to a homogenous population. In reviewing this topic, we explore possible functions that might be carried by a heterogeneous ensemble of cells; however, this question has proven difficult to test, both because methods to manipulate molecular variation are limited and because it is complicated to define, and measure, population-level function. We consider several possible methods to further pursue the hypothesis that variation is function through the use of comparative analysis and novel experimental techniques.
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Affiliation(s)
- Hannah Dueck
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Philadelphia, PA, USA
| | - James Eberwine
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.,Penn Program in Single Cell Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhyong Kim
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Philadelphia, PA, USA.,Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.,Penn Program in Single Cell Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.,Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA
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Gough A, Shun TY, Lansing Taylor D, Schurdak M. A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens. Methods 2015; 96:12-26. [PMID: 26476369 DOI: 10.1016/j.ymeth.2015.10.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2015] [Revised: 10/12/2015] [Accepted: 10/13/2015] [Indexed: 12/14/2022] Open
Abstract
Heterogeneity is well recognized as a common property of cellular systems that impacts biomedical research and the development of therapeutics and diagnostics. Several studies have shown that analysis of heterogeneity: gives insight into mechanisms of action of perturbagens; can be used to predict optimal combination therapies; and can be applied to tumors where heterogeneity is believed to be associated with adaptation and resistance. Cytometry methods including high content screening (HCS), high throughput microscopy, flow cytometry, mass spec imaging and digital pathology capture cell level data for populations of cells. However it is often assumed that the population response is normally distributed and therefore that the average adequately describes the results. A deeper understanding of the results of the measurements and more effective comparison of perturbagen effects requires analysis that takes into account the distribution of the measurements, i.e. the heterogeneity. However, the reproducibility of heterogeneous data collected on different days, and in different plates/slides has not previously been evaluated. Here we show that conventional assay quality metrics alone are not adequate for quality control of the heterogeneity in the data. To address this need, we demonstrate the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in an SAR screen, describe a workflow for quality control in heterogeneity analysis. One major challenge in high throughput biology is the evaluation and interpretation of heterogeneity in thousands of samples, such as compounds in a cell-based screen. In this study we also demonstrate that three heterogeneity indices previously reported, capture the shapes of the distributions and provide a means to filter and browse big data sets of cellular distributions in order to compare and identify distributions of interest. These metrics and methods are presented as a workflow for analysis of heterogeneity in large scale biology projects.
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Affiliation(s)
- Albert Gough
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA; Dept. of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA, USA.
| | - Tong Ying Shun
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA
| | - D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA; Dept. of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA, USA
| | - Mark Schurdak
- University of Pittsburgh Drug Discovery Institute, 3501 Fifth Avenue, Pittsburgh, PA, USA; Dept. of Computational and Systems Biology, University of Pittsburgh, 3501 Fifth Avenue, Pittsburgh, PA, USA
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44
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Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment. Acta Pharmacol Sin 2015; 36:1219-27. [PMID: 26388155 PMCID: PMC4648179 DOI: 10.1038/aps.2015.92] [Citation(s) in RCA: 149] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 09/06/2015] [Indexed: 02/06/2023] Open
Abstract
Recent studies have revealed extensive genetic and non-genetic variation across different geographical regions of a tumor or throughout different stages of tumor progression, which is referred to as intra-tumor heterogeneity. Several causes contribute to this phenomenon, including genomic instability, epigenetic alteration, plastic gene expression, signal transduction, and microenvironmental differences. These variables may affect key signaling pathways that regulate cancer cell growth, drive phenotypic diversity, and pose challenges to cancer treatment. Understanding the mechanisms underlying this heterogeneity will support the development of effective therapeutic strategies.
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45
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Lin JR, Fallahi-Sichani M, Sorger PK. Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat Commun 2015; 6:8390. [PMID: 26399630 PMCID: PMC4587398 DOI: 10.1038/ncomms9390] [Citation(s) in RCA: 357] [Impact Index Per Article: 39.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Accepted: 08/17/2015] [Indexed: 12/23/2022] Open
Abstract
Single-cell analysis reveals aspects of cellular physiology not evident from population-based studies, particularly in the case of highly multiplexed methods such as mass cytometry (CyTOF) able to correlate the levels of multiple signalling, differentiation and cell fate markers. Immunofluorescence (IF) microscopy adds information on cell morphology and the microenvironment that are not obtained using flow-based techniques, but the multiplicity of conventional IF is limited. This has motivated development of imaging methods that require specialized instrumentation, exotic reagents or proprietary protocols that are difficult to reproduce in most laboratories. Here we report a public-domain method for achieving high multiplicity single-cell IF using cyclic immunofluorescence (CycIF), a simple and versatile procedure in which four-colour staining alternates with chemical inactivation of fluorophores to progressively build a multichannel image. Because CycIF uses standard reagents and instrumentation and is no more expensive than conventional IF, it is suitable for high-throughput assays and screening applications. Multiplexed single cell measurements provide insight into connections between cell state and phenotype. Here Lin et al. present CycIF, a high throughput, public domain immunofluorescence method for multiplexed single-cell analysis of adherent cells following live-cell imaging.
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Affiliation(s)
- Jia-Ren Lin
- HMS LINCS Center &Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts 02115 USA
| | - Mohammad Fallahi-Sichani
- Department of Systems Biology Harvard Medical School 200 Longwood Avenue, Boston, Massachusetts 02115 USA
| | - Peter K Sorger
- HMS LINCS Center &Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts 02115 USA.,Department of Systems Biology Harvard Medical School 200 Longwood Avenue, Boston, Massachusetts 02115 USA
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46
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Stochastic sensitivity analysis and kernel inference via distributional data. Biophys J 2015; 107:1247-1255. [PMID: 25185560 DOI: 10.1016/j.bpj.2014.07.025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2014] [Revised: 07/08/2014] [Accepted: 07/15/2014] [Indexed: 12/18/2022] Open
Abstract
Cellular processes are noisy due to the stochastic nature of biochemical reactions. As such, it is impossible to predict the exact quantity of a molecule or other attributes at the single-cell level. However, the distribution of a molecule over a population is often deterministic and is governed by the underlying regulatory networks relevant to the cellular functionality of interest. Recent studies have started to exploit this property to infer network states. To facilitate the analysis of distributional data in a general experimental setting, we introduce a computational framework to efficiently characterize the sensitivity of distributional output to changes in external stimuli. Further, we establish a probability-divergence-based kernel regression model to accurately infer signal level based on distribution measurements. Our methodology is applicable to any biological system subject to stochastic dynamics and can be used to elucidate how population-based information processing may contribute to organism-level functionality. It also lays the foundation for engineering synthetic biological systems that exploit population decoding to more robustly perform various biocomputation tasks, such as disease diagnostics and environmental-pollutant sensing.
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47
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Abstract
Cell signaling pathways control cells' responses to their environment through an intricate network of proteins and small molecules partitioned by intracellular structures, such as the cytoskeleton and nucleus. Our understanding of these pathways has been revised recently with the advent of more advanced experimental techniques; no longer are signaling pathways viewed as linear cascades of information flowing from membrane-bound receptors to the nucleus. Instead, such pathways must be understood in the context of networks, and studying such networks requires an integration of computational and experimental approaches. This understanding is becoming more important in designing novel therapies for diseases such as cancer. Using the MAPK (mitogen-activated protein kinase) and PI3K (class I phosphoinositide-3' kinase) pathways as case studies of cellular signaling, we give an overview of these pathways and their functions. We then describe, using a number of case studies, how computational modeling has aided in understanding these pathways' deregulation in cancer, and how such understanding can be used to optimally tailor current therapies or help design new therapies against cancer.
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Affiliation(s)
- Julio Saez-Rodriguez
- Current address: Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, D-52074 Aachen, Germany;
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom;
| | - Aidan MacNamara
- European Bioinformatics Institute, European Molecular Biology Laboratory, Wellcome Trust Genome Campus, Hinxton, Cambridgeshire CB10 1SD, United Kingdom;
| | - Simon Cook
- Signalling Laboratory, The Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, United Kingdom;
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48
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Flusberg DA, Sorger PK. Surviving apoptosis: life-death signaling in single cells. Trends Cell Biol 2015; 25:446-58. [PMID: 25920803 PMCID: PMC4570028 DOI: 10.1016/j.tcb.2015.03.003] [Citation(s) in RCA: 97] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Revised: 03/19/2015] [Accepted: 03/19/2015] [Indexed: 12/16/2022]
Abstract
Tissue development and homeostasis are regulated by opposing pro-survival and pro-death signals. An interesting feature of the Tumor Necrosis Factor (TNF) family of ligands is that they simultaneously activate opposing signals within a single cell via the same ligand-receptor complex. The magnitude of pro-death events such as caspase activation and pro-survival events such as Nuclear Factor (NF)-κB activation vary not only from one cell type to the next but also among individual cells of the same type due to intrinsic and extrinsic noise. The molecules involved in these pro-survival and/or pro-death pathways, and the different phenotypes that result from their activities, have been recently reviewed. Here we focus on the impact of cell-to-cell variability in the strength of these opposing signals on shaping cell fate decisions.
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Affiliation(s)
- Deborah A Flusberg
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA.
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49
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Xue Q, Lu Y, Eisele MR, Sulistijo ES, Khan N, Fan R, Miller-Jensen K. Analysis of single-cell cytokine secretion reveals a role for paracrine signaling in coordinating macrophage responses to TLR4 stimulation. Sci Signal 2015; 8:ra59. [PMID: 26082435 DOI: 10.1126/scisignal.aaa2155] [Citation(s) in RCA: 107] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Macrophages not only produce multiple cytokines but also respond to multiple cytokines, which likely shapes the ultimate response of the population. To determine the role of paracrine signaling in shaping the profile of inflammatory cytokines secreted by macrophages in response to stimulation of Toll-like receptor 4 (TLR4) with lipopolysaccharide (LPS), we combined multiplexed, microwell-based measurements of cytokine secretion by single cells with analysis of cytokine secretion by cell populations. Loss of paracrine signaling as a result of cell isolation reduced the secretion by macrophage-like U937 cells and human monocyte-derived macrophages (MDMs) of a subset of LPS-stimulated cytokines, including interleukin-6 (IL-6) and IL-10. Graphical Gaussian modeling (GGM) of the single-cell data defined a regulatory network of paracrine signals, which was validated experimentally in the population through antibody-mediated neutralization of individual cytokines. Tumor necrosis factor-α (TNF-α) was the most influential cytokine in the GGM network. Paracrine signaling by TNF-α secreted from a small subpopulation of "high-secreting" cells was necessary, but not sufficient, for the secretion of large amounts of IL-6 and IL-10 by the cell population. Decreased relative IL-10 secretion by isolated MDMs was linked to increased TNF-α secretion, suggesting that inhibition of the inflammatory response also depends on paracrine signaling. Our results reveal a previously uncharacterized role for cell-to-cell communication within a population in coordinating a rapid innate immune response despite underlying cell-to-cell heterogeneity.
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Affiliation(s)
- Qiong Xue
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Yao Lu
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Markus R Eisele
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA. Institute for System Dynamics, University of Stuttgart, 70569 Stuttgart, Germany
| | - Endah S Sulistijo
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Nafeesa Khan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA.
| | - Kathryn Miller-Jensen
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520, USA. Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT 06520, USA.
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50
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Abstract
Akt/PKB, a serine/threonine kinase member of the AGC family of proteins, is involved in the regulation of a plethora of cellular processes triggered by a wide diversity of extracellular signals and is thus considered a key signalling molecule in higher eukaryotes. Deregulation of Akt signalling is associated with a variety of human diseases, revealing Akt-dependent pathways as an attractive target for therapeutic intervention. Since its discovery in the early 1990s, a large body of work has focused on Akt phosphorylation of two residues, Thr308 and Ser473, and modification of these two sites has been established as being equivalent to Akt activation. More recently, Akt has been identified as a substrate for many different post-translational modifications, including not only phosphorylation of other residues, but also acetylation, glycosylation, oxidation, ubiquitination and SUMOylation. These modifications could provide additional regulatory steps for fine-tuning Akt function, Akt trafficking within the cell and/or for determining the substrate specificity of this signalling molecule. In the present review, we provide an overview of these different post-translational modifications identified for Akt, focusing on their consequences for this kinase activity.
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