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Aslan MK, Ding Y, Stavrakis S, deMello AJ. Smartphone Imaging Flow Cytometry for High-Throughput Single-Cell Analysis. Anal Chem 2023; 95:14526-14532. [PMID: 37733469 DOI: 10.1021/acs.analchem.3c03213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
We present a portable imaging flow cytometer comprising a smartphone, a small-footprint optical framework, and a PDMS-based microfluidic device. Flow cytometric analysis is performed in a sheathless manner via elasto-inertial focusing with a custom-written Android program, integrating a graphical user interface (GUI) that provides a high degree of user control over image acquisition. The proposed system offers two different operational modes. First, "post-processing" mode enables particle/cell sizing at throughputs of up to 67 000 particles/s. Alternatively, "real-time" mode allows for integrated cell/particle classification with machine learning at throughputs of 100 particles/s. To showcase the efficacy of our platform, polystyrene particles are accurately enumerated within heterogeneous populations using the post-processing mode. In real-time mode, an open-source machine learning algorithm is deployed within a custom-developed Android application to classify samples containing cells of similar size but with different morphologies. The flow cytometer can extract high-resolution bright-field images with a spatial resolution <700 nm using the developed machine learning-based algorithm, achieving classification accuracies of 97% and 93% for Jurkat and EL4 cells, respectively. Our results confirm that the smartphone imaging flow cytometer (sIFC) is capable of both enumerating single particles in flow and identifying morphological features with high resolution and minimal hardware.
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Affiliation(s)
- Mahmut Kamil Aslan
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zürich, Switzerland
| | - Yun Ding
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zürich, Switzerland
| | - Stavros Stavrakis
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zürich, Switzerland
| | - Andrew J deMello
- Institute for Chemical and Bioengineering, ETH Zurich, 8093 Zürich, Switzerland
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Abstract
Cell cycle involves a series of changes that lead to cell growth and division. Cell cycle analysis is crucial to understand cellular responses to changing environmental conditions. Since its inception, flow cytometry has been particularly useful for cell cycle analysis at single cell level due to its speed and precision. Previously, flow cytometric cell cycle analysis relied solely on the measurement of cellular DNA content. Later, methods were developed for multiparametric analysis. This review explains the journey of flow cytometry to understand different molecular and cellular events underlying cell cycle using various protocols. Recent advances in the field that overcome the shortcomings of traditional flow cytometry and expand its scope for cell cycle studies are also discussed.
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Reisman BJ, Barone SM, Bachmann BO, Irish JM. DebarcodeR increases fluorescent cell barcoding capacity and accuracy. Cytometry A 2021; 99:946-953. [PMID: 33960644 PMCID: PMC8410645 DOI: 10.1002/cyto.a.24363] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 03/09/2021] [Accepted: 04/20/2021] [Indexed: 12/25/2022]
Abstract
Fluorescent cell barcoding (FCB) enables efficient collection of tens to hundreds of flow cytometry samples by covalently marking cells with varying concentration of spectrally distinct dyes. A key consideration in FCB is to balance the density of dye barcodes, the complexity of cells in the sample, and the desired accuracy of the debarcoding. Unfortunately, barcoding bench and computational methods have not benefited from the high dimensional revolution in cytometry due to a lack of automated computational tools that effectively balance these common cytometry needs. DebarcodeR addresses these unmet needs by providing a framework for computational debarcoding augmented by improvements to experimental methods. Adaptive regression modeling accounted for differential dye uptake between different cell types and Gaussian mixture modeling provided a robust method to probabilistically assign cells to samples. Assignment tolerance parameters are available to allow users to balance high cell recovery with accurate assignments. Improvements to experimental methods include: (1) inclusion of an "external standard" control where a pool of all cells was stained a single level of each barcoding dyes and (2) an "internal standard" where each cell is stained with a single level of a separate dye. DebarcodeR significantly improved speed, accuracy, and reproducibility of FCB while avoiding selective loss of unusual cell subsets when debarcoding microtiter plates of cell lines and heterogenous mixtures of primary cells. DebarcodeR is available on Github as an R package that works with flowCore and Cytoverse packages at github.com/cytolab/DebarcodeR.
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Affiliation(s)
| | - Sierra M. Barone
- Department of Cell & Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology & Immunology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Jonathan M. Irish
- Department of Cell & Developmental Biology, Vanderbilt University, Nashville, TN, USA
- Department of Pathology, Microbiology & Immunology, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN, USA
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4
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Mekhemar M, Tölle J, Dörfer C, Fawzy El‐Sayed K. TLR3 ligation affects differentiation and stemness properties of gingival mesenchymal stem/progenitor cells. J Clin Periodontol 2020; 47:991-1005. [DOI: 10.1111/jcpe.13323] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Revised: 05/07/2020] [Accepted: 05/21/2020] [Indexed: 12/18/2022]
Affiliation(s)
- Mohamed Mekhemar
- Clinic for Conservative Dentistry and Periodontology School of Dental Medicine Christian‐Albrecht’s University Kiel Germany
- Universitätsklinikum SchleswigȐHolstein Ȑ Campus, Kiel
| | - Johannes Tölle
- Clinic for Conservative Dentistry and Periodontology School of Dental Medicine Christian‐Albrecht’s University Kiel Germany
| | - Christof Dörfer
- Clinic for Conservative Dentistry and Periodontology School of Dental Medicine Christian‐Albrecht’s University Kiel Germany
| | - Karim Fawzy El‐Sayed
- Clinic for Conservative Dentistry and Periodontology School of Dental Medicine Christian‐Albrecht’s University Kiel Germany
- Oral Medicine and Periodontology Department Faculty of Oral and Dental Medicine Cairo University Cairo Egypt
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5
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Operating regimes in a single enzymatic cascade at ensemble-level. PLoS One 2019; 14:e0220243. [PMID: 31369598 PMCID: PMC6675077 DOI: 10.1371/journal.pone.0220243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 07/11/2019] [Indexed: 01/19/2023] Open
Abstract
Single enzymatic cascade, ubiquitously found in cellular signaling networks, is a phosphorylation-dephosphorylation reaction cycle causing a transition between inactive and active states of a protein catalysed by kinase and phosphatase, respectively. Steady-state information processing ability of such a cycle (e.g., MAPK cascade) has been classified into four qualitatively different operating regimes, viz., hyperbolic (H), signal-transducing (ST), threshold-hyperbolic (TH) and ultrasensitive (U). These four regimes represent qualitatively different dose-response curves, that is, relationship between concentrations of input kinase (e.g., pMEK) and response activated protein (e.g., pERK). Regimes were identified using a deterministic model accounting for population-averaged behavior only. Operating regimes can be strongly influenced by the inherently present cell-to-cell variability in an ensemble of cells which is captured in the form of pMEK and pERK distributions using reporter-based single-cell experimentation. In this study, we show that such experimentally acquired snapshot pMEK and pERK distribution data of a single MAPK cascade can be directly used to infer the underlying operating regime even in the absence of a dose-response curve. This deduction is possible primarily due to the presence of a monotonic relationship between experimental observables RIQR, ratio of the inter-quartile range of the pERK and pMEK distribution pairs and RM, ratio of the medians of the distribution pair. We demonstrate this relationship by systematic analysis of a quasi-steady state approximated model superimposed with an input gamma distribution constrained by the stimulus strength specific pMEK distribution measured on Jurkat-T cells stimulated with PMA. As a first, we show that introduction of cell-to-cell variability only in the upstream kinase achieved by superimposition of an appropriate input pMEK distribution on the dose-response curve can predict bimodal response pERK distribution in ST regime. Implementation of the proposed method on the input-response distribution pair obtained in stimulated Jurkat-T cells revealed that while low-dosage PMA stimulation preserves the H regime observed in resting cells, high-dosage causes H to ST regime transition.
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Cheung P, Vallania F, Dvorak M, Chang SE, Schaffert S, Donato M, Rao AM, Mao R, Utz PJ, Khatri P, Kuo AJ. Single-cell epigenetics - Chromatin modification atlas unveiled by mass cytometry. Clin Immunol 2018; 196:40-48. [PMID: 29960011 DOI: 10.1016/j.clim.2018.06.009] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 06/21/2018] [Accepted: 06/22/2018] [Indexed: 12/13/2022]
Abstract
Modifications of histone proteins are fundamental to the regulation of epigenetic phenotypes. Dysregulations of histone modifications have been linked to the pathogenesis of diverse human diseases. However, identifying differential histone modifications in patients with immune-mediated diseases has been challenging, in part due to the lack of a powerful analytic platform to study histone modifications in the complex human immune system. We recently developed a highly multiplexed platform, Epigenetic landscape profiling using cytometry by Time-Of-Flight (EpiTOF), to analyze the global levels of a broad array of histone modifications in single cells using mass cytometry. In this review, we summarize the development of EpiTOF and discuss its potential applications in biomedical research. We anticipate that this platform will provide new insights into the roles of epigenetic regulation in hematopoiesis, immune cell functions, and immune system aging, and reveal aberrant epigenetic patterns associated with immune-mediated diseases.
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Affiliation(s)
- Peggie Cheung
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Francesco Vallania
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Mai Dvorak
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Sarah E Chang
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Steven Schaffert
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Michele Donato
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Aditya M Rao
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Rong Mao
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Paul J Utz
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, California 94305, USA.
| | - Purvesh Khatri
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Biomedical Informatics Research, Stanford University School of Medicine, Stanford, California 94305, USA.
| | - Alex J Kuo
- Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, California 94305, USA; Department of Medicine, Division of Immunology and Rheumatology, Stanford University School of Medicine, Stanford, California 94305, USA.
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7
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Lin CC, Kurashige M, Liu Y, Terabayashi T, Ishimoto Y, Wang T, Choudhary V, Hobbs R, Liu LK, Lee PH, Outeda P, Zhou F, Restifo NP, Watnick T, Kawano H, Horie S, Prinz W, Xu H, Menezes LF, Germino GG. A cleavage product of Polycystin-1 is a mitochondrial matrix protein that affects mitochondria morphology and function when heterologously expressed. Sci Rep 2018; 8:2743. [PMID: 29426897 PMCID: PMC5807443 DOI: 10.1038/s41598-018-20856-6] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2017] [Accepted: 01/22/2018] [Indexed: 12/18/2022] Open
Abstract
Recent studies have reported intrinsic metabolic reprogramming in Pkd1 knock-out cells, implicating dysregulated cellular metabolism in the pathogenesis of polycystic kidney disease. However, the exact nature of the metabolic changes and their underlying cause remains controversial. We show herein that Pkd1 k o /ko renal epithelial cells have impaired fatty acid utilization, abnormal mitochondrial morphology and function, and that mitochondria in kidneys of ADPKD patients have morphological alterations. We further show that a C-terminal cleavage product of polycystin-1 (CTT) translocates to the mitochondria matrix and that expression of CTT in Pkd1 ko/ko cells rescues some of the mitochondrial phenotypes. Using Drosophila to model in vivo effects, we find that transgenic expression of mouse CTT results in decreased viability and exercise endurance but increased CO2 production, consistent with altered mitochondrial function. Our results suggest that PC1 may play a direct role in regulating mitochondrial function and cellular metabolism and provide a framework to understand how impaired mitochondrial function could be linked to the regulation of tubular diameter in both physiological and pathological conditions.
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Affiliation(s)
- Cheng-Chao Lin
- Kidney Disease Branch; National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Mahiro Kurashige
- Kidney Disease Branch; National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Yi Liu
- Laboratory of Molecular Genetics; National Heart, Lung and Blood Institute, NIH, Bethesda, MD, USA
| | - Takeshi Terabayashi
- Kidney Disease Branch; National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Yu Ishimoto
- Division of Nephrology and Endocrinology and the Division of CKD Pathophysiology, University of Tokyo Graduate School of Medicine, Tokyo, Japan
| | - Tanchun Wang
- Kidney Disease Branch; National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Vineet Choudhary
- Laboratory of Cell and Molecular Biology; National Institute of Diabetes and Digestive and Kidney Disease, NIH, Bethesda, MD, USA
| | - Ryan Hobbs
- Kidney Disease Branch; National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Li-Ka Liu
- Laboratory of Cell and Molecular Biology; National Institute of Diabetes and Digestive and Kidney Disease, NIH, Bethesda, MD, USA
| | - Ping-Hsien Lee
- Center for Cell-Based Therapy, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Patricia Outeda
- Department of Medicine, Division of Nephrology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Fang Zhou
- Kidney Disease Branch; National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health (NIH), Bethesda, MD, USA
| | - Nicholas P Restifo
- Center for Cell-Based Therapy, National Cancer Institute, NIH, Bethesda, MD, USA
| | - Terry Watnick
- Department of Medicine, Division of Nephrology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Haruna Kawano
- Department of Urology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shigeo Horie
- Department of Urology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - William Prinz
- Laboratory of Cell and Molecular Biology; National Institute of Diabetes and Digestive and Kidney Disease, NIH, Bethesda, MD, USA
| | - Hong Xu
- Laboratory of Molecular Genetics; National Heart, Lung and Blood Institute, NIH, Bethesda, MD, USA
| | - Luis F Menezes
- Kidney Disease Branch; National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health (NIH), Bethesda, MD, USA.
| | - Gregory G Germino
- Kidney Disease Branch; National Institute of Diabetes and Digestive and Kidney Disease, National Institutes of Health (NIH), Bethesda, MD, USA.
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Earl DC, Ferrell PB, Leelatian N, Froese JT, Reisman BJ, Irish JM, Bachmann BO. Discovery of human cell selective effector molecules using single cell multiplexed activity metabolomics. Nat Commun 2018; 9:39. [PMID: 29295987 PMCID: PMC5750220 DOI: 10.1038/s41467-017-02470-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Accepted: 12/01/2017] [Indexed: 01/06/2023] Open
Abstract
Discovering bioactive metabolites within a metabolome is challenging because there is generally little foreknowledge of metabolite molecular and cell-targeting activities. Here, single-cell response profiles and primary human tissue comprise a response platform used to discover novel microbial metabolites with cell-type-selective effector properties in untargeted metabolomic inventories. Metabolites display diverse effector mechanisms, including targeting protein synthesis, cell cycle status, DNA damage repair, necrosis, apoptosis, or phosphoprotein signaling. Arrayed metabolites are tested against acute myeloid leukemia patient bone marrow and molecules that specifically targeted blast cells or nonleukemic immune cell subsets within the same tissue biopsy are revealed. Cell-targeting polyketides are identified in extracts from biosynthetically prolific bacteria, including a previously unreported leukemia blast-targeting anthracycline and a polyene macrolactam that alternates between targeting blasts or nonmalignant cells by way of light-triggered photochemical isomerization. High-resolution cell profiling with mass cytometry confirms response mechanisms and is used to validate initial observations.
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Affiliation(s)
- David C Earl
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN, 37235, USA
| | - P Brent Ferrell
- Department of Medicine, Vanderbilt University Medical Center, 1161 21st Avenue South, D-3100 Medical Center North, Nashville, TN, 37232, USA
| | - Nalin Leelatian
- Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN, 37232, USA
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, 2220 Pierce Avenue, Nashville, TN, 37232, USA
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, 1161 21st Avenue South, D-2220 Medical Center North, Nashville, TN, 37232, USA
| | - Jordan T Froese
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN, 37235, USA
| | - Benjamin J Reisman
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN, 37235, USA
| | - Jonathan M Irish
- Department of Cell and Developmental Biology, Vanderbilt University, 465 21st Avenue South, Nashville, TN, 37232, USA.
- Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, 2220 Pierce Avenue, Nashville, TN, 37232, USA.
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, 1161 21st Avenue South, D-2220 Medical Center North, Nashville, TN, 37232, USA.
| | - Brian O Bachmann
- Department of Chemistry, Vanderbilt University, 7330 Stevenson Center, Station B 351822, Nashville, TN, 37235, USA.
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Abstract
Peroxisome proliferation involves signal recognition and computation by molecular networks that direct molecular events of gene expression, metabolism, membrane biogenesis, organelle proliferation, protein import, and organelle inheritance. Peroxisome biogenesis in yeast has served as a model system for exploring the regulatory networks controlling this process. Yeast is an outstanding model system to develop tools and approaches to study molecular networks and cellular responses and because the mechanisms of peroxisome biogenesis and key aspects of the transcriptional regulatory networks are remarkably conserved from yeast to humans. In this chapter, we focus on the complex regulatory networks that respond to environmental cues leading to peroxisome assembly and the molecular events of organelle assembly. Ultimately, understanding the mechanisms of the entire peroxisome biogenesis program holds promise for predictive modeling approaches and for guiding rational intervention strategies that could treat human conditions associated with peroxisome function.
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Abstract
Cell growth is a complex phenotype widely used in systems biology to gauge the impact of genetic and environmental perturbations. Due to the magnitude of genome-wide studies, resolution is often sacrificed in favor of throughput, creating a demand for scalable, time-resolved, quantitative methods of growth assessment. We present ODELAY (One-cell Doubling Evaluation by Living Arrays of Yeast), an automated and scalable growth analysis platform. High measurement density and single-cell resolution provide a powerful tool for large-scale multiparameter growth analysis based on the modeling of microcolony expansion on solid media. Pioneered in yeast but applicable to other colony forming organisms, ODELAY extracts the three key growth parameters (lag time, doubling time, and carrying capacity) that define microcolony expansion from single cells, simultaneously permitting the assessment of population heterogeneity. The utility of ODELAY is illustrated using yeast mutants, revealing a spectrum of phenotypes arising from single and combinatorial growth parameter perturbations.
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11
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Brink DP, Borgström C, Tueros FG, Gorwa-Grauslund MF. Real-time monitoring of the sugar sensing in Saccharomyces cerevisiae indicates endogenous mechanisms for xylose signaling. Microb Cell Fact 2016; 15:183. [PMID: 27776527 PMCID: PMC5078928 DOI: 10.1186/s12934-016-0580-x] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Accepted: 10/14/2016] [Indexed: 12/03/2022] Open
Abstract
Background The sugar sensing and carbon catabolite repression in Baker’s yeast Saccharomyces cerevisiae is governed by three major signaling pathways that connect carbon source recognition with transcriptional regulation. Here we present a screening method based on a non-invasive in vivo reporter system for real-time, single-cell screening of the sugar signaling state in S. cerevisiae in response to changing carbon conditions, with a main focus on the response to glucose and xylose. Results The artificial reporter system was constructed by coupling a green fluorescent protein gene (yEGFP3) downstream of endogenous yeast promoters from the Snf3p/Rgt2p, SNF1/Mig1p and cAMP/PKA signaling pathways: HXT1p/2p/4p; SUC2p, CAT8p; TPS1p/2p and TEF4p respectively. A panel of eight biosensors strains was generated by single copy chromosomal integration of the different constructs in a W303-derived strain. The signaling biosensors were validated for their functionality with flow cytometry by comparing the fluorescence intensity (FI) response in the presence of high or nearly depleted glucose to the known induction/repression conditions of the eight different promoters. The FI signal correlated with the known patterns of the selected promoters while maintaining a non-invasive property on the cellular phenotype, as was demonstrated in terms of growth, metabolites and enzyme activity. Conclusions Once verified, the sensors were used to evaluate the signaling response to varying conditions of extracellular glucose, glycerol and xylose by screening in 96-well microtiter plates. We show that these yeast strains, which do not harbor any recombinant pathways for xylose utilization, are lacking a signaling response for extracellular xylose. However, for the HXT2p/4p sensors, a shift in the flow cytometry population dynamics indicated that internalized xylose does affect the signaling. These results suggest that the previously observed effects of this pentose on the S. cerevisiae physiology and gene regulation can be attributed to xylose and not only to a lack of glucose. Electronic supplementary material The online version of this article (doi:10.1186/s12934-016-0580-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Daniel P Brink
- Applied Microbiology, Department of Chemistry, Lund University, P.O. Box 124, 22100, Lund, Sweden.
| | - Celina Borgström
- Applied Microbiology, Department of Chemistry, Lund University, P.O. Box 124, 22100, Lund, Sweden
| | - Felipe G Tueros
- Applied Microbiology, Department of Chemistry, Lund University, P.O. Box 124, 22100, Lund, Sweden
| | - Marie F Gorwa-Grauslund
- Applied Microbiology, Department of Chemistry, Lund University, P.O. Box 124, 22100, Lund, Sweden
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12
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Hewitt SK, Foster DS, Dyer PS, Avery SV. Phenotypic heterogeneity in fungi: Importance and methodology. FUNGAL BIOL REV 2016. [DOI: 10.1016/j.fbr.2016.09.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
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13
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Activation of RNase L by Murine Coronavirus in Myeloid Cells Is Dependent on Basal Oas Gene Expression and Independent of Virus-Induced Interferon. J Virol 2016; 90:3160-72. [PMID: 26739051 DOI: 10.1128/jvi.03036-15] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2015] [Accepted: 12/30/2015] [Indexed: 12/24/2022] Open
Abstract
UNLABELLED The oligoadenylate synthetase (OAS)-RNase L pathway is a potent interferon (IFN)-induced antiviral activity. Upon sensing double-stranded RNA, OAS produces 2',5'-oligoadenylates (2-5A), which activate RNase L. Murine coronavirus (mouse hepatitis virus [MHV]) nonstructural protein 2 (ns2) is a 2',5'-phosphodiesterase (PDE) that cleaves 2-5A, thereby antagonizing RNase L activation. PDE activity is required for robust replication in myeloid cells, as a mutant of MHV (ns2(H126R)) encoding an inactive PDE fails to antagonize RNase L activation and replicates poorly in bone marrow-derived macrophages (BMM), while ns2(H126R) replicates to high titer in several types of nonmyeloid cells, as well as in IFN receptor-deficient (Ifnar1(-/-)) BMM. We reported previously that myeloid cells express significantly higher basal levels of OAS transcripts than nonmyeloid cells. Here, we investigated the contributions of Oas gene expression, basal IFN signaling, and virus-induced IFN to RNase L activation. Infection with ns2(H126R) activated RNase L in Ifih1(-/-) BMM to a similar extent as in wild-type (WT) BMM, despite the lack of IFN induction in the absence of MDA5 expression. However, ns2(H126R) failed to induce RNase L activation in BMM treated with IFNAR1-blocking antibody, as well as in Ifnar1(-/-) BMM, both expressing low basal levels of Oas genes. Thus, activation of RNase L does not require virus-induced IFN but rather correlates with adequate levels of basal Oas gene expression, maintained by basal IFN signaling. Finally, overexpression of RNase L is not sufficient to compensate for inadequate basal OAS levels. IMPORTANCE The oligoadenylate synthetase (OAS)-RNase L pathway is a potent antiviral activity. Activation of RNase L during murine coronavirus (mouse hepatitis virus [MHV]) infection of myeloid cells correlates with high basal Oas gene expression and is independent of virus-induced interferon secretion. Thus, our data suggest that cells with high basal Oas gene expression levels can activate RNase L and thereby inhibit virus replication early in infection upon exposure to viral double-stranded RNA (dsRNA) before the induction of interferon and prior to transcription of interferon-stimulated antiviral genes. These findings challenge the notion that activation of the OAS-RNase L pathway requires virus to induce type I IFN, which in turn upregulates OAS gene expression, as well as to provide dsRNA to activate OAS. Our data further suggest that myeloid cells may serve as sentinels to restrict viral replication, thus protecting other cell types from infection.
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14
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Romagnoli G, Knijnenburg TA, Liti G, Louis EJ, Pronk JT, Daran JM. Deletion of theSaccharomyces cerevisiae ARO8gene, encoding an aromatic amino acid transaminase, enhances phenylethanol production from glucose. Yeast 2014; 32:29-45. [DOI: 10.1002/yea.3015] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Revised: 03/18/2014] [Accepted: 04/04/2014] [Indexed: 11/11/2022] Open
Affiliation(s)
- Gabriele Romagnoli
- Department of Biotechnology; Delft University of Technology; Delft The Netherlands
- Kluyver Centre for Genomics of Industrial Fermentation; Delft The Netherlands
| | | | - Gianni Liti
- Centre for Genetics and Genomics, Queens Medical Centre; University of Nottingham; UK
- Institute for Research on Cancer and Ageing; CNRS UMR 7284-INSERM U 1081- UNS NICE; Nice France
| | - Edward J. Louis
- Centre for Genetics and Genomics, Queens Medical Centre; University of Nottingham; UK
- Centre for Genetic Architecture of Complex Traits, Department of Genetics; University of Leicester; UK
| | - Jack T. Pronk
- Department of Biotechnology; Delft University of Technology; Delft The Netherlands
- Kluyver Centre for Genomics of Industrial Fermentation; Delft The Netherlands
- Platform Green Synthetic Biology; Delft The Netherlands
| | - Jean-Marc Daran
- Department of Biotechnology; Delft University of Technology; Delft The Netherlands
- Kluyver Centre for Genomics of Industrial Fermentation; Delft The Netherlands
- Platform Green Synthetic Biology; Delft The Netherlands
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15
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Zuleta IA, Aranda-Díaz A, Li H, El-Samad H. Dynamic characterization of growth and gene expression using high-throughput automated flow cytometry. Nat Methods 2014; 11:443-8. [PMID: 24608180 PMCID: PMC4016179 DOI: 10.1038/nmeth.2879] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2013] [Accepted: 01/16/2014] [Indexed: 01/06/2023]
Abstract
Cells adjust to changes in environmental conditions using complex regulatory programs. These cellular programs are the result of an intricate interplay between gene expression, cellular growth and protein degradation. Technologies that enable simultaneous and time-resolved measurements of these variables are necessary to dissect cellular homeostatic strategies. Here we report the development of an automated flow cytometry robotic setup that enables real-time measurement of precise and simultaneous relative growth and protein synthesis rates of multiplexed microbial populations across many conditions. These measurements generate quantitative profiles of dynamically evolving protein synthesis and degradation rates. We demonstrate this setup in the context of gene regulation of the unfolded protein response (UPR) of Saccharomyces cerevisiae and uncover a dynamic and complex landscape of gene expression, growth dynamics and proteolysis following perturbations.
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Affiliation(s)
- Ignacio A Zuleta
- 1] Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA. [2] The California Institute for Quantitative Biosciences, San Francisco, California, USA
| | - Andrés Aranda-Díaz
- 1] Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA. [2] The California Institute for Quantitative Biosciences, San Francisco, California, USA
| | - Hao Li
- 1] Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA. [2] The California Institute for Quantitative Biosciences, San Francisco, California, USA
| | - Hana El-Samad
- 1] Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, California, USA. [2] The California Institute for Quantitative Biosciences, San Francisco, California, USA
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16
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Abstract
Peroxisomes carry out various oxidative reactions that are tightly regulated to adapt to the changing needs of the cell and varying external environments. Accordingly, they are remarkably fluid and can change dramatically in abundance, size, shape and content in response to numerous cues. These dynamics are controlled by multiple aspects of peroxisome biogenesis that are coordinately regulated with each other and with other cellular processes. Ongoing studies are deciphering the diverse molecular mechanisms that underlie biogenesis and how they cooperate to dynamically control peroxisome utility. These important challenges should lead to an understanding of peroxisome dynamics that can be capitalized upon for bioengineering and the development of therapies to improve human health.
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Affiliation(s)
- Jennifer J Smith
- 1] Seattle Biomedical Research Institute, 307 Westlake Avenue North, 98109-5240, USA. [2] Institute for Systems Biology, 401 Terry Avenue North, Seattle, Washington 98109-5219, USA
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17
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Manninen T, Huttunen H, Ruusuvuori P, Nykter M. Leukemia prediction using sparse logistic regression. PLoS One 2013; 8:e72932. [PMID: 24023658 PMCID: PMC3758279 DOI: 10.1371/journal.pone.0072932] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 07/12/2013] [Indexed: 01/28/2023] Open
Abstract
We describe a supervised prediction method for diagnosis of acute myeloid leukemia (AML) from patient samples based on flow cytometry measurements. We use a data driven approach with machine learning methods to train a computational model that takes in flow cytometry measurements from a single patient and gives a confidence score of the patient being AML-positive. Our solution is based on an [Formula: see text] regularized logistic regression model that aggregates AML test statistics calculated from individual test tubes with different cell populations and fluorescent markers. The model construction is entirely data driven and no prior biological knowledge is used. The described solution scored a 100% classification accuracy in the DREAM6/FlowCAP2 Molecular Classification of Acute Myeloid Leukaemia Challenge against a golden standard consisting of 20 AML-positive and 160 healthy patients. Here we perform a more extensive validation of the prediction model performance and further improve and simplify our original method showing that statistically equal results can be obtained by using simple average marker intensities as features in the logistic regression model. In addition to the logistic regression based model, we also present other classification models and compare their performance quantitatively. The key benefit in our prediction method compared to other solutions with similar performance is that our model only uses a small fraction of the flow cytometry measurements making our solution highly economical.
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Affiliation(s)
- Tapio Manninen
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland
- * E-mail:
| | - Heikki Huttunen
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Pekka Ruusuvuori
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland
| | - Matti Nykter
- Institute of Biomedical Technology, University of Tampere, Tampere, Finland
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18
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Barteneva NS, Fasler-Kan E, Vorobjev IA. Imaging flow cytometry: coping with heterogeneity in biological systems. J Histochem Cytochem 2012; 60:723-33. [PMID: 22740345 DOI: 10.1369/0022155412453052] [Citation(s) in RCA: 117] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023] Open
Abstract
Imaging flow cytometry (IFC) platforms combine features of flow cytometry and fluorescent microscopy with advances in data-processing algorithms. IFC allows multiparametric fluorescent and morphological analysis of thousands of cellular events and has the unique capability of identifying collected events by their real images. IFC allows the analysis of heterogeneous cell populations, where one of the cellular components has low expression (<0.03%) and can be described by Poisson distribution. With the help of IFC, one can address a critical question of statistical analysis of subcellular distribution of proteins in a cell. Here the authors review advantages of IFC in comparison with more traditional technologies, such as Western blotting and flow cytometry (FC), as well as new high-throughput fluorescent microscopy (HTFM), and discuss further developments of this novel analytical technique.
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Affiliation(s)
- Natasha S Barteneva
- Immune Disease Institute and Program in Cellular and Molecular Medicine, Children's Hospital Boston and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
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