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Asmar AJ, Benson ZA, Peskin AP, Chalfoun J, Simon M, Halter M, Plant AL. High-volume, label-free imaging for quantifying single-cell dynamics in induced pluripotent stem cell colonies. PLoS One 2024; 19:e0298446. [PMID: 38377138 PMCID: PMC10878516 DOI: 10.1371/journal.pone.0298446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/23/2024] [Indexed: 02/22/2024] Open
Abstract
To facilitate the characterization of unlabeled induced pluripotent stem cells (iPSCs) during culture and expansion, we developed an AI pipeline for nuclear segmentation and mitosis detection from phase contrast images of individual cells within iPSC colonies. The analysis uses a 2D convolutional neural network (U-Net) plus a 3D U-Net applied on time lapse images to detect and segment nuclei, mitotic events, and daughter nuclei to enable tracking of large numbers of individual cells over long times in culture. The analysis uses fluorescence data to train models for segmenting nuclei in phase contrast images. The use of classical image processing routines to segment fluorescent nuclei precludes the need for manual annotation. We optimize and evaluate the accuracy of automated annotation to assure the reliability of the training. The model is generalizable in that it performs well on different datasets with an average F1 score of 0.94, on cells at different densities, and on cells from different pluripotent cell lines. The method allows us to assess, in a non-invasive manner, rates of mitosis and cell division which serve as indicators of cell state and cell health. We assess these parameters in up to hundreds of thousands of cells in culture for more than 36 hours, at different locations in the colonies, and as a function of excitation light exposure.
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Affiliation(s)
- Anthony J. Asmar
- Biosystems and Biomaterials Division Material Measurement Lab, NIST Gaithersburg, Gaithersburg, Maryland, United States of America
| | - Zackery A. Benson
- Biosystems and Biomaterials Division Material Measurement Lab, NIST Gaithersburg, Gaithersburg, Maryland, United States of America
| | - Adele P. Peskin
- Software and Systems Division Information Technology Lab, NIST Gaithersburg, Gaithersburg, Maryland, United States of America
| | - Joe Chalfoun
- Software and Systems Division Information Technology Lab, NIST Gaithersburg, Gaithersburg, Maryland, United States of America
| | - Mylene Simon
- Software and Systems Division Information Technology Lab, NIST Gaithersburg, Gaithersburg, Maryland, United States of America
| | - Michael Halter
- Biosystems and Biomaterials Division Material Measurement Lab, NIST Gaithersburg, Gaithersburg, Maryland, United States of America
| | - Anne L. Plant
- Biosystems and Biomaterials Division Material Measurement Lab, NIST Gaithersburg, Gaithersburg, Maryland, United States of America
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2
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Bianchini RM, Kurz EU. The analysis of protein recruitment to laser microirradiation-induced DNA damage in live cells: Best practices for data analysis. DNA Repair (Amst) 2023; 129:103545. [PMID: 37524003 DOI: 10.1016/j.dnarep.2023.103545] [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: 05/05/2023] [Revised: 07/20/2023] [Accepted: 07/24/2023] [Indexed: 08/02/2023]
Abstract
Laser microirradiation coupled with live-cell fluorescence microscopy is a powerful technique that has been used widely in studying the recruitment and retention of proteins at sites of DNA damage. Results obtained from this technique can be found in published works by both seasoned and infrequent users of microscopy. However, like many other microscopy-based techniques, the presentation of data from laser microirradiation experiments is inconsistent; papers report a wide assortment of analytic techniques, not all of which result in accurate and/or appropriate representation of the data. In addition to the varied methods of analysis, experimental and analytical details are commonly under-reported. Consequently, publications reporting data from laser microirradiation coupled with fluorescence microscopy experiments need to be carefully and critically assessed by readers. Here, we undertake a systematic investigation of commonly reported corrections used in the analysis of laser microirradiation data. We validate the critical need to correct data for photobleaching and we identify key experimental parameters that must be accounted for when presenting data from laser microirradiation experiments. Furthermore, we propose a straightforward, four-step analytical protocol that can readily be applied across platforms and that aims to improve the quality of data reporting in the DNA damage field.
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Affiliation(s)
- Ryan M Bianchini
- Robson DNA Science Centre, Arnie Charbonneau Cancer Institute, and Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada
| | - Ebba U Kurz
- Robson DNA Science Centre, Arnie Charbonneau Cancer Institute, and Department of Physiology and Pharmacology, Cumming School of Medicine, University of Calgary, 3330 Hospital Drive NW, Calgary, AB T2N 4N1, Canada.
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3
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Kwee E, Peterson A, Halter M, Elliott J. Practical application of microsphere samples for benchmarking a quantitative phase imaging system. Cytometry A 2020; 99:1022-1032. [PMID: 33305901 PMCID: PMC8195315 DOI: 10.1002/cyto.a.24291] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 11/11/2020] [Accepted: 12/01/2020] [Indexed: 01/12/2023]
Abstract
Quantitative phase imaging (QPI) provides an approach for monitoring the dry mass of individual cells by measuring the optical pathlength of visible light as it passes through cells. A distinct advantage of QPI is that the measurements result in optical path length quantities that are, in principle, instrument independent. Reference materials that induce a well‐defined optical pathlength shift and are compatible with QPI imaging systems will be valuable in assuring the accuracy of such measurements on different instruments. In this study, we evaluate seven combinations of microspheres embedded in index refraction matching media as candidate reference materials for benchmarking the performance of a QPI system and as calibration standards for the optical pathlength measurement. Poly(methyl metharylate) microspheres and mineral oil were used to evaluate the range of illumination apertures, signal‐to‐noise ratios, and focus positions that allow an accurate quantitative optical pathlength measurement. The microsphere‐based reference material can be used to verify settings on an instrument that are suitable for obtaining an accurate pathlength measurement from biological cells. The microsphere/media reference material is applied to QPI‐based dry mass measurements of a population of HEK293 cells to benchmark and provide evidence that the QPI image data are accurate.
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Affiliation(s)
- Edward Kwee
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Alexander Peterson
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - Michael Halter
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
| | - John Elliott
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, Maryland, USA
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4
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Plant AL, Halter M, Stinson J. Probing pluripotency gene regulatory networks with quantitative live cell imaging. Comput Struct Biotechnol J 2020; 18:2733-2743. [PMID: 33101611 PMCID: PMC7560648 DOI: 10.1016/j.csbj.2020.09.025] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 09/14/2020] [Accepted: 09/15/2020] [Indexed: 11/12/2022] Open
Abstract
Live cell imaging uniquely enables the measurement of dynamic events in single cells, but it has not been used often in the study of gene regulatory networks. Network components can be examined in relation to one another by quantitative live cell imaging of fluorescent protein reporter cell lines that simultaneously report on more than one network component. A series of dual-reporter cell lines would allow different combinations of network components to be examined in individual cells. Dynamical information about interacting network components in individual cells is critical to predictive modeling of gene regulatory networks, and such information is not accessible through omics and other end point techniques. Achieving this requires that gene-edited cell lines are appropriately designed and adequately characterized to assure the validity of the biological conclusions derived from the expression of the reporters. In this brief review we discuss what is known about the importance of dynamics to network modeling and review some recent advances in optical microscopy methods and image analysis approaches that are making the use of quantitative live cell imaging for network analysis possible. We also discuss how strategies for genetic engineering of reporter cell lines can influence the biological relevance of the data.
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Affiliation(s)
- Anne L Plant
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, United States
| | - Michael Halter
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, United States
| | - Jeffrey Stinson
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, United States
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5
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Hubbard JB, Halter M, Sarkar S, Plant AL. The role of fluctuations in determining cellular network thermodynamics. PLoS One 2020; 15:e0230076. [PMID: 32160263 PMCID: PMC7065797 DOI: 10.1371/journal.pone.0230076] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 02/20/2020] [Indexed: 12/28/2022] Open
Abstract
The steady state distributions of phenotypic responses within an isogenic population of cells result from both deterministic and stochastic characteristics of biochemical networks. A biochemical network can be characterized by a multidimensional potential landscape based on the distribution of responses and a diffusion matrix of the correlated dynamic fluctuations between N-numbers of intracellular network variables. In this work, we develop a thermodynamic description of biological networks at the level of microscopic interactions between network variables. The Boltzmann H-function defines the rate of free energy dissipation of a network system and provides a framework for determining the heat associated with the nonequilibrium steady state and its network components. The magnitudes of the landscape gradients and the dynamic correlated fluctuations of network variables are experimentally accessible. We describe the use of Fokker-Planck dynamics to calculate housekeeping heat from the experimental data by a method that we refer to as Thermo-FP. The method provides insight into the composition of the network and the relative thermodynamic contributions from network components. We surmise that these thermodynamic quantities allow determination of the relative importance of network components to overall network control. We conjecture that there is an upper limit to the rate of dissipative heat produced by a biological system that is associated with system size or modularity, and we show that the dissipative heat has a lower bound.
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Affiliation(s)
- Joseph B. Hubbard
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Michael Halter
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Swarnavo Sarkar
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Anne L. Plant
- Biosystems and Biomaterials Division, National Institute of Standards and Technology, Gaithersburg, MD, United States of America
- * E-mail:
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6
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May M, Denecke B, Schroeder T, Götz M, Faissner A. Cell tracking in vitro reveals that the extracellular matrix glycoprotein Tenascin-C modulates cell cycle length and differentiation in neural stem/progenitor cells of the developing mouse spinal cord. Biol Open 2018; 7:7/7/bio027730. [PMID: 30045859 PMCID: PMC6078350 DOI: 10.1242/bio.027730] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Generation of astrocytes during the development of the mammalian spinal cord is poorly understood. Previously, we have shown that the glycoprotein of the extracellular matrix (ECM) tenascin-C (Tnc) modulates the expression territories of the patterning genes Nkx6.1 and Nkx2.2 in the developing ventral spinal cord, tunes the responsiveness of neural stem/progenitor cells towards the cytokines FGF2 and EGF and thereby promotes astrocyte maturation. In order to obtain further mechanistic insight into these processes, we have compared embryonic day-15 spinal cord neural progenitor cells (NPCs) from wild-type and Tnc knockout mice using continuous single-cell live imaging and cell lineage analysis in vitroTnc knockout cells displayed a significantly reduced rate of cell division both in response to FGF2 and EGF. When individual clones of dividing cells were investigated with regard to their cell lineage trees using the tTt tracking software, it appeared that the cell cycle length in response to growth factors was reduced in the knockout. Furthermore, when Tnc knockout NPCs were induced to differentiate by the removal of FGF2 and EGF glial differentiation was enhanced. We conclude that the constituent of the stem cell niche Tnc contributes to preserve stemness of NPCs.
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Affiliation(s)
- Marcus May
- Department for Cell Morphology and Molecular Neurobiology, Ruhr-University Bochum, 44780 Bochum, Germany
| | - Bernd Denecke
- Aachen Interdisciplinary Center for Clinical Research, Faculty of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen, 52074 Aachen, Germany
| | - Timm Schroeder
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland
| | - Magdalena Götz
- Physiological Genomics, Biomedical Center, Ludwig-Maximilians University Munich, 82152 Planegg/Martinsried, Germany.,Department of Physiological Genomics, Biomedical Center, Ludwig-Maximilians University Munich, 82152 Planegg/Martinsried, Germany.,Munich Cluster for Systems Neurology (SyNergy), Ludwig-Maximilians University Munich, 81377 Munich, Germany
| | - Andreas Faissner
- Department for Cell Morphology and Molecular Neurobiology, Ruhr-University Bochum, 44780 Bochum, Germany
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7
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Lineage mapper: A versatile cell and particle tracker. Sci Rep 2016; 6:36984. [PMID: 27853188 PMCID: PMC5113068 DOI: 10.1038/srep36984] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2016] [Accepted: 10/19/2016] [Indexed: 12/23/2022] Open
Abstract
The ability to accurately track cells and particles from images is critical to many biomedical problems. To address this, we developed Lineage Mapper, an open-source tracker for time-lapse images of biological cells, colonies, and particles. Lineage Mapper tracks objects independently of the segmentation method, detects mitosis in confluence, separates cell clumps mistakenly segmented as a single cell, provides accuracy and scalability even on terabyte-sized datasets, and creates division and/or fusion lineages. Lineage Mapper has been tested and validated on multiple biological and simulated problems. The software is available in ImageJ and Matlab at isg.nist.gov.
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8
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Bhadriraju K, Halter M, Amelot J, Bajcsy P, Chalfoun J, Vandecreme A, Mallon BS, Park KY, Sista S, Elliott JT, Plant AL. Large-scale time-lapse microscopy of Oct4 expression in human embryonic stem cell colonies. Stem Cell Res 2016; 17:122-9. [PMID: 27286574 PMCID: PMC5012928 DOI: 10.1016/j.scr.2016.05.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 04/15/2016] [Accepted: 05/20/2016] [Indexed: 01/06/2023] Open
Abstract
Identification and quantification of the characteristics of stem cell preparations is critical for understanding stem cell biology and for the development and manufacturing of stem cell based therapies. We have developed image analysis and visualization software that allows effective use of time-lapse microscopy to provide spatial and dynamic information from large numbers of human embryonic stem cell colonies. To achieve statistically relevant sampling, we examined >680 colonies from 3 different preparations of cells over 5 days each, generating a total experimental dataset of 0.9 terabyte (TB). The 0.5 Giga-pixel images at each time point were represented by multi-resolution pyramids and visualized using the Deep Zoom Javascript library extended to support viewing Giga-pixel images over time and extracting data on individual colonies. We present a methodology that enables quantification of variations in nominally-identical preparations and between colonies, correlation of colony characteristics with Oct4 expression, and identification of rare events.
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Affiliation(s)
- Kiran Bhadriraju
- Department of Bioengineering, University of Maryland, College Park, MD 20742, USA
| | - Michael Halter
- Biosystems and Biomaterials Division, Materials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Julien Amelot
- Software Systems Division, Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Peter Bajcsy
- Software Systems Division, Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Joe Chalfoun
- Software Systems Division, Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Antoine Vandecreme
- Software Systems Division, Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Barbara S Mallon
- The NIH Stem Cell Unit, Division of Intramural Research, National Institute of Neurological Disorders and Stroke, NIH, U.S. Department of Health and Human Services, Bethesda, MD, USA
| | - Kye-Yoon Park
- The NIH Stem Cell Unit, Division of Intramural Research, National Institute of Neurological Disorders and Stroke, NIH, U.S. Department of Health and Human Services, Bethesda, MD, USA
| | - Subhash Sista
- Biosystems and Biomaterials Division, Materials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - John T Elliott
- Biosystems and Biomaterials Division, Materials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
| | - Anne L Plant
- Biosystems and Biomaterials Division, Materials Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 20899, USA
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9
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CHALFOUN J, MAJURSKI M, PESKIN A, BREEN C, BAJCSY P, BRADY M. Empirical gradient threshold technique for automated segmentation across image modalities and cell lines. J Microsc 2015; 260:86-99. [DOI: 10.1111/jmi.12269] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Accepted: 04/30/2015] [Indexed: 02/02/2023]
Affiliation(s)
- J. CHALFOUN
- Information Technology Laboratory; National Institute of Standards and Technology
| | - M. MAJURSKI
- Information Technology Laboratory; National Institute of Standards and Technology
| | - A. PESKIN
- Information Technology Laboratory; National Institute of Standards and Technology
| | - C. BREEN
- Princeton University; Princeton NJ 08544 USA
| | - P. BAJCSY
- Information Technology Laboratory; National Institute of Standards and Technology
| | - M. BRADY
- Information Technology Laboratory; National Institute of Standards and Technology
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10
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Model M. Intensity calibration and flat-field correction for fluorescence microscopes. CURRENT PROTOCOLS IN CYTOMETRY 2014; 68:10.14.1-10.14.10. [PMID: 24692055 DOI: 10.1002/0471142956.cy1014s68] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Standardization in fluorescence microscopy involves calibration of intensity in reproducible units and correction for spatial nonuniformity of illumination (flat-field or shading correction). Both goals can be achieved using concentrated solutions of fluorescent dyes. When a drop of a highly concentrated fluorescent dye is placed between a slide and a coverslip it produces a spatially uniform field, resistant to photobleaching and with reproducible quantum yield; it can be used as a brightness standard for wide-field and confocal microscopes. For wide-field microscopes, calibration can be further extended to absolute molecular units. This can be done by imaging a solution of known concentration and known depth; the latter can be prepared by placing a small spherical lens in a diluted solution of the same fluorophore that is used in the biological specimen.
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Affiliation(s)
- Michael Model
- Department of Biological Sciences, Kent State University, Kent, Ohio
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11
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Birkenkamp-Demtröder K, Hahn SA, Mansilla F, Thorsen K, Maghnouj A, Christensen R, Øster B, Ørntoft TF. Keratin23 (KRT23) knockdown decreases proliferation and affects the DNA damage response of colon cancer cells. PLoS One 2013; 8:e73593. [PMID: 24039993 PMCID: PMC3767798 DOI: 10.1371/journal.pone.0073593] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2012] [Accepted: 07/25/2013] [Indexed: 11/18/2022] Open
Abstract
Keratin 23 (KRT23) is strongly expressed in colon adenocarcinomas but absent in normal colon mucosa. Array based methylation profiling of 40 colon samples showed that the promoter of KRT23 was methylated in normal colon mucosa, while hypomethylated in most adenocarcinomas. Promoter methylation correlated with absent expression, while increased KRT23 expression in tumor samples correlated with promoter hypomethylation, as confirmed by bisulfite sequencing. Demethylation induced KRT23 expression in vitro. Expression profiling of shRNA mediated stable KRT23 knockdown in colon cancer cell lines showed that KRT23 depletion affected molecules of the cell cycle and DNA replication, recombination and repair. In vitro analyses confirmed that KRT23 depletion significantly decreased the cellular proliferation of SW948 and LS1034 cells and markedly decreased the expression of genes involved in DNA damage response, mainly molecules of the double strand break repair homologous recombination pathway. KRT23 knockdown decreased the transcript and protein expression of key molecules as e.g. MRE11A, E2F1, RAD51 and BRCA1. Knockdown of KRT23 rendered colon cancer cells more sensitive to irradiation and reduced proliferation of the KRT23 depleted cells compared to irradiated control cells.
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Affiliation(s)
| | - Stephan A. Hahn
- Department of Molecular GI-Oncology, Center of Clinical Research, Ruhr-University Bochum, Bochum, Germany
| | - Francisco Mansilla
- Department of Molecular Medicine MOMA, Aarhus University Hospital, Skejby, Aarhus N, Denmark
| | - Kasper Thorsen
- Department of Molecular Medicine MOMA, Aarhus University Hospital, Skejby, Aarhus N, Denmark
| | - Abdelouahid Maghnouj
- Department of Molecular GI-Oncology, Center of Clinical Research, Ruhr-University Bochum, Bochum, Germany
| | - Rikke Christensen
- Department of Clinical Genetics, Aarhus University Hospital, Skejby, Aarhus N, Denmark
| | - Bodil Øster
- Department of Molecular Medicine MOMA, Aarhus University Hospital, Skejby, Aarhus N, Denmark
| | - Torben Falck Ørntoft
- Department of Molecular Medicine MOMA, Aarhus University Hospital, Skejby, Aarhus N, Denmark
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12
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Chalfoun J, Kociolek M, Dima A, Halter M, Cardone A, Peskin A, Bajcsy P, Brady M. Segmenting time-lapse phase contrast images of adjacent NIH 3T3 cells. J Microsc 2012; 249:41-52. [PMID: 23126432 DOI: 10.1111/j.1365-2818.2012.03678.x] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
We present a new method for segmenting phase contrast images of NIH 3T3 fibroblast cells that is accurate even when cells are physically in contact with each other. The problem of segmentation, when cells are in contact, poses a challenge to the accurate automation of cell counting, tracking and lineage modelling in cell biology. The segmentation method presented in this paper consists of (1) background reconstruction to obtain noise-free foreground pixels and (2) incorporation of biological insight about dividing and nondividing cells into the segmentation process to achieve reliable separation of foreground pixels defined as pixels associated with individual cells. The segmentation results for a time-lapse image stack were compared against 238 manually segmented images (8219 cells) provided by experts, which we consider as reference data. We chose two metrics to measure the accuracy of segmentation: the 'Adjusted Rand Index' which compares similarities at a pixel level between masks resulting from manual and automated segmentation, and the 'Number of Cells per Field' (NCF) which compares the number of cells identified in the field by manual versus automated analysis. Our results show that the automated segmentation compared to manual segmentation has an average adjusted rand index of 0.96 (1 being a perfect match), with a standard deviation of 0.03, and an average difference of the two numbers of cells per field equal to 5.39% with a standard deviation of 4.6%.
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Affiliation(s)
- J Chalfoun
- National Institute of Standards and Technology, Gaithersburg, Maryland 20899, USA.
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13
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Predicting rates of cell state change caused by stochastic fluctuations using a data-driven landscape model. Proc Natl Acad Sci U S A 2012; 109:19262-7. [PMID: 23115330 DOI: 10.1073/pnas.1207544109] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023] Open
Abstract
We develop a potential landscape approach to quantitatively describe experimental data from a fibroblast cell line that exhibits a wide range of GFP expression levels under the control of the promoter for tenascin-C. Time-lapse live-cell microscopy provides data about short-term fluctuations in promoter activity, and flow cytometry measurements provide data about the long-term kinetics, because isolated subpopulations of cells relax from a relatively narrow distribution of GFP expression back to the original broad distribution of responses. The landscape is obtained from the steady state distribution of GFP expression and connected to a potential-like function using a stochastic differential equation description (Langevin/Fokker-Planck). The range of cell states is constrained by a force that is proportional to the gradient of the potential, and biochemical noise causes movement of cells within the landscape. Analyzing the mean square displacement of GFP intensity changes in live cells indicates that these fluctuations are described by a single diffusion constant in log GFP space. This finding allows application of the Kramers' model to calculate rates of switching between two attractor states and enables an accurate simulation of the dynamics of relaxation back to the steady state with no adjustable parameters. With this approach, it is possible to use the steady state distribution of phenotypes and a quantitative description of the short-term fluctuations in individual cells to accurately predict the rates at which different phenotypes will arise from an isolated subpopulation of cells.
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14
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Nandy K, Gudla PR, Amundsen R, Meaburn KJ, Misteli T, Lockett SJ. Automatic segmentation and supervised learning-based selection of nuclei in cancer tissue images. Cytometry A 2012; 81:743-54. [PMID: 22899462 PMCID: PMC6362837 DOI: 10.1002/cyto.a.22097] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2012] [Revised: 05/18/2012] [Accepted: 06/12/2012] [Indexed: 01/14/2023]
Abstract
Analysis of preferential localization of certain genes within the cell nuclei is emerging as a new technique for the diagnosis of breast cancer. Quantitation requires accurate segmentation of 100-200 cell nuclei in each tissue section to draw a statistically significant result. Thus, for large-scale analysis, manual processing is too time consuming and subjective. Fortuitously, acquired images generally contain many more nuclei than are needed for analysis. Therefore, we developed an integrated workflow that selects, following automatic segmentation, a subpopulation of accurately delineated nuclei for positioning of fluorescence in situ hybridization-labeled genes of interest. Segmentation was performed by a multistage watershed-based algorithm and screening by an artificial neural network-based pattern recognition engine. The performance of the workflow was quantified in terms of the fraction of automatically selected nuclei that were visually confirmed as well segmented and by the boundary accuracy of the well-segmented nuclei relative to a 2D dynamic programming-based reference segmentation method. Application of the method was demonstrated for discriminating normal and cancerous breast tissue sections based on the differential positioning of the HES5 gene. Automatic results agreed with manual analysis in 11 out of 14 cancers, all four normal cases, and all five noncancerous breast disease cases, thus showing the accuracy and robustness of the proposed approach.
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Affiliation(s)
- Kaustav Nandy
- Optical Microscopy and Analysis Laboratory, Advanced Technology Program, SAIC-Frederick, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland 21702, USA.
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15
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Mancia A, Elliott JT, Halter M, Bhadriraju K, Tona A, Spurlin TA, Middlebrooks BL, Baatz JE, Warr GW, Plant AL. Quantitative methods to characterize morphological properties of cell lines. Biotechnol Prog 2012; 28:1069-78. [DOI: 10.1002/btpr.1564] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2011] [Revised: 03/23/2012] [Indexed: 12/22/2022]
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16
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Erdmann G, Volz C, Boutros M. Systematic approaches to dissect biological processes in stem cells by image-based screening. Biotechnol J 2012; 7:768-78. [DOI: 10.1002/biot.201200117] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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