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Hall D. Equations describing semi-confluent cell growth (I) Analytical approximations. Biophys Chem 2024; 307:107173. [PMID: 38241828 DOI: 10.1016/j.bpc.2024.107173] [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: 08/24/2023] [Revised: 12/27/2023] [Accepted: 01/05/2024] [Indexed: 01/21/2024]
Abstract
A set of differential equations with analytical solutions are presented that can quantitatively account for variable degrees of contact inhibition on cell growth in two- and three-dimensional cultures. The developed equations can be used for comparative purposes when assessing contribution of higher-order effects, such as culture geometry and nutrient depletion, on mean cell growth rate. These equations also offer experimentalists the opportunity to characterize cell culture experiments using a single reductive parameter.
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Affiliation(s)
- Damien Hall
- WPI Nano Life Science Institute, Kanazawa University, Kakumamachi, Kanazawa, Ishikawa 920-1164, Japan.
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2
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Hall D. MIL-CELL: a tool for multi-scale simulation of yeast replication and prion transmission. EUROPEAN BIOPHYSICS JOURNAL : EBJ 2023; 52:673-704. [PMID: 37670150 PMCID: PMC10682183 DOI: 10.1007/s00249-023-01679-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 08/08/2023] [Accepted: 08/14/2023] [Indexed: 09/07/2023]
Abstract
The single-celled baker's yeast, Saccharomyces cerevisiae, can sustain a number of amyloid-based prions, the three most prominent examples being [URE3], [PSI+], and [PIN+]. In the laboratory, haploid S. cerevisiae cells of a single mating type can acquire an amyloid prion in one of two ways (i) spontaneous nucleation of the prion within the yeast cell, and (ii) receipt via mother-to-daughter transmission during the cell division cycle. Similarly, prions can be lost due to (i) dissolution of the prion amyloid by its breakage into non-amyloid monomeric units, or (ii) preferential donation/retention of prions between the mother and daughter during cell division. Here we present a computational tool (Monitoring Induction and Loss of prions in Cells; MIL-CELL) for modelling these four general processes using a multiscale approach describing both spatial and kinetic aspects of the yeast life cycle and the amyloid-prion behavior. We describe the workings of the model, assumptions upon which it is based and some interesting simulation results pertaining to the wave-like spread of the epigenetic prion elements through the yeast population. MIL-CELL is provided as a stand-alone GUI executable program for free download with the paper. MIL-CELL is equipped with a relational database allowing all simulated properties to be searched, collated and graphed. Its ability to incorporate variation in heritable properties means MIL-CELL is also capable of simulating loss of the isogenic nature of a cell population over time. The capability to monitor both chronological and reproductive age also makes MIL-CELL potentially useful in studies of cell aging.
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Affiliation(s)
- Damien Hall
- WPI Nano Life Science Institute, Kanazawa University, Kakumamachi, Kanazawa, Ishikawa, 920-1164, Japan.
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Britton SJ, Rogers LJ, White JS, Maskell DL. HYPHAEdelity: a quantitative image analysis tool for assessing peripheral whole colony filamentation. FEMS Yeast Res 2022; 22:6832773. [PMID: 36398755 PMCID: PMC9697609 DOI: 10.1093/femsyr/foac060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/13/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022] Open
Abstract
The yeast Saccharomyces cerevisiae, also known as brewer's yeast, can undergo a reversible stress-responsive transition from individual ellipsoidal cells to chains of elongated cells in response to nitrogen- or carbon starvation. Whole colony morphology is frequently used to evaluate phenotypic switching response; however, quantifying two-dimensional top-down images requires each pixel to be characterized as belonging to the colony or background. While feasible for a small number of colonies, this labor-intensive assessment process is impracticable for larger datasets. The software tool HYPHAEdelity has been developed to semi-automate the assessment of two-dimensional whole colony images and quantify the magnitude of peripheral whole colony yeast filamentation using image analysis tools intrinsic to the OpenCV Python library. The software application functions by determining the total area of filamentous growth, referred to as the f-measure, by subtracting the area of the inner colony boundary from the outer-boundary area associated with hyphal projections. The HYPHAEdelity application was validated against automated and manually pixel-counted two-dimensional top-down images of S. cerevisiae colonies exhibiting varying degrees of filamentation. HYPHAEdelity's f-measure results were comparable to areas determined through a manual pixel enumeration method and found to be more accurate than other whole colony filamentation software solutions.
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Affiliation(s)
- Scott J Britton
- Corresponding author: Institute for Biological Chemistry, Biophysics and Bioengineering, John Muir Building, Heriot-Watt University, Riccarton, Edinburgh, Scotland, United Kingdom, EH14 4AS. Tel: +32470205380; E-mail:
| | | | - Jane S White
- Institute of Biological Chemistry, Biophysics, and Bioengineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom EH14 4AS
| | - Dawn L Maskell
- Institute of Biological Chemistry, Biophysics, and Bioengineering, School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, United Kingdom EH14 4AS
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4
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Quantifying yeast colony morphologies with feature engineering from time-lapse photography. Sci Data 2022; 9:216. [PMID: 35581201 PMCID: PMC9114130 DOI: 10.1038/s41597-022-01340-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/12/2022] [Indexed: 11/13/2022] Open
Abstract
Baker’s yeast (Saccharomyces cerevisiae) is a model organism for studying the morphology that emerges at the scale of multi-cell colonies. To look at how morphology develops, we collect a dataset of time-lapse photographs of the growth of different strains of S. cerevisiae. We discuss the general statistical challenges that arise when using time-lapse photographs to extract time-dependent features. In particular, we show how texture-based feature engineering and representative clustering can be successfully applied to categorize the development of yeast colony morphology using our dataset. The Local binary pattern (LBP) from image processing is used to score the surface texture of colonies. This texture score develops along a smooth trajectory during growth. The path taken depends on how the morphology emerges. A hierarchical clustering of the colonies is performed according to their texture development trajectories. The clustering method is designed for practical interpretability; it obtains the best representative colony image for any hierarchical cluster. Measurement(s) | Yeast colony morphology | Technology Type(s) | Time-lapse photographs | Factor Type(s) | Genotype | Sample Characteristic - Organism | Saccharomyces cerevisiae |
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Abstract
Breeding and domestication have generated widely exploited crops, animals and microbes. However, many Saccharomyces cerevisiae industrial strains have complex polyploid genomes and are sterile, preventing genetic improvement strategies based on breeding. Here, we present a strain improvement approach based on the budding yeasts' property to promote genetic recombination when meiosis is interrupted and cells return-to-mitotic-growth (RTG). We demonstrate that two unrelated sterile industrial strains with complex triploid and tetraploid genomes are RTG-competent and develop a visual screening for easy and high-throughput identification of recombined RTG clones based on colony phenotypes. Sequencing of the evolved clones reveal unprecedented levels of RTG-induced genome-wide recombination. We generate and extensively phenotype a RTG library and identify clones with superior biotechnological traits. Thus, we propose the RTG-framework as a fully non-GMO workflow to rapidly improve industrial yeasts that can be easily brought to the market.
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TAMMiCol: Tool for analysis of the morphology of microbial colonies. PLoS Comput Biol 2018; 14:e1006629. [PMID: 30507938 PMCID: PMC6292648 DOI: 10.1371/journal.pcbi.1006629] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 12/13/2018] [Accepted: 11/08/2018] [Indexed: 01/21/2023] Open
Abstract
Many microbes are studied by examining colony morphology via two-dimensional top-down images. The quantification of such images typically requires each pixel to be labelled as belonging to either the colony or background, producing a binary image. While this may be achieved manually for a single colony, this process is infeasible for large datasets containing thousands of images. The software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol) has been developed to efficiently and automatically convert colony images to binary. TAMMiCol exploits the structure of the images to choose a thresholding tolerance and produce a binary image of the colony. The images produced are shown to compare favourably with images processed manually, while TAMMiCol is shown to outperform standard segmentation methods. Multiple images may be imported together for batch processing, while the binary data may be exported as a CSV or MATLAB MAT file for quantification, or analysed using statistics built into the software. Using the in-built statistics, it is found that images produced by TAMMiCol yield values close to those computed from binary images processed manually. Analysis of a new large dataset using TAMMiCol shows that colonies of Saccharomyces cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM. TAMMiCol is accessed through a graphical user interface, making it easy to use for those without specialist knowledge of image processing, statistical methods or coding. Many microbes are studied by examining the colony morphology via a two-dimensional top-down image. In order to quantify such images, we typically need to label each pixel as belonging either to the colony or the background, creating a binary image. This task is laborious when performed manually and proves infeasible for large datasets. To overcome this, we have developed the software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol), which automatically and efficiently converts colony images to binary. Multiple images may be imported and processed simultaneously, and TAMMiCol exploits the structure of the images to identify an appropriate threshold for the binary conversion of each image. The images produced by TAMMiCol, which take around 20 seconds each to process, compare favourably with images processed manually, which take anywhere up to 15 minutes, while TAMMiCol outperforms several standard image segmentation methods. After processing, the images may be exported as a CSV or MATLAB MAT file for further analysis, or may be quantified by TAMMiCol using the in-built statistics. Using TAMMiCol, we have found that colonies of S. cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM.
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Gontar A, Bottema MJ, Binder BJ, Tronnolone H. Characterizing the shape patterns of dimorphic yeast pseudohyphae. ROYAL SOCIETY OPEN SCIENCE 2018; 5:180820. [PMID: 30473830 PMCID: PMC6227998 DOI: 10.1098/rsos.180820] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 09/13/2018] [Indexed: 06/09/2023]
Abstract
Pseudohyphal growth of the dimorphic yeast Saccharomyces cerevisiae is analysed using two-dimensional top-down binary images. The colony morphology is characterized using clustered shape primitives (CSPs), which are learned automatically from the data and thus do not require a list of predefined features or a priori knowledge of the shape. The power of CSPs is demonstrated through the classification of pseudohyphal yeast colonies known to produce different morphologies. The classifier categorizes the yeast colonies considered with an accuracy of 0.969 and standard deviation 0.041, demonstrating that CSPs capture differences in morphology, while CSPs are found to provide greater discriminatory power than spatial indices previously used to quantify pseudohyphal growth. The analysis demonstrates that CSPs provide a promising avenue for analysing morphology in high-throughput assays.
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Affiliation(s)
- Amelia Gontar
- Flinders Mathematical Sciences Laboratory and Medical Device Research Institute, School of Computer Science, Engineering and Mathematics, Flinders University, GPO Box 2100, Adelaide, South Australia 5001, Australia
| | - Murk J. Bottema
- Flinders Mathematical Sciences Laboratory and Medical Device Research Institute, School of Computer Science, Engineering and Mathematics, Flinders University, GPO Box 2100, Adelaide, South Australia 5001, Australia
| | - Benjamin J. Binder
- School of Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Hayden Tronnolone
- School of Mathematical Sciences, University of Adelaide, Adelaide, South Australia 5005, Australia
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Tronnolone H, Gardner JM, Sundstrom JF, Jiranek V, Oliver SG, Binder BJ. Quantifying the dominant growth mechanisms of dimorphic yeast using a lattice-based model. J R Soc Interface 2018; 14:rsif.2017.0314. [PMID: 28954849 DOI: 10.1098/rsif.2017.0314] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2017] [Accepted: 08/31/2017] [Indexed: 12/22/2022] Open
Abstract
A mathematical model is presented for the growth of yeast that incorporates both dimorphic behaviour and nutrient diffusion. The budding patterns observed in the standard and pseudohyphal growth modes are represented by a bias in the direction of cell proliferation. A set of spatial indices is developed to quantify the morphology and compare the relative importance of the directional bias to nutrient concentration and diffusivity on colony shape. It is found that there are three different growth modes: uniform growth, diffusion-limited growth (DLG) and an intermediate region in which the bias determines the morphology. The dimorphic transition due to nutrient limitation is investigated by relating the directional bias to the nutrient concentration, and this is shown to replicate the behaviour observed in vivo Comparisons are made with experimental data, from which it is found that the model captures many of the observed features. Both DLG and pseudohyphal growth are found to be capable of generating observed experimental morphologies.
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Affiliation(s)
- Hayden Tronnolone
- School of Mathematical Sciences, Waite Campus, University of Adelaide, Adelaide, South Australia 5005, Australia
| | - Jennifer M Gardner
- Department of Wine and Food Science, Waite Campus, University of Adelaide, Urrbrae, SA 5064, Australia
| | - Joanna F Sundstrom
- Department of Wine and Food Science, Waite Campus, University of Adelaide, Urrbrae, SA 5064, Australia
| | - Vladimir Jiranek
- Department of Wine and Food Science, Waite Campus, University of Adelaide, Urrbrae, SA 5064, Australia
| | - Stephen G Oliver
- Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Cambridge CB2 1GA, UK
| | - Benjamin J Binder
- School of Mathematical Sciences, Waite Campus, University of Adelaide, Adelaide, South Australia 5005, Australia
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Cromie GA, Tan Z, Hays M, Sirr A, Jeffery EW, Dudley AM. Transcriptional Profiling of Biofilm Regulators Identified by an Overexpression Screen in Saccharomyces cerevisiae. G3 (BETHESDA, MD.) 2017; 7:2845-2854. [PMID: 28673928 PMCID: PMC5555487 DOI: 10.1534/g3.117.042440] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2017] [Accepted: 06/27/2017] [Indexed: 12/25/2022]
Abstract
Biofilm formation by microorganisms is a major cause of recurring infections and removal of biofilms has proven to be extremely difficult given their inherent drug resistance . Understanding the biological processes that underlie biofilm formation is thus extremely important and could lead to the development of more effective drug therapies, resulting in better infection outcomes. Using the yeast Saccharomyces cerevisiae as a biofilm model, overexpression screens identified DIG1, SFL1, HEK2, TOS8, SAN1, and ROF1/YHR177W as regulators of biofilm formation. Subsequent RNA-seq analysis of biofilm and nonbiofilm-forming strains revealed that all of the overexpression strains, other than DIG1 and TOS8, were adopting a single differential expression profile, although induced to varying degrees. TOS8 adopted a separate profile, while the expression profile of DIG1 reflected the common pattern seen in most of the strains, plus substantial DIG1-specific expression changes. We interpret the existence of the common transcriptional pattern seen across multiple, unrelated overexpression strains as reflecting a transcriptional state, that the yeast cell can access through regulatory signaling mechanisms, allowing an adaptive morphological change between biofilm-forming and nonbiofilm states.
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Affiliation(s)
- Gareth A Cromie
- Pacific Northwest Research Institute, Seattle, Washington 98122
| | - Zhihao Tan
- Pacific Northwest Research Institute, Seattle, Washington 98122
- Institute of Medical Biology, Agency for Science, Technology and Research, Singapore 138648
| | - Michelle Hays
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington 98195
| | - Amy Sirr
- Pacific Northwest Research Institute, Seattle, Washington 98122
| | - Eric W Jeffery
- Pacific Northwest Research Institute, Seattle, Washington 98122
| | - Aimée M Dudley
- Pacific Northwest Research Institute, Seattle, Washington 98122
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington 98195
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Lee KB, Wang J, Palme J, Escalante-Chong R, Hua B, Springer M. Polymorphisms in the yeast galactose sensor underlie a natural continuum of nutrient-decision phenotypes. PLoS Genet 2017; 13:e1006766. [PMID: 28542190 PMCID: PMC5464677 DOI: 10.1371/journal.pgen.1006766] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Revised: 06/08/2017] [Accepted: 04/19/2017] [Indexed: 01/26/2023] Open
Abstract
In nature, microbes often need to "decide" which of several available nutrients to utilize, a choice that depends on a cell's inherent preference and external nutrient levels. While natural environments can have mixtures of different nutrients, phenotypic variation in microbes' decisions of which nutrient to utilize is poorly studied. Here, we quantified differences in the concentration of glucose and galactose required to induce galactose-responsive (GAL) genes across 36 wild S. cerevisiae strains. Using bulk segregant analysis, we found that a locus containing the galactose sensor GAL3 was associated with differences in GAL signaling in eight different crosses. Using allele replacements, we confirmed that GAL3 is the major driver of GAL induction variation, and that GAL3 allelic variation alone can explain as much as 90% of the variation in GAL induction in a cross. The GAL3 variants we found modulate the diauxic lag, a selectable trait. These results suggest that ecological constraints on the galactose pathway may have led to variation in a single protein, allowing cells to quantitatively tune their response to nutrient changes in the environment.
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Affiliation(s)
- Kayla B. Lee
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, Massachusetts, United States of America
| | - Jue Wang
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
- Systems Biology Graduate Program, Harvard University, Cambridge, Massachusetts, United States of America
- Ginkgo Bioworks, Boston, Massachusetts, United States of America
| | - Julius Palme
- Plant Systems Biology, School of Life Sciences Weihenstephan, Technische Universität, München, Freising, Germany
| | | | - Bo Hua
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
- Systems Biology Graduate Program, Harvard University, Cambridge, Massachusetts, United States of America
| | - Michael Springer
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, United States of America
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Choudhry P. High-Throughput Method for Automated Colony and Cell Counting by Digital Image Analysis Based on Edge Detection. PLoS One 2016; 11:e0148469. [PMID: 26848849 PMCID: PMC4746068 DOI: 10.1371/journal.pone.0148469] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2015] [Accepted: 01/17/2016] [Indexed: 11/25/2022] Open
Abstract
Counting cells and colonies is an integral part of high-throughput screens and quantitative cellular assays. Due to its subjective and time-intensive nature, manual counting has hindered the adoption of cellular assays such as tumor spheroid formation in high-throughput screens. The objective of this study was to develop an automated method for quick and reliable counting of cells and colonies from digital images. For this purpose, I developed an ImageJ macro Cell Colony Edge and a CellProfiler Pipeline Cell Colony Counting, and compared them to other open-source digital methods and manual counts. The ImageJ macro Cell Colony Edge is valuable in counting cells and colonies, and measuring their area, volume, morphology, and intensity. In this study, I demonstrate that Cell Colony Edge is superior to other open-source methods, in speed, accuracy and applicability to diverse cellular assays. It can fulfill the need to automate colony/cell counting in high-throughput screens, colony forming assays, and cellular assays.
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Affiliation(s)
- Priya Choudhry
- Department of Chemistry, California Institute of Technology, Pasadena, California, United States of America
- * E-mail:
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Ruusuvuori P, Valkonen M, Nykter M, Visakorpi T, Latonen L. Feature-based analysis of mouse prostatic intraepithelial neoplasia in histological tissue sections. J Pathol Inform 2016; 7:5. [PMID: 26955503 PMCID: PMC4763506 DOI: 10.4103/2153-3539.175378] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2015] [Accepted: 12/20/2015] [Indexed: 12/13/2022] Open
Abstract
This paper describes work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. Prostatic intraepithelial neoplasia (PIN) represents premalignant tissue involving epithelial growth confined in the lumen of prostatic acini. In the attempts to understand oncogenesis in the human prostate, early neoplastic changes can be modeled in the mouse with genetic manipulation of certain tumor suppressor genes or oncogenes. As with many early pathological changes, the PIN lesions in the mouse prostate are macroscopically small, but microscopically spanning areas often larger than single high magnification focus fields in microscopy. This poses a challenge to utilize full potential of the data acquired in histological specimens. We use whole prostates fixed in molecular fixative PAXgene™, embedded in paraffin, sectioned through and stained with H&E. To visualize and analyze the microscopic information spanning whole mouse PIN (mPIN) lesions, we utilize automated whole slide scanning and stacked sections through the tissue. The region of interests is masked, and the masked areas are processed using a cascade of automated image analysis steps. The images are normalized in color space, after which exclusion of secretion areas and feature extraction is performed. Machine learning is utilized to build a model of early PIN lesions for determining the probability for histological changes based on the calculated features. We performed a feature-based analysis to mPIN lesions. First, a quantitative representation of over 100 features was built, including several features representing pathological changes in PIN, especially describing the spatial growth pattern of lesions in the prostate tissue. Furthermore, we built a classification model, which is able to align PIN lesions corresponding to grading by visual inspection to more advanced and mild lesions. The classifier allowed both determining the probability of early histological changes for uncategorized tissue samples and interpretation of the model parameters. Here, we develop quantitative image analysis pipeline to describe morphological changes in histological images. Even subtle changes in mPIN lesion characteristics can be described with feature analysis and machine learning. Constructing and using multidimensional feature data to represent histological changes enables richer analysis and interpretation of early pathological lesions.
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Affiliation(s)
- Pekka Ruusuvuori
- Institute of Biosciences and Medical Technology - BioMediTech, University of Tampere, Tampere, Finland; Tampere University of Technology, Pori, Finland
| | - Mira Valkonen
- Institute of Biosciences and Medical Technology - BioMediTech, University of Tampere, Tampere, Finland
| | - Matti Nykter
- Institute of Biosciences and Medical Technology - BioMediTech, University of Tampere, Tampere, Finland
| | - Tapio Visakorpi
- Institute of Biosciences and Medical Technology - BioMediTech, University of Tampere, Tampere, Finland; Fimlab Laboratories, Tampere University Hospital, Tampere, Finland
| | - Leena Latonen
- Institute of Biosciences and Medical Technology - BioMediTech, University of Tampere, Tampere, Finland; Fimlab Laboratories, Tampere University Hospital, Tampere, Finland
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Ploidy-regulated variation in biofilm-related phenotypes in natural isolates of Saccharomyces cerevisiae. G3-GENES GENOMES GENETICS 2014; 4:1773-86. [PMID: 25060625 PMCID: PMC4169170 DOI: 10.1534/g3.114.013250] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The ability of yeast to form biofilms contributes to better survival under stressful conditions. We see the impact of yeast biofilms and “flocs” (clumps) in human health and industry, where forming clumps enables yeast to act as a natural filter in brewing and forming biofilms enables yeast to remain virulent in cases of fungal infection. Despite the importance of biofilms in yeast natural isolates, the majority of our knowledge about yeast biofilm genetics comes from work with a few tractable laboratory strains. A new collection of sequenced natural isolates from the Saccharomyces Genome Resequencing Project enabled us to examine the breadth of biofilm-related phenotypes in geographically, ecologically, and genetically diverse strains of Saccharomyces cerevisiae. We present a panel of 31 haploid and 24 diploid strains for which we have characterized six biofilm-related phenotypes: complex colony morphology, complex mat formation, flocculation, agar invasion, polystyrene adhesion, and psuedohyphal growth. Our results show that there is extensive phenotypic variation between and within strains, and that these six phenotypes are primarily uncorrelated or weakly correlated, with the notable exception of complex colony and complex mat formation. We also show that the phenotypic strength of these strains varies significantly depending on ploidy, and the diploid strains demonstrate both decreased and increased phenotypic strength with respect to their haploid counterparts. This is a more complex view of the impact of ploidy on biofilm-related phenotypes than previous work with laboratory strains has suggested, demonstrating the importance and enormous potential of working with natural isolates of yeast.
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