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Sadanandan B, Vijayalakshmi V, Ashrit P, Babu UV, Sharath Kumar LM, Sampath V, Shetty K, Joglekar AP, Awaknavar R. Aqueous spice extracts as alternative antimycotics to control highly drug resistant extensive biofilm forming clinical isolates of Candida albicans. PLoS One 2023; 18:e0281035. [PMID: 37315001 DOI: 10.1371/journal.pone.0281035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 05/31/2023] [Indexed: 06/16/2023] Open
Abstract
Candida albicans form biofilm by associating with biotic and abiotic surfaces. Biofilm formation by C. albicans is relevant and significant as the organisms residing within, gain resistance to conventional antimycotics and are therefore difficult to treat. This study targeted the potential of spice-based antimycotics to control C. albicans biofilms. Ten clinical isolates of C. albicans along with a standard culture MTCC-3017 (ATCC-90028) were screened for their biofilm-forming ability. C. albicans M-207 and C. albicans S-470 were identified as high biofilm formers by point inoculation on Trypticase Soy Agar (TSA) medium as they formed a lawn within 16 h and exhibited resistance to fluconazole and caspofungin at 25 mcg and 8 mcg respectively. Aqueous and organic spice extracts were screened for their antimycotic activity against C. albicans M-207 and S-470 by agar and disc diffusion and a Zone of Inhibition was observed. Minimal Inhibitory Concentration was determined based on growth absorbance and cell viability measurements. The whole aqueous extract of garlic inhibited biofilms of C. albicans M-207, whereas whole aqueous extracts of garlic, clove, and Indian gooseberry were effective in controlling C. albicans S-470 biofilm within 12 h of incubation. The presence of allicin, ellagic acid, and gallic acid as dominant compounds in the aqueous extracts of garlic, clove, and Indian gooseberry respectively was determined by High-Performance Thin Layer Chromatography and Liquid Chromatography-Mass Spectrometry. The morphology of C. albicans biofilm at different growth periods was also determined through bright field microscopy, phase contrast microscopy, and fluorescence microscopy. The results of this study indicated that the alternate approach in controlling high biofilm-forming, multi-drug resistant clinical isolates of C. albicans M-207 and S-470 using whole aqueous extracts of garlic, clove, and Indian gooseberry is a safe, potential, and cost-effective one that can benefit the health care needs with additional effective therapeutics to treat biofilm infections.
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Affiliation(s)
- Bindu Sadanandan
- Department of Biotechnology, M S Ramaiah Institute of Technology, Bengaluru, Karnataka, India
| | | | - Priya Ashrit
- Department of Biotechnology, M S Ramaiah Institute of Technology, Bengaluru, Karnataka, India
| | - Uddagiri Venkanna Babu
- Department of Phytochemistry, Research and Development, The Himalaya Drug Company, Bengaluru, Karnataka, India
| | | | - Vasulingam Sampath
- Department of Phytochemistry, Research and Development, The Himalaya Drug Company, Bengaluru, Karnataka, India
| | - Kalidas Shetty
- Department of Plant Sciences, North Dakota State University, Fargo, North Dakota, United States of America
| | | | - Rashmi Awaknavar
- Department of Biotechnology, M S Ramaiah Institute of Technology, Bengaluru, Karnataka, India
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2
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Mukherjee S, Sarkar R, Manich M, Labruyere E, Olivo-Marin JC. Domain Adapted Multitask Learning for Segmenting Amoeboid Cells in Microscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:42-54. [PMID: 36044485 DOI: 10.1109/tmi.2022.3203022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The method proposed in this paper is a robust combination of multi-task learning and unsupervised domain adaptation for segmenting amoeboid cells in microscopy. A highlight of this work is the manner in which the model's hyperparameters are estimated. The detriments of ad-hoc parameter estimation are well known, but this issue remains largely unaddressed in the context of CNN-based segmentation. Using a novel min-max formulation of the segmentation cost function our proposed method analytically estimates the model's hyperparameters, while simultaneously learning the CNN weights during training. This end-to-end framework provides a consolidated mechanism to harness the potential of multi-task learning to isolate and segment clustered cells from low contrast brightfield images, and it simultaneously leverages deep domain adaptation to segment fluorescent cells without explicit pixel-level re- annotation of the data. Experimental validations on multi-cellular images strongly suggest the effectiveness of the proposed technique, and our quantitative results show at least 15% and 10% improvement in cell segmentation on brightfield and fluorescence images respectively compared to contemporary supervised segmentation methods.
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3
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Fukai YT, Kawaguchi K. LapTrack: linear assignment particle tracking with tunable metrics. Bioinformatics 2022; 39:6887138. [PMID: 36495181 PMCID: PMC9825786 DOI: 10.1093/bioinformatics/btac799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/09/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION Particle tracking is an important step of analysis in a variety of scientific fields and is particularly indispensable for the construction of cellular lineages from live images. Although various supervised machine learning methods have been developed for cell tracking, the diversity of the data still necessitates heuristic methods that require parameter estimations from small amounts of data. For this, solving tracking as a linear assignment problem (LAP) has been widely applied and demonstrated to be efficient. However, there has been no implementation that allows custom connection costs, parallel parameter tuning with ground truth annotations, and the functionality to preserve ground truth connections, limiting the application to datasets with partial annotations. RESULTS We developed LapTrack, a LAP-based tracker which allows including arbitrary cost functions and inputs, parallel parameter tuning and ground-truth track preservation. Analysis of real and artificial datasets demonstrates the advantage of custom metric functions for tracking score improvement from distance-only cases. The tracker can be easily combined with other Python-based tools for particle detection, segmentation and visualization. AVAILABILITY AND IMPLEMENTATION LapTrack is available as a Python package on PyPi, and the notebook examples are shared at https://github.com/yfukai/laptrack. The data and code for this publication are hosted at https://github.com/NoneqPhysLivingMatterLab/laptrack-optimisation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Kyogo Kawaguchi
- Nonequilibrium Physics of Living Matter RIKEN Hakubi Research Team, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan,RIKEN Cluster for Pioneering Research, Kobe 650-0047, Japan,Universal Biology Institute, The University of Tokyo, Tokyo 113-0033, Japan
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4
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GEMA-An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices. J Imaging 2022; 8:jimaging8100281. [PMID: 36286375 PMCID: PMC9605644 DOI: 10.3390/jimaging8100281] [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: 07/28/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 01/24/2023] Open
Abstract
Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.
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5
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Cuny AP, Ponti A, Kündig T, Rudolf F, Stelling J. Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction. Nat Methods 2022; 19:1276-1285. [PMID: 36138173 DOI: 10.1038/s41592-022-01603-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 08/02/2022] [Indexed: 11/09/2022]
Abstract
Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking-assigning identical cells on consecutive images to a track-is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.
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Affiliation(s)
- Andreas P Cuny
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Aaron Ponti
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Tomas Kündig
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Fabian Rudolf
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Jörg Stelling
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland. .,Swiss Institute of Bioinformatics, Basel, Switzerland.
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6
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Cimini BA. Improved tracking via cell region fingerprints. Nat Methods 2022; 19:1177-1178. [PMID: 36138172 PMCID: PMC10275404 DOI: 10.1038/s41592-022-01525-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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7
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Lawrimore J, de Larminat SC, Cook D, Friedman B, Doshi A, Yeh E, Bloom K. Polymer models reveal how chromatin modification can modulate force at the kinetochore. Mol Biol Cell 2022; 33:ar97. [PMID: 35704466 DOI: 10.1091/mbc.e22-02-0041] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
A key feature of chromosome segregation is the ability to sense tension between sister kinetochores. DNA between sister kinetochores must be packaged in a way that sustains tension propagation from one kinetochore to its sister, approximately 1 micron away. A molecular bottlebrush consisting of a primary axis populated with a crowded array of side chains provides a means to build tension over length scales considerably larger than the stiffness of the individual elements, that is, DNA polymer. Evidence for the bottlebrush organization of chromatin between sister kinetochores comes from genetic, cell biological, and polymer modeling of the budding yeast centromere. In this study, we have used polymer dynamic simulations of the bottlebrush to recapitulate experimental observations of kinetochore structure. Several aspects of the spatial distribution of kinetochore proteins and their response to perturbation lack a mechanistic understanding. Changes in physical parameters of bottlebrush, DNA stiffness, and DNA loops directly impact the architecture of the inner kinetochore. This study reveals that the bottlebrush is an active participant in building tension between sister kinetochores and proposes a mechanism for chromatin feedback to the kinetochore.
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Affiliation(s)
- Josh Lawrimore
- Biology Department, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Solenn C de Larminat
- Biology Department, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Diana Cook
- Biology Department, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Brandon Friedman
- Biology Department, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Ayush Doshi
- Biology Department, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Elaine Yeh
- Biology Department, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
| | - Kerry Bloom
- Biology Department, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599
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8
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Padovani F, Mairhörmann B, Falter-Braun P, Lengefeld J, Schmoller KM. Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC. BMC Biol 2022; 20:174. [PMID: 35932043 PMCID: PMC9356409 DOI: 10.1186/s12915-022-01372-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 07/08/2022] [Indexed: 12/12/2022] Open
Abstract
Background High-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed. Results We present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae, histone Htb1 concentrations decrease with replicative age. Conclusions Cell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC Supplementary Information The online version contains supplementary material available at 10.1186/s12915-022-01372-6.
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Affiliation(s)
- Francesco Padovani
- Institute of Functional Epigenetics (IFE), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Center Munich, 85764, Munich-Neuherberg, Germany.
| | - Benedikt Mairhörmann
- Institute of Functional Epigenetics (IFE), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Center Munich, 85764, Munich-Neuherberg, Germany.,Institute of Network Biology (INET), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Center Munich, 85764, Munich-Neuherberg, Germany
| | - Pascal Falter-Braun
- Institute of Network Biology (INET), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Center Munich, 85764, Munich-Neuherberg, Germany.,Microbe-Host Interactions, Faculty of Biology, Ludwig-Maximilians-University (LMU) München, 82152, Planegg-, Martinsried, Germany
| | - Jette Lengefeld
- Institute of Biotechnology, HiLIFE, University of Helsinki, Biocenter 2, P.O.Box 56 (Viikinkaari 5 D), 00014, Helsinki, Finland.,Department of Biosciences and Nutrition (BioNut), Karolinska Institutet, Huddinge, Sweden
| | - Kurt M Schmoller
- Institute of Functional Epigenetics (IFE), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Center Munich, 85764, Munich-Neuherberg, Germany. .,German Center for Diabetes Research (DZD), 85764, Munich-Neuherberg, Germany.
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9
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Cuny AP, Schlottmann FP, Ewald JC, Pelet S, Schmoller KM. Live cell microscopy: From image to insight. BIOPHYSICS REVIEWS 2022; 3:021302. [PMID: 38505412 PMCID: PMC10903399 DOI: 10.1063/5.0082799] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2021] [Accepted: 03/18/2022] [Indexed: 03/21/2024]
Abstract
Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.
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Affiliation(s)
| | - Fabian P. Schlottmann
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Jennifer C. Ewald
- Interfaculty Institute of Cell Biology, University of Tuebingen, 72076 Tuebingen, Germany
| | - Serge Pelet
- Department of Fundamental Microbiology, University of Lausanne, 1015 Lausanne, Switzerland
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10
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Abstract
Intragenic regions that are removed during maturation of the RNA transcript—introns—are universally present in the nuclear genomes of eukaryotes1. The budding yeast, an otherwise intron-poor species, preserves two sets of ribosomal protein genes that differ primarily in their introns2,3. Although studies have shed light on the role of ribosomal protein introns under stress and starvation4–6, understanding the contribution of introns to ribosome regulation remains challenging. Here, by combining isogrowth profiling7 with single-cell protein measurements8, we show that introns can mediate inducible phenotypic heterogeneity that confers a clear fitness advantage. Osmotic stress leads to bimodal expression of the small ribosomal subunit protein Rps22B, which is mediated by an intron in the 5′ untranslated region of its transcript. The two resulting yeast subpopulations differ in their ability to cope with starvation. Low levels of Rps22B protein result in prolonged survival under sustained starvation, whereas high levels of Rps22B enable cells to grow faster after transient starvation. Furthermore, yeasts growing at high concentrations of sugar, similar to those in ripe grapes, exhibit bimodal expression of Rps22B when approaching the stationary phase. Differential intron-mediated regulation of ribosomal protein genes thus provides a way to diversify the population when starvation threatens in natural environments. Our findings reveal a role for introns in inducing phenotypic heterogeneity in changing environments, and suggest that duplicated ribosomal protein genes in yeast contribute to resolving the evolutionary conflict between precise expression control and environmental responsiveness9. Experiments in yeast show that introns have a role in inducing phenotypic heterogeneity and that intron-mediated regulation of ribosomal proteins confers a fitness advantage by enabling yeast populations to diversify under nutrient-scarce conditions.
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11
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Kruitbosch HT, Mzayek Y, Omlor S, Guerra P, Milias-Argeitis A. A convolutional neural network for segmentation of yeast cells without manual training annotations. Bioinformatics 2021; 38:1427-1433. [PMID: 34893817 PMCID: PMC8825468 DOI: 10.1093/bioinformatics/btab835] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Revised: 10/09/2021] [Accepted: 12/07/2021] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process. RESULTS We developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism. AVAILABILITY AND IMPLEMENTATION The synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Herbert T Kruitbosch
- Center for Information Technology, University of Groningen, 9747 AJ Groningen, The Netherlands
| | - Yasmin Mzayek
- Center for Information Technology, University of Groningen, 9747 AJ Groningen, The Netherlands
| | - Sara Omlor
- Center for Information Technology, University of Groningen, 9747 AJ Groningen, The Netherlands
| | - Paolo Guerra
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, 9747 AG Groningen, The Netherlands
| | - Andreas Milias-Argeitis
- Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, 9747 AG Groningen, The Netherlands,To whom correspondence should be addressed.
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12
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Löffler K, Scherr T, Mikut R. A graph-based cell tracking algorithm with few manually tunable parameters and automated segmentation error correction. PLoS One 2021; 16:e0249257. [PMID: 34492015 PMCID: PMC8423278 DOI: 10.1371/journal.pone.0249257] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Accepted: 08/03/2021] [Indexed: 11/29/2022] Open
Abstract
Automatic cell segmentation and tracking enables to gain quantitative insights into the processes driving cell migration. To investigate new data with minimal manual effort, cell tracking algorithms should be easy to apply and reduce manual curation time by providing automatic correction of segmentation errors. Current cell tracking algorithms, however, are either easy to apply to new data sets but lack automatic segmentation error correction, or have a vast set of parameters that needs either manual tuning or annotated data for parameter tuning. In this work, we propose a tracking algorithm with only few manually tunable parameters and automatic segmentation error correction. Moreover, no training data is needed. We compare the performance of our approach to three well-performing tracking algorithms from the Cell Tracking Challenge on data sets with simulated, degraded segmentation—including false negatives, over- and under-segmentation errors. Our tracking algorithm can correct false negatives, over- and under-segmentation errors as well as a mixture of the aforementioned segmentation errors. On data sets with under-segmentation errors or a mixture of segmentation errors our approach performs best. Moreover, without requiring additional manual tuning, our approach ranks several times in the top 3 on the 6th edition of the Cell Tracking Challenge.
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Affiliation(s)
- Katharina Löffler
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
- Institute of Biological and Chemical Systems - Biological Information Processing, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
- * E-mail:
| | - Tim Scherr
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Ralf Mikut
- Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
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13
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YeastNet: Deep-Learning-Enabled Accurate Segmentation of Budding Yeast Cells in Bright-Field Microscopy. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11062692] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Accurate and efficient segmentation of live-cell images is critical in maximizing data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of the non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast-cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labeling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub.
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14
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Abstract
Synthetic biology has so far made limited use of mathematical models, mostly because their inference has been traditionally perceived as expensive and/or difficult. We have recently demonstrated how in silico simulations and in vitro/vivo experiments can be integrated to develop a cyber-physical platform that automates model calibration and leads to saving 60-80% of the effort. In this book chapter, we illustrate the protocol used to attain such results. By providing a comprehensive list of steps and pointing the reader to the code we use to operate our platform, we aim at providing synthetic biologists with an additional tool to accelerate the pace at which the field progresses toward applications.
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15
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Bheda P, Aguilar-Gómez D, Kukhtevich I, Becker J, Charvin G, Kirmizis A, Schneider R. Microfluidics for single-cell lineage tracking over time to characterize transmission of phenotypes in Saccharomyces cerevisiae. STAR Protoc 2020; 1:100228. [PMID: 33377118 PMCID: PMC7757727 DOI: 10.1016/j.xpro.2020.100228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The budding yeast Saccharomyces cerevisiae is an excellent model organism to dissect the maintenance and inheritance of phenotypes due to its asymmetric division. This requires following individual cells over time as they go through divisions to define pedigrees. Here, we provide a detailed protocol for collecting and analyzing time-lapse imaging data of yeast cells. The microfluidics protocol can achieve improved time resolution for single-cell tracking to enable characterization of maintenance and inheritance of phenotypes. For complete details on the use and execution of this protocol, please refer to Bheda et al. (2020a).
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Affiliation(s)
- Poonam Bheda
- Institute of Functional Epigenetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | | | - Igor Kukhtevich
- Institute of Functional Epigenetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Johannes Becker
- Institute of Bioengineering, School of Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Gilles Charvin
- Development and Stem Cells, IGBMC, 67400 Illkirch, France
| | - Antonis Kirmizis
- Department of Biological Sciences, University of Cyprus, 2109 Nicosia, Cyprus
| | - Robert Schneider
- Institute of Functional Epigenetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
- Faculty of Biology, Ludwig-Maximilians Universität München, 80333 Munich, Germany
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16
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Dietler N, Minder M, Gligorovski V, Economou AM, Joly DAHL, Sadeghi A, Chan CHM, Koziński M, Weigert M, Bitbol AF, Rahi SJ. A convolutional neural network segments yeast microscopy images with high accuracy. Nat Commun 2020; 11:5723. [PMID: 33184262 PMCID: PMC7665014 DOI: 10.1038/s41467-020-19557-4] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Accepted: 10/15/2020] [Indexed: 11/14/2022] Open
Abstract
The identification of cell borders ('segmentation') in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application ( www.quantsysbio.com/data-and-software ) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient large-scale image processing uncovers new biology, we analyze the geometries of ≈2200 wild-type and cyclin mutant cells and find that morphogenesis control occurs unexpectedly early and gradually.
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Affiliation(s)
- Nicola Dietler
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Institute of Bioengineering, School of Life Sciences, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Matthias Minder
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vojislav Gligorovski
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Augoustina Maria Economou
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Denis Alain Henri Lucien Joly
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ahmad Sadeghi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Chun Hei Michael Chan
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Mateusz Koziński
- Computer Vision Laboratory, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Martin Weigert
- Institute of Bioengineering, School of Life Sciences, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Anne-Florence Bitbol
- Institute of Bioengineering, School of Life Sciences, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sahand Jamal Rahi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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17
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Bagamery LE, Justman QA, Garner EC, Murray AW. A Putative Bet-Hedging Strategy Buffers Budding Yeast against Environmental Instability. Curr Biol 2020; 30:4563-4578.e4. [PMID: 32976801 DOI: 10.1016/j.cub.2020.08.092] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 07/20/2020] [Accepted: 08/26/2020] [Indexed: 12/19/2022]
Abstract
To grow and divide, cells must extract resources from dynamic and unpredictable environments. Many organisms use different metabolic strategies for distinct contexts. Budding yeast can produce ATP from carbon sources by mechanisms that prioritize either speed (fermentation) or yield (respiration). Withdrawing glucose from exponentially growing cells reveals variability in their ability to switch from fermentation to respiration. We observe two subpopulations of glucose-starved cells: recoverers, which rapidly adapt and resume growth, and arresters, which enter a shock state characterized by deformation of many cellular structures, including mitochondria. These states are heritable, and on high glucose, arresters grow and divide faster than recoverers. Recoverers have a fitness advantage during a carbon source shift but are less fit in a constant, high-glucose environment, and we observe natural variation in the frequency of the two states across wild yeast strains. These experiments suggest that bet hedging has evolved in budding yeast.
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Affiliation(s)
- Laura E Bagamery
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
| | - Quincey A Justman
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA
| | - Ethan C Garner
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA.
| | - Andrew W Murray
- Department of Molecular and Cellular Biology, Harvard University, 52 Oxford Street, Cambridge, MA 02138, USA.
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18
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Ren H, Zhao M, Liu B, Yao R, Liu Q, Ren Z, Wu Z, Gao Z, Yang X, Tang C. Cellbow: a robust customizable cell segmentation program. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0213-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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19
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Lu AX, Zarin T, Hsu IS, Moses AM. YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells. Bioinformatics 2020; 35:4525-4527. [PMID: 31095270 PMCID: PMC6821424 DOI: 10.1093/bioinformatics/btz402] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 03/22/2019] [Accepted: 05/07/2019] [Indexed: 01/01/2023] Open
Abstract
SUMMARY We introduce YeastSpotter, a web application for the segmentation of yeast microscopy images into single cells. YeastSpotter is user-friendly and generalizable, reducing the computational expertise required for this critical preprocessing step in many image analysis pipelines. AVAILABILITY AND IMPLEMENTATION YeastSpotter is available at http://yeastspotter.csb.utoronto.ca/. Code is available at https://github.com/alexxijielu/yeast_segmentation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Alex X Lu
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Taraneh Zarin
- Department of Cells and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Ian S Hsu
- Department of Cells and Systems Biology, University of Toronto, Toronto, ON, Canada
| | - Alan M Moses
- Department of Computer Science, University of Toronto, Toronto, ON, Canada.,Department of Cells and Systems Biology, University of Toronto, Toronto, ON, Canada.,Center for Analysis of Genome Evolution and Function, University of Toronto, Toronto, ON, Canada
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20
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Barber F, Amir A, Murray AW. Cell-size regulation in budding yeast does not depend on linear accumulation of Whi5. Proc Natl Acad Sci U S A 2020; 117:14243-14250. [PMID: 32518113 PMCID: PMC7321981 DOI: 10.1073/pnas.2001255117] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Cells must couple cell-cycle progress to their growth rate to restrict the spread of cell sizes present throughout a population. Linear, rather than exponential, accumulation of Whi5, was proposed to provide this coordination by causing a higher Whi5 concentration in cells born at a smaller size. We tested this model using the inducible GAL1 promoter to make the Whi5 concentration independent of cell size. At an expression level that equalizes the mean cell size with that of wild-type cells, the size distributions of cells with galactose-induced Whi5 expression and wild-type cells are indistinguishable. Fluorescence microscopy confirms that the endogenous and GAL1 promoters produce different relationships between Whi5 concentration and cell volume without diminishing size control in the G1 phase. We also expressed Cln3 from the GAL1 promoter, finding that the spread in cell sizes for an asynchronous population is unaffected by this perturbation. Our findings indicate that size control in budding yeast does not fundamentally originate from the linear accumulation of Whi5, contradicting a previous claim and demonstrating the need for further models of cell-cycle regulation to explain how cell size controls passage through Start.
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Affiliation(s)
- Felix Barber
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138
| | - Ariel Amir
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138
| | - Andrew W Murray
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138;
- FAS Center for Systems Biology, Harvard University, Cambridge, MA 02138
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21
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Lugagne JB, Lin H, Dunlop MJ. DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning. PLoS Comput Biol 2020; 16:e1007673. [PMID: 32282792 PMCID: PMC7153852 DOI: 10.1371/journal.pcbi.1007673] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 01/22/2020] [Indexed: 12/20/2022] Open
Abstract
Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image analysis pipeline that performs segmentation, tracking, and lineage reconstruction. Our analysis focuses on time-lapse movies of Escherichia coli cells trapped in a "mother machine" microfluidic device, a scalable platform for long-term single-cell analysis that is widely used in the field. While deep learning has been applied to cell segmentation problems before, our approach is fundamentally innovative in that it also uses machine learning to perform cell tracking and lineage reconstruction. With this framework we are able to get high fidelity results (1% error rate), without human intervention. Further, the algorithm is fast, with complete analysis of a typical frame containing ~150 cells taking <700msec. The framework is not constrained to a particular experimental set up and has the potential to generalize to time-lapse images of other organisms or different experimental configurations. These advances open the door to a myriad of applications including real-time tracking of gene expression and high throughput analysis of strain libraries at single-cell resolution.
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Affiliation(s)
- Jean-Baptiste Lugagne
- Department of Biomedical Engineering, Boston University, Boston, Massachussets, United States of America
| | - Haonan Lin
- Department of Biomedical Engineering, Boston University, Boston, Massachussets, United States of America
| | - Mary J. Dunlop
- Department of Biomedical Engineering, Boston University, Boston, Massachussets, United States of America
- * E-mail:
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22
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Lawrimore J, Doshi A, Walker B, Bloom K. AI-Assisted Forward Modeling of Biological Structures. Front Cell Dev Biol 2019; 7:279. [PMID: 31799251 PMCID: PMC6868055 DOI: 10.3389/fcell.2019.00279] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 10/30/2019] [Indexed: 01/01/2023] Open
Abstract
The rise of machine learning and deep learning technologies have allowed researchers to automate image classification. We describe a method that incorporates automated image classification and principal component analysis to evaluate computational models of biological structures. We use a computational model of the kinetochore to demonstrate our artificial-intelligence (AI)-assisted modeling method. The kinetochore is a large protein complex that connects chromosomes to the mitotic spindle to facilitate proper cell division. The kinetochore can be divided into two regions: the inner kinetochore, including proteins that interact with DNA; and the outer kinetochore, comprised of microtubule-binding proteins. These two kinetochore regions have been shown to have different distributions during metaphase in live budding yeast and therefore act as a test case for our forward modeling technique. We find that a simple convolutional neural net (CNN) can correctly classify fluorescent images of inner and outer kinetochore proteins and show a CNN trained on simulated, fluorescent images can detect difference in experimental images. A polymer model of the ribosomal DNA locus serves as a second test for the method. The nucleolus surrounds the ribosomal DNA locus and appears amorphous in live-cell, fluorescent microscopy experiments in budding yeast, making detection of morphological changes challenging. We show a simple CNN can detect subtle differences in simulated images of the ribosomal DNA locus, demonstrating our CNN-based classification technique can be used on a variety of biological structures.
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Affiliation(s)
- Josh Lawrimore
- Department of Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Ayush Doshi
- Department of Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Benjamin Walker
- Department of Mathematics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Kerry Bloom
- Department of Biology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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23
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Altenburg T, Goldenbogen B, Uhlendorf J, Klipp E. Osmolyte homeostasis controls single-cell growth rate and maximum cell size of Saccharomyces cerevisiae. NPJ Syst Biol Appl 2019; 5:34. [PMID: 31583116 PMCID: PMC6763471 DOI: 10.1038/s41540-019-0111-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 08/23/2019] [Indexed: 11/09/2022] Open
Abstract
Cell growth is well described at the population level, but precisely how nutrient and water uptake and cell wall expansion drive the growth of single cells is poorly understood. Supported by measurements of single-cell growth trajectories and cell wall elasticity, we present a single-cell growth model for yeast. The model links the thermodynamic quantities, such as turgor pressure, osmolarity, cell wall elasto-plasticity, and cell size, applying concepts from rheology and thin shell theory. It reproduces cell size dynamics during single-cell growth, budding, and hyper-osmotic or hypo-osmotic stress. We find that single-cell growth rate and final size are primarily governed by osmolyte uptake and consumption, while bud expansion requires additionally different cell wall extensibilities between mother and bud. Based on first principles the model provides a more accurate description of size dynamics than previous attempts and its analytical simplification allows for easy combination with models for other cell processes.
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Affiliation(s)
- Tom Altenburg
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
- Robert Koch-Institut, Berlin, Germany
| | - Björn Goldenbogen
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jannis Uhlendorf
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Edda Klipp
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Berlin, Germany
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24
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DiSalvo M, Smiddy NM, Allbritton NL. Automated sensing and splitting of stem cell colonies on microraft arrays. APL Bioeng 2019; 3:036106. [PMID: 31489396 PMCID: PMC6715441 DOI: 10.1063/1.5113719] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 08/17/2019] [Indexed: 01/24/2023] Open
Abstract
Human induced pluripotent stem cells (hiPSCs) are widely used for disease modeling, tissue engineering, and clinical applications. Although the development of new disease-relevant or customized hiPSC lines is of high importance, current automated hiPSC isolation technologies rely largely on the fluorescent labeling of cells, thus limiting the cell line development from many applications. The objective of this research was to develop a platform for high-throughput hiPSC cytometry and splitting that utilized a label-free cell sensing approach. An image analysis pipeline utilizing background subtraction and standard deviation projections was implemented to detect hiPSC colonies from bright-field microscopy data. The pipeline was incorporated into an automated microscopy system coupling quad microraft cell-isolation arrays, computer-based vision, and algorithms for smart decision making and cell sorting. The pipeline exhibited a hiPSC detection specificity of 98% and a sensitivity of 88%, allowing for the successful tracking of growth for hundreds of microcolonies over 7 days. The automated platform split 170 mother colonies from a microarray within 80 min, and the harvested daughter biopsies were expanded into viable hiPSC colonies suitable for downstream assays, such as polymerase chain reaction (PCR) or continued culture. Transmitted light microscopy offers an alternative, label-free modality for isolating hiPSCs, yet its low contrast and specificity for adherent cells remain a challenge for automation. This novel approach to label-free sensing and microcolony subsampling with the preservation of the mother colony holds the potential for hiPSC colony screening based on a wide range of properties including those measurable only by a cell destructive assay.
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Affiliation(s)
- Matthew DiSalvo
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill/Raleigh, North Carolina 27599/27607, USA
| | - Nicole M. Smiddy
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
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25
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Vicar T, Balvan J, Jaros J, Jug F, Kolar R, Masarik M, Gumulec J. Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison. BMC Bioinformatics 2019; 20:360. [PMID: 31253078 PMCID: PMC6599268 DOI: 10.1186/s12859-019-2880-8] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Accepted: 05/07/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Because of its non-destructive nature, label-free imaging is an important strategy for studying biological processes. However, routine microscopic techniques like phase contrast or DIC suffer from shadow-cast artifacts making automatic segmentation challenging. The aim of this study was to compare the segmentation efficacy of published steps of segmentation work-flow (image reconstruction, foreground segmentation, cell detection (seed-point extraction) and cell (instance) segmentation) on a dataset of the same cells from multiple contrast microscopic modalities. RESULTS We built a collection of routines aimed at image segmentation of viable adherent cells grown on the culture dish acquired by phase contrast, differential interference contrast, Hoffman modulation contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data. We demonstrated that it is crucial to perform the image reconstruction step, enabling the use of segmentation methods originally not applicable on label-free images. Further we compared foreground segmentation methods (thresholding, feature-extraction, level-set, graph-cut, learning-based), seed-point extraction methods (Laplacian of Gaussians, radial symmetry and distance transform, iterative radial voting, maximally stable extremal region and learning-based) and single cell segmentation methods. We validated suitable set of methods for each microscopy modality and published them online. CONCLUSIONS We demonstrate that image reconstruction step allows the use of segmentation methods not originally intended for label-free imaging. In addition to the comprehensive comparison of methods, raw and reconstructed annotated data and Matlab codes are provided.
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Affiliation(s)
- Tomas Vicar
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3058/10, Brno, CZ-61600 Czech Republic
- Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500 Czech Republic
| | - Jan Balvan
- Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500 Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, Brno, CZ-612 00 Czech Republic
| | - Josef Jaros
- Department of Histology and Embryology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500 Czech Republic
- International Clinical Research Center, St. Anne’s University Hospital, Pekarska 664/53, Brno, CZ-65691 Czech Republic
| | - Florian Jug
- Max Planck Institute of Molecular Cell Biology and Genetics, Pfotenhauerstr. 108, Dresden, DE-01307 Germany
| | - Radim Kolar
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3058/10, Brno, CZ-61600 Czech Republic
| | - Michal Masarik
- Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500 Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, Brno, CZ-612 00 Czech Republic
| | - Jaromir Gumulec
- Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500 Czech Republic
- Department of Pathological Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, CZ-62500 Czech Republic
- Central European Institute of Technology, Brno University of Technology, Purkynova 656/123, Brno, CZ-612 00 Czech Republic
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26
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Wood NE, Doncic A. A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking. PLoS One 2019; 14:e0206395. [PMID: 30917124 PMCID: PMC6436761 DOI: 10.1371/journal.pone.0206395] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 03/08/2019] [Indexed: 12/17/2022] Open
Abstract
Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein dynamics in single cells, relies on segmentation and tracking of individual cells for data generation. The potential of the data that can be extracted from this technique is limited by the inability to accurately segment a large number of cells from such microscopy images and track them over long periods of time. Existing segmentation and tracking algorithms either require additional dyes or markers specific to segmentation or they are highly specific to one imaging condition and cell morphology and/or necessitate manual correction. Here we introduce a fully automated, fast and robust segmentation and tracking algorithm for budding yeast that overcomes these limitations. Full automatization is achieved through a novel automated seeding method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately segment and track individual yeast cells without any specific dye or biomarker. Moreover, we show how existing channels devoted to a biological process of interest can be used to improve the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and pheromone treated cells). In addition, the algorithm is independent of the specific imaging method (bright-field/phase) and objective used (40X/63X/100X). We validate our algorithm's performance on 9 cases each entailing a different imaging condition, objective magnification and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and tracking tool that can be adapted to numerous budding yeast single-cell studies.
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Affiliation(s)
- N. Ezgi Wood
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, Texas, United States of America
- * E-mail:
| | - Andreas Doncic
- Department of Cell Biology, UT Southwestern Medical Center, Dallas, Texas, United States of America
- Green Center for Systems Biology, UT Southwestern Medical Center, Dallas, Texas, United States of America
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27
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Liu G, Dong F, Fu C, Smith ZJ. Automated morphometry toolbox for analysis of microscopic model organisms using simple bright-field imaging. Biol Open 2019; 8:bio.037788. [PMID: 30814065 PMCID: PMC6451328 DOI: 10.1242/bio.037788] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Model organisms with compact genomes, such as yeast and C aenorhabditis elegans, are particularly useful for understanding organism growth and life/cell cycle. Organism morphology is a critical parameter to measure in monitoring growth and stage in the life cycle. However, manual measurements are both time consuming and potentially inaccurate, due to variations among users and user fatigue. In this paper we present an automated method to segment bright-field images of fission yeast, budding yeast, and C. elegans roundworm, reporting a wide range of morphometric parameters, such as length, width, eccentricity, and others. Comparisons between automated and manual methods on fission yeast reveal good correlation in size values, with the 95% confidence interval lying between -0.8 and +0.6 μm in cell length, similar to the 95% confidence interval between two manual users. In a head-to-head comparison with other published algorithms on multiple datasets, our method achieves more accurate and robust results with substantially less computation time. We demonstrate the method's versatility on several model organisms, and demonstrate its utility through automated analysis of changes in fission yeast growth due to single kinase deletions. The algorithm has additionally been implemented as a stand-alone executable program to aid dissemination to other researchers.
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Affiliation(s)
- Guanghui Liu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Fenfen Dong
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Chuanhai Fu
- School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Zachary J Smith
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, China
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28
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Bakker E, Swain PS, Crane MM. Morphologically constrained and data informed cell segmentation of budding yeast. Bioinformatics 2018; 34:88-96. [PMID: 28968663 DOI: 10.1093/bioinformatics/btx550] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 09/03/2017] [Indexed: 01/11/2023] Open
Abstract
Motivation Although high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most techniques for segmentation perform poorly. Although potentially constraining the generalizability of software, incorporating information about the microfluidic features, flow of media and the morphology of the cells can substantially improve performance. Results Here we present DISCO (Data Informed Segmentation of Cell Objects), a framework for using the physical constraints imposed by microfluidic traps, the shape based morphological constraints of budding yeast and temporal information about cell growth and motion to allow tracking and segmentation of cells in microfluidic devices. Using manually curated datasets, we demonstrate substantial improvements in both tracking and segmentation when compared with existing software. Availability and implementation The MATLAB code for the algorithm and for measuring performance is available at https://github.com/pswain/segmentation-software and the test images and the curated ground-truth results used for comparing the algorithms are available at http://datashare.is.ed.ac.uk/handle/10283/2002. Contact mcrane2@uw.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Elco Bakker
- SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.,School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Peter S Swain
- SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.,School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
| | - Matthew M Crane
- SynthSys-Synthetic and Systems Biology, University of Edinburgh, Edinburgh EH9 3BF, UK.,School of Biological Sciences, University of Edinburgh, Edinburgh EH9 3BF, UK
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29
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Identification of individual cells from z-stacks of bright-field microscopy images. Sci Rep 2018; 8:11455. [PMID: 30061662 PMCID: PMC6065389 DOI: 10.1038/s41598-018-29647-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Accepted: 07/16/2018] [Indexed: 12/25/2022] Open
Abstract
Obtaining single cell data from time-lapse microscopy images is critical for quantitative biology, but bottlenecks in cell identification and segmentation must be overcome. We propose a novel, versatile method that uses machine learning classifiers to identify cell morphologies from z-stack bright-field microscopy images. We show that axial information is enough to successfully classify the pixels of an image, without the need to consider in focus morphological features. This fast, robust method can be used to identify different cell morphologies, including the features of E. coli, S. cerevisiae and epithelial cells, even in mixed cultures. Our method demonstrates the potential of acquiring and processing Z-stacks for single-layer, single-cell imaging and segmentation.
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Alanazi H, Canul AJ, Garman A, Quimby J, Vasdekis AE. Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements. Cytometry A 2017; 91:443-449. [PMID: 28371011 PMCID: PMC6585648 DOI: 10.1002/cyto.a.23099] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rate, without any computationally-intensive, post-acquisition processing. We also detail how the method can be expanded to bacterial cell segmentation with 98% success rates with substantially reduced processing requirements in comparison to existing methods. We attribute this improved performance to the remarkably uniform background, elimination of cell-to-cell and intracellular optical artifacts, and enhanced signal-to-background ratio-all innate properties of imaging in the optical-phase domain. © 2017 International Society for Advancement of Cytometry.
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Affiliation(s)
- H. Alanazi
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
| | - A. J. Canul
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
| | - A. Garman
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
| | - J. Quimby
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
| | - A. E. Vasdekis
- Department of PhysicsUniversity of IdahoMoscowIdaho83844
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