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Thiermann R, Sandler M, Ahir G, Sauls JT, Schroeder J, Brown S, Le Treut G, Si F, Li D, Wang JD, Jun S. Tools and methods for high-throughput single-cell imaging with the mother machine. eLife 2024; 12:RP88463. [PMID: 38634855 PMCID: PMC11026091 DOI: 10.7554/elife.88463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning-based segmentation, 'what you put is what you get' (WYPIWYG) - that is, pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother machine-based high-throughput imaging and analysis methods in their research.
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Affiliation(s)
- Ryan Thiermann
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | - Michael Sandler
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | - Gursharan Ahir
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | - John T Sauls
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | - Jeremy Schroeder
- Department of Biological Chemistry, University of Michigan Medical SchoolAnn ArborUnited States
| | - Steven Brown
- Department of Physics, University of California, San DiegoLa JollaUnited States
| | | | - Fangwei Si
- Department of Physics, Carnegie Mellon UniversityPittsburghUnited States
| | - Dongyang Li
- Division of Biology and Biological Engineering, California Institute of TechnologyPasadenaUnited States
| | - Jue D Wang
- Department of Bacteriology, University of Wisconsin–MadisonMadisonUnited States
| | - Suckjoon Jun
- Department of Physics, University of California, San DiegoLa JollaUnited States
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Thiermann R, Sandler M, Ahir G, Sauls JT, Schroeder JW, Brown SD, Le Treut G, Si F, Li D, Wang JD, Jun S. Tools and methods for high-throughput single-cell imaging with the mother machine. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.27.534286. [PMID: 37066401 PMCID: PMC10103947 DOI: 10.1101/2023.03.27.534286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Despite much progress, image processing remains a significant bottleneck for high-throughput analysis of microscopy data. One popular platform for single-cell time-lapse imaging is the mother machine, which enables long-term tracking of microbial cells under precisely controlled growth conditions. While several mother machine image analysis pipelines have been developed in the past several years, adoption by a non-expert audience remains a challenge. To fill this gap, we implemented our own software, MM3, as a plugin for the multidimensional image viewer napari. napari-MM3 is a complete and modular image analysis pipeline for mother machine data, which takes advantage of the high-level interactivity of napari. Here, we give an overview of napari-MM3 and test it against several well-designed and widely-used image analysis pipelines, including BACMMAN and DeLTA. Researchers often analyze mother machine data with custom scripts using varied image analysis methods, but a quantitative comparison of the output of different pipelines has been lacking. To this end, we show that key single-cell physiological parameter correlations and distributions are robust to the choice of analysis method. However, we also find that small changes in thresholding parameters can systematically alter parameters extracted from single-cell imaging experiments. Moreover, we explicitly show that in deep learning based segmentation, "what you put is what you get" (WYPIWYG) - i.e., pixel-level variation in training data for cell segmentation can propagate to the model output and bias spatial and temporal measurements. Finally, while the primary purpose of this work is to introduce the image analysis software that we have developed over the last decade in our lab, we also provide information for those who want to implement mother-machine-based high-throughput imaging and analysis methods in their research.
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Affiliation(s)
- Ryan Thiermann
- Department of Physics, University of California San Diego, La Jolla CA
| | - Michael Sandler
- Department of Physics, University of California San Diego, La Jolla CA
| | - Gursharan Ahir
- Department of Physics, University of California San Diego, La Jolla CA
| | - John T. Sauls
- Department of Physics, University of California San Diego, La Jolla CA
| | - Jeremy W. Schroeder
- Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, MI
| | - Steven D. Brown
- Department of Physics, University of California San Diego, La Jolla CA
| | | | - Fangwei Si
- Department of Physics, Carnegie Mellon University, Pittsburgh, PA
| | - Dongyang Li
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA
| | - Jue D. Wang
- Department of Bacteriology, University of Wisconsin-Madison, Madison, WI
| | - Suckjoon Jun
- Department of Physics, University of California San Diego, La Jolla CA
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Allard P, Papazotos F, Potvin-Trottier L. Microfluidics for long-term single-cell time-lapse microscopy: Advances and applications. Front Bioeng Biotechnol 2022; 10:968342. [PMID: 36312536 PMCID: PMC9597311 DOI: 10.3389/fbioe.2022.968342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/21/2022] [Indexed: 11/13/2022] Open
Abstract
Cells are inherently dynamic, whether they are responding to environmental conditions or simply at equilibrium, with biomolecules constantly being made and destroyed. Due to their small volumes, the chemical reactions inside cells are stochastic, such that genetically identical cells display heterogeneous behaviors and gene expression profiles. Studying these dynamic processes is challenging, but the development of microfluidic methods enabling the tracking of individual prokaryotic cells with microscopy over long time periods under controlled growth conditions has led to many discoveries. This review focuses on the recent developments of one such microfluidic device nicknamed the mother machine. We overview the original device design, experimental setup, and challenges associated with this platform. We then describe recent methods for analyzing experiments using automated image segmentation and tracking. We further discuss modifications to the experimental setup that allow for time-varying environmental control, replicating batch culture conditions, cell screening based on their dynamic behaviors, and to accommodate a variety of microbial species. Finally, this review highlights the discoveries enabled by this technology in diverse fields, such as cell-size control, genetic mutations, cellular aging, and synthetic biology.
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Affiliation(s)
- Paige Allard
- Department of Biology, Concordia University, Montréal, QC, Canada
| | - Fotini Papazotos
- Department of Biology, Concordia University, Montréal, QC, Canada
| | - Laurent Potvin-Trottier
- Department of Biology, Concordia University, Montréal, QC, Canada
- Department of Physics, Concordia University, Montréal, QC, Canada
- Centre for Applied Synthetic Biology, Concordia University, Montréal, QC, Canada
- *Correspondence: Laurent Potvin-Trottier,
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Täuber S, Schmitz J, Blöbaum L, Fante N, Steinhoff H, Grünberger A. How to Perform a Microfluidic Cultivation Experiment—A Guideline to Success. BIOSENSORS 2021; 11:bios11120485. [PMID: 34940242 PMCID: PMC8699335 DOI: 10.3390/bios11120485] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 11/23/2021] [Accepted: 11/26/2021] [Indexed: 12/19/2022]
Abstract
As a result of the steadily ongoing development of microfluidic cultivation (MC) devices, a plethora of setups is used in biological laboratories for the cultivation and analysis of different organisms. Because of their biocompatibility and ease of fabrication, polydimethylsiloxane (PDMS)-glass-based devices are most prominent. Especially the successful and reproducible cultivation of cells in microfluidic systems, ranging from bacteria over algae and fungi to mammalians, is a fundamental step for further quantitative biological analysis. In combination with live-cell imaging, MC devices allow the cultivation of small cell clusters (or even single cells) under defined environmental conditions and with high spatio-temporal resolution. Yet, most setups in use are custom made and only few standardised setups are available, making trouble-free application and inter-laboratory transfer tricky. Therefore, we provide a guideline to overcome the most frequently occurring challenges during a MC experiment to allow untrained users to learn the application of continuous-flow-based MC devices. By giving a concise overview of the respective workflow, we give the reader a general understanding of the whole procedure and its most common pitfalls. Additionally, we complement the listing of challenges with solutions to overcome these hurdles. On selected case studies, covering successful and reproducible growth of cells in MC devices, we demonstrate detailed solutions to solve occurring challenges as a blueprint for further troubleshooting. Since developer and end-user of MC devices are often different persons, we believe that our guideline will help to enhance a broader applicability of MC in the field of life science and eventually promote the ongoing advancement of MC.
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Affiliation(s)
- Sarah Täuber
- Multiscale Bioengineering, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany; (S.T.); (J.S.); (L.B.); (N.F.); (H.S.)
- Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany
| | - Julian Schmitz
- Multiscale Bioengineering, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany; (S.T.); (J.S.); (L.B.); (N.F.); (H.S.)
- Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany
| | - Luisa Blöbaum
- Multiscale Bioengineering, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany; (S.T.); (J.S.); (L.B.); (N.F.); (H.S.)
- Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany
| | - Niklas Fante
- Multiscale Bioengineering, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany; (S.T.); (J.S.); (L.B.); (N.F.); (H.S.)
| | - Heiko Steinhoff
- Multiscale Bioengineering, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany; (S.T.); (J.S.); (L.B.); (N.F.); (H.S.)
- Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Faculty of Technology, Bielefeld University, Universitätsstraße 25, 33615 Bielefeld, Germany; (S.T.); (J.S.); (L.B.); (N.F.); (H.S.)
- Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstraße 27, 33615 Bielefeld, Germany
- Correspondence:
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Hare PJ, LaGree TJ, Byrd BA, DeMarco AM, Mok WWK. Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters. Microorganisms 2021; 9:2277. [PMID: 34835403 PMCID: PMC8620850 DOI: 10.3390/microorganisms9112277] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/20/2021] [Accepted: 10/27/2021] [Indexed: 11/16/2022] Open
Abstract
Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.
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Affiliation(s)
- Patricia J. Hare
- Department of Molecular Biology & Biophysics, UConn Health, Farmington, CT 06032, USA; (P.J.H.); (T.J.L.); (B.A.B.); (A.M.D.)
- School of Dental Medicine, University of Connecticut, Farmington, CT 06032, USA
| | - Travis J. LaGree
- Department of Molecular Biology & Biophysics, UConn Health, Farmington, CT 06032, USA; (P.J.H.); (T.J.L.); (B.A.B.); (A.M.D.)
| | - Brandon A. Byrd
- Department of Molecular Biology & Biophysics, UConn Health, Farmington, CT 06032, USA; (P.J.H.); (T.J.L.); (B.A.B.); (A.M.D.)
- School of Medicine, University of Connecticut, Farmington, CT 06032, USA
| | - Angela M. DeMarco
- Department of Molecular Biology & Biophysics, UConn Health, Farmington, CT 06032, USA; (P.J.H.); (T.J.L.); (B.A.B.); (A.M.D.)
| | - Wendy W. K. Mok
- Department of Molecular Biology & Biophysics, UConn Health, Farmington, CT 06032, USA; (P.J.H.); (T.J.L.); (B.A.B.); (A.M.D.)
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The Trends of Single-Cell Analysis: A Global Study. BIOMED RESEARCH INTERNATIONAL 2020; 2020:7425397. [PMID: 33313317 PMCID: PMC7719492 DOI: 10.1155/2020/7425397] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 09/07/2020] [Accepted: 11/17/2020] [Indexed: 11/17/2022]
Abstract
Objective The field of single-cell analysis has rapidly grown worldwide, and a bibliometric analysis and visualization of data and publications pertaining to such single-cell research has the potential to offer insights into the development of this field over the past two decades while also highlighting future avenues of research. Methods Single-cell analysis-related studies published from 2000-2019 were identified through searches of the Web of Science, Scopus, and PubMed databases, and corresponding bibliometric data were systematically compiled. Extracted data from each study included author names, country of origin, and affiliations. GraphPad Prism was used to analyze these data, while VOSviewer was used to perform global analyses of bibliographic coupling, coauthorship, cocitation, and co-occurrence. Results In total, 4,071 relevant studies were included in this analysis. The number of publications increased substantially with time, suggesting that single-cell analyses are becoming increasingly more prevalent in recent years. Studies from the USA had the greatest impact in this field, with higher H-index values and numbers of citations relative to other countries, whereas Israel exhibited the highest average number of citations per publication. Bibliographic coupling, coauthorship, cocitation, and co-occurrence analyses revealed that Analytical Chemistry was associated with the highest number of publications in this field, and the University of Stanford contributed the most to this field. The most cited study included in this analysis was published by Macosko et al. in 2015 in Cell. Co-occurrence analyses revealed that the most common single-cell research topics included “mechanistic studies,” “in vitro studies,” “in vivo studies,” and “fabrication studies.” Conclusions Single-cell analyses are a rapidly growing area of scientific interest, and higher volumes of publications in this field are expected in the coming years, particularly for studies conducting fabrication and in vivo single-cell analyses.
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