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Garcia-Fossa F, Moraes-Lacerda T, Rodrigues-da-Silva M, Diaz-Rohrer B, Singh S, Carpenter AE, Cimini BA, de Jesus MB. Live Cell Painting: image-based profiling in live cells using Acridine Orange. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.28.610144. [PMID: 39257795 PMCID: PMC11383656 DOI: 10.1101/2024.08.28.610144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Image-based profiling has been used to analyze cell health, drug mechanism of action, CRISPR-edited cells, and overall cytotoxicity. Cell Painting is a broadly used image-based assay that uses morphological features to capture how cells respond to treatments. However, this method requires cell fixation for staining, which prevents examining live cells. To address this limitation, here we present Live Cell Painting (LCP), a high-content method based on Acridine orange, a metachromatic dye that labels different organelles and cellular structures. We began by showing that LCP can be applied to follow acidic vesicle redistribution of cells exposed to acidic vesicles inhibitors. Next, we show that LCP can identify subtle changes in cells exposed to silver nanoparticles that are not detected by techniques such as MTT assay. In drug treatments, LCP was helpful in assessing the dose-response relationship and creating profiles that allow clustering of drugs that cause liver injury. Here, we present an affordable and easy-to-use image-based assay capable of assessing overall cell health and showing promise for use in various applications such as assessing drugs and nanoparticles. We envisage the use of Live Cell Painting as an initial screening of overall cell health while providing insights into new biological questions.
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Morgado L, Gómez-de-Mariscal E, Heil HS, Henriques R. The rise of data-driven microscopy powered by machine learning. J Microsc 2024; 295:85-92. [PMID: 38445705 DOI: 10.1111/jmi.13282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 02/13/2024] [Indexed: 03/07/2024]
Abstract
Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field of view and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse and adapt promise to transform optical imaging by opening new experimental possibilities.
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Affiliation(s)
- Leonor Morgado
- Optical Cell Biology, Instituto Gulbenkian de Ciência, Oeiras, Portugal
- Abbelight, Cachan, France
| | | | - Hannah S Heil
- Optical Cell Biology, Instituto Gulbenkian de Ciência, Oeiras, Portugal
| | - Ricardo Henriques
- Optical Cell Biology, Instituto Gulbenkian de Ciência, Oeiras, Portugal
- UCL-Laboratory for Molecular Cell Biology, University College London, London, UK
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Dibus M, Joshi O, Ivaska J. Novel tools to study cell-ECM interactions, cell adhesion dynamics and migration. Curr Opin Cell Biol 2024; 88:102355. [PMID: 38631101 DOI: 10.1016/j.ceb.2024.102355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/15/2024] [Accepted: 03/15/2024] [Indexed: 04/19/2024]
Abstract
Integrin-mediated cell adhesion is essential for cell migration, mechanotransduction and tissue integrity. In vivo, these processes are regulated by complex physicochemical signals from the extracellular matrix (ECM). These nuanced cues, including molecular composition, rigidity and topology, call for sophisticated systems to faithfully explore cell behaviour. Here, we discuss recent methodological advances in cell-ECM adhesion research and compile a toolbox of techniques that we expect to shape this field in future. We outline methodological breakthroughs facilitating the transition from rigid 2D substrates to more complex and dynamic 3D systems, as well as advances in super-resolution imaging for an in-depth understanding of adhesion nanostructure. Selected methods are exemplified with relevant biological findings to underscore their applicability in cell adhesion research. We expect this new "toolbox" of methods will allow for a closer approximation of in vitro experimental setups to in vivo conditions, providing deeper insights into physiological and pathophysiological processes associated with cell-ECM adhesion.
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Affiliation(s)
- Michal Dibus
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Omkar Joshi
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland
| | - Johanna Ivaska
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland; InFLAMES Research Flagship Center, University of Turku, Turku, Finland; Department of Life Technologies, University of Turku, FI-20520 Turku, Finland; Western Finnish Cancer Center (FICAN West), University of Turku, FI-20520 Turku, Finland; Foundation for the Finnish Cancer Institute, Tukholmankatu 8, FI-00014 Helsinki, Finland.
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4
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Krog J, Dvirnas A, Ström OE, Beech JP, Tegenfeldt JO, Müller V, Westerlund F, Ambjörnsson T. Photophysical image analysis: Unsupervised probabilistic thresholding for images from electron-multiplying charge-coupled devices. PLoS One 2024; 19:e0300122. [PMID: 38578724 PMCID: PMC10997106 DOI: 10.1371/journal.pone.0300122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 02/22/2024] [Indexed: 04/07/2024] Open
Abstract
We introduce the concept photophysical image analysis (PIA) and an associated pipeline for unsupervised probabilistic image thresholding for images recorded by electron-multiplying charge-coupled device (EMCCD) cameras. We base our approach on a closed-form analytic expression for the characteristic function (Fourier-transform of the probability mass function) for the image counts recorded in an EMCCD camera, which takes into account both stochasticity in the arrival of photons at the imaging camera and subsequent noise induced by the detection system of the camera. The only assumption in our method is that the background photon arrival to the imaging system is described by a stationary Poisson process (we make no assumption about the photon statistics for the signal). We estimate the background photon statistics parameter, λbg, from an image which contains both background and signal pixels by use of a novel truncated fit procedure with an automatically determined image count threshold. Prior to this, the camera noise model parameters are estimated using a calibration step. Utilizing the estimates for the camera parameters and λbg, we then introduce a probabilistic thresholding method, where, for the first time, the fraction of misclassified pixels can be determined a priori for a general image in an unsupervised way. We use synthetic images to validate our a priori estimates and to benchmark against the Otsu method, which is a popular unsupervised non-probabilistic image thresholding method (no a priori estimates for the error rates are provided). For completeness, we lastly present a simple heuristic general-purpose segmentation method based on the thresholding results, which we apply to segmentation of synthetic images and experimental images of fluorescent beads and lung cell nuclei. Our publicly available software opens up for fully automated, unsupervised, probabilistic photophysical image analysis.
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Affiliation(s)
- Jens Krog
- Centre for Environmental and Climate Science, Lund University, Lund, Sweden
| | - Albertas Dvirnas
- Centre for Environmental and Climate Science, Lund University, Lund, Sweden
| | - Oskar E. Ström
- Department of Physics and NanoLund, Lund University, Lund, Sweden
| | - Jason P. Beech
- Department of Physics and NanoLund, Lund University, Lund, Sweden
| | | | - Vilhelm Müller
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Fredrik Westerlund
- Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Tobias Ambjörnsson
- Centre for Environmental and Climate Science, Lund University, Lund, Sweden
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Gómez-de-Mariscal E, Del Rosario M, Pylvänäinen JW, Jacquemet G, Henriques R. Harnessing artificial intelligence to reduce phototoxicity in live imaging. J Cell Sci 2024; 137:jcs261545. [PMID: 38324353 PMCID: PMC10912813 DOI: 10.1242/jcs.261545] [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: 02/08/2024] Open
Abstract
Fluorescence microscopy is essential for studying living cells, tissues and organisms. However, the fluorescent light that switches on fluorescent molecules also harms the samples, jeopardizing the validity of results - particularly in techniques such as super-resolution microscopy, which demands extended illumination. Artificial intelligence (AI)-enabled software capable of denoising, image restoration, temporal interpolation or cross-modal style transfer has great potential to rescue live imaging data and limit photodamage. Yet we believe the focus should be on maintaining light-induced damage at levels that preserve natural cell behaviour. In this Opinion piece, we argue that a shift in role for AIs is needed - AI should be used to extract rich insights from gentle imaging rather than recover compromised data from harsh illumination. Although AI can enhance imaging, our ultimate goal should be to uncover biological truths, not just retrieve data. It is essential to prioritize minimizing photodamage over merely pushing technical limits. Our approach is aimed towards gentle acquisition and observation of undisturbed living systems, aligning with the essence of live-cell fluorescence microscopy.
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Affiliation(s)
| | | | - Joanna W. Pylvänäinen
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku 20500, Finland
| | - Guillaume Jacquemet
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku 20500, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
- Turku Bioimaging, University of Turku and Åbo Akademi University, Turku 20520, Finland
- InFLAMES Research Flagship Center, Åbo Akademi University, Turku 20100, Finland
| | - Ricardo Henriques
- Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
- UCL Laboratory for Molecular Cell Biology, University College London, London WC1E 6BT, UK
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Shi Y, Tabet JS, Milkie DE, Daugird TA, Yang CQ, Ritter AT, Giovannucci A, Legant WR. Smart lattice light-sheet microscopy for imaging rare and complex cellular events. Nat Methods 2024; 21:301-310. [PMID: 38167656 PMCID: PMC11216155 DOI: 10.1038/s41592-023-02126-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Accepted: 11/09/2023] [Indexed: 01/05/2024]
Abstract
Light-sheet microscopes enable rapid high-resolution imaging of biological specimens; however, biological processes span spatiotemporal scales. Moreover, long-term phenotypes are often instigated by rare or fleeting biological events that are difficult to capture with a single imaging modality. Here, to overcome this limitation, we present smartLLSM, a microscope that incorporates artificial intelligence-based instrument control to autonomously switch between epifluorescent inverted imaging and lattice light-sheet microscopy (LLSM). We apply this approach to two unique processes: cell division and immune synapse formation. In each context, smartLLSM provides population-level statistics across thousands of cells and autonomously captures multicolor three-dimensional datasets or four-dimensional time-lapse movies of rare events at rates that dramatically exceed human capabilities. From this, we quantify the effects of Taxol dose on spindle structure and kinetochore dynamics in dividing cells and of antigen strength on cytotoxic T lymphocyte engagement and lytic granule polarization at the immune synapse. Overall, smartLLSM efficiently detects rare events within heterogeneous cell populations and records these processes with high spatiotemporal four-dimensional imaging over statistically significant replicates.
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Affiliation(s)
- Yu Shi
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jimmy S Tabet
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Daniel E Milkie
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Timothy A Daugird
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Chelsea Q Yang
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | | - Andrea Giovannucci
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Wesley R Legant
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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Balasubramanian H, Hobson CM, Chew TL, Aaron JS. Imagining the future of optical microscopy: everything, everywhere, all at once. Commun Biol 2023; 6:1096. [PMID: 37898673 PMCID: PMC10613274 DOI: 10.1038/s42003-023-05468-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 10/16/2023] [Indexed: 10/30/2023] Open
Abstract
The optical microscope has revolutionized biology since at least the 17th Century. Since then, it has progressed from a largely observational tool to a powerful bioanalytical platform. However, realizing its full potential to study live specimens is hindered by a daunting array of technical challenges. Here, we delve into the current state of live imaging to explore the barriers that must be overcome and the possibilities that lie ahead. We venture to envision a future where we can visualize and study everything, everywhere, all at once - from the intricate inner workings of a single cell to the dynamic interplay across entire organisms, and a world where scientists could access the necessary microscopy technologies anywhere.
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Affiliation(s)
| | - Chad M Hobson
- Advanced Imaging Center; Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA, 20147, USA
| | - Teng-Leong Chew
- Advanced Imaging Center; Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA, 20147, USA
| | - Jesse S Aaron
- Advanced Imaging Center; Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA, 20147, USA.
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