1
|
Falcó C, Cohen DJ, Carrillo JA, Baker RE. Quantifying cell cycle regulation by tissue crowding. Biophys J 2024:S0006-3495(24)00317-5. [PMID: 38715360 DOI: 10.1016/j.bpj.2024.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/24/2024] [Accepted: 05/02/2024] [Indexed: 05/25/2024] Open
Abstract
The spatiotemporal coordination and regulation of cell proliferation is fundamental in many aspects of development and tissue maintenance. Cells have the ability to adapt their division rates in response to mechanical constraints, yet we do not fully understand how cell proliferation regulation impacts cell migration phenomena. Here, we present a minimal continuum model of cell migration with cell cycle dynamics, which includes density-dependent effects and hence can account for cell proliferation regulation. By combining minimal mathematical modeling, Bayesian inference, and recent experimental data, we quantify the impact of tissue crowding across different cell cycle stages in epithelial tissue expansion experiments. Our model suggests that cells sense local density and adapt cell cycle progression in response, during G1 and the combined S/G2/M phases, providing an explicit relationship between each cell-cycle-stage duration and local tissue density, which is consistent with several experimental observations. Finally, we compare our mathematical model's predictions to different experiments studying cell cycle regulation and present a quantitative analysis on the impact of density-dependent regulation on cell migration patterns. Our work presents a systematic approach for investigating and analyzing cell cycle data, providing mechanistic insights into how individual cells regulate proliferation, based on population-based experimental measurements.
Collapse
Affiliation(s)
- Carles Falcó
- Mathematical Institute, University of Oxford, Oxford, United Kingdom.
| | - Daniel J Cohen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey; Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey
| | - José A Carrillo
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Ruth E Baker
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| |
Collapse
|
2
|
Liu Y, Suh K, Maini PK, Cohen DJ, Baker RE. Parameter identifiability and model selection for partial differential equation models of cell invasion. J R Soc Interface 2024; 21:20230607. [PMID: 38442862 PMCID: PMC10914513 DOI: 10.1098/rsif.2023.0607] [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] [Received: 10/18/2023] [Accepted: 01/31/2024] [Indexed: 03/07/2024] Open
Abstract
When employing mechanistic models to study biological phenomena, practical parameter identifiability is important for making accurate predictions across wide ranges of unseen scenarios, as well as for understanding the underlying mechanisms. In this work, we use a profile-likelihood approach to investigate parameter identifiability for four extensions of the Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) model, given experimental data from a cell invasion assay. We show that more complicated models tend to be less identifiable, with parameter estimates being more sensitive to subtle differences in experimental procedures, and that they require more data to be practically identifiable. As a result, we suggest that parameter identifiability should be considered alongside goodness-of-fit and model complexity as criteria for model selection.
Collapse
Affiliation(s)
- Yue Liu
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Kevin Suh
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA
| | | | - Daniel J. Cohen
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA
| | - Ruth E. Baker
- Mathematical Institute, University of Oxford, Oxford, UK
| |
Collapse
|
3
|
Suh K, Cho YK, Breinyn IB, Cohen DJ. E-cadherin biomaterials reprogram collective cell migration and cell cycling by forcing homeostatic conditions. Cell Rep 2024; 43:113743. [PMID: 38358889 DOI: 10.1016/j.celrep.2024.113743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 01/02/2024] [Accepted: 01/18/2024] [Indexed: 02/17/2024] Open
Abstract
Cells attach to the world through either cell-extracellular matrix adhesion or cell-cell adhesion, and traditional biomaterials imitate the matrix for integrin-based adhesion. However, materials incorporating cadherin proteins that mimic cell-cell adhesion offer an alternative to program cell behavior and integrate into living tissues. We investigated how cadherin substrates affect collective cell migration and cell cycling in epithelia. Our approach involved biomaterials with matrix proteins on one-half and E-cadherin proteins on the other, forming a "Janus" interface across which we grew a single sheet of cells. Tissue regions over the matrix side exhibited normal collective dynamics, but an abrupt behavior shift occurred across the Janus boundary onto the E-cadherin side, where cells attached to the substrate via E-cadherin adhesions, resulting in stalled migration and slowing of the cell cycle. E-cadherin surfaces disrupted long-range mechanical coordination and nearly doubled the length of the G0/G1 phase of the cell cycle, linked to the lack of integrin focal adhesions on the E-cadherin surface.
Collapse
Affiliation(s)
- Kevin Suh
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Youn Kyoung Cho
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Isaac B Breinyn
- Department of Quantitative and Computational Biology, Princeton University, Princeton, NJ 08544, USA
| | - Daniel J Cohen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544, USA.
| |
Collapse
|
4
|
Sonneck J, Zhou Y, Chen J. MMV_Im2Im: an open-source microscopy machine vision toolbox for image-to-image transformation. Gigascience 2024; 13:giad120. [PMID: 38280188 PMCID: PMC10821710 DOI: 10.1093/gigascience/giad120] [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: 04/06/2023] [Revised: 09/30/2023] [Accepted: 12/28/2023] [Indexed: 01/29/2024] Open
Abstract
Over the past decade, deep learning (DL) research in computer vision has been growing rapidly, with many advances in DL-based image analysis methods for biomedical problems. In this work, we introduce MMV_Im2Im, a new open-source Python package for image-to-image transformation in bioimaging applications. MMV_Im2Im is designed with a generic image-to-image transformation framework that can be used for a wide range of tasks, including semantic segmentation, instance segmentation, image restoration, image generation, and so on. Our implementation takes advantage of state-of-the-art machine learning engineering techniques, allowing researchers to focus on their research without worrying about engineering details. We demonstrate the effectiveness of MMV_Im2Im on more than 10 different biomedical problems, showcasing its general potentials and applicabilities. For computational biomedical researchers, MMV_Im2Im provides a starting point for developing new biomedical image analysis or machine learning algorithms, where they can either reuse the code in this package or fork and extend this package to facilitate the development of new methods. Experimental biomedical researchers can benefit from this work by gaining a comprehensive view of the image-to-image transformation concept through diversified examples and use cases. We hope this work can give the community inspirations on how DL-based image-to-image transformation can be integrated into the assay development process, enabling new biomedical studies that cannot be done only with traditional experimental assays. To help researchers get started, we have provided source code, documentation, and tutorials for MMV_Im2Im at [https://github.com/MMV-Lab/mmv_im2im] under MIT license.
Collapse
Affiliation(s)
- Justin Sonneck
- Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V., Bunsen-Kirchhoff-Str. 11, Dortmund 44139, Germany
- Faculty of Computer Science, Ruhr-University Bochum, Universitätsstraße 150, Bochum 44801, Germany
| | - Yu Zhou
- Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V., Bunsen-Kirchhoff-Str. 11, Dortmund 44139, Germany
| | - Jianxu Chen
- Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V., Bunsen-Kirchhoff-Str. 11, Dortmund 44139, Germany
| |
Collapse
|
5
|
Liu X, Li B, Liu C, Ta D. Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:408-420. [PMID: 37589024 PMCID: PMC10425324 DOI: 10.1007/s43657-023-00094-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 01/03/2023] [Accepted: 01/05/2023] [Indexed: 08/18/2023]
Abstract
Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues, playing a crucial role in the field of histopathology. However, when labeling and imaging biological tissues, there are still some challenges, e.g., time-consuming tissue preparation steps, expensive reagents, and signal bias due to photobleaching. To overcome these limitations, we present a deep-learning-based method for fluorescence translation of tissue sections, which is achieved by conditional generative adversarial network (cGAN). Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image, and implement the virtual multi-label fluorescent staining by merging the generated different fluorescence images as well. Moreover, this proposed method can also effectively reduce the time-consuming and laborious preparation in imaging processes, and further saves the cost and time. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-023-00094-1.
Collapse
Affiliation(s)
- Xin Liu
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433 China
- State Key Laboratory of Medical Neurobiology, Fudan University, Shanghai, 200433 China
| | - Boyi Li
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433 China
| | - Chengcheng Liu
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433 China
| | - Dean Ta
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433 China
- Center for Biomedical Engineering, Fudan University, Shanghai, 200433 China
| |
Collapse
|
6
|
Suh K, Cho YK, Breinyn IB, Cohen DJ. E-cadherin biointerfaces reprogram collective cell migration and cell cycling by forcing homeostatic conditions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.25.550505. [PMID: 37546933 PMCID: PMC10402016 DOI: 10.1101/2023.07.25.550505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Cells attach to the world around them in two ways-cell:extracellular-matrix adhesion and cell:cell adhesion-and conventional biomaterials are made to resemble the matrix to encourage integrin-based cell adhesion. However, interest is growing for cell-mimetic interfaces that mimic cell-cell interactions using cadherin proteins, as this offers a new way to program cell behavior and design synthetic implants and objects that can integrate directly into living tissues. Here, we explore how these cadherin-based materials affect collective cell behaviors, focusing specifically on collective migration and cell cycle regulation in cm-scale epithelia. We built culture substrates where half of the culture area was functionalized with matrix proteins and the contiguous half was functionalized with E-cadherin proteins, and we grew large epithelia across this 'Janus' interface. Parts of the tissues in contact with the matrix side of the Janus interface exhibited normal collective dynamics, but an abrupt shift in behaviors happened immediately across the Janus boundary onto the E-cadherin side, where cells formed hybrid E-cadherin junctions with the substrate, migration effectively froze in place, and cell-cycling significantly decreased. E-cadherin materials suppressed long-range mechanical correlations in the tissue and mechanical information reflected off the substrate interface. These effects could not be explained by conventional density, shape index, or contact inhibition explanations. E-cadherin surfaces nearly doubled the length of the G0/G1 phase of the cell cycle, which we ultimately connected to the exclusion of matrix focal adhesions induced by the E-cadherin culture surface.
Collapse
Affiliation(s)
- Kevin Suh
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA, 08544
| | - Youn Kyoung Cho
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA, 08544
| | - Isaac B Breinyn
- Department of Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA, 08544
| | - Daniel J Cohen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ, USA, 08544
| |
Collapse
|
7
|
Chen J, Viana MP, Rafelski SM. When seeing is not believing: application-appropriate validation matters for quantitative bioimage analysis. Nat Methods 2023; 20:968-970. [PMID: 37433995 DOI: 10.1038/s41592-023-01881-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Affiliation(s)
- Jianxu Chen
- Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany
| | | | | |
Collapse
|
8
|
Self-assembly of tessellated tissue sheets by expansion and collision. Nat Commun 2022; 13:4026. [PMID: 35821232 PMCID: PMC9276766 DOI: 10.1038/s41467-022-31459-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 06/17/2022] [Indexed: 11/28/2022] Open
Abstract
Tissues do not exist in isolation—they interact with other tissues within and across organs. While cell-cell interactions have been intensely investigated, less is known about tissue-tissue interactions. Here, we studied collisions between monolayer tissues with different geometries, cell densities, and cell types. First, we determine rules for tissue shape changes during binary collisions and describe complex cell migration at tri-tissue boundaries. Next, we propose that genetically identical tissues displace each other based on pressure gradients, which are directly linked to gradients in cell density. We present a physical model of tissue interactions that allows us to estimate the bulk modulus of the tissues from collision dynamics. Finally, we introduce TissEllate, a design tool for self-assembling complex tessellations from arrays of many tissues, and we use cell sheet engineering techniques to transfer these composite tissues like cellular films. Overall, our work provides insight into the mechanics of tissue collisions, harnessing them to engineer tissue composites as designable living materials. Tissue boundaries in our body separate organs and enable healing, but boundary mechanics are not well known. Here, the authors define mechanical rules for colliding cell monolayers and use these rules to make complex, predictable tessellations.
Collapse
|
9
|
Jiang CF, Sun YM. Label-free monitoring of spatiotemporal changes in the stem cell cytoskeletons in time-lapse phase-contrast microscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:2323-2333. [PMID: 35519244 PMCID: PMC9045902 DOI: 10.1364/boe.452822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/12/2022] [Accepted: 03/15/2022] [Indexed: 06/14/2023]
Abstract
Investigation of the dynamic structural changes in the actin cytoskeleton during cell migration provides crucial information about the physiological conditions of a stem cell during in-vitro culture. Here we proposed a quantitative analytical model associated with texture extraction with cell tracking techniques for in situ monitoring of the cytoskeletal density change of stem cells in phase-contrast microscopy without fluorescence staining. The reliability of the model in quantifying the texture density with different orientation was first validated using a series of simulated textural images. The capability of the method to reflect the spatiotemporal regulation of the cytoskeletal structure of a living stem cell was further proved by applying it to a set of 72 h phase-contrast microscopic video of the growth dynamics of mesenchymal stem cells in vitro culture.
Collapse
Affiliation(s)
- Ching-Fen Jiang
- Graduate Degree Program of Smart Healthcare & Bioinformatics, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Man Sun
- Department of Biomedical Engineering, I-Shou University, Kaohsiung, Taiwan
| |
Collapse
|
10
|
LaChance J, Suh K, Clausen J, Cohen DJ. Learning the rules of collective cell migration using deep attention networks. PLoS Comput Biol 2022; 18:e1009293. [PMID: 35476698 PMCID: PMC9106212 DOI: 10.1371/journal.pcbi.1009293] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 05/13/2022] [Accepted: 03/23/2022] [Indexed: 11/18/2022] Open
Abstract
Collective, coordinated cellular motions underpin key processes in all multicellular organisms, yet it has been difficult to simultaneously express the ‘rules’ behind these motions in clear, interpretable forms that effectively capture high-dimensional cell-cell interaction dynamics in a manner that is intuitive to the researcher. Here we apply deep attention networks to analyze several canonical living tissues systems and present the underlying collective migration rules for each tissue type using only cell migration trajectory data. We use these networks to learn the behaviors of key tissue types with distinct collective behaviors—epithelial, endothelial, and metastatic breast cancer cells—and show how the results complement traditional biophysical approaches. In particular, we present attention maps indicating the relative influence of neighboring cells to the learned turning decisions of a ‘focal cell’–the primary cell of interest in a collective setting. Colloquially, we refer to this learned relative influence as ‘attention’, as it serves as a proxy for the physical parameters modifying the focal cell’s future motion as a function of each neighbor cell. These attention networks reveal distinct patterns of influence and attention unique to each model tissue. Endothelial cells exhibit tightly focused attention on their immediate forward-most neighbors, while cells in more expansile epithelial tissues are more broadly influenced by neighbors in a relatively large forward sector. Attention maps of ensembles of more mesenchymal, metastatic cells reveal completely symmetric attention patterns, indicating the lack of any particular coordination or direction of interest. Moreover, we show how attention networks are capable of detecting and learning how these rules change based on biophysical context, such as location within the tissue and cellular crowding. That these results require only cellular trajectories and no modeling assumptions highlights the potential of attention networks for providing further biological insights into complex cellular systems. Collective behaviors are crucial to the function of multicellular life, with large-scale, coordinated cell migration enabling processes spanning organ formation to coordinated skin healing. However, we lack effective tools to discover and cleanly express collective rules at the level of an individual cell. Here, we employ a carefully structured neural network to extract collective information directly from cell trajectory data. The network is trained on data from various systems, including canonical collective cell systems (HUVEC and MDCK cells) which display visually distinct forms of collective motion, and metastatic cancer cells (MDA-MB-231) which are highly uncoordinated. Using these trained networks, we can produce attention maps for each system, which indicate how a cell within a tissue takes in information from its surrounding neighbors, as a function of weights assigned to those neighbors. Thus for a cell type in which cells tend to follow the path of the cell in front, the attention maps will display high weights for cells spatially forward of the focal cell. We present results in terms of additional metrics, such as accuracy plots and number of interacting cells, and encourage future development of improved metrics.
Collapse
Affiliation(s)
- Julienne LaChance
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Kevin Suh
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Jens Clausen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
| | - Daniel J. Cohen
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, United States of America
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey, United States of America
- * E-mail:
| |
Collapse
|
11
|
Wolf AE, Heinrich MA, Breinyn IB, Zajdel TJ, Cohen DJ. Short-term bioelectric stimulation of collective cell migration in tissues reprograms long-term supracellular dynamics. PNAS NEXUS 2022; 1:pgac002. [PMID: 35360553 PMCID: PMC8962779 DOI: 10.1093/pnasnexus/pgac002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/03/2021] [Accepted: 01/07/2022] [Indexed: 01/28/2023]
Abstract
The ability to program collective cell migration can allow us to control critical multicellular processes in development, regenerative medicine, and invasive disease. However, while various technologies exist to make individual cells migrate, translating these tools to control myriad, collectively interacting cells within a single tissue poses many challenges. For instance, do cells within the same tissue interpret a global migration 'command' differently based on where they are in the tissue? Similarly, since no stimulus is permanent, what are the long-term effects of transient commands on collective cell dynamics? We investigate these questions by bioelectrically programming large epithelial tissues to globally migrate 'rightward' via electrotaxis. Tissues clearly developed distinct rear, middle, side, and front responses to a single global migration stimulus. Furthermore, at no point poststimulation did tissues return to their prestimulation behavior, instead equilibrating to a 3rd, new migratory state. These unique dynamics suggested that programmed migration resets tissue mechanical state, which was confirmed by transient chemical disruption of cell-cell junctions, analysis of strain wave propagation patterns, and quantification of cellular crowd dynamics. Overall, this work demonstrates how externally driving the collective migration of a tissue can reprogram baseline cell-cell interactions and collective dynamics, even well beyond the end of the global migratory cue, and emphasizes the importance of considering the supracellular context of tissues and other collectives when attempting to program crowd behaviors.
Collapse
Affiliation(s)
- Abraham E Wolf
- Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA
| | | | | | - Tom J Zajdel
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544, USA
| | - Daniel J Cohen
- To whom correspondence should be addressed. Address: Attn. , 111 Hoyt Laboratory, Princeton, NJ 08544, USA. E-mail:
| |
Collapse
|
12
|
von Chamier L, Laine RF, Jukkala J, Spahn C, Krentzel D, Nehme E, Lerche M, Hernández-Pérez S, Mattila PK, Karinou E, Holden S, Solak AC, Krull A, Buchholz TO, Jones ML, Royer LA, Leterrier C, Shechtman Y, Jug F, Heilemann M, Jacquemet G, Henriques R. Democratising deep learning for microscopy with ZeroCostDL4Mic. Nat Commun 2021; 12:2276. [PMID: 33859193 PMCID: PMC8050272 DOI: 10.1038/s41467-021-22518-0] [Citation(s) in RCA: 184] [Impact Index Per Article: 61.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2020] [Accepted: 03/10/2021] [Indexed: 02/02/2023] Open
Abstract
Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier that novice users often find difficult to overcome. Here, we present ZeroCostDL4Mic, an entry-level platform simplifying DL access by leveraging the free, cloud-based computational resources of Google Colab. ZeroCostDL4Mic allows researchers with no coding expertise to train and apply key DL networks to perform tasks including segmentation (using U-Net and StarDist), object detection (using YOLOv2), denoising (using CARE and Noise2Void), super-resolution microscopy (using Deep-STORM), and image-to-image translation (using Label-free prediction - fnet, pix2pix and CycleGAN). Importantly, we provide suitable quantitative tools for each network to evaluate model performance, allowing model optimisation. We demonstrate the application of the platform to study multiple biological processes.
Collapse
Affiliation(s)
- Lucas von Chamier
- MRC-Laboratory for Molecular Cell Biology, University College London, London, UK
| | - Romain F Laine
- MRC-Laboratory for Molecular Cell Biology, University College London, London, UK
- The Francis Crick Institute, London, UK
| | - Johanna Jukkala
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland
| | - Christoph Spahn
- Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany
| | - Daniel Krentzel
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
- Department of Bioengineering, Imperial College London, London, UK
| | - Elias Nehme
- Department of Electrical Engineering, Technion-Israel Institute of Technology, Haifa, Israel
- Department of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, Israel
| | - Martina Lerche
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
| | - Sara Hernández-Pérez
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, and MediCity Research Laboratories, University of Turku, Turku, Finland
| | - Pieta K Mattila
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland
- Institute of Biomedicine, and MediCity Research Laboratories, University of Turku, Turku, Finland
| | - Eleni Karinou
- Centre for Bacterial Cell Biology, Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | - Séamus Holden
- Centre for Bacterial Cell Biology, Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, UK
| | | | - Alexander Krull
- Center for Systems Biology Dresden (CSBD), Dresden, Germany
- Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany
- Max Planck Institute for Physics of Complex Systems, Dresden, Germany
| | - Tim-Oliver Buchholz
- Center for Systems Biology Dresden (CSBD), Dresden, Germany
- Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany
| | - Martin L Jones
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
| | | | | | - Yoav Shechtman
- Department of Biomedical Engineering, Technion-Israel Institute of Technology, Haifa, Israel
| | - Florian Jug
- Center for Systems Biology Dresden (CSBD), Dresden, Germany
- Max Planck Institute for Molecular Cell Biology and Genetics, Dresden, Germany
- Fondatione Human Technopole, Milano, Italy
| | - Mike Heilemann
- Institute of Physical and Theoretical Chemistry, Goethe-University Frankfurt, Frankfurt, Germany
| | - Guillaume Jacquemet
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku, Finland.
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku, Finland.
| | - Ricardo Henriques
- MRC-Laboratory for Molecular Cell Biology, University College London, London, UK.
- The Francis Crick Institute, London, UK.
- Instituto Gulbenkian de Ciência, Oeiras, Portugal.
| |
Collapse
|