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Nabi IR, Cardoen B, Khater IM, Gao G, Wong TH, Hamarneh G. AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth. J Cell Biol 2024; 223:e202311073. [PMID: 38865088 PMCID: PMC11169916 DOI: 10.1083/jcb.202311073] [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: 11/15/2023] [Revised: 04/02/2024] [Accepted: 05/21/2024] [Indexed: 06/13/2024] Open
Abstract
Super-resolution microscopy, or nanoscopy, enables the use of fluorescent-based molecular localization tools to study molecular structure at the nanoscale level in the intact cell, bridging the mesoscale gap to classical structural biology methodologies. Analysis of super-resolution data by artificial intelligence (AI), such as machine learning, offers tremendous potential for the discovery of new biology, that, by definition, is not known and lacks ground truth. Herein, we describe the application of weakly supervised paradigms to super-resolution microscopy and its potential to enable the accelerated exploration of the nanoscale architecture of subcellular macromolecules and organelles.
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Affiliation(s)
- Ivan R. Nabi
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, Canada
- School of Biomedical Engineering, University of British Columbia, Vancouver, Canada
| | - Ben Cardoen
- School of Computing Science, Simon Fraser University, Burnaby, Canada
| | - Ismail M. Khater
- School of Computing Science, Simon Fraser University, Burnaby, Canada
- Department of Electrical and Computer Engineering, Faculty of Engineering and Technology, Birzeit University, Birzeit, Palestine
| | - Guang Gao
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, Canada
| | - Timothy H. Wong
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, Canada
| | - Ghassan Hamarneh
- School of Computing Science, Simon Fraser University, Burnaby, Canada
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2
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Liu Y, Uttam S. Perspective on quantitative phase imaging to improve precision cancer medicine. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:S22705. [PMID: 38584967 PMCID: PMC10996848 DOI: 10.1117/1.jbo.29.s2.s22705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/03/2024] [Accepted: 03/15/2024] [Indexed: 04/09/2024]
Abstract
Significance Quantitative phase imaging (QPI) offers a label-free approach to non-invasively characterize cellular processes by exploiting their refractive index based intrinsic contrast. QPI captures this contrast by translating refractive index associated phase shifts into intensity-based quantifiable data with nanoscale sensitivity. It holds significant potential for advancing precision cancer medicine by providing quantitative characterization of the biophysical properties of cells and tissue in their natural states. Aim This perspective aims to discuss the potential of QPI to increase our understanding of cancer development and its response to therapeutics. It also explores new developments in QPI methods towards advancing personalized cancer therapy and early detection. Approach We begin by detailing the technical advancements of QPI, examining its implementations across transmission and reflection geometries and phase retrieval methods, both interferometric and non-interferometric. The focus then shifts to QPI's applications in cancer research, including dynamic cell mass imaging for drug response assessment, cancer risk stratification, and in-vivo tissue imaging. Results QPI has emerged as a crucial tool in precision cancer medicine, offering insights into tumor biology and treatment efficacy. Its sensitivity to detecting nanoscale changes holds promise for enhancing cancer diagnostics, risk assessment, and prognostication. The future of QPI is envisioned in its integration with artificial intelligence, morpho-dynamics, and spatial biology, broadening its impact in cancer research. Conclusions QPI presents significant potential in advancing precision cancer medicine and redefining our approach to cancer diagnosis, monitoring, and treatment. Future directions include harnessing high-throughput dynamic imaging, 3D QPI for realistic tumor models, and combining artificial intelligence with multi-omics data to extend QPI's capabilities. As a result, QPI stands at the forefront of cancer research and clinical application in cancer care.
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Affiliation(s)
- Yang Liu
- University of Illinois Urbana-Champaign, Beckman Institute for Advanced Science and Technology, Cancer Center at Illinois, Department of Bioengineering, Department of Electrical and Computer Engineering, Urbana, Illinois, United States
- University of Pittsburgh, Departments of Medicine and Bioengineering, Pittsburgh, Pennsylvania, United States
| | - Shikhar Uttam
- University of Pittsburgh, Department of Computational and Systems Biology, Pittsburgh, Pennsylvania, United States
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3
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Shakarchy A, Zarfati G, Hazak A, Mealem R, Huk K, Ziv T, Avinoam O, Zaritsky A. Machine learning inference of continuous single-cell state transitions during myoblast differentiation and fusion. Mol Syst Biol 2024; 20:217-241. [PMID: 38238594 PMCID: PMC10912675 DOI: 10.1038/s44320-024-00010-3] [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: 03/02/2023] [Revised: 12/19/2023] [Accepted: 01/04/2024] [Indexed: 03/06/2024] Open
Abstract
Cells modify their internal organization during continuous state transitions, supporting functions from cell division to differentiation. However, tools to measure dynamic physiological states of individual transitioning cells are lacking. We combined live-cell imaging and machine learning to monitor ERK1/2-inhibited primary murine skeletal muscle precursor cells, that transition rapidly and robustly from proliferating myoblasts to post-mitotic myocytes and then fuse, forming multinucleated myotubes. Our models, trained using motility or actin intensity features from single-cell tracking data, effectively tracked real-time continuous differentiation, revealing that differentiation occurs 7.5-14.5 h post induction, followed by fusion ~3 h later. Co-inhibition of ERK1/2 and p38 led to differentiation without fusion. Our model inferred co-inhibition leads to terminal differentiation, indicating that p38 is specifically required for transitioning from terminal differentiation to fusion. Our model also predicted that co-inhibition leads to changes in actin dynamics. Mass spectrometry supported these in silico predictions and suggested novel fusion and maturation regulators downstream of differentiation. Collectively, this approach can be adapted to various biological processes to uncover novel links between dynamic single-cell states and their functional outcomes.
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Affiliation(s)
- Amit Shakarchy
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel
| | - Giulia Zarfati
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 761001, Israel
| | - Adi Hazak
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 761001, Israel
| | - Reut Mealem
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel
| | - Karina Huk
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 761001, Israel
| | - Tamar Ziv
- The Smoler Proteomics Center, Lorry I. Lokey Interdisciplinary Center for Life Sciences and Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
| | - Ori Avinoam
- Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, 761001, Israel.
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel.
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4
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Burgess J, Nirschl JJ, Zanellati MC, Lozano A, Cohen S, Yeung-Levy S. Orientation-invariant autoencoders learn robust representations for shape profiling of cells and organelles. Nat Commun 2024; 15:1022. [PMID: 38310122 PMCID: PMC10838319 DOI: 10.1038/s41467-024-45362-4] [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: 07/09/2023] [Accepted: 01/19/2024] [Indexed: 02/05/2024] Open
Abstract
Cell and organelle shape are driven by diverse genetic and environmental factors and thus accurate quantification of cellular morphology is essential to experimental cell biology. Autoencoders are a popular tool for unsupervised biological image analysis because they learn a low-dimensional representation that maps images to feature vectors to generate a semantically meaningful embedding space of morphological variation. The learned feature vectors can also be used for clustering, dimensionality reduction, outlier detection, and supervised learning problems. Shape properties do not change with orientation, and thus we argue that representation learning methods should encode this orientation invariance. We show that conventional autoencoders are sensitive to orientation, which can lead to suboptimal performance on downstream tasks. To address this, we develop O2-variational autoencoder (O2-VAE), an unsupervised method that learns robust, orientation-invariant representations. We use O2-VAE to discover morphology subgroups in segmented cells and mitochondria, detect outlier cells, and rapidly characterise cellular shape and texture in large datasets, including in a newly generated synthetic benchmark.
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Affiliation(s)
- James Burgess
- Institute for Computational & Mathematical Engineering, Stanford University, Stanford, CA, USA.
| | - Jeffrey J Nirschl
- Department of Pathology, School of Medicine, Stanford University, Stanford, CA, USA
| | - Maria-Clara Zanellati
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alejandro Lozano
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Sarah Cohen
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Serena Yeung-Levy
- Departments of Biomedical Data Science, Computer Science, and Electrical Engineering, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
- Clinical Excellence Research Center, School of Medicine, Stanford University, Stanford, CA, USA.
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5
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Engels SM, Kamat P, Pafilis GS, Li Y, Agrawal A, Haller DJ, Phillip JM, Contreras LM. Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells. PNAS NEXUS 2024; 3:pgad415. [PMID: 38156290 PMCID: PMC10754159 DOI: 10.1093/pnasnexus/pgad415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 11/21/2023] [Indexed: 12/30/2023]
Abstract
Particulate matter (PM) is a ubiquitous component of air pollution that is epidemiologically linked to human pulmonary diseases. PM chemical composition varies widely, and the development of high-throughput experimental techniques enables direct profiling of cellular effects using compositionally unique PM mixtures. Here, we show that in a human bronchial epithelial cell model, exposure to three chemically distinct PM mixtures drive unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability, DNA damage responses, and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Finally, we observed that PM mixtures with higher cadmium content induced increased DNA damage and drove redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of individual cellular morphologies provides a robust, high-throughput approach to gauge the effects of environmental stressors on biological systems and score cellular susceptibilities to pollution.
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Affiliation(s)
- Sean M Engels
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX 78712, USA
| | - Pratik Kamat
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - G Stavros Pafilis
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX 78712, USA
| | - Yukang Li
- Department of Biology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Anshika Agrawal
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Daniel J Haller
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27606, USA
| | - Jude M Phillip
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD 21231, USA
| | - Lydia M Contreras
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, TX 78712, USA
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, TX, 78712, USA
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6
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Alieva M, Wezenaar AKL, Wehrens EJ, Rios AC. Bridging live-cell imaging and next-generation cancer treatment. Nat Rev Cancer 2023; 23:731-745. [PMID: 37704740 DOI: 10.1038/s41568-023-00610-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/25/2023] [Indexed: 09/15/2023]
Abstract
By providing spatial, molecular and morphological data over time, live-cell imaging can provide a deeper understanding of the cellular and signalling events that determine cancer response to treatment. Understanding this dynamic response has the potential to enhance clinical outcome by identifying biomarkers or actionable targets to improve therapeutic efficacy. Here, we review recent applications of live-cell imaging for uncovering both tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. Given the increasing uses of T cell therapies, we discuss the unique opportunity of time-lapse imaging for capturing the interactivity and motility of immunotherapies. Although traditionally limited in the number of molecular features captured, novel developments in multidimensional imaging and multi-omics data integration offer strategies to connect single-cell dynamics to molecular phenotypes. We review the effect of these recent technological advances on our understanding of the cellular dynamics of tumour targeting and discuss their implication for next-generation precision medicine.
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Affiliation(s)
- Maria Alieva
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Instituto de Investigaciones Biomedicas Sols-Morreale (IIBM), CSIC-UAM, Madrid, Spain
| | - Amber K L Wezenaar
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Ellen J Wehrens
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
| | - Anne C Rios
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
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7
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Soelistyo CJ, Ulicna K, Lowe AR. Machine learning enhanced cell tracking. FRONTIERS IN BIOINFORMATICS 2023; 3:1228989. [PMID: 37521315 PMCID: PMC10380934 DOI: 10.3389/fbinf.2023.1228989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 07/03/2023] [Indexed: 08/01/2023] Open
Abstract
Quantifying cell biology in space and time requires computational methods to detect cells, measure their properties, and assemble these into meaningful trajectories. In this aspect, machine learning (ML) is having a transformational effect on bioimage analysis, now enabling robust cell detection in multidimensional image data. However, the task of cell tracking, or constructing accurate multi-generational lineages from imaging data, remains an open challenge. Most cell tracking algorithms are largely based on our prior knowledge of cell behaviors, and as such, are difficult to generalize to new and unseen cell types or datasets. Here, we propose that ML provides the framework to learn aspects of cell behavior using cell tracking as the task to be learned. We suggest that advances in representation learning, cell tracking datasets, metrics, and methods for constructing and evaluating tracking solutions can all form part of an end-to-end ML-enhanced pipeline. These developments will lead the way to new computational methods that can be used to understand complex, time-evolving biological systems.
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Affiliation(s)
- Christopher J. Soelistyo
- Department of Structural and Molecular Biology, University College London, London, United Kingdom
- Institute for the Physics of Living Systems, London, United Kingdom
| | - Kristina Ulicna
- Department of Structural and Molecular Biology, University College London, London, United Kingdom
- Institute for the Physics of Living Systems, London, United Kingdom
| | - Alan R. Lowe
- Department of Structural and Molecular Biology, University College London, London, United Kingdom
- Institute for the Physics of Living Systems, London, United Kingdom
- Alan Turing Institute, London, United Kingdom
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8
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Engels SM, Kamat P, Pafilis GS, Li Y, Agrawal A, Haller DJ, Phillip JM, Contreras LM. Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.17.541204. [PMID: 37292596 PMCID: PMC10245696 DOI: 10.1101/2023.05.17.541204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Particulate matter (PM) is a ubiquitous component of indoor and outdoor air pollution that is epidemiologically linked to many human pulmonary diseases. PM has many emission sources, making it challenging to understand the biological effects of exposure due to the high variance in chemical composition. However, the effects of compositionally unique particulate matter mixtures on cells have not been analyzed using both biophysical and biomolecular approaches. Here, we show that in a human bronchial epithelial cell model (BEAS-2B), exposure to three chemically distinct PM mixtures drives unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability and DNA damage responses and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization and structure, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Lastly, we observed that particulate matter mixtures with high contents of heavy metals, such as cadmium and lead, induced larger drops in viability, increased DNA damage, and drove a redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of cellular morphology provides a robust approach to gauge the effects of environmental stressors on biological systems and determine cellular susceptibilities to pollution.
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Affiliation(s)
- Sean M. Engels
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, 78712
| | - Pratik Kamat
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - G. Stavros Pafilis
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, 78712
| | - Yukang Li
- Department of Biology, Johns Hopkins University, Baltimore, Maryland 21218
| | - Anshika Agrawal
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
| | - Daniel J. Haller
- Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, North Carolina, 27606
| | - Jude M. Phillip
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, 21218
- Institute for Nanobiotechnology, Johns Hopkins University, Baltimore, Maryland, 21218
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Baltimore, Maryland, 21231
| | - Lydia M. Contreras
- McKetta Department of Chemical Engineering, University of Texas at Austin, Austin, Texas, 78712
- Institute for Cellular and Molecular Biology, The University of Texas at Austin, Austin, Texas, USA
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9
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Copperman J, Gross SM, Chang YH, Heiser LM, Zuckerman DM. Morphodynamical cell state description via live-cell imaging trajectory embedding. Commun Biol 2023; 6:484. [PMID: 37142678 PMCID: PMC10160022 DOI: 10.1038/s42003-023-04837-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 04/10/2023] [Indexed: 05/06/2023] Open
Abstract
Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories-that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.
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Affiliation(s)
- Jeremy Copperman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
| | - Sean M Gross
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA
| | - Laura M Heiser
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA.
| | - Daniel M Zuckerman
- Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, 97239, USA.
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, 97239, USA.
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10
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Mencattini A, D'Orazio M, Casti P, Comes MC, Di Giuseppe D, Antonelli G, Filippi J, Corsi F, Ghibelli L, Veith I, Di Natale C, Parrini MC, Martinelli E. Deep-Manager: a versatile tool for optimal feature selection in live-cell imaging analysis. Commun Biol 2023; 6:241. [PMID: 36869080 PMCID: PMC9984362 DOI: 10.1038/s42003-023-04585-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 02/13/2023] [Indexed: 03/05/2023] Open
Abstract
One of the major problems in bioimaging, often highly underestimated, is whether features extracted for a discrimination or regression task will remain valid for a broader set of similar experiments or in the presence of unpredictable perturbations during the image acquisition process. Such an issue is even more important when it is addressed in the context of deep learning features due to the lack of a priori known relationship between the black-box descriptors (deep features) and the phenotypic properties of the biological entities under study. In this regard, the widespread use of descriptors, such as those coming from pre-trained Convolutional Neural Networks (CNNs), is hindered by the fact that they are devoid of apparent physical meaning and strongly subjected to unspecific biases, i.e., features that do not depend on the cell phenotypes, but rather on acquisition artifacts, such as brightness or texture changes, focus shifts, autofluorescence or photobleaching. The proposed Deep-Manager software platform offers the possibility to efficiently select those features having lower sensitivity to unspecific disturbances and, at the same time, a high discriminating power. Deep-Manager can be used in the context of both handcrafted and deep features. The unprecedented performances of the method are proven using five different case studies, ranging from selecting handcrafted green fluorescence protein intensity features in chemotherapy-related breast cancer cell death investigation to addressing problems related to the context of Deep Transfer Learning. Deep-Manager, freely available at https://github.com/BEEuniroma2/Deep-Manager , is suitable for use in many fields of bioimaging and is conceived to be constantly upgraded with novel image acquisition perturbations and modalities.
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Affiliation(s)
- A Mencattini
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy
| | - M D'Orazio
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy
| | - P Casti
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy
| | - M C Comes
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy
| | - D Di Giuseppe
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy
| | - G Antonelli
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy
| | - J Filippi
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy
| | - F Corsi
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy
- Department of Biology, University of Rome Tor Vergata, 00133, Rome, Italy
| | - L Ghibelli
- Department of Biology, University of Rome Tor Vergata, 00133, Rome, Italy
| | - I Veith
- Inserm U830, Stress and Cancer Lab, Institut Curie, Centre de Recherche, Paris Sciences et Lettres Research University, 75005, Paris, France
| | - C Di Natale
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy
| | - M C Parrini
- Inserm U830, Stress and Cancer Lab, Institut Curie, Centre de Recherche, Paris Sciences et Lettres Research University, 75005, Paris, France
| | - E Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133, Rome, Italy.
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (IC-LOC), University of Rome Tor Vergata, 00133, Rome, Italy.
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11
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Siu DMD, Lee KCM, Chung BMF, Wong JSJ, Zheng G, Tsia KK. Optofluidic imaging meets deep learning: from merging to emerging. LAB ON A CHIP 2023; 23:1011-1033. [PMID: 36601812 DOI: 10.1039/d2lc00813k] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.
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Affiliation(s)
- Dickson M D Siu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Bob M F Chung
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Justin S J Wong
- Conzeb Limited, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Guoan Zheng
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
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Alderfer S, Sun J, Tahtamouni L, Prasad A. Morphological signatures of actin organization in single cells accurately classify genetic perturbations using CNNs with transfer learning. SOFT MATTER 2022; 18:8342-8354. [PMID: 36222484 DOI: 10.1039/d2sm01000c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The actin cytoskeleton plays essential roles in countless cell processes, from cell division to migration to signaling. In cancer cells, cytoskeletal dynamics, cytoskeletal filament organization, and overall cell morphology are known to be altered substantially. We hypothesize that actin fiber organization and cell shape may carry specific signatures of genetic or signaling perturbations. We used convolutional neural networks (CNNs) on a small fluorescence microscopy image dataset of retinal pigment epithelial (RPE) cells and triple-negative breast cancer (TNBC) cells for identifying morphological signatures in cancer cells. Using a transfer learning approach, CNNs could be trained to accurately distinguish between normal and oncogenically transformed RPE cells with an accuracy of about 95% or better at the single cell level. Furthermore, CNNs could distinguish transformed cell lines differing by an oncogenic mutation from each other and could also detect knockdown of cofilin in TNBC cells, indicating that each single oncogenic mutation or cytoskeletal perturbation produces a unique signature in actin morphology. Application of the Local Interpretable Model-Agnostic Explanations (LIME) method for visually interpreting the CNN results revealed features of the global actin structure relevant for some cells and classification tasks. Interestingly, many of these features were supported by previous biological observation. Actin fiber organization is thus a sensitive marker for cell identity, and identification of its perturbations could be very useful for assaying cell phenotypes, including disease states.
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Affiliation(s)
- Sydney Alderfer
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA.
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523, USA
| | - Jiangyu Sun
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - Lubna Tahtamouni
- Department of Biochemistry and Molecular Biology, Colorado State University, Fort Collins, CO 80523, USA
- Department of Biology and Biotechnology, The Hashemite University, Zarqa, Jordan
| | - Ashok Prasad
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO 80523, USA.
- School of Biomedical Engineering, Colorado State University, Fort Collins, CO 80523, USA
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Nowakowski TJ, Salama SR. Cerebral Organoids as an Experimental Platform for Human Neurogenomics. Cells 2022; 11:2803. [PMID: 36139380 PMCID: PMC9496777 DOI: 10.3390/cells11182803] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/06/2022] [Accepted: 09/07/2022] [Indexed: 01/25/2023] Open
Abstract
The cerebral cortex forms early in development according to a series of heritable neurodevelopmental instructions. Despite deep evolutionary conservation of the cerebral cortex and its foundational six-layered architecture, significant variations in cortical size and folding can be found across mammals, including a disproportionate expansion of the prefrontal cortex in humans. Yet our mechanistic understanding of neurodevelopmental processes is derived overwhelmingly from rodent models, which fail to capture many human-enriched features of cortical development. With the advent of pluripotent stem cells and technologies for differentiating three-dimensional cultures of neural tissue in vitro, cerebral organoids have emerged as an experimental platform that recapitulates several hallmarks of human brain development. In this review, we discuss the merits and limitations of cerebral organoids as experimental models of the developing human brain. We highlight innovations in technology development that seek to increase its fidelity to brain development in vivo and discuss recent efforts to use cerebral organoids to study regeneration and brain evolution as well as to develop neurological and neuropsychiatric disease models.
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Affiliation(s)
- Tomasz J. Nowakowski
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA 94158, USA
- Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94158, USA
| | - Sofie R. Salama
- Department of Molecular, Cellular and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA 95060, USA
- UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95060, USA
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Soelistyo CJ, Vallardi G, Charras G, Lowe AR. Learning biophysical determinants of cell fate with deep neural networks. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00503-6] [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]
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