Journal articles: 'Imaging systems in biology'

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Megason, Sean G., and Scott E. Fraser. "Imaging in Systems Biology." Cell 130, no. 5 (September 2007): 784–95. http://dx.doi.org/10.1016/j.cell.2007.08.031.

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Smith, Sarah E., Brian D. Slaughter, and Jay R. Unruh. "Imaging methodologies for systems biology." Cell Adhesion & Migration 8, no. 5 (September 3, 2014): 468–77. http://dx.doi.org/10.4161/cam.29152.

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Sung, Myong-Hee, and James G. McNally. "Live cell imaging and systems biology." Wiley Interdisciplinary Reviews: Systems Biology and Medicine 3, no. 2 (August 20, 2010): 167–82. http://dx.doi.org/10.1002/wsbm.108.

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Huang, Kerwyn Casey. "Applications of imaging for bacterial systems biology." Current Opinion in Microbiology 27 (October 2015): 114–20. http://dx.doi.org/10.1016/j.mib.2015.08.003.

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Kherlopian, Armen R., Ting Song, Qi Duan, Mathew A. Neimark, Ming J. Po, John K. Gohagan, and Andrew F. Laine. "A review of imaging techniques for systems biology." BMC Systems Biology 2, no. 1 (2008): 74. http://dx.doi.org/10.1186/1752-0509-2-74.

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Müller, Ralph. "High-throughput cell imaging in bone systems biology." Journal of Orthopaedic Translation 2, no. 4 (October 2014): 196–97. http://dx.doi.org/10.1016/j.jot.2014.07.115.

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Mullassery, Dhanya, Caroline A. Horton, Christopher D. Wood, and Michael R. H. White. "Single live-cell imaging for systems biology 9." Essays in Biochemistry 45 (September 30, 2008): 121–34. http://dx.doi.org/10.1042/bse0450121.

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Understanding how mammalian cells function requires a dynamic perspective. However, owing to the complexity of signalling networks, these non-linear systems can easily elude human intuition. The central aim of systems biology is to improve our understanding of the temporal complexity of cell signalling pathways, using a combination of experimental and computational approaches. Live-cell imaging and computational modelling are compatible techniques which allow quantitative analysis of cell signalling pathway dynamics. Non-invasive imaging techniques, based on the use of various luciferases and fluorescent proteins, trace cellular events such as gene expression, protein–protein interactions and protein localization in cells. By employing a number of markers in a single assay, multiple parameters can be measured simultaneously in the same cell. Following acquisition using specialized microscopy, analysis of multi-parameter time-lapse images facilitates the identification of important qualitative and quantitative relationships–linking intracellular signalling, gene expression and cell fate.

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Ritchie, Ken. "S01H3 Single molecule imaging of diffusion in E. Coll membranes(Systems Biology of Intracellular Signaling as Studied by Single-Molecule Imaging)." Seibutsu Butsuri 47, supplement (2007): S1. http://dx.doi.org/10.2142/biophys.47.s1_3.

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Sako, Yasushi. "Imaging single molecules in living cells for systems biology." Molecular Systems Biology 2, no. 1 (January 2006): 56. http://dx.doi.org/10.1038/msb4100100.

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Liu, Zhe, and Philipp J. Keller. "Emerging Imaging and Genomic Tools for Developmental Systems Biology." Developmental Cell 36, no. 6 (March 2016): 597–610. http://dx.doi.org/10.1016/j.devcel.2016.02.016.

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Hacker, Marcus, Rodney J. Hicks, and Thomas Beyer. "Applied Systems Biology—embracing molecular imaging for systemic medicine." European Journal of Nuclear Medicine and Molecular Imaging 47, no. 12 (April 7, 2020): 2721–25. http://dx.doi.org/10.1007/s00259-020-04798-8.

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Whichard, Zakary L., Casim A. Sarkar, Marek Kimmel, and Seth J. Corey. "Hematopoiesis and its disorders: a systems biology approach." Blood 115, no. 12 (March 25, 2010): 2339–47. http://dx.doi.org/10.1182/blood-2009-08-215798.

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Scientists have traditionally studied complex biologic systems by reducing them to simple building blocks. Genome sequencing, high-throughput screening, and proteomics have, however, generated large datasets, revealing a high level of complexity in components and interactions. Systems biology embraces this complexity with a combination of mathematical, engineering, and computational tools for constructing and validating models of biologic phenomena. The validity of mathematical modeling in hematopoiesis was established early by the pioneering work of Till and McCulloch. In reviewing more recent papers, we highlight deterministic, stochastic, statistical, and network-based models that have been used to better understand a range of topics in hematopoiesis, including blood cell production, the periodicity of cyclical neutropenia, stem cell production in response to cytokine administration, and the emergence of imatinib resistance in chronic myeloid leukemia. Future advances require technologic improvements in computing power, imaging, and proteomics as well as greater collaboration between experimentalists and modelers. Altogether, systems biology will improve our understanding of normal and abnormal hematopoiesis, better define stem cells and their daughter cells, and potentially lead to more effective therapies.

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Saleem, Ramsey A., and John D. Aitchison. "Systems cell biology of the mitotic spindle." Journal of Cell Biology 188, no. 1 (January 11, 2010): 7–9. http://dx.doi.org/10.1083/jcb.200912028.

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Cell division depends critically on the temporally controlled assembly of mitotic spindles, which are responsible for the distribution of duplicated chromosomes to each of the two daughter cells. To gain insight into the process, Vizeacoumar et al., in this issue (Vizeacoumar et al. 2010. J. Cell Biol. doi:10.1083/jcb.200909013), have combined systems genetics with high-throughput and high-content imaging to comprehensively identify and classify novel components that contribute to the morphology and function of the mitotic spindle.

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Conrad, Christian, Annelie Wünsche, Tze Heng Tan, Jutta Bulkescher, Frank Sieckmann, Fatima Verissimo, Arthur Edelstein, et al. "Micropilot: automation of fluorescence microscopy–based imaging for systems biology." Nature Methods 8, no. 3 (January 23, 2011): 246–49. http://dx.doi.org/10.1038/nmeth.1558.

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Henkelman, R. Mark. "Systems Biology through Mouse Imaging Centers: Experience and New Directions." Annual Review of Biomedical Engineering 12, no. 1 (July 2010): 143–66. http://dx.doi.org/10.1146/annurev-bioeng-070909-105343.

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Du, Wei, Ying Wang, Qingming Luo, and Bi-Feng Liu. "Optical molecular imaging for systems biology: from molecule to organism." Analytical and Bioanalytical Chemistry 386, no. 3 (July 19, 2006): 444–57. http://dx.doi.org/10.1007/s00216-006-0541-z.

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Albanese, Chris, Olga C. Rodriguez, John VanMeter, Stanley T. Fricke, Brian R. Rood, YiChien Lee, Sean S. Wang, et al. "Preclinical Magnetic Resonance Imaging and Systems Biology in Cancer Research." American Journal of Pathology 182, no. 2 (February 2013): 312–18. http://dx.doi.org/10.1016/j.ajpath.2012.09.024.

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Borst, Jan Willem, Jack Fransen, Stefanie Weidtkamp‐Peters, and Yvonne Stahl. "Microspectroscopy: functional imaging of biological systems." FEBS Letters 596, no. 19 (October 2022): 2469–71. http://dx.doi.org/10.1002/1873-3468.14498.

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Stewart, Theodora J. "Across the spectrum: integrating multidimensional metal analytics for in situ metallomic imaging." Metallomics 11, no. 1 (2019): 29–49. http://dx.doi.org/10.1039/c8mt00235e.

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Taking a systems analytical approach to systems biology questions requires a network of multidimensional analytical tools to illuminate the many different functional and structural aspects of metals in biology.

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Sung, Myong-Hee. "A Checklist for Successful Quantitative Live Cell Imaging in Systems Biology." Cells 2, no. 2 (April 29, 2013): 284–93. http://dx.doi.org/10.3390/cells2020284.

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Lubeck, Eric, and Long Cai. "Single-cell systems biology by super-resolution imaging and combinatorial labeling." Nature Methods 9, no. 7 (June 3, 2012): 743–48. http://dx.doi.org/10.1038/nmeth.2069.

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Gao, Jing, Yuncong Chen, Zijian Guo, and Weijiang He. "Recent Endeavors on Molecular Imaging for Mapping Metals in Biology." Biophysics Reports 6, no. 5 (October 2020): 159–78. http://dx.doi.org/10.1007/s41048-020-00118-7.

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Abstract Transition metals such as zinc, copper and iron play vital roles in maintaining physiological functions and homeostasis of living systems. Molecular imaging, including two-photon imaging (TPI), bioluminescence imaging (BLI) and photoacoustic imaging (PAI), could act as non-invasive toolkits for capturing dynamic events in living cells, tissues and whole animals. Herein, we review the recent progress in the development of molecular probes for essential transition metals and their biological applications. We emphasize the contributions of metallostasis to health and disease, and discuss the future research directions about how to harness the great potential of metal sensors. Graphic Abstract

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Ponsioen, Bas, and Hugo J. Snippert. "Cancer systems biology: Live imaging of intestinal tissue in health and disease." Current Opinion in Systems Biology 2 (April 2017): 19–28. http://dx.doi.org/10.1016/j.coisb.2017.02.001.

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Lubeck, Eric, and Long Cai. "Towards Single-Cell Systems Biology through Super-Resolution Imaging and Molecular Barcoding." Biophysical Journal 104, no. 2 (January 2013): 371a. http://dx.doi.org/10.1016/j.bpj.2012.11.2062.

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Bezanilla, Magdalena. "What can plants do for cell biology?" Molecular Biology of the Cell 24, no. 16 (August 15, 2013): 2491–93. http://dx.doi.org/10.1091/mbc.e12-10-0706.

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Historically, cell biologists studied organisms that represented a reasonable sampling of life's diversity, whereas recently research has narrowed into a few model systems. As a result, the cells of plants have been relatively neglected. Here I choose three examples to illustrate how plants have been informative and could be even more so. Owing to their ease of imaging and genetic tractability, multicellular plant model systems provide a unique opportunity to address long-standing questions in cell biology.

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Weigert, Roberto, Natalie Porat-Shliom, and Panomwat Amornphimoltham. "Imaging cell biology in live animals: Ready for prime time." Journal of Cell Biology 201, no. 7 (June 24, 2013): 969–79. http://dx.doi.org/10.1083/jcb.201212130.

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Time-lapse fluorescence microscopy is one of the main tools used to image subcellular structures in living cells. Yet for decades it has been applied primarily to in vitro model systems. Thanks to the most recent advancements in intravital microscopy, this approach has finally been extended to live rodents. This represents a major breakthrough that will provide unprecedented new opportunities to study mammalian cell biology in vivo and has already provided new insight in the fields of neurobiology, immunology, and cancer biology.

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Barcellos-Hoff, Mary Helen. "WHAT IS THE USE OF SYSTEMS BIOLOGY APPROACHES IN RADIATION BIOLOGY?" Health Physics 100, no. 3 (March 2011): 272–73. http://dx.doi.org/10.1097/hp.0b013e318209c69b.

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Gallot, Guilhem. "Terahertz sensing in biology and medicine." Photoniques, no. 101 (March 2020): 53–58. http://dx.doi.org/10.1051/photon/202010153.

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Terahertz radiation offers new contrasts with biological systems, without markers or staining, at the molecular, cellular or tissue level. Thanks to technological advances, it is increasingly emerging as a solution of choice for directly probing the interaction with molecules and biological solutions. Applications range from dynamics of biological molecules to imaging of cancerous tissues, including ion, protein and membrane sensors.

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Mittendorf, Elizabeth A., John M. S. Bartlett, Daphne L. Lichtensztajn, and Sarat Chandarlapaty. "Incorporating Biology Into Breast Cancer Staging: American Joint Committee on Cancer, Eighth Edition, Revisions and Beyond." American Society of Clinical Oncology Educational Book, no. 38 (May 2018): 38–46. http://dx.doi.org/10.1200/edbk_200981.

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Higher-quality imaging, refined surgical procedures, enhanced pathologic evaluation, and improved understanding of the impact of tumor biology on treatment and prognosis have necessitated revisions of the AJCC breast cancer staging system. The eighth edition includes clinical and pathologic prognostic stages that incorporate biologic variables—grade, estrogen and progesterone receptor status, HER2 status, and multigene panels—with the anatomic extent of disease defined by tumor, node, and metastasis categories. The prognostic staging systems facilitate more refined stratification with respect to survival than anatomic stage alone. Because the prognostic staging systems are dependent on biologic factors, accuracy is dependent on rigorous pathologic evaluation of tumors and on administration of treatment dictated by tumor biology. It is anticipated that technological advances will facilitate even more refined determination of underlying biology within tumors and in the peripheral blood, which increasingly is being evaluated as a compartment that reflects the primary tumor and sites of distant metastases. Diseases should be staged according to the eighth edition staging system to accurately reflect prognosis and to allow standardized data collection. Such standardization will facilitate assessment of the impact of advances in diagnosis and treatment of patients with breast cancer.

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Verbeek, Fons J., and Lu Cao. "L-systems from 3D-imaging of Phenotypes of Arborized Structures." Fundamenta Informaticae 175, no. 1-4 (September 28, 2020): 327–45. http://dx.doi.org/10.3233/fi-2020-1959.

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Biology is 3D. Therefore, it is important to be able to analyze phenomena in a spatiotemporal manner. Different fields in computational sciences are useful for analysis in biology; i.e. image analysis, pattern recognition and machine learning. To fit an empirical model to a higher abstraction, however, theoretical computer science methods are probed. We explore the construction of empirical 3D graphical models and develop abstractions from these models in L-systems. These systems are provided with a profound formalization in a grammar allowing generalization and exploration of mathematical structures in topologies. The connections between these computational approaches are illustrated by a case study of the development of the lactiferous duct in mice and the phenotypical effects from different environmental conditions we can observe on it. We have constructed a workflow to get 3D models from different experimental conditions and use these models to extract features. Our aim is to construct an abstraction of these 3D models to an L-system from features that we have measured. From our measurements we can make the productions for an L-system. In this manner we can formalize the arborization of the lactiferous duct under different environmental conditions and capture different observations. All considered, this paper illustrates the joint of empirical with theoretical computational sciences and the augmentation of the interpretation of the results. At the same time, it shows a method to analyze complex 3D topologies and produces archetypes for developmental configurations.

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Weiskittel, Taylor M., Cristina Correia, Grace T. Yu, Choong Yong Ung, Scott H. Kaufmann, Daniel D. Billadeau, and Hu Li. "The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches." Genes 12, no. 7 (July 20, 2021): 1098. http://dx.doi.org/10.3390/genes12071098.

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Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.

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Bhargava, Akanksha, Benjamin Monteagudo, Priyanka Kushwaha, Janaka Senarathna, Yunke Ren, Ryan C. Riddle, Manisha Aggarwal, and Arvind P. Pathak. "VascuViz: a multimodality and multiscale imaging and visualization pipeline for vascular systems biology." Nature Methods 19, no. 2 (February 2022): 242–54. http://dx.doi.org/10.1038/s41592-021-01363-5.

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Liu, Shirley, Yichi Su, Michael Z. Lin, and John A. Ronald. "Brightening up Biology: Advances in Luciferase Systems for in Vivo Imaging." ACS Chemical Biology 16, no. 12 (November 15, 2021): 2707–18. http://dx.doi.org/10.1021/acschembio.1c00549.

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Yun, Anthony J., Patrick Y. Lee, and Anthony N. Gerber. "Integrating Systems Biology and Medical Imaging: Understanding Disease Distribution in the Lung Model." American Journal of Roentgenology 186, no. 4 (April 2006): 925–30. http://dx.doi.org/10.2214/ajr.05.0072.

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Cheng, J. X., and X. S. Xie. "Vibrational spectroscopic imaging of living systems: An emerging platform for biology and medicine." Science 350, no. 6264 (November 26, 2015): aaa8870. http://dx.doi.org/10.1126/science.aaa8870.

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Ueda, Hiroki R. "Whole-body/organ Imaging with Single-cell Resolution Toward Organism-level Systems Biology." Proceedings for Annual Meeting of The Japanese Pharmacological Society WCP2018 (2018): SY32–1. http://dx.doi.org/10.1254/jpssuppl.wcp2018.0_sy32-1.

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Tanrıverdi Eçik, Esra, Emrah Özcan, Hasan Hüseyin Kazan, Ismail Erol, Elif Şenkuytu, and Bünyemin Çoşut. "Dual color triads: synthesis, photophysics and applications in live cell imaging." New Journal of Chemistry 45, no. 22 (2021): 9984–94. http://dx.doi.org/10.1039/d1nj00900a.

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Fluorescent labels and probes constitute an important class of organic materials used in the development of sensor systems and imaging platforms for various chemical and molecular biology applications.

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Myers, Kenneth A., and Christopher Janetopoulos. "Recent advances in imaging subcellular processes." F1000Research 5 (June 30, 2016): 1553. http://dx.doi.org/10.12688/f1000research.8399.1.

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Cell biology came about with the ability to first visualize cells. As microscopy techniques advanced, the early microscopists became the first cell biologists to observe the inner workings and subcellular structures that control life. This ability to see organelles within a cell provided scientists with the first understanding of how cells function. The visualization of the dynamic architecture of subcellular structures now often drives questions as researchers seek to understand the intricacies of the cell. With the advent of fluorescent labeling techniques, better and new optical techniques, and more sensitive and faster cameras, a whole array of questions can now be asked. There has been an explosion of new light microscopic techniques, and the race is on to build better and more powerful imaging systems so that we can further our understanding of the spatial and temporal mechanisms controlling molecular cell biology.

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Park, Eun-Yeong, Haeni Lee, Seongyi Han, Chulhong Kim, and Jeesu Kim. "Photoacoustic imaging systems based on clinical ultrasound platform." Experimental Biology and Medicine 247, no. 7 (January 24, 2022): 551–60. http://dx.doi.org/10.1177/15353702211073684.

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Photoacoustic imaging has drawn a significant amount of attention due to its unique capacity for functional, metabolic, and molecular imaging, which is achieved by the combination of optical excitation and acoustic detection. With both strengths of light and ultrasound, photoacoustic images can provide strong optical contrast at high ultrasound resolution in deep tissue. As photoacoustic imaging can be used to visualize complementary information to ultrasound imaging using the same data acquisition process, several studies have been conducted on combining photoacoustic imaging with existing clinical ultrasound systems. This review highlights our development of a photoacoustic/ultrasound dual-modal imaging system, various features and functionalities implemented for clinical translation, and preclinical/clinical studies performed by using the systems.

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Stueber, Deanna D., Jake Villanova, Itzel Aponte, Zhen Xiao, and Vicki L. Colvin. "Magnetic Nanoparticles in Biology and Medicine: Past, Present, and Future Trends." Pharmaceutics 13, no. 7 (June 24, 2021): 943. http://dx.doi.org/10.3390/pharmaceutics13070943.

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The use of magnetism in medicine has changed dramatically since its first application by the ancient Greeks in 624 BC. Now, by leveraging magnetic nanoparticles, investigators have developed a range of modern applications that use external magnetic fields to manipulate biological systems. Drug delivery systems that incorporate these particles can target therapeutics to specific tissues without the need for biological or chemical cues. Once precisely located within an organism, magnetic nanoparticles can be heated by oscillating magnetic fields, which results in localized inductive heating that can be used for thermal ablation or more subtle cellular manipulation. Biological imaging can also be improved using magnetic nanoparticles as contrast agents; several types of iron oxide nanoparticles are US Food and Drug Administration (FDA)-approved for use in magnetic resonance imaging (MRI) as contrast agents that can improve image resolution and information content. New imaging modalities, such as magnetic particle imaging (MPI), directly detect magnetic nanoparticles within organisms, allowing for background-free imaging of magnetic particle transport and collection. “Lab-on-a-chip” technology benefits from the increased control that magnetic nanoparticles provide over separation, leading to improved cellular separation. Magnetic separation is also becoming important in next-generation immunoassays, in which particles are used to both increase sensitivity and enable multiple analyte detection. More recently, the ability to manipulate material motion with external fields has been applied in magnetically actuated soft robotics that are designed for biomedical interventions. In this review article, the origins of these various areas are introduced, followed by a discussion of current clinical applications, as well as emerging trends in the study and application of these materials.

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Karacosta, Loukia G. "From imaging a single cell to implementing precision medicine: an exciting new era." Emerging Topics in Life Sciences 5, no. 6 (December 10, 2021): 837–47. http://dx.doi.org/10.1042/etls20210219.

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In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.

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Ritchie, Ken, Yoriko Lill, Chetan Sood, Hochan Lee, and Shunyuan Zhang. "Single-molecule imaging in live bacteria cells." Philosophical Transactions of the Royal Society B: Biological Sciences 368, no. 1611 (February 5, 2013): 20120355. http://dx.doi.org/10.1098/rstb.2012.0355.

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Bacteria, such as Escherichia coli and Caulobacter crescentus , are the most studied and perhaps best-understood organisms in biology. The advances in understanding of living systems gained from these organisms are immense. Application of single-molecule techniques in bacteria have presented unique difficulties owing to their small size and highly curved form. The aim of this review is to show advances made in single-molecule imaging in bacteria over the past 10 years, and to look to the future where the combination of implementing such high-precision techniques in well-characterized and controllable model systems such as E. coli could lead to a greater understanding of fundamental biological questions inaccessible through classic ensemble methods.

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Zhang, Feng. "Biology and Application of Genome Editing." Blood 134, Supplement_1 (November 13, 2019): SCI—22—SCI—22. http://dx.doi.org/10.1182/blood-2019-121282.

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Precision genome editing, which can be used to alter specific DNA sequences, is a powerful tool for understanding the molecular circuitry underlying cellular processes. Over the past several years, we and others have harnessed microbial CRISPR-Cas systems for use as platforms for a range of genome manipulations, including single and multiplex gene knockout, gene activation, and large-scale screening applications. Recently, we discovered and characterized several novel CRISPR systems that target RNA, including the CRISPR-Cas13 family. We developed a toolbox for RNA modulation based on Cas13, including methods for highly specific RNA knockdown, transcript imaging, and precision base editing. During our initial characterization of Cas13, we observed that Cas13 also exhibits so-called non-specific "collateral" RNase activity in vitro, which we capitalized on to create SHERLOCK, a highly sensitive and specific CRISPR diagnostic platform. We are continuing to refine and extend CRISPR-based technologies as well as explore microbial diversity to find new enzymes and systems that can be adapted for use as molecular biology tools and novel therapeutics. Disclosures Zhang: Arbor Biotechnologies: Consultancy, Equity Ownership; Sherlock Biosciences: Consultancy, Equity Ownership; Pairwise Plants: Consultancy, Equity Ownership; Beam Therapeutics: Consultancy, Equity Ownership; Editas Medicine: Consultancy, Equity Ownership.

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Chen, Xue-Wen, and Satoru Miyano. "INTRODUCTION — The First IEEE Conference on Healthcare Informatics, Imaging, and Systems biology HISB'11." Journal of Bioinformatics and Computational Biology 09, no. 05 (October 2011): v—vi. http://dx.doi.org/10.1142/s0219720011005719.

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Fenton, James M., and Antony R. Crofts. "Computer aided fluorescence imaging of photosynthetic systems." Photosynthesis Research 26, no. 1 (October 1990): 59–66. http://dx.doi.org/10.1007/bf00048977.

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Kim, Sangpil, Juhee Kim, Batakrishna Jana, and Ja-Hyoung Ryu. "Intra-mitochondrial reaction for cancer cell imaging and anti-cancer therapy by aggregation-induced emission." RSC Advances 10, no. 71 (2020): 43383–88. http://dx.doi.org/10.1039/d0ra07471c.

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Danuser, Gaudenz. "S01H2 Probing cytoskeleton structural dynamics and function by single particle tracking methods(Systems Biology of Intracellular Signaling as Studied by Single-Molecule Imaging)." Seibutsu Butsuri 47, supplement (2007): S1. http://dx.doi.org/10.2142/biophys.47.s1_2.

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Cheng, C.-M., P.-Y. Chu, K.-H. Chuang, S. R. Roffler, C.-H. Kao, W.-L. Tseng, J. Shiea, et al. "Hapten-derivatized nanoparticle targeting and imaging of gene expression by multimodality imaging systems." Cancer Gene Therapy 16, no. 1 (September 19, 2008): 83–90. http://dx.doi.org/10.1038/cgt.2008.50.

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Lee, Dongwoo, Jihye Kim, Eunjoo Song, Ji-Young Jeong, Eun-chae Jeon, Pilhan Kim, and Wonhee Lee. "Micromirror-Embedded Coverslip Assembly for Bidirectional Microscopic Imaging." Micromachines 11, no. 6 (June 10, 2020): 582. http://dx.doi.org/10.3390/mi11060582.

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3D imaging of a biological sample provides information about cellular and subcellular structures that are important in cell biology and related diseases. However, most 3D imaging systems, such as confocal and tomographic microscopy systems, are complex and expensive. Here, we developed a quasi-3D imaging tool that is compatible with most conventional microscopes by integrating micromirrors and microchannel structures on coverslips to provide bidirectional imaging. Microfabricated micromirrors had a precisely 45° reflection angle and optically clean reflective surfaces with high reflectance over 95%. The micromirrors were embedded on coverslips that could be assembled as a microchannel structure. We demonstrated that this simple disposable device allows a conventional microscope to perform bidirectional imaging with simple control of a focal plane. Images of microbeads and cells under bright-field and fluorescent microscopy show that the device can provide a quick analysis of 3D information, such as 3D positions and subcellular structures.

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Zhou, Pan, Haipeng He, Hanbin Ma, Shurong Wang, and Siyi Hu. "A Review of Optical Imaging Technologies for Microfluidics." Micromachines 13, no. 2 (February 8, 2022): 274. http://dx.doi.org/10.3390/mi13020274.

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Abstract:

Microfluidics can precisely control and manipulate micro-scale fluids, and are also known as lab-on-a-chip or micro total analysis systems. Microfluidics have huge application potential in biology, chemistry, and medicine, among other fields. Coupled with a suitable detection system, the detection and analysis of small-volume and low-concentration samples can be completed. This paper reviews an optical imaging system combined with microfluidics, including bright-field microscopy, chemiluminescence imaging, spectrum-based microscopy imaging, and fluorescence-based microscopy imaging. At the end of the article, we summarize the advantages and disadvantages of each imaging technology.

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