Ultrafast confocal fluorescence microscopy beyond the fluorescence lifetime limit

Mikami, Hideharu; Harmon, Jeffrey; Kobayashi, Hirofumi; Hamad, Syed; Wang, Yisen; Iwata, Osamu; Suzuki, Kengo; Ito, Takuro; Aisaka, Yuri; Kutsuna, Natsumaro; Nagasawa, Kazuya; Watarai, Hiroshi; Ozeki, Yasuyuki; Goda, Keisuke

doi:10.1364/optica.5.000117

Cited by 100 publications

(70 citation statements)

References 37 publications

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“…As Rodenacker et al (62) point out a critical aspect of this microscopic approach is the overlap of cells and detritus particles. Several new IFC approaches for phytoplankton are emerging (47,63,65) and are based on time-stretch imaging in microfluidic systems (49) or fluorescence microscopy (63,65). With respect to analysis time and particle overlap, the application of standard FCM overcomes all mentioned disadvantages of microscopy but it is limited due to low taxonomic resolution.…”

Section: Discussionmentioning

confidence: 99%

“…A common standard method of species counting is done via microscopy and includes adverse aspects of subjectivity, time (14,61) and non-preserved raw data. An alternative to manual microscopic counting is an automatized microscope as presented by Rodenacker et al (62) or Mikami et al (63). As Rodenacker et al (62) point out a critical aspect of this microscopic approach is the overlap of cells and detritus particles.…”

Section: Discussionmentioning

confidence: 99%

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Hidden Secrets Behind Dots: Improved Phytoplankton Taxonomic Resolution Using High‐Throughput Imaging Flow Cytometry

Dunker

2019

Cytometry Pt A

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Phytoplankton are aquatic, microscopically small primary producers, accounting for almost half of the worldwide carbon fixation. As early indicators of environmental change, they play a crucial role in water quality management. Human activities like climate change, eutrophication, or international shipping traffic strongly impact diversity of these organisms. Phytoplankton monitoring is a crucial step in the recognition of changes in community composition. The common standard for monitoring programs is manual microscopic counting, which strongly limits sample number and sampling frequency. In contrast, high-throughput technologies like standard flow cytometry (FCM) are restricted to a low taxonomic resolution, which makes them unsuitable for the identification of indicator species. Imaging flow cytometers (IFC) could overcome these limitations as they combine microscopy and high-throughput analysis. In comparison to single fluorescence values, image information not only allows for a wide variety of possibilities to characterize different species as well as immediate and fast measurements but also provides an archivable data output. Taxonomic resolution of IFC (ImageStream X Mk II) was proven comparable to standard FCM (FACSAria II) by the help of numerical evaluations. This is demonstrated on different levels of taxonomic differentiation of laboratory grown cultures in this study. Phytoplankton species discrimination by an imaging flow cytometer could be useful as supportive tool to make machine-learning classifications more robust, reliable, and flexible. Furthermore, this study provides examples, demonstrating the possibility of discrimination between species with similar fluorescence properties, strains, and even subpopulations. In contrast to standard FCM, each cell is not only represented as a dot in a cytogram but is also linked to microscopic brightfield and the author presents a new way to visualize this as image-based cytograms. The source code is supplied and could be useful for all kind of IFC data in general.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Hidden Secrets Behind Dots: Improved Phytoplankton Taxonomic Resolution Using High‐Throughput Imaging Flow Cytometry

Dunker

2019

Cytometry Pt A

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“…This technique is capable of fluorescence microscopy in a flow rate of 1 m s −1 at the throughput of 50 000 cell s −1 . Besides, such a system can be used for ultrafast cell microscopy beyond the fluorescence lifetime limit . Figure e displays an example output image of this method.…”

Section: Imaging Approaches For Dynamic Platformsmentioning

confidence: 99%

Optical Imaging Approaches to Monitor Static and Dynamic Cell‐on‐Chip Platforms: A Tutorial Review

Arandian

Bagheri

Ehtesabi

et al. 2019

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Miniaturized laboratories on chip platforms play an important role in handling life sciences studies. The platforms may contain static or dynamic biological cells. Examples are a fixed medium of an organ‐on‐a‐chip and individual cells moving in a microfluidic channel, respectively. Due to feasibility of control or investigation and ethical implications of live targets, both static and dynamic cell‐on‐chip platforms promise various applications in biology. To extract necessary information from the experiments, the demand for direct monitoring is rapidly increasing. Among different microscopy methods, optical imaging is a straightforward choice. Considering light interaction with biological agents, imaging signals may be generated as a result of scattering or emission effects from a sample. Thus, optical imaging techniques could be categorized into scattering‐based and emission‐based techniques. In this review, various optical imaging approaches used in monitoring static and dynamic platforms are introduced along with their optical systems, advantages, challenges, and applications. This review may help biologists to find a suitable imaging technique for different cell‐on‐chip studies and might also be useful for the people who are going to develop optical imaging systems in life sciences studies.

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“…WITH the combined merits of optical microscopy and flow cytometry, imaging flow cytometry has become an established tool for rapid, high-content analysis of single cells in large heterogeneous populations (1)(2)(3)(4)(5)(6)(7)(8)(9). It has found a wide range of applications in microbiology, stem cell biology, immunology, and marine biology (3,10,11).…”

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confidence: 99%

Intelligent Image De‐Blurring for Imaging Flow Cytometry

et al. 2019

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By virtue of the combined merits of optical microscopy and flow cytometry, imaging flow cytometry is a powerful tool for rapid, high‐content analysis of single cells in large heterogeneous populations. However, its efficiency (defined by the ratio of the number of clearly imaged cells to the total cell population) is not high (typically 50–80%), due to out‐of‐focus image blurring caused by imperfect fluidic focusing of cells, a common drawback that not only reduces the number of cell images useable for high‐content analysis but also increases the probability of false events and missed rare cells. To address this challenge and expand the efficacy of imaging flow cytometry, here, we propose and demonstrate intelligent deblurring of out‐of‐focus cell images in imaging flow cytometry. Specifically, by using our machine learning algorithms, we show an 11% increase in variance and a 95% increase in first‐order gradient summation of cell images taken with an optofluidic time‐stretch microscope. Without strict hardware requirements, our intelligent de‐blurring method provides a promising solution to the out‐of‐focus blurring problem of imaging flow cytometers and holds promise for significantly improving their performance. © 2019 International Society for Advancement of Cytometry

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Ultrafast confocal fluorescence microscopy beyond the fluorescence lifetime limit

Cited by 100 publications

References 37 publications

Hidden Secrets Behind Dots: Improved Phytoplankton Taxonomic Resolution Using High‐Throughput Imaging Flow Cytometry

Hidden Secrets Behind Dots: Improved Phytoplankton Taxonomic Resolution Using High‐Throughput Imaging Flow Cytometry

Optical Imaging Approaches to Monitor Static and Dynamic Cell‐on‐Chip Platforms: A Tutorial Review

Intelligent Image De‐Blurring for Imaging Flow Cytometry

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