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Cartwright HN, Hobson CM, Chew TL, Reiche MA, Aaron JS. The challenges and opportunities of open-access microscopy facilities. J Microsc 2024; 294:386-396. [PMID: 36779652 DOI: 10.1111/jmi.13176] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 02/14/2023]
Abstract
Microscopy core facilities are increasingly utilised research resources, but they are generally only available to users within the host institution. Such localised access misses an opportunity to facilitate research across a broader user base. Here, we present the model of an open-access microscopy facility, using the Advanced Imaging Center (AIC) at Howard Hughes Medical Institute Janelia Research Campus as an example. The AIC has pioneered a model whereby advanced microscopy technologies and expertise are made accessible to researchers on a global scale. We detail our experiences in addressing the considerable challenges associated with this model for those who may be interested in launching an open-access imaging facility. Importantly, we focus on how this model can empower researchers, particularly those from resource-constrained settings.
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Affiliation(s)
- Heather N Cartwright
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia
| | - Chad M Hobson
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia
| | - Teng-Leong Chew
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia
| | - Michael A Reiche
- Africa Microscopy Initiative, University of Cape Town, Cape Town, South Africa
| | - Jesse S Aaron
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, Virginia
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Nagaki K, Furuta T, Yamaji N, Kuniyoshi D, Ishihara M, Kishima Y, Murata M, Hoshino A, Takatsuka H. Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists. Chromosome Res 2021; 29:361-371. [PMID: 34648121 DOI: 10.1007/s10577-021-09676-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 09/03/2021] [Accepted: 10/01/2021] [Indexed: 11/29/2022]
Abstract
Observing chromosomes is a time-consuming and labor-intensive process, and chromosomes have been analyzed manually for many years. In the last decade, automated acquisition systems for microscopic images have advanced dramatically due to advances in their controlling computer systems, and nowadays, it is possible to automatically acquire sets of tiling-images consisting of large number, more than 1000, of images from large areas of specimens. However, there has been no simple and inexpensive system to efficiently select images containing mitotic cells among these images. In this paper, a classification system of chromosomal images by deep learning artificial intelligence (AI) that can be easily handled by non-data scientists was applied. With this system, models suitable for our own samples could be easily built on a Macintosh computer with Create ML. As examples, models constructed by learning using chromosome images derived from various plant species were able to classify images containing mitotic cells among samples from plant species not used for learning in addition to samples from the species used. The system also worked for cells in tissue sections and tetrads. Since this system is inexpensive and can be easily trained via deep learning using scientists' own samples, it can be used not only for chromosomal image analysis but also for analysis of other biology-related images.
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Affiliation(s)
- Kiyotaka Nagaki
- Institute of Plant Science and Resources, Okayama University, Kurashiki, 710-0046, Japan.
| | - Tomoyuki Furuta
- Institute of Plant Science and Resources, Okayama University, Kurashiki, 710-0046, Japan
| | - Naoki Yamaji
- Institute of Plant Science and Resources, Okayama University, Kurashiki, 710-0046, Japan
| | - Daichi Kuniyoshi
- Laboratory of Plant Breeding, Research Faculty of Agriculture, Hokkaido University, Sapporo, 060-8589, Japan
| | - Megumi Ishihara
- Laboratory of Plant Breeding, Research Faculty of Agriculture, Hokkaido University, Sapporo, 060-8589, Japan
| | - Yuji Kishima
- Laboratory of Plant Breeding, Research Faculty of Agriculture, Hokkaido University, Sapporo, 060-8589, Japan
| | - Minoru Murata
- Department of Agricultural and Food Science, Universiti Tunku Abdul Rahman, 31900, Kampar, Perak, Malaysia
| | - Atsushi Hoshino
- National Institute for Basic Biology, Okazaki, 444-8585, Japan.,Department of Basic Biology, SOKENDAI (The Graduate University for Advanced Studies), Okazaki, 444-8585, Japan
| | - Hirotomo Takatsuka
- Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan.,School of Biological Science and Technology, College of Science and Engineering, Kanazawa University, Kakuma-machi, Kanazawa, 920-1192, Japan
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A two-stage method for automated detection of ring-like endosomes in fluorescent microscopy images. PLoS One 2019; 14:e0218931. [PMID: 31246999 PMCID: PMC6597078 DOI: 10.1371/journal.pone.0218931] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 06/12/2019] [Indexed: 01/21/2023] Open
Abstract
Endosomes are subcellular organelles which serve as important transport compartments in eukaryotic cells. Fluorescence microscopy is a widely applied technology to study endosomes at the subcellular level. In general, a microscopy image can contain a large number of organelles and endosomes in particular. Detecting and annotating endosomes in fluorescence microscopy images is a critical part in the study of subcellular trafficking processes. Such annotation is usually performed by human inspection, which is time-consuming and prone to inaccuracy if carried out by inexperienced analysts. This paper proposes a two-stage method for automated detection of ring-like endosomes. The method consists of a localization stage cascaded by an identification stage. Given a test microscopy image, the localization stage generates a voting-map by locally comparing the query endosome patches and the test image based on a bag-of-words model. Using the voting-map, a number of candidate patches of endosomes are determined. Subsequently, in the identification stage, a support vector machine (SVM) is trained using the endosome patches and the background pattern patches. Each of the candidate patches is classified by the SVM to rule out those patches of endosome-like background patterns. The performance of the proposed method is evaluated with real microscopy images of human myeloid endothelial cells. It is shown that the proposed method significantly outperforms several state-of-the-art competing methods using multiple performance metrics.
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