1
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Ghanegolmohammadi F, Eslami M, Ohya Y. Systematic data analysis pipeline for quantitative morphological cell phenotyping. Comput Struct Biotechnol J 2024; 23:2949-2962. [PMID: 39104709 PMCID: PMC11298594 DOI: 10.1016/j.csbj.2024.07.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 08/07/2024] Open
Abstract
Quantitative morphological phenotyping (QMP) is an image-based method used to capture morphological features at both the cellular and population level. Its interdisciplinary nature, spanning from data collection to result analysis and interpretation, can lead to uncertainties, particularly among those new to this actively growing field. High analytical specificity for a typical QMP is achieved through sophisticated approaches that can leverage subtle cellular morphological changes. Here, we outline a systematic workflow to refine the QMP methodology. For a practical review, we describe the main steps of a typical QMP; in each step, we discuss the available methods, their applications, advantages, and disadvantages, along with the R functions and packages for easy implementation. This review does not cover theoretical backgrounds, but provides several references for interested researchers. It aims to broaden the horizons for future phenome studies and demonstrate how to exploit years of endeavors to achieve more with less.
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Affiliation(s)
- Farzan Ghanegolmohammadi
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab, Schepen’s Eye Research Institute of Massachusetts Eye and Ear Infirmary, Harvard Medical School, Boston, USA
| | - Yoshikazu Ohya
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
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2
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Teranishi K, Wagatsuma K, Toda K, Nomaru H, Yanagihashi Y, Ochiai H, Akai S, Mochizuki E, Onda Y, Nakagawa K, Sugimoto K, Takahashi S, Yamaguchi H, Ota S. Label-free ghost cytometry for manufacturing of cell therapy products. Sci Rep 2024; 14:21848. [PMID: 39300150 DOI: 10.1038/s41598-024-72016-8] [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: 10/03/2023] [Accepted: 09/03/2024] [Indexed: 09/22/2024] Open
Abstract
Automation and quality control (QC) are critical in manufacturing safe and effective cell and gene therapy products. However, current QC methods, reliant on molecular staining, pose difficulty in in-line testing and can increase manufacturing costs. Here we demonstrate the potential of using label-free ghost cytometry (LF-GC), a machine learning-driven, multidimensional, high-content, and high-throughput flow cytometry approach, in various stages of the cell therapy manufacturing processes. LF-GC accurately quantified cell count and viability of human peripheral blood mononuclear cells (PBMCs) and identified non-apoptotic live cells and early apoptotic/dead cells in PBMCs (ROC-AUC: area under receiver operating characteristic curve = 0.975), T cells and non-T cells in white blood cells (ROC-AUC = 0.969), activated T cells and quiescent T cells in PBMCs (ROC-AUC = 0.990), and particulate impurities in PBMCs (ROC-AUC ≧ 0.998). The results support that LF-GC is a non-destructive label-free cell analytical method that can be used to monitor cell numbers, assess viability, identify specific cell subsets or phenotypic states, and remove impurities during cell therapy manufacturing. Thus, LF-GC holds the potential to enable full automation in the manufacturing of cell therapy products with reduced cost and increased efficiency.
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Affiliation(s)
| | | | - Keisuke Toda
- Thinkcyte, K.K., 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Hiroko Nomaru
- Thinkcyte, K.K., 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | | | - Hiroshi Ochiai
- Thinkcyte, K.K., 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Satoru Akai
- Thinkcyte, K.K., 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Emi Mochizuki
- Thinkcyte, K.K., 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Yuuki Onda
- Thinkcyte, K.K., 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Keiji Nakagawa
- Thinkcyte, K.K., 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Keiki Sugimoto
- Thinkcyte, K.K., 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Shinya Takahashi
- Astellas Pharma, Inc., 5-2-3 Tokodai, Tsukuba-shi, Ibaraki, 300-2698, Japan
| | - Hideto Yamaguchi
- Astellas Pharma, Inc., 5-2-3 Tokodai, Tsukuba-shi, Ibaraki, 300-2698, Japan
| | - Sadao Ota
- Thinkcyte, K.K., 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
- Research Center for Advanced Science and Technology, The University of Tokyo, Meguro-ku, Tokyo, 153-8904, Japan.
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3
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Kage D, Eirich A, Heinrich K, Kirsch J, Popien J, Wolf A, Volkmann KV, Chang HD, Kaiser T. Cell sorting based on pulse shapes from angle resolved detection of scattered light. Commun Biol 2024; 7:1063. [PMID: 39215170 PMCID: PMC11364749 DOI: 10.1038/s42003-024-06759-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 08/15/2024] [Indexed: 09/04/2024] Open
Abstract
Flow cytometry is a key technology for the analysis and sorting of cells or particles at high throughput. Conventional and current flow cytometry is primarily based on fluorescent stains to detect the cells of interest. However, such stains also have disadvantages, as their effect on cells must be carefully tested to avoid effects on the results of the experiments. Alternative approaches using imaging or other label-free techniques often require highly sophisticated setups, are commonly limited in resolution, and produce challenging amounts of data. Our technology exploits scattered light instead. The custom-built flow cytometry setup comprises a fiber array in forward scatter detection for angular resolution and captures the whole pulse shape with advanced signal processing. Thereby this setup enables cell analysis and sorting purely based on scattered light signals without the need for fluorescent labels. We demonstrate the feasibility of this cell sorting technology by sorting cell lines for their cell cycle stages based on scattered light. Furthermore, we demonstrate the ability to classify human peripheral blood T- and B-cell subsets.
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Affiliation(s)
- Daniel Kage
- German Rheumatology Research Center (DRFZ) - Flow Cytometry Core Facility, Charitéplatz 1 (Virchowweg 12), 10117, Berlin, Germany
| | - Andrej Eirich
- APE Angewandte Physik und Elektronik GmbH, Plauener Straße 163-165 / Haus N, 13053, Berlin, Germany
| | - Kerstin Heinrich
- German Rheumatology Research Center (DRFZ) - Flow Cytometry Core Facility, Charitéplatz 1 (Virchowweg 12), 10117, Berlin, Germany
| | - Jenny Kirsch
- German Rheumatology Research Center (DRFZ) - Flow Cytometry Core Facility, Charitéplatz 1 (Virchowweg 12), 10117, Berlin, Germany
| | - Jan Popien
- APE Angewandte Physik und Elektronik GmbH, Plauener Straße 163-165 / Haus N, 13053, Berlin, Germany
| | - Alexander Wolf
- German Rheumatology Research Center (DRFZ) - Flow Cytometry Core Facility, Charitéplatz 1 (Virchowweg 12), 10117, Berlin, Germany
| | - Konrad V Volkmann
- APE Angewandte Physik und Elektronik GmbH, Plauener Straße 163-165 / Haus N, 13053, Berlin, Germany
| | - Hyun-Dong Chang
- German Rheumatology Research Center (DRFZ) - Flow Cytometry Core Facility, Charitéplatz 1 (Virchowweg 12), 10117, Berlin, Germany
- Department of Cytometry, Institute for Biotechnology, Technische Universität Berlin, Berlin, Germany
| | - Toralf Kaiser
- German Rheumatology Research Center (DRFZ) - Flow Cytometry Core Facility, Charitéplatz 1 (Virchowweg 12), 10117, Berlin, Germany.
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4
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Mante J, Groover KE, Pullen RM. Environmental community transcriptomics: strategies and struggles. Brief Funct Genomics 2024:elae033. [PMID: 39183066 DOI: 10.1093/bfgp/elae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/02/2024] [Accepted: 08/08/2024] [Indexed: 08/27/2024] Open
Abstract
Transcriptomics is the study of RNA transcripts, the portion of the genome that is transcribed, in a specific cell, tissue, or organism. Transcriptomics provides insight into gene expression patterns, regulation, and the underlying mechanisms of cellular processes. Community transcriptomics takes this a step further by studying the RNA transcripts from environmental assemblies of organisms, with the intention of better understanding the interactions between members of the community. Community transcriptomics requires successful extraction of RNA from a diverse set of organisms and subsequent analysis via mapping those reads to a reference genome or de novo assembly of the reads. Both, extraction protocols and the analysis steps can pose hurdles for community transcriptomics. This review covers advances in transcriptomic techniques and assesses the viability of applying them to community transcriptomics.
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Affiliation(s)
- Jeanet Mante
- Oak Ridge Associated Universities, Oak Ridge, 37831, TN, USA
| | - Kyra E Groover
- Department of Molecular Biosciences, University of Texas at Austin, Austin, 78705, TX, USA
| | - Randi M Pullen
- DEVCOM Army Research Laboratory, Adelphi, 20783, MD, USA
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5
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Tsubouchi A, An Y, Kawamura Y, Yanagihashi Y, Nakayama H, Murata Y, Teranishi K, Ishiguro S, Aburatani H, Yachie N, Ota S. Pooled CRISPR screening of high-content cellular phenotypes using ghost cytometry. CELL REPORTS METHODS 2024; 4:100737. [PMID: 38531306 PMCID: PMC10985231 DOI: 10.1016/j.crmeth.2024.100737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/30/2023] [Accepted: 02/27/2024] [Indexed: 03/28/2024]
Abstract
Recent advancements in image-based pooled CRISPR screening have facilitated the mapping of diverse genotype-phenotype associations within mammalian cells. However, the rapid enrichment of cells based on morphological information continues to pose a challenge, constraining the capacity for large-scale gene perturbation screening across diverse high-content cellular phenotypes. In this study, we demonstrate the applicability of multimodal ghost cytometry-based cell sorting, including both fluorescent and label-free high-content phenotypes, for rapid pooled CRISPR screening within vast cell populations. Using the high-content cell sorter operating in fluorescence mode, we successfully executed kinase-specific CRISPR screening targeting genes influencing the nuclear translocation of RelA. Furthermore, using the multiparametric, label-free mode, we performed large-scale screening to identify genes involved in macrophage polarization. Notably, the label-free platform can enrich target phenotypes without requiring invasive staining, preserving untouched cells for downstream assays and expanding the potential for screening cellular phenotypes even when suitable markers are absent.
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Affiliation(s)
| | - Yuri An
- ThinkCyte Inc., Tokyo 113-8654, Japan
| | | | | | | | | | | | - Soh Ishiguro
- School of Biomedical Engineering, Faculty of Medicine and Faculty of Applied Science, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Hiroyuki Aburatani
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
| | - Nozomu Yachie
- School of Biomedical Engineering, Faculty of Medicine and Faculty of Applied Science, University of British Columbia, Vancouver, BC V6T 1Z3, Canada; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
| | - Sadao Ota
- ThinkCyte Inc., Tokyo 113-8654, Japan; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan.
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6
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Dimitriadis S, Dova L, Kotsianidis I, Hatzimichael E, Kapsali E, Markopoulos GS. Imaging Flow Cytometry: Development, Present Applications, and Future Challenges. Methods Protoc 2024; 7:28. [PMID: 38668136 PMCID: PMC11054958 DOI: 10.3390/mps7020028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 03/13/2024] [Accepted: 03/21/2024] [Indexed: 04/29/2024] Open
Abstract
Imaging flow cytometry (ImFC) represents a significant technological advancement in the field of cytometry, effectively merging the high-throughput capabilities of flow analysis with the detailed imaging characteristics of microscopy. In our comprehensive review, we adopt a historical perspective to chart the development of ImFC, highlighting its origins and current state of the art and forecasting potential future advancements. The genesis of ImFC stemmed from merging the hydraulic system of a flow cytometer with advanced camera technology. This synergistic coupling facilitates the morphological analysis of cell populations at a high-throughput scale, effectively evolving the landscape of cytometry. Nevertheless, ImFC's implementation has encountered hurdles, particularly in developing software capable of managing its sophisticated data acquisition and analysis needs. The scale and complexity of the data generated by ImFC necessitate the creation of novel analytical tools that can effectively manage and interpret these data, thus allowing us to unlock the full potential of ImFC. Notably, artificial intelligence (AI) algorithms have begun to be applied to ImFC, offering promise for enhancing its analytical capabilities. The adaptability and learning capacity of AI may prove to be essential in knowledge mining from the high-dimensional data produced by ImFC, potentially enabling more accurate analyses. Looking forward, we project that ImFC may become an indispensable tool, not only in research laboratories, but also in clinical settings. Given the unique combination of high-throughput cytometry and detailed imaging offered by ImFC, we foresee a critical role for this technology in the next generation of scientific research and diagnostics. As such, we encourage both current and future scientists to consider the integration of ImFC as an addition to their research toolkit and clinical diagnostic routine.
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Affiliation(s)
- Savvas Dimitriadis
- Hematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece; (S.D.); (L.D.)
| | - Lefkothea Dova
- Hematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece; (S.D.); (L.D.)
| | - Ioannis Kotsianidis
- Department of Hematology, University Hospital of Alexandroupolis, Democritus University of Thrace, 69100 Alexandroupolis, Greece;
| | - Eleftheria Hatzimichael
- Department of Hematology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.H.); (E.K.)
| | - Eleni Kapsali
- Department of Hematology, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece; (E.H.); (E.K.)
| | - Georgios S. Markopoulos
- Hematology Laboratory, Unit of Molecular Biology and Translational Flow Cytometry, University Hospital of Ioannina, 45100 Ioannina, Greece; (S.D.); (L.D.)
- Department of Surgery, Faculty of Medicine, University of Ioannina, 45110 Ioannina, Greece
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7
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Kawamura Y, Nakanishi K, Murata Y, Teranishi K, Miyazaki R, Toda K, Imai T, Kajiwara Y, Nakagawa K, Matsuo H, Adachi S, Ota S, Hiramatsu H. Label-free cell detection of acute leukemia using ghost cytometry. Cytometry A 2024; 105:196-202. [PMID: 38087915 DOI: 10.1002/cyto.a.24821] [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: 07/17/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 01/05/2024]
Abstract
Early diagnosis and prompt initiation of appropriate treatment are critical for improving the prognosis of acute leukemia. Acute leukemia is diagnosed by microscopic morphological examination of bone marrow smears and flow cytometric immunophenotyping of bone marrow cells stained with fluorophore-conjugated antibodies. However, these diagnostic processes require trained professionals and are time and resource-intensive. Here, we present a novel diagnostic approach using ghost cytometry, a recently developed high-content flow cytometric approach, which enables machine vision-based, stain-free, high-speed analysis of cells, leveraging their detailed morphological information. We demonstrate that ghost cytometry can detect leukemic cells from the bone marrow cells of patients diagnosed with acute lymphoblastic leukemia and acute myeloid leukemia without relying on biological staining. The approach presented here holds promise as a precise, simple, swift, and cost-effective diagnostic method for acute leukemia in clinical practice.
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Affiliation(s)
| | - Kayoko Nakanishi
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | | | | | | | | | | | | | - Hidemasa Matsuo
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Souichi Adachi
- Department of Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Sadao Ota
- ThinkCyte K.K, Tokyo, Japan
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan
| | - Hidefumi Hiramatsu
- Department of Pediatrics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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8
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Iwama Y, Nomaru H, Masuda T, Kawamura Y, Matsumura M, Murata Y, Teranishi K, Nishida K, Ota S, Mandai M, Takahashi M. Label-free enrichment of human pluripotent stem cell-derived early retinal progenitor cells for cell-based regenerative therapies. Stem Cell Reports 2024; 19:254-269. [PMID: 38181785 PMCID: PMC10874851 DOI: 10.1016/j.stemcr.2023.12.001] [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: 07/20/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 01/07/2024] Open
Abstract
Pluripotent stem cell-based therapy for retinal degenerative diseases is a promising approach to restoring visual function. A clinical study using retinal organoid (RO) sheets was recently conducted in patients with retinitis pigmentosa. However, the graft preparation currently requires advanced skills to identify and excise suitable segments from the transplantable area of the limited number of suitable ROs. This remains a challenge for consistent clinical implementations. Herein, we enabled the enrichment of wild-type (non-reporter) retinal progenitor cells (RPCs) from dissociated ROs using a label-free ghost cytometry (LF-GC)-based sorting system, where a machine-based classifier was trained in advance with another RPC reporter line. The sorted cells reproducibly formed retinal spheroids large enough for transplantation and developed mature photoreceptors in the retinal degeneration rats. This method of enriching early RPCs with no specific surface antigens and without any reporters or chemical labeling is promising for robust preparation of graft tissues during cell-based therapy.
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Affiliation(s)
- Yasuaki Iwama
- Laboratory for Retinal Regeneration, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan; Department of Ophthalmology, Kobe City Eye Hospital, Kobe, Hyogo 650-0047, Japan; Cell and Gene Therapy in Ophthalmology Laboratory, BZP, RIKEN, Wako, Saitama 351-0198, Japan; Department of Ophthalmology, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan
| | | | - Tomohiro Masuda
- Laboratory for Retinal Regeneration, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan; Department of Ophthalmology, Kobe City Eye Hospital, Kobe, Hyogo 650-0047, Japan; Cell and Gene Therapy in Ophthalmology Laboratory, BZP, RIKEN, Wako, Saitama 351-0198, Japan.
| | | | - Michiru Matsumura
- Laboratory for Retinal Regeneration, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan; Department of Ophthalmology, Kobe City Eye Hospital, Kobe, Hyogo 650-0047, Japan
| | | | | | - Kohji Nishida
- Department of Ophthalmology, Osaka University Graduate School of Medicine, Osaka 565-0871, Japan
| | - Sadao Ota
- ThinkCyte K.K., Tokyo 113-8654, Japan; Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
| | - Michiko Mandai
- Laboratory for Retinal Regeneration, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan; Department of Ophthalmology, Kobe City Eye Hospital, Kobe, Hyogo 650-0047, Japan.
| | - Masayo Takahashi
- Laboratory for Retinal Regeneration, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo 650-0047, Japan; Department of Ophthalmology, Kobe City Eye Hospital, Kobe, Hyogo 650-0047, Japan
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9
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Wei J, Gao W, Yang X, Yu Z, Su F, Han C, Xing X. Machine learning classification of cellular states based on the impedance features derived from microfluidic single-cell impedance flow cytometry. BIOMICROFLUIDICS 2024; 18:014103. [PMID: 38274201 PMCID: PMC10807927 DOI: 10.1063/5.0181287] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 12/19/2023] [Indexed: 01/27/2024]
Abstract
Mitosis is a crucial biological process where a parental cell undergoes precisely controlled functional phases and divides into two daughter cells. Some drugs can inhibit cell mitosis, for instance, the anti-cancer drugs interacting with the tumor cell proliferation and leading to mitosis arrest at a specific phase or cell death eventually. Combining machine learning with microfluidic impedance flow cytometry (IFC) offers a concise way for label-free and high-throughput classification of drug-treated cells at single-cell level. IFC-based single-cell analysis generates a large amount of data related to the cell electrophysiology parameters, and machine learning helps establish correlations between these data and specific cell states. This work demonstrates the application of machine learning for cell state classification, including the binary differentiations between the G1/S and apoptosis states and between the G2/M and apoptosis states, as well as the classification of three subpopulations comprising a subgroup insensitive to the drug beyond the two drug-induced states of G2/M arrest and apoptosis. The impedance amplitudes and phases used as input features for the model training were extracted from the IFC-measured datasets for the drug-treated tumor cells. The deep neural network (DNN) model was exploited here with the structure (e.g., hidden layer number and neuron number in each layer) optimized for each given cell type and drug. For the H1650 cells, we obtained an accuracy of 78.51% for classification between the G1/S and apoptosis states and 82.55% for the G2/M and apoptosis states. For HeLa cells, we achieved a high accuracy of 96.94% for classification between the G2/M and apoptosis states, both of which were induced by taxol treatment. Even higher accuracy approaching 100% was achieved for the vinblastine-treated HeLa cells for the differentiation between the viable and non-viable states, and between the G2/M and apoptosis states. We also demonstrate the capability of the DNN model for high-accuracy classification of the three subpopulations in a complete cell sample treated by taxol or vinblastine.
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Affiliation(s)
- Jian Wei
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
| | - Wenbing Gao
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
| | - Xinlong Yang
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
| | - Zhuotong Yu
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
| | - Fei Su
- Department of Integrative Oncology, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing 100029, China
| | - Chengwu Han
- Department of Clinical Laboratory, China-Japan Friendship Hospital, No. 2 Yinghuayuan East Street, Chaoyang District, Beijing 100029, China
| | - Xiaoxing Xing
- College of Information Science and Technology, Beijing University of Chemical Technology, No. 15 North 3rd Ring Road, Chaoyang District, Beijing 100029, China
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10
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Salek M, Li N, Chou HP, Saini K, Jovic A, Jacobs KB, Johnson C, Lu V, Lee EJ, Chang C, Nguyen P, Mei J, Pant KP, Wong-Thai AY, Smith QF, Huang S, Chow R, Cruz J, Walker J, Chan B, Musci TJ, Ashley EA, Masaeli MM. COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning. Commun Biol 2023; 6:971. [PMID: 37740030 PMCID: PMC10516940 DOI: 10.1038/s42003-023-05325-9] [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: 01/30/2023] [Accepted: 09/06/2023] [Indexed: 09/24/2023] Open
Abstract
Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
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Affiliation(s)
- Mahyar Salek
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA.
| | - Nianzhen Li
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Hou-Pu Chou
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Kiran Saini
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Andreja Jovic
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Kevin B Jacobs
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | | | - Vivian Lu
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Esther J Lee
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | | | - Phuc Nguyen
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Jeanette Mei
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Krishna P Pant
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | | | | | | | - Ryan Chow
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Janifer Cruz
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Jeff Walker
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Bryan Chan
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Thomas J Musci
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
| | - Euan A Ashley
- Deepcell Inc; 4025 Bohannon Dr., Menlo Park, CA, 94025, USA
- Department of Medicine, Genetics, & Biomedical Data Science, Stanford University, Stanford, CA, USA
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11
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Julian T, Tang T, Hosokawa Y, Yalikun Y. Machine learning implementation strategy in imaging and impedance flow cytometry. BIOMICROFLUIDICS 2023; 17:051506. [PMID: 37900052 PMCID: PMC10613093 DOI: 10.1063/5.0166595] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/06/2023] [Indexed: 10/31/2023]
Abstract
Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.
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Affiliation(s)
- Trisna Julian
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan
| | - Tao Tang
- Department of Biomedical Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore 117583, Singapore
| | - Yoichiroh Hosokawa
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayamacho, Ikoma, Nara 630-0192, Japan
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12
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 PMCID: PMC10141994 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan;
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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13
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Midani FS, David LA. Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity. Front Microbiol 2023; 13:910390. [PMID: 36687598 PMCID: PMC9849913 DOI: 10.3389/fmicb.2022.910390] [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: 04/01/2022] [Accepted: 11/29/2022] [Indexed: 01/07/2023] Open
Abstract
Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.
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Affiliation(s)
- Firas S. Midani
- Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
- Department of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX, United States
| | - Lawrence A. David
- Center for Genomic and Computational Biology, Duke University, Durham, NC, United States
- Department of Molecular Genetics and Microbiology, Duke University, Durham, NC, United States
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14
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Kawasaki J, Tomonaga K, Horie M. Large-scale investigation of zoonotic viruses in the era of high-throughput sequencing. Microbiol Immunol 2023; 67:1-13. [PMID: 36259224 DOI: 10.1111/1348-0421.13033] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 09/28/2022] [Accepted: 10/16/2022] [Indexed: 01/10/2023]
Abstract
Zoonotic diseases considerably impact public health and socioeconomics. RNA viruses reportedly caused approximately 94% of zoonotic diseases documented from 1990 to 2010, emphasizing the importance of investigating RNA viruses in animals. Furthermore, it has been estimated that hundreds of thousands of animal viruses capable of infecting humans are yet to be discovered, warning against the inadequacy of our understanding of viral diversity. High-throughput sequencing (HTS) has enabled the identification of viral infections with relatively little bias. Viral searches using both symptomatic and asymptomatic animal samples by HTS have revealed hidden viral infections. This review introduces the history of viral searches using HTS, current analytical limitations, and future potentials. We primarily summarize recent research on large-scale investigations on viral infections reusing HTS data from public databases. Furthermore, considering the accumulation of uncultivated viruses, we discuss current studies and challenges for connecting viral sequences to their phenotypes using various approaches: performing data analysis, developing predictive modeling, or implementing high-throughput platforms of virological experiments. We believe that this article provides a future direction in large-scale investigations of potential zoonotic viruses using the HTS technology.
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Affiliation(s)
- Junna Kawasaki
- Laboratory of RNA Viruses, Department of Virus Research, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan.,Laboratory of RNA Viruses, Department of Mammalian Regulatory Network, Graduate School of Biostudies, Kyoto University, Kyoto, Japan.,Faculty of Science and Engineering, Waseda University, Tokyo, Japan
| | - Keizo Tomonaga
- Laboratory of RNA Viruses, Department of Virus Research, Institute for Frontier Life and Medical Sciences, Kyoto University, Kyoto, Japan.,Laboratory of RNA Viruses, Department of Mammalian Regulatory Network, Graduate School of Biostudies, Kyoto University, Kyoto, Japan.,Department of Molecular Virology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Masayuki Horie
- Division of Veterinary Sciences, Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Osaka, Japan.,Osaka International Research Center for Infectious Diseases, Osaka Prefecture University, Osaka, Japan
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15
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Rees P, Summers HD, Filby A, Carpenter AE, Doan M. Imaging flow cytometry: a primer. NATURE REVIEWS. METHODS PRIMERS 2022; 2:86. [PMID: 37655209 PMCID: PMC10468826 DOI: 10.1038/s43586-022-00167-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/08/2022] [Indexed: 09/02/2023]
Abstract
Imaging flow cytometry combines the high throughput nature of flow cytometry with the advantages of single cell image acquisition associated with microscopy. The measurement of large numbers of features from the resulting images provides rich datasets which have resulted in a wide range of novel biomedical applications. In this primer we discuss the typical imaging flow instrumentation, the form of data acquired and the typical analysis tools that can be applied to this data. Using examples from the literature we discuss the progression of the analysis methods that have been applied to imaging flow cytometry data. These methods start from the use of simple single image features and multiple channel gating strategies, followed by the design and use of custom features for phenotype classification, through to powerful machine and deep learning methods. For each of these methods, we outline the processes involved in analyzing typical datasets and provide details of example applications. Finally we discuss the current limitations of imaging flow cytometry and the innovations which are addressing these challenges.
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Affiliation(s)
- Paul Rees
- Department of Biomedical Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, United Kingdom
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA 02142, United States of America
| | - Huw D Summers
- Department of Biomedical Engineering, Swansea University, Bay Campus, Swansea SA1 8EN, United Kingdom
| | - Andrew Filby
- Flow Cytometry Core Facility and Innovation, Methodology and Application Research Theme, Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Anne E Carpenter
- Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, Massachusetts MA 02142, United States of America
| | - Minh Doan
- Bioimaging Analytics, GlaxoSmithKline, Collegeville, PA, United States of America
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Kusumoto D, Yuasa S, Fukuda K. Induced Pluripotent Stem Cell-Based Drug Screening by Use of Artificial Intelligence. Pharmaceuticals (Basel) 2022; 15:562. [PMID: 35631387 PMCID: PMC9145330 DOI: 10.3390/ph15050562] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/25/2022] [Accepted: 04/27/2022] [Indexed: 12/10/2022] Open
Abstract
Induced pluripotent stem cells (iPSCs) are terminally differentiated somatic cells that differentiate into various cell types. iPSCs are expected to be used for disease modeling and for developing novel treatments because differentiated cells from iPSCs can recapitulate the cellular pathology of patients with genetic mutations. However, a barrier to using iPSCs for comprehensive drug screening is the difficulty of evaluating their pathophysiology. Recently, the accuracy of image analysis has dramatically improved with the development of artificial intelligence (AI) technology. In the field of cell biology, it has become possible to estimate cell types and states by examining cellular morphology obtained from simple microscopic images. AI can evaluate disease-specific phenotypes of iPS-derived cells from label-free microscopic images; thus, AI can be utilized for disease-specific drug screening using iPSCs. In addition to image analysis, various AI-based methods can be applied to drug development, including phenotype prediction by analyzing genomic data and virtual screening by analyzing structural formulas and protein-protein interactions of compounds. In the future, combining AI methods may rapidly accelerate drug discovery using iPSCs. In this review, we explain the details of AI technology and the application of AI for iPSC-based drug screening.
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Affiliation(s)
- Dai Kusumoto
- Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan;
- Center for Preventive Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan
| | - Shinsuke Yuasa
- Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan;
| | - Keiichi Fukuda
- Department of Cardiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan;
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