1
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Wang Z, Liu Q, Zhou J, Su X. Single-detector multiplex imaging flow cytometry for cancer cell classification with deep learning. Cytometry A 2024. [PMID: 39101554 DOI: 10.1002/cyto.a.24890] [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: 12/21/2023] [Revised: 06/27/2024] [Accepted: 07/22/2024] [Indexed: 08/06/2024]
Abstract
Imaging flow cytometry, which combines the advantages of flow cytometry and microscopy, has emerged as a powerful tool for cell analysis in various biomedical fields such as cancer detection. In this study, we develop multiplex imaging flow cytometry (mIFC) by employing a spatial wavelength division multiplexing technique. Our mIFC can simultaneously obtain brightfield and multi-color fluorescence images of individual cells in flow, which are excited by a metal halide lamp and measured by a single detector. Statistical analysis results of multiplex imaging experiments with resolution test lens, magnification test lens, and fluorescent microspheres validate the operation of the mIFC with good imaging channel consistency and micron-scale differentiation capabilities. A deep learning method is designed for multiplex image processing that consists of three deep learning networks (U-net, very deep super resolution, and visual geometry group 19). It is demonstrated that the cluster of differentiation 24 (CD24) imaging channel is more sensitive than the brightfield, nucleus, or cancer antigen 125 (CA125) imaging channel in classifying the three types of ovarian cell lines (IOSE80 normal cell, A2780, and OVCAR3 cancer cells). An average accuracy rate of 97.1% is achieved for the classification of these three types of cells by deep learning analysis when all four imaging channels are considered. Our single-detector mIFC is promising for the development of future imaging flow cytometers and for the automatic single-cell analysis with deep learning in various biomedical fields.
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Affiliation(s)
- Zhiwen Wang
- School of Integrated Circuits, Shandong University, Jinan, China
- Institute of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, China
| | - Qiao Liu
- Department of Molecular Medicine and Genetics, School of Basic Medicine Sciences, Shandong University, Jinan, China
| | - Jie Zhou
- School of Integrated Circuits, Shandong University, Jinan, China
| | - Xuantao Su
- School of Integrated Circuits, Shandong University, Jinan, China
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2
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Kuhn TM, Paulsen M, Cuylen-Haering S. Accessible high-speed image-activated cell sorting. Trends Cell Biol 2024; 34:657-670. [PMID: 38789300 DOI: 10.1016/j.tcb.2024.04.007] [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: 09/06/2023] [Revised: 04/15/2024] [Accepted: 04/23/2024] [Indexed: 05/26/2024]
Abstract
Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.
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Affiliation(s)
- Terra M Kuhn
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Malte Paulsen
- Novo Nordisk Foundation Center for Stem Cell Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Sara Cuylen-Haering
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
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3
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Yamashita M, Tamamitsu M, Kirisako H, Goda Y, Chen X, Hattori K, Ota S. High-Throughput 3D Imaging Flow Cytometry of Suspended Adherent 3D Cell Cultures. SMALL METHODS 2024; 8:e2301318. [PMID: 38133483 DOI: 10.1002/smtd.202301318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/27/2023] [Indexed: 12/23/2023]
Abstract
3D cell cultures are indispensable in recapitulating in vivo environments. Among the many 3D culture methods, culturing adherent cells on hydrogel beads to form spheroid-like structures is a powerful strategy for maintaining high cell viability and functions in the adherent states. However, high-throughput, scalable technologies for 3D imaging of individual cells cultured on the hydrogel scaffolds are lacking. This study reports the development of a high throughput, scalable 3D imaging flow cytometry platform for analyzing spheroid models. This platform is realized by integrating a single objective fluorescence light-sheet microscopy with a microfluidic device that combines hydrodynamic and acoustofluidic focusing techniques. This integration enabled unprecedentedly high-throughput and scalable optofluidic 3D imaging, processing 1310 spheroids consisting of 28 117 cells min-1. The large dataset obtained enables precise quantification and comparison of the nuclear morphology of adhering and suspended cells, revealing that the adhering cells have smaller nuclei with less rounded surfaces. This platform's high throughput, robustness, and precision for analyzing the morphology of subcellular structures in 3D culture models hold promising potential for various biomedical analyses, including image-based phenotypic screening of drugs with spheroids or organoids.
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Affiliation(s)
- Minato Yamashita
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan
| | - Miu Tamamitsu
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan
| | - Hiromi Kirisako
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan
| | - Yuki Goda
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan
| | - Xiaoyao Chen
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan
| | - Kazuki Hattori
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan
| | - Sadao Ota
- Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan
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4
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Yun HG, Cadierno YA, Kim TW, Muñoz-Barrutia A, Garica-Gonzalez D, Choi S. Computational Hyperspectral Microflow Cytometry. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2024; 20:e2400019. [PMID: 38770741 DOI: 10.1002/smll.202400019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/22/2024] [Indexed: 05/22/2024]
Abstract
Miniaturized flow cytometry has significant potential for portable applications, such as cell-based diagnostics and the monitoring of therapeutic cell manufacturing, however, the performance of current techniques is often limited by the inability to resolve spectrally-overlapping fluorescence labels. Here, the study presents a computational hyperspectral microflow cytometer (CHC) that enables accurate discrimination of spectrally-overlapping fluorophores labeling single cells. CHC employs a dispersive optical element and an optimization algorithm to detect the full fluorescence emission spectrum from flowing cells, with a high spectral resolution of ≈3 nm in the range from 450 to 650 nm. CHC also includes a dedicated microfluidic device that ensures in-focus imaging through viscoelastic sheathless focusing, thereby enhancing the accuracy and reliability of microflow cytometry analysis. The potential of CHC for analyzing T lymphocyte subpopulations and monitoring changes in cell composition during T cell expansion is demonstrated. Overall, CHC represents a major breakthrough in microflow cytometry and can facilitate its use for immune cell monitoring.
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Affiliation(s)
- Hyo Geun Yun
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea
| | - Yoel Alonso Cadierno
- Bioengineering Department, Universidad Carlos III De Madrid, Avda. de la Universidad 30, Leganés, Madrid, 28911, Spain
| | - Tae Won Kim
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea
| | - Arrate Muñoz-Barrutia
- Bioengineering Department, Universidad Carlos III De Madrid, Avda. de la Universidad 30, Leganés, Madrid, 28911, Spain
| | - Daniel Garica-Gonzalez
- Department of Continuum Mechanics and Structural Analysis, Universidad Carlos III De Madrid, Avda. de la Universidad 30, Leganés, Madrid, 28911, Spain
| | - Sungyoung Choi
- Department of Electronic Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Department of Biomedical Engineering, Hanyang University, Seoul, 04763, Republic of Korea
- Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Seoul, 04763, Republic of Korea
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5
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Hybel TE, Jensen SH, Rodrigues MA, Hybel TE, Pedersen MN, Qvick SH, Enemark MH, Bill M, Rosenberg CA, Ludvigsen M. Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia. Int J Mol Sci 2024; 25:6465. [PMID: 38928171 PMCID: PMC11203419 DOI: 10.3390/ijms25126465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 06/07/2024] [Accepted: 06/10/2024] [Indexed: 06/28/2024] Open
Abstract
Acute myeloid leukemia (AML) is a heterogenous blood cancer with a dismal prognosis. It emanates from leukemic stem cells (LSCs) arising from the genetic transformation of hematopoietic stem cells (HSCs). LSCs hold prognostic value, but their molecular and immunophenotypic heterogeneity poses challenges: there is no single marker for identifying all LSCs across AML samples. We hypothesized that imaging flow cytometry (IFC) paired with artificial intelligence-driven image analysis could visually distinguish LSCs from HSCs based solely on morphology. Initially, a seven-color IFC panel was employed to immunophenotypically identify LSCs and HSCs in bone marrow samples from five AML patients and ten healthy donors, respectively. Next, we developed convolutional neural network (CNN) models for HSC-LSC discrimination using brightfield (BF), side scatter (SSC), and DNA images. Classification using only BF images achieved 86.96% accuracy, indicating significant morphological differences. Accuracy increased to 93.42% when combining BF with DNA images, highlighting differences in nuclear morphology, although DNA images alone were inadequate for accurate HSC-LSC discrimination. Model development using SSC images revealed minor granularity differences. Performance metrics varied substantially between AML patients, indicating considerable morphologic variations among LSCs. Overall, we demonstrate proof-of-concept results for accurate CNN-based HSC-LSC differentiation, instigating the development of a novel technique within AML monitoring.
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Affiliation(s)
- Trine Engelbrecht Hybel
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Sofie Hesselberg Jensen
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | | | - Thomas Engelbrecht Hybel
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
| | - Maya Nautrup Pedersen
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Signe Håkansson Qvick
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
| | - Marie Hairing Enemark
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Marie Bill
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
| | - Carina Agerbo Rosenberg
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
| | - Maja Ludvigsen
- Department of Hematology, Aarhus University Hospital, 8200 Aarhus N, Denmark; (T.E.H.); (M.H.E.)
- Department of Clinical Medicine, Aarhus University, 8200 Aarhus N, Denmark
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6
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Hou D, Zhou J, Xiao R, Yang K, Ding Y, Wang D, Wu G, Lei C. Optofluidic time-stretch imaging flow cytometry with a real-time storage rate beyond 5.9 GB/s. Cytometry A 2024. [PMID: 38842356 DOI: 10.1002/cyto.a.24854] [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: 02/20/2024] [Revised: 04/09/2024] [Accepted: 05/10/2024] [Indexed: 06/07/2024]
Abstract
Optofluidic time-stretch imaging flow cytometry (OTS-IFC) provides a suitable solution for high-precision cell analysis and high-sensitivity detection of rare cells due to its high-throughput and continuous image acquisition. However, transferring and storing continuous big data streams remains a challenge. In this study, we designed a high-speed streaming storage strategy to store OTS-IFC data in real-time, overcoming the imbalance between the fast generation speed in the data acquisition and processing subsystem and the comparatively slower storage speed in the transmission and storage subsystem. This strategy, utilizing an asynchronous buffer structure built on the producer-consumer model, optimizes memory usage for enhanced data throughput and stability. We evaluated the storage performance of the high-speed streaming storage strategy in ultra-large-scale blood cell imaging on a common commercial device. The experimental results show that it can provide a continuous data throughput of up to 5891 MB/s.
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Affiliation(s)
- Dan Hou
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Jiehua Zhou
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Ruidong Xiao
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Kaining Yang
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Yan Ding
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Du Wang
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Guoqiang Wu
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
| | - Cheng Lei
- The Institute of Technological Sciences, Wuhan University, Wuhan, China
- Shenzhen Institute of Wuhan University, Shenzhen, China
- Suzhou Institute of Wuhan University, Suzhou, China
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7
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Mandracchia B, Zheng C, Rajendran S, Liu W, Forghani P, Xu C, Jia S. High-speed optical imaging with sCMOS pixel reassignment. Nat Commun 2024; 15:4598. [PMID: 38816394 PMCID: PMC11139943 DOI: 10.1038/s41467-024-48987-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 05/13/2024] [Indexed: 06/01/2024] Open
Abstract
Fluorescence microscopy has undergone rapid advancements, offering unprecedented visualization of biological events and shedding light on the intricate mechanisms governing living organisms. However, the exploration of rapid biological dynamics still poses a significant challenge due to the limitations of current digital camera architectures and the inherent compromise between imaging speed and other capabilities. Here, we introduce sHAPR, a high-speed acquisition technique that leverages the operating principles of sCMOS cameras to capture fast cellular and subcellular processes. sHAPR harnesses custom fiber optics to convert microscopy images into one-dimensional recordings, enabling acquisition at the maximum camera readout rate, typically between 25 and 250 kHz. We have demonstrated the utility of sHAPR with a variety of phantom and dynamic systems, including high-throughput flow cytometry, cardiomyocyte contraction, and neuronal calcium waves, using a standard epi-fluorescence microscope. sHAPR is highly adaptable and can be integrated into existing microscopy systems without requiring extensive platform modifications. This method pushes the boundaries of current fluorescence imaging capabilities, opening up new avenues for investigating high-speed biological phenomena.
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Affiliation(s)
- Biagio Mandracchia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- E.T.S.I. Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Corey Zheng
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Suraj Rajendran
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Wenhao Liu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Parvin Forghani
- Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Chunhui Xu
- Department of Pediatrics, School of Medicine, Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shu Jia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
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8
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Sharma S, Boyer J, Teyton L. A practitioner's view of spectral flow cytometry. Nat Methods 2024; 21:740-743. [PMID: 37789184 PMCID: PMC10991079 DOI: 10.1038/s41592-023-02042-3] [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] [Indexed: 10/05/2023]
Abstract
Spectral flow cytometry allows the simultaneous analysis of a large number of cell surface markers at the single cell level
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Affiliation(s)
- Siddhartha Sharma
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Josh Boyer
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA
| | - Luc Teyton
- Department of Immunology and Microbiology, Scripps Research, La Jolla, CA, USA.
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9
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Bai Q, Huang X, Du S, Guo Y, Li C, Li W, Li J, Gu C. Multiple-polarization-sensitive photodetector Based on a plasmonic metasurface. NANOSCALE 2024. [PMID: 38625084 DOI: 10.1039/d4nr00808a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
On-chip polarization-sensitive photodetectors are highly desired for ultra-compact optoelectronic systems. It has been demonstrated that polarization-sensitive photodetection can be realized using intrinsic chiral and anisotropy materials. However, these photodetectors can only realize the detection of either circularly polarized light (CPL) or linear polarized light (LPL) and are not applicable to multiple-polarization-sensitive photodetection. Herein, we experimentally demonstrate a metasurface-integrated semiconductor to realize multiple-polarization-sensitive photodetection at visible wavelengths. This device is composed of a MoSe2 monolayer on an H-shaped plasmonic nanostructure. The geometric chirality and anisotropy of the H-shaped nanostructure result in CPL and LPL resolved optical responses. By integrating a plasmonic metasurface with monolayer MoSe2, we converted polarization-sensitive optical absorption to the polarization-sensitive photocurrent of the device through the photoconductive effect. Polarization-sensitive photocurrent responses to both CPL and LPL are systematically investigated, which demonstrate a high photocurrent circular dichroism (CD) of 0.35 at a wavelength of 810 nm and photocurrent linear polarization (LP) of 0.4 at a wavelength of 633 nm. Our results provide a potential pathway to realize multiple-polarization-sensitive applications in medicine analysis, biology, and remote sensing.
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Affiliation(s)
- Qinghu Bai
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
- School of Physical Sciences, CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Xin Huang
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
- School of Physical Sciences, CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Shuo Du
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
- School of Physical Sciences, CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Yang Guo
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
- School of Physical Sciences, CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Chensheng Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
- School of Physical Sciences, CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Wei Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
- School of Physical Sciences, CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Junjie Li
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
- Songshan Lake Materials Laboratory, Dongguan 523808, People's Republic of China
| | - Changzhi Gu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China.
- School of Physical Sciences, CAS Key Laboratory of Vacuum Physics, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
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10
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Hua X, Han K, Mandracchia B, Radmand A, Liu W, Kim H, Yuan Z, Ehrlich SM, Li K, Zheng C, Son J, Silva Trenkle AD, Kwong GA, Zhu C, Dahlman JE, Jia S. Light-field flow cytometry for high-resolution, volumetric and multiparametric 3D single-cell analysis. Nat Commun 2024; 15:1975. [PMID: 38438356 PMCID: PMC10912605 DOI: 10.1038/s41467-024-46250-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 02/15/2024] [Indexed: 03/06/2024] Open
Abstract
Imaging flow cytometry (IFC) combines flow cytometry and fluorescence microscopy to enable high-throughput, multiparametric single-cell analysis with rich spatial details. However, current IFC techniques remain limited in their ability to reveal subcellular information with a high 3D resolution, throughput, sensitivity, and instrumental simplicity. In this study, we introduce a light-field flow cytometer (LFC), an IFC system capable of high-content, single-shot, and multi-color acquisition of up to 5,750 cells per second with a near-diffraction-limited resolution of 400-600 nm in all three dimensions. The LFC system integrates optical, microfluidic, and computational strategies to facilitate the volumetric visualization of various 3D subcellular characteristics through convenient access to commonly used epi-fluorescence platforms. We demonstrate the effectiveness of LFC in assaying, analyzing, and enumerating intricate subcellular morphology, function, and heterogeneity using various phantoms and biological specimens. The advancement offered by the LFC system presents a promising methodological pathway for broad cell biological and translational discoveries, with the potential for widespread adoption in biomedical research.
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Affiliation(s)
- Xuanwen Hua
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Keyi Han
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Biagio Mandracchia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Afsane Radmand
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
- Department of Chemical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Wenhao Liu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Hyejin Kim
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Zhou Yuan
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
- Georgia W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Samuel M Ehrlich
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
- Georgia W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Kaitao Li
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Corey Zheng
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Jeonghwan Son
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Aaron D Silva Trenkle
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Gabriel A Kwong
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Cheng Zhu
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - James E Dahlman
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA
| | - Shu Jia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.
- Parker H. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA.
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11
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Huang Z, Yu X, Liu Q, Maki T, Alam K, Wang Y, Xue F, Tang S, Du P, Dong Q, Wang D, Huang J. Bioaerosols in the atmosphere: A comprehensive review on detection methods, concentration and influencing factors. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168818. [PMID: 38036132 DOI: 10.1016/j.scitotenv.2023.168818] [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: 08/24/2023] [Revised: 11/17/2023] [Accepted: 11/21/2023] [Indexed: 12/02/2023]
Abstract
In the past few decades, especially since the outbreak of the coronavirus disease (COVID-19), the effects of atmospheric bioaerosols on human health, the environment, and climate have received great attention. To evaluate the impacts of bioaerosols quantitatively, it is crucial to determine the types of bioaerosols in the atmosphere and their spatial-temporal distribution. We provide a concise summary of the online and offline observation strategies employed by the global research community to sample and analyze atmospheric bioaerosols. In addition, the quantitative distribution of bioaerosols is described by considering the atmospheric bioaerosols concentrations at various time scales (daily and seasonal changes, for example), under various weather, and different underlying surfaces. Finally, a comprehensive summary of the reasons for the spatiotemporal distribution of bioaerosols is discussed, including differences in emission sources, the impact process of meteorological factors and environmental factors. This review of information on the latest research progress contributes to the emergence of further observation strategies that determine the quantitative dynamics of public health and ecological effects of bioaerosols.
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Affiliation(s)
- Zhongwei Huang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China
| | - Xinrong Yu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Qiantao Liu
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Teruya Maki
- Department of Life Science, Faculty of Science and Engineering, Kindai University, Higashiosaka, Osaka, Japan
| | - Khan Alam
- Department of Physics, University of Peshawar, Peshawar 25120, Pakistan
| | - Yongkai Wang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Fanli Xue
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Shihan Tang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Pengyue Du
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Qing Dong
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Danfeng Wang
- Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China
| | - Jianping Huang
- Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China; Collaborative Innovation Center for Western Ecological Safety, Lanzhou University, Lanzhou 730000, China.
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12
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Ciaparrone G, Pirone D, Fiore P, Xin L, Xiao W, Li X, Bardozzo F, Bianco V, Miccio L, Pan F, Memmolo P, Tagliaferri R, Ferraro P. Label-free cell classification in holographic flow cytometry through an unbiased learning strategy. LAB ON A CHIP 2024; 24:924-932. [PMID: 38264771 DOI: 10.1039/d3lc00385j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Nowadays, label-free imaging flow cytometry at the single-cell level is considered the stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology, life sciences and healthcare. In this framework, digital holography in microscopy promises to be a powerful imaging modality thanks to its multi-refocusing and label-free quantitative phase imaging capabilities, along with the encoding of the highest information content within the imaged samples. Moreover, the recent achievements of new data analysis tools for cell classification based on deep/machine learning, combined with holographic imaging, are urging these systems toward the effective implementation of point of care devices. However, the generalization capabilities of learning-based models may be limited from biases caused by data obtained from other holographic imaging settings and/or different processing approaches. In this paper, we propose a combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder, used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features. We demonstrate the proposed approach in the challenging classification task related to the identification of drug-resistant endometrial cancer cells.
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Affiliation(s)
- Gioele Ciaparrone
- Neurone Lab, Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Fisciano, Italy.
| | - Daniele Pirone
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Pierpaolo Fiore
- Neurone Lab, Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Fisciano, Italy.
| | - Lu Xin
- Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education, School of Instrumentation Science & Optoelectronics Engineering, Beihang University, 100191 Beijing, China.
| | - Wen Xiao
- Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education, School of Instrumentation Science & Optoelectronics Engineering, Beihang University, 100191 Beijing, China.
| | - Xiaoping Li
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - Francesco Bardozzo
- Neurone Lab, Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Fisciano, Italy.
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Vittorio Bianco
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Lisa Miccio
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Feng Pan
- Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education, School of Instrumentation Science & Optoelectronics Engineering, Beihang University, 100191 Beijing, China.
| | - Pasquale Memmolo
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Roberto Tagliaferri
- Neurone Lab, Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Fisciano, Italy.
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Pietro Ferraro
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
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13
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Bianco V, D'Agostino M, Pirone D, Giugliano G, Mosca N, Di Summa M, Scerra G, Memmolo P, Miccio L, Russo T, Stella E, Ferraro P. Label-Free Intracellular Multi-Specificity in Yeast Cells by Phase-Contrast Tomographic Flow Cytometry. SMALL METHODS 2023; 7:e2300447. [PMID: 37670547 DOI: 10.1002/smtd.202300447] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 08/14/2023] [Indexed: 09/07/2023]
Abstract
In-flow phase-contrast tomography provides a 3D refractive index of label-free cells in cytometry systems. Its major limitation, as with any quantitative phase imaging approach, is the lack of specificity compared to fluorescence microscopy, thus restraining its huge potentialities in single-cell analysis and diagnostics. Remarkable results in introducing specificity are obtained through artificial intelligence (AI), but only for adherent cells. However, accessing the 3D fluorescence ground truth and obtaining accurate voxel-level co-registration of image pairs for AI training is not viable for high-throughput cytometry. The recent statistical inference approach is a significant step forward for label-free specificity but remains limited to cells' nuclei. Here, a generalized computational strategy based on a self-consistent statistical inference to achieve intracellular multi-specificity is shown. Various subcellular compartments (i.e., nuclei, cytoplasmic vacuoles, the peri-vacuolar membrane area, cytoplasm, vacuole-nucleus contact site) can be identified and characterized quantitatively at different phases of the cells life cycle by using yeast cells as a biological model. Moreover, for the first time, virtual reality is introduced for handling the information content of multi-specificity in single cells. Full fruition is proofed for exploring and interacting with 3D quantitative biophysical parameters of the identified compartments on demand, thus opening the route to a metaverse for 3D microscopy.
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Affiliation(s)
- Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli, Napoli, 80078, Italy
| | - Massimo D'Agostino
- Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Via S. Pansini 5, Naples, 80131, Italy
| | - Daniele Pirone
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli, Napoli, 80078, Italy
| | - Giusy Giugliano
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli, Napoli, 80078, Italy
| | - Nicola Mosca
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122/D-O, Bari, 70125, Italy
| | - Maria Di Summa
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122/D-O, Bari, 70125, Italy
| | - Gianluca Scerra
- Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Via S. Pansini 5, Naples, 80131, Italy
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli, Napoli, 80078, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli, Napoli, 80078, Italy
| | - Tommaso Russo
- Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Via S. Pansini 5, Naples, 80131, Italy
| | - Ettore Stella
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Via Amendola 122/D-O, Bari, 70125, Italy
| | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, Pozzuoli, Napoli, 80078, Italy
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14
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Sun A, Li Y, Zhu P, He X, Jiang Z, Kong Y, Liu C, Wang S. Dual-view transport of intensity phase imaging flow cytometry. BIOMEDICAL OPTICS EXPRESS 2023; 14:5199-5207. [PMID: 37854577 PMCID: PMC10581798 DOI: 10.1364/boe.504863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 09/03/2023] [Indexed: 10/20/2023]
Abstract
In this work, we design multi-parameter phase imaging flow cytometry based on dual-view transport of intensity (MPFC), which integrates phase imaging and microfluidics to a microscope, to obtain single-shot quantitative phase imaging on cells flowing in the microfluidic channel. The MPFC system has been proven with simple configuration, accurate phase retrieval, high imaging contrast, and real-time imaging and has been successfully employed not only in imaging, recognizing, and analyzing the flowing cells even with high-flowing velocities but also in tracking cell motilities, including rotation and binary rotation. Current results suggest that our proposed MPFC provides an effective tool for imaging and analyzing cells in microfluidics and can be potentially used in both fundamental and clinical studies.
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Affiliation(s)
- Aihui Sun
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yaxi Li
- Radiology Department, Jiangnan University Medical Center, Wuxi, Jiangsu, 214122, China
| | - Pengfei Zhu
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Xiaoliang He
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Zhilong Jiang
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
| | - Yan Kong
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
- Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China
| | - Cheng Liu
- Department of Optoelectronic Information Science and Engineering, School of Science, Jiangnan University, Wuxi, Jiangsu, 214122, China
- Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai, 201800, China
| | - Shouyu Wang
- Jiangsu Province Engineering Research Center of Integrated Circuit Reliability Technology and Testing System & School of Electronics and Information Engineering, OptiX+ Laboratory, Wuxi University, Wuxi, Jiangsu 214105, China
- Single Molecule Nanometry Laboratory, China
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15
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Haddad M, Frickenstein A, Wilhelm S. High-Throughput Single-Cell Analysis of Nanoparticle-Cell Interactions. Trends Analyt Chem 2023; 166:117172. [PMID: 37520860 PMCID: PMC10373476 DOI: 10.1016/j.trac.2023.117172] [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] [Indexed: 08/01/2023]
Abstract
Understanding nanoparticle-cell interactions at single-nanoparticle and single-cell resolutions is crucial to improving the design of next-generation nanoparticles for safer, more effective, and more efficient applications in nanomedicine. This review focuses on recent advances in the continuous high-throughput analysis of nanoparticle-cell interactions at the single-cell level. We highlight and discuss the current trends in continual flow high-throughput methods for analyzing single cells, such as advanced flow cytometry techniques and inductively coupled plasma mass spectrometry methods, as well as their intersection in the form of mass cytometry. This review further discusses the challenges and opportunities with current single-cell analysis approaches and provides proposed directions for innovation in the high-throughput analysis of nanoparticle-cell interactions.
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Affiliation(s)
- Majood Haddad
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Alex Frickenstein
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
| | - Stefan Wilhelm
- Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, 73019, USA
- Stephenson Cancer Center, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma, 73104, USA
- Institute for Biomedical Engineering, Science, and Technology (IBEST), University of Oklahoma, Norman, Oklahoma, 73019, USA
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16
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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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17
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Mermans F, Mattelin V, Van den Eeckhoudt R, García-Timermans C, Van Landuyt J, Guo Y, Taurino I, Tavernier F, Kraft M, Khan H, Boon N. Opportunities in optical and electrical single-cell technologies to study microbial ecosystems. Front Microbiol 2023; 14:1233705. [PMID: 37692384 PMCID: PMC10486927 DOI: 10.3389/fmicb.2023.1233705] [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: 06/02/2023] [Accepted: 08/03/2023] [Indexed: 09/12/2023] Open
Abstract
New techniques are revolutionizing single-cell research, allowing us to study microbes at unprecedented scales and in unparalleled depth. This review highlights the state-of-the-art technologies in single-cell analysis in microbial ecology applications, with particular attention to both optical tools, i.e., specialized use of flow cytometry and Raman spectroscopy and emerging electrical techniques. The objectives of this review include showcasing the diversity of single-cell optical approaches for studying microbiological phenomena, highlighting successful applications in understanding microbial systems, discussing emerging techniques, and encouraging the combination of established and novel approaches to address research questions. The review aims to answer key questions such as how single-cell approaches have advanced our understanding of individual and interacting cells, how they have been used to study uncultured microbes, which new analysis tools will become widespread, and how they contribute to our knowledge of ecological interactions.
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Affiliation(s)
- Fabian Mermans
- Center for Microbial Ecology and Technology (CMET), Department of Biotechnology, Ghent University, Ghent, Belgium
- Department of Oral Health Sciences, KU Leuven, Leuven, Belgium
| | - Valérie Mattelin
- Center for Microbial Ecology and Technology (CMET), Department of Biotechnology, Ghent University, Ghent, Belgium
| | - Ruben Van den Eeckhoudt
- Micro- and Nanosystems (MNS), Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
| | - Cristina García-Timermans
- Center for Microbial Ecology and Technology (CMET), Department of Biotechnology, Ghent University, Ghent, Belgium
| | - Josefien Van Landuyt
- Center for Microbial Ecology and Technology (CMET), Department of Biotechnology, Ghent University, Ghent, Belgium
| | - Yuting Guo
- Center for Microbial Ecology and Technology (CMET), Department of Biotechnology, Ghent University, Ghent, Belgium
| | - Irene Taurino
- Micro- and Nanosystems (MNS), Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- Semiconductor Physics, Department of Physics and Astronomy, KU Leuven, Leuven, Belgium
| | - Filip Tavernier
- MICAS, Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
| | - Michael Kraft
- Micro- and Nanosystems (MNS), Department of Electrical Engineering (ESAT), KU Leuven, Leuven, Belgium
- Leuven Institute of Micro- and Nanoscale Integration (LIMNI), KU Leuven, Leuven, Belgium
| | - Hira Khan
- Center for Microbial Ecology and Technology (CMET), Department of Biotechnology, Ghent University, Ghent, Belgium
| | - Nico Boon
- Center for Microbial Ecology and Technology (CMET), Department of Biotechnology, Ghent University, Ghent, Belgium
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18
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Liu X, Zhou J, Yan R, Tang T, Wei S, Li R, Hou D, Weng Y, Wang D, Shen H, Zhou F, Tanaka Y, Li M, Hosokawa Y, Yalikun Y, Lei C. An optimized PDMS microfluidic device for ultra-fast and high-throughput imaging flow cytometry. LAB ON A CHIP 2023; 23:3571-3580. [PMID: 37401791 DOI: 10.1039/d3lc00237c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
Imaging flow cytometry (IFC) is a powerful tool for cell detection and analysis due to its high throughput and compatibility in image acquisition. Optical time-stretch (OTS) imaging is considered as one of the most promising imaging techniques for IFC because it can realize cell imaging at a flow speed of around 60 m s-1. However, existing PDMS-based microchannels cannot function at flow velocities higher than 10 m s-1; thus the capability of OTS-based IFC is significantly limited. To overcome the velocity barrier for PDMS-based microchannels, we proposed an optimized design of PDMS-based microchannels with reduced hydraulic resistance and 3D hydrodynamic focusing capability, which can drive fluids at an ultra-high flow velocity (of up to 40 m s-1) by using common syringe pumps. To verify the feasibility of our design, we fabricated and installed the microchannel in an OTS IFC system. The experimental results first proved that the proposed microchannel can support a stable flow velocity of up to 40 m s-1 without any leakage or damage. Then, we demonstrated that the OTS IFC is capable of imaging cells at a velocity of up to 40 m s-1 with good quality. To the best of our knowledge, it is the first time that IFC has achieved such a high flow velocity just by using a PDMS-glass chip. Moreover, high velocity can enhance the focusing of cells on the optical focal plane, increasing the number of detected cells and the throughput. This work provides a promising solution for IFC to fully release its capability of advanced imaging techniques by operating at an extremely high screening throughput.
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Affiliation(s)
- Xun Liu
- The Institute of Technological Sciences, Wuhan University, Wuhan, 430072, China.
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
| | - Jiehua Zhou
- The Institute of Technological Sciences, Wuhan University, Wuhan, 430072, China.
| | - Ruopeng Yan
- The Institute of Technological Sciences, Wuhan University, Wuhan, 430072, China.
| | - Tao Tang
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
| | - Shubin Wei
- The Institute of Technological Sciences, Wuhan University, Wuhan, 430072, China.
| | - Rubing Li
- The Institute of Technological Sciences, Wuhan University, Wuhan, 430072, China.
| | - Dan Hou
- The Institute of Technological Sciences, Wuhan University, Wuhan, 430072, China.
| | - Yueyun Weng
- The Institute of Technological Sciences, Wuhan University, Wuhan, 430072, China.
| | - Du Wang
- The Institute of Technological Sciences, Wuhan University, Wuhan, 430072, China.
| | - Hui Shen
- Department of Hematology, Renmin Hospital of Wuhan University, Wuhan, 430072, China
| | - Fuling Zhou
- Department of Hematology, Renmin Hospital of Wuhan University, Wuhan, 430072, China
| | - Yo Tanaka
- Center for Biosystems Dynamics Research (BDR), RIKEN, 1-3 Yamadaoka Suita, Osaka, 565-0871, Japan
| | - Ming Li
- School of Engineering, Macquarie University, Sydney, 2109, Australia
| | - Yoichiroh Hosokawa
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
| | - Yaxiaer Yalikun
- Division of Materials Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan.
| | - Cheng Lei
- The Institute of Technological Sciences, Wuhan University, Wuhan, 430072, China.
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19
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Wang Y, Huang Z, Wang X, Yang F, Yao X, Pan T, Li B, Chu J. Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms. LAB ON A CHIP 2023; 23:3615-3627. [PMID: 37458395 DOI: 10.1039/d3lc00194f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from heterogeneous cellular populations. However, the high-speed flow of fluorescent cells leads to motion blur in cell images, making it challenging to identify cell types from the raw images. In this study, we present a real-time single-cell imaging and classification system based on a fluorescence microscope and deep learning algorithm, which is able to directly identify cell types from motion-blur images. To obtain annotated datasets of blurred images for deep learning model training, we developed a motion deblurring algorithm for the reconstruction of blur-free images. To demonstrate the ability of this system, deblurred images of HeLa cells with various fluorescent labels and HeLa cells at different cell cycle stages were acquired. The trained ResNet achieved a high accuracy of 96.6% for single-cell classification of HeLa cells in three different mitotic stages, with a short processing time of only 2 ms. This technology provides a simple way to realize single-cell fluorescence IFC and real-time cell classification, offering significant potential in various biological and medical applications.
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Affiliation(s)
- Yiming Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Ziwei Huang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Xiaojie Wang
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Fengrui Yang
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, University of Science and Technology of China School of Life Sciences, Hefei, 230026, China
| | - Xuebiao Yao
- MOE Key Laboratory for Membraneless Organelles and Cellular Dynamics, University of Science and Technology of China School of Life Sciences, Hefei, 230026, China
| | - Tingrui Pan
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, 215123, China
| | - Baoqing Li
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
| | - Jiaru Chu
- Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, 230027, China.
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, University of Science and Technology of China, Hefei, 230027, China
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20
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Dudaie M, Barnea I, Nissim N, Shaked NT. On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning. Sci Rep 2023; 13:12370. [PMID: 37524884 PMCID: PMC10390541 DOI: 10.1038/s41598-023-38160-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 07/04/2023] [Indexed: 08/02/2023] Open
Abstract
We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.
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Affiliation(s)
- Matan Dudaie
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Itay Barnea
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Noga Nissim
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel
| | - Natan T Shaked
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, 69978, Tel Aviv, Israel.
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21
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Bacon B, Repin M, Shuryak I, Wu HC, Santella RM, Terry MB, Brenner DJ, Turner HC. High-throughput measurement of double strand break global repair phenotype in peripheral blood mononuclear cells after long-term cryopreservation. Cytometry A 2023; 103:575-583. [PMID: 36823754 PMCID: PMC10680149 DOI: 10.1002/cyto.a.24725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 01/02/2023] [Accepted: 02/21/2023] [Indexed: 02/25/2023]
Abstract
Peripheral blood mononuclear cells (PBMCs) are a useful model for biochemical assays, particularly for etiological studies. We describe here a method for measuring DNA repair capacity (DRC) in archival cryogenically preserved PBMCs. To model DRC, we measured γ-H2AX repair kinetics in thawed PBMCs after irradiation with 3 Gy gamma rays. Time-dependent fluorescently labeled γ-H2AX levels were measured at five time points from 1 to 20 h, yielding an estimate of global DRC repair kinetics as well as a measure of unrepaired double strand breaks at 20 h. While γ-H2AX levels are traditionally measured by either microscopy or flow-cytometry, we developed a protocol for imaging flow cytometry (IFC) that combines the detailed information of microscopy with the statistical power of flow methods. The visual imaging component of the IFC allows for monitoring aspects such as cellular health and apoptosis as well as fluorescence localization of the γ-H2AX signal, which ensures the power and significance of this technique. Application of a machine-learning based image classification improved flow cytometry fluorescent measurements by identifying apoptotic cells unable to undergo DNA repair. We present here DRC repair parameters from 18 frozen archival PBMCs and 28 fresh blood samples collected from a demographically diverse cohort of women measured in a high-throughput IFC format. This thaw method and assay can be used alone or in conjunction with other assays to measure etiological phenotypes in cryogenic biobanks of PBMCs.
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Affiliation(s)
- Bezalel Bacon
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
| | - Mikhail Repin
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
| | - Igor Shuryak
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
| | - Hui-Chen Wu
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center
| | - Regina M. Santella
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center
| | - Mary Beth Terry
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center
- Department of Epidemiology, Mailman School of Public Health, Columbia University, Irving Medical Center, New York
| | - David J. Brenner
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
| | - Helen C. Turner
- Center for Radiological Research, Columbia University Irving Medical Center, New York, (NY)
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22
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Bouzid K, Greener J, Carrara S, Gosselin B. Portable impedance-sensing device for microorganism characterization in the field. Sci Rep 2023; 13:10526. [PMID: 37386229 PMCID: PMC10310846 DOI: 10.1038/s41598-023-37506-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/22/2023] [Indexed: 07/01/2023] Open
Abstract
A variety of biosensors have been proposed to quickly detect and measure the properties of individual microorganisms among heterogeneous populations, but challenges related to cost, portability, stability, sensitivity, and power consumption limit their applicability. This study proposes a portable microfluidic device based on impedance flow-cytometry and electrical impedance spectroscopy that can detect and quantify the size of microparticles larger than 45 µm, such as algae and microplastics. The system is low cost ($300), portable (5 cm [Formula: see text] 5 cm), low-power (1.2 W), and easily fabricated utilizing a 3D-printer and industrial printed circuit board technology. The main novelty we demonstrate is the use of square wave excitation signal for impedance measurements with quadrature phase-sensitive detectors. A linked algorithm removes the errors associated to higher order harmonics. After validating the performance of the device for complex impedance models, we used it to detect and differentiate between polyethylene microbeads of sizes between 63 and 83 µm, and buccal cells between 45 and 70 µm. A precision of 3% is reported for the measured impedance and a minimum size of 45 µm is reported for the particle characterization.
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Affiliation(s)
- Karim Bouzid
- Department of Electrical and Computer Engineering, Laval University, Quebec-City, G1V 0A6, Canada.
| | - Jesse Greener
- Department of Chemistry, Laval University, Quebec-City, G1V 0A6, Canada
| | - Sandro Carrara
- Institute of Electrical and Micro Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Benoit Gosselin
- Department of Electrical and Computer Engineering, Laval University, Quebec-City, G1V 0A6, Canada
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23
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Conley HE, Sheats MK. Targeting Neutrophil β 2-Integrins: A Review of Relevant Resources, Tools, and Methods. Biomolecules 2023; 13:892. [PMID: 37371473 DOI: 10.3390/biom13060892] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/15/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
Neutrophils are important innate immune cells that respond during inflammation and infection. These migratory cells utilize β2-integrin cell surface receptors to move out of the vasculature into inflamed tissues and to perform various anti-inflammatory responses. Although critical for fighting off infection, neutrophil responses can also become dysregulated and contribute to disease pathophysiology. In order to limit neutrophil-mediated damage, investigators have focused on β2-integrins as potential therapeutic targets, but so far these strategies have failed in clinical trials. As the field continues to move forward, a better understanding of β2-integrin function and signaling will aid the design of future therapeutics. Here, we provide a detailed review of resources, tools, experimental methods, and in vivo models that have been and will continue to be utilized to investigate the vitally important cell surface receptors, neutrophil β2-integrins.
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Affiliation(s)
- Haleigh E Conley
- Department of Clinical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27607, USA
| | - M Katie Sheats
- Department of Clinical Sciences, College of Veterinary Medicine, North Carolina State University, Raleigh, NC 27607, USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27607, USA
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24
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Bravo DD, Shi Y, Sheu A, Liang WC, Lin W, Wu Y, Yan M, Wang J. A Real-Time Image-Based Efferocytosis Assay for the Discovery of Functionally Inhibitory Anti-MerTK Antibodies. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 210:1166-1176. [PMID: 36881873 PMCID: PMC10067786 DOI: 10.4049/jimmunol.2200597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 02/12/2023] [Indexed: 03/09/2023]
Abstract
Efferocytosis is a phagocytic process by which apoptotic cells are cleared by professional and nonprofessional phagocytic cells. In tumors, efferocytosis of apoptotic cancer cells by tumor-associated macrophages prevents Ag presentation and suppresses the host immune response against the tumor. Therefore, reactivating the immune response by blockade of tumor-associated macrophage-mediated efferocytosis is an attractive strategy for cancer immunotherapy. Even though several methods have been developed to monitor efferocytosis, an automated and high-throughput quantitative assay should offer highly desirable advantages for drug discovery. In this study, we describe a real-time efferocytosis assay with an imaging system for live-cell analysis. Using this assay, we successfully discovered potent anti-MerTK Abs that block tumor-associated macrophage-mediated efferocytosis in mice. Furthermore, we used primary human and cynomolgus monkey macrophages to identify and characterize anti-MerTK Abs for potential clinical development. By studying the phagocytic activities of different types of macrophages, we demonstrated that our efferocytosis assay is robust for screening and characterization of drug candidates that inhibit unwanted efferocytosis. Moreover, our assay is also applicable to investigating the kinetics and molecular mechanisms of efferocytosis/phagocytosis.
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Affiliation(s)
- Daniel D. Bravo
- Department of Biochemical and Cellular Pharmacology, Genentech, Inc., South San Francisco, CA
| | - Yongchang Shi
- Department of Biochemical and Cellular Pharmacology, Genentech, Inc., South San Francisco, CA
| | - Allison Sheu
- Department of Molecular and Cellular Biology, University of California, Berkeley, Berkeley, CA
| | - Wei-Ching Liang
- Department of Antibody Engineering, Genentech, Inc., South San Francisco, CA
| | - WeiYu Lin
- Department of Antibody Engineering, Genentech, Inc., South San Francisco, CA
| | - Yan Wu
- Department of Antibody Engineering, Genentech, Inc., South San Francisco, CA
| | - Minhong Yan
- Department of Molecular Oncology, Genentech, Inc., South San Francisco, CA
| | - Jianyong Wang
- Department of Biochemical and Cellular Pharmacology, Genentech, Inc., South San Francisco, CA
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25
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Son J, Mandracchia B, Silva Trenkle AD, Kwong GA, Jia S. Portable light-sheet optofluidic microscopy for 3D fluorescence imaging flow cytometry. LAB ON A CHIP 2023; 23:624-630. [PMID: 36633262 PMCID: PMC9931680 DOI: 10.1039/d2lc01024k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Imaging flow cytometry (IFC) combines conventional flow cytometry with optical microscopy, allowing for high-throughput, multi-parameter screening of single-cell specimens with morphological and spatial information. However, current 3D IFC systems are limited by instrumental complexity and incompatibility with available microfluidic devices or operations. Here, we report portable light-sheet optofluidic microscopy (PLSOM) for 3D fluorescence cytometric imaging. PLSOM exploits a compact, open-top light-sheet configuration compatible with commonly adopted microfluidic chips. The system offers a subcellular resolution (2-4 μm) in all three dimensions, high throughput (∼1000 cells per s), and portability (30 cm (l) × 10 cm (w) × 26 cm (h)). We demonstrated PLSOM for 3D IFC using various phantom and cell systems. The low-cost and custom-built architecture of PLSOM permits easy adaptability and dissemination for broad 3D flow cytometric investigations.
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Affiliation(s)
- Jeonghwan Son
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
| | - Biagio Mandracchia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
| | - Aaron D Silva Trenkle
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
| | - Gabriel A Kwong
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
- Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Shu Jia
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA 30332, USA.
- Parker H. Petit Institute of Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
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26
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Welsh JA, Arkesteijn GJA, Bremer M, Cimorelli M, Dignat-George F, Giebel B, Görgens A, Hendrix A, Kuiper M, Lacroix R, Lannigan J, van Leeuwen TG, Lozano-Andrés E, Rao S, Robert S, de Rond L, Tang VA, Tertel T, Yan X, Wauben MHM, Nolan JP, Jones JC, Nieuwland R, van der Pol E. A compendium of single extracellular vesicle flow cytometry. J Extracell Vesicles 2023; 12:e12299. [PMID: 36759917 PMCID: PMC9911638 DOI: 10.1002/jev2.12299] [Citation(s) in RCA: 46] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 11/29/2022] [Accepted: 12/17/2022] [Indexed: 02/11/2023] Open
Abstract
Flow cytometry (FCM) offers a multiparametric technology capable of characterizing single extracellular vesicles (EVs). However, most flow cytometers are designed to detect cells, which are larger than EVs. Whereas cells exceed the background noise, signals originating from EVs partly overlap with the background noise, thereby making EVs more difficult to detect than cells. This technical mismatch together with complexity of EV-containing fluids causes limitations and challenges with conducting, interpreting and reproducing EV FCM experiments. To address and overcome these challenges, researchers from the International Society for Extracellular Vesicles (ISEV), International Society for Advancement of Cytometry (ISAC), and the International Society on Thrombosis and Haemostasis (ISTH) joined forces and initiated the EV FCM working group. To improve the interpretation, reporting, and reproducibility of future EV FCM data, the EV FCM working group published an ISEV position manuscript outlining a framework of minimum information that should be reported about an FCM experiment on single EVs (MIFlowCyt-EV). However, the framework contains limited background information. Therefore, the goal of this compendium is to provide the background information necessary to design and conduct reproducible EV FCM experiments. This compendium contains background information on EVs, the interaction between light and EVs, FCM hardware, experimental design and preanalytical procedures, sample preparation, assay controls, instrument data acquisition and calibration, EV characterization, and data reporting. Although this compendium focuses on EVs, many concepts and explanations could also be applied to FCM detection of other particles within the EV size range, such as bacteria, lipoprotein particles, milk fat globules, and viruses.
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Affiliation(s)
- Joshua A Welsh
- Translational Nanobiology Section, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Ger J A Arkesteijn
- Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Michel Bremer
- Institute for Transfusion Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Michael Cimorelli
- Vesicle Observation Center, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Experimental Clinical Chemistry, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Department of Chemical Engineering, Drexel University, Philadelphia, Pennsylvania, USA
| | - Françoise Dignat-George
- Aix Marseille Univ, INSERM, INRAE, C2VN, UFR de Pharmacie, Marseille, France
- Hematology and Vascular Biology Department, CHU La Conception, Assistance Publique-Hôpitaux de Marseille, Marseille, France
| | - Bernd Giebel
- Institute for Transfusion Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - André Görgens
- Institute for Transfusion Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Clinical Research Center, Department for Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
- Evox Therapeutics Ltd, Oxford, UK
| | - An Hendrix
- Laboratory of Experimental Cancer Research, Department of Human Structure and Repair, Ghent University, Ghent, Belgium
| | - Martine Kuiper
- Vesicle Observation Center, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Experimental Clinical Chemistry, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Biomedical Engineering & Physics, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Dutch Metrology Institute, VSL, Delft, The Netherlands
| | - Romaric Lacroix
- Aix Marseille Univ, INSERM, INRAE, C2VN, UFR de Pharmacie, Marseille, France
- Hematology and Vascular Biology Department, CHU La Conception, Assistance Publique-Hôpitaux de Marseille, Marseille, France
| | - Joanne Lannigan
- Flow Cytometry Support Services, LLC, Arlington, Virginia, USA
| | - Ton G van Leeuwen
- Vesicle Observation Center, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Biomedical Engineering & Physics, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis and Ischemic Syndromes, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Estefanía Lozano-Andrés
- Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - Shoaib Rao
- Institute for Transfusion Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Stéphane Robert
- Aix Marseille Univ, INSERM, INRAE, C2VN, UFR de Pharmacie, Marseille, France
- Hematology and Vascular Biology Department, CHU La Conception, Assistance Publique-Hôpitaux de Marseille, Marseille, France
| | - Leonie de Rond
- Vesicle Observation Center, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Experimental Clinical Chemistry, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Biomedical Engineering & Physics, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Vera A Tang
- Flow Cytometry & Virometry Core Facility, Faculty of Medicine, Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, Ontario, Canada
| | - Tobias Tertel
- Institute for Transfusion Medicine, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Xiaomei Yan
- MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, Key Laboratory for Chemical Biology of Fujian Province, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, Fujian, People's Republic of China
| | - Marca H M Wauben
- Department of Biomolecular Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
| | - John P Nolan
- Scintillon Institute, San Diego, California, USA
- Cellarcus Biosciences, San Diego, California, USA
| | - Jennifer C Jones
- Translational Nanobiology Section, Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Rienk Nieuwland
- Vesicle Observation Center, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Experimental Clinical Chemistry, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis and Ischemic Syndromes, Amsterdam, The Netherlands
| | - Edwin van der Pol
- Vesicle Observation Center, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Experimental Clinical Chemistry, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Biomedical Engineering & Physics, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Cardiovascular Sciences, Atherosclerosis and Ischemic Syndromes, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
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27
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Gao K, Kaye NM, Ayati M, Koyuturk M, Calabrese JR, Christian E, Lazarus HM, Kaplan D. Divergent Directionality of Immune Cell-Specific Protein Expression between Bipolar Lithium Responders and Non-Responders Revealed by Enhanced Flow Cytometry. Medicina (B Aires) 2023; 59:medicina59010120. [PMID: 36676744 PMCID: PMC9860624 DOI: 10.3390/medicina59010120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/02/2023] [Accepted: 01/05/2023] [Indexed: 01/11/2023] Open
Abstract
Background and Objectives: There is no biomarker to predict lithium response. This study used CellPrint™ enhanced flow cytometry to study 28 proteins representing a spectrum of cellular pathways in monocytes and CD4+ lymphocytes before and after lithium treatment in patients with bipolar disorder (BD). Materials and Methods: Symptomatic patients with BD type I or II received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks. Patients were assessed with standard rating scales and divided into two groups, responders (≥50% improvement from baseline) and non-responders. Twenty-eight intracellular proteins in CD4+ lymphocytes and monocytes were analyzed with CellPrint™, an enhanced flow cytometry procedure. Data were analyzed for differences in protein expression levels. Results: The intent-to-treat sample included 13 lithium-responders (12 blood samples before treatment and 9 after treatment) and 11 lithium-non-responders (11 blood samples before treatment and 4 after treatment). No significant differences in expression between the groups was observed prior to lithium treatment. After treatment, the majority of analytes increased expression in responders and decreased expression in non-responders. Significant increases were seen for PDEB4 and NR3C1 in responders. A significant decrease was seen for NR3C1 in non-responders. Conclusions: Lithium induced divergent directionality of protein expression depending on the whether the patient was a responder or non-responder, elucidating molecular characteristics of lithium responsiveness. A subsequent study with a larger sample size is warranted.
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Affiliation(s)
- Keming Gao
- Department of Psychiatry, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
- Correspondence: ; Tel.: +1-216-844-2400; Fax: +1-214-844-2877
| | | | - Marzieh Ayati
- Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX 78539, USA
| | - Mehmet Koyuturk
- Department of Computer and Data Sciences, Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Joseph R. Calabrese
- Department of Psychiatry, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | | | - Hillard M. Lazarus
- Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
- CellPrint Biotechnology, Cleveland, OH 44106, USA
| | - David Kaplan
- CellPrint Biotechnology, Cleveland, OH 44106, USA
- Department of Medicine-Hematology/Oncology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
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28
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Jiang B, Xie D, Wang S, Li X, Wu G. Advances in early detection methods for solid tumors. Front Genet 2023; 14:1091223. [PMID: 36911396 PMCID: PMC9998680 DOI: 10.3389/fgene.2023.1091223] [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: 11/06/2022] [Accepted: 02/13/2023] [Indexed: 02/26/2023] Open
Abstract
During the last decade, non-invasive methods such as liquid biopsy have slowly replaced traditional imaging and invasive pathological methods used to diagnose and monitor cancer. Improvements in the available detection methods have enabled the early screening and diagnosis of solid tumors. In addition, advances in early detection methods have made the continuous monitoring of tumor progression using repeat sampling possible. Previously, the focus of liquid biopsy techniques included the following: 1) the isolation of circulating tumor cells, circulating tumor DNA, and extracellular tumor vesicles from solid tumor cells in the patient's blood; in addition to 2) analyzing genomic and proteomic data contained within the isolates. Recently, there has been a rapid devolvement in the techniques used to isolate and analyze molecular markers. This rapid evolvement in detection techniques improves their accuracy, especially when few samples are available. In addition, there is a tremendous expansion in the acquisition of samples and targets for testing; solid tumors can be detected from blood and other body fluids. Test objects have also expanded from samples taken directly from cancer to include indirect objects affected in cancer development. Liquid biopsy technology has limitations. Even so, this detection technique is the key to a new phase of oncogenetics. This review aims to provide an overview of the current advances in liquid biopsy marker selection, isolation, and detection methods for solid tumors. The advantages and disadvantages of liquid biopsy technology will also be explored.
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Affiliation(s)
- Bowen Jiang
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Deqian Xie
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shijin Wang
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Xiunan Li
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Guangzhen Wu
- Department of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
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29
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Tang R, Xia L, Gutierrez B, Gagne I, Munoz A, Eribez K, Jagnandan N, Chen X, Zhang Z, Waller L, Alaynick W, Cho SH, An C, Lo YH. Low-latency label-free image-activated cell sorting using fast deep learning and AI inferencing. Biosens Bioelectron 2023; 220:114865. [DOI: 10.1016/j.bios.2022.114865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/27/2022] [Accepted: 10/25/2022] [Indexed: 11/09/2022]
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30
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Railean V, Buszewski B. Flow Cytometry - Sophisticated Tool for Basic Research or/and Routine Diagnosis; Impact of the Complementarity in Both Pre- as Well as Clinical Studies. Crit Rev Anal Chem 2022:1-23. [PMID: 36576036 DOI: 10.1080/10408347.2022.2154596] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Flow cytometry is a sophisticated technology used widely in both basic research and as a routine tool in clinical diagnosis. The technology has progressed from single parameter detection in the 1970s and 1980s to high end multicolor analysis, with currently 30 parameters detected simultaneously, allowing the identification and purification of rare subpopulations of cells of interest. Flow cytometry continues to evolve and expand to facilitate the investigation of new diagnostic and therapeutic avenues. The present review gives an overview of basic theory and instrumentation, presents and compares the advantages and disadvantages of conventional, spectral and imaging flow cytometry as well as mass cytometry. Current methodologies and applications in both research, pre- and clinical settings are discussed, as well as potential limitations and future evolution. This finding encourages the reader to promote such relationship between basic science, diagnosis and multidisciplinary approach since the standard methods have limitations (e.g., in differentiating the cells after staining). Moreover, such path inspires future cytometry specialists develop new/alternative frontiers between pre- and clinical diagnosis and be more flexible in designing the study for both human as well as veterinary medicine.
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Affiliation(s)
- Viorica Railean
- Department of Infectious, Invasive Diseases and Veterinary Administration, Institute of Veterinary Medicine, Toruń, Poland
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Torun, Poland
| | - Bogusław Buszewski
- Centre for Modern Interdisciplinary Technologies, Nicolaus Copernicus University, Torun, Poland
- Department of Environmental Chemistry and Bioanalysis, Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Toruń, Poland
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31
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Zhang T, Gao M, Chen X, Gao C, Feng S, Chen D, Wang J, Zhao X, Chen J. Demands and technical developments of clinical flow cytometry with emphasis in quantitative, spectral, and imaging capabilities. NANOTECHNOLOGY AND PRECISION ENGINEERING 2022. [DOI: 10.1063/10.0015301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
As the gold-standard method for single-cell analysis, flow cytometry enables high-throughput and multiple-parameter characterization of individual biological cells. This review highlights the demands for clinical flow cytometry in laboratory hematology (e.g., diagnoses of minimal residual disease and various types of leukemia), summarizes state-of-the-art clinical flow cytometers (e.g., FACSLyricTM by Becton Dickinson, DxFLEX by Beckman Coulter), then considers innovative technical improvements in flow cytometry (including quantitative, spectral, and imaging approaches) to address the limitations of clinical flow cytometry in hematology diagnosis. Finally, driven by these clinical demands, future developments in clinical flow cytometry are suggested.
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Affiliation(s)
- Ting Zhang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Mengge Gao
- Peking University People’s Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing 100044, People’s Republic of China
| | - Xiao Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Chiyuan Gao
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Shilun Feng
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, People’s Republic of China
| | - Deyong Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Junbo Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
| | - Xiaosu Zhao
- Peking University People’s Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing 100044, People’s Republic of China
| | - Jian Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, People’s Republic of China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
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Ni C, Zhou Z, Zhu Z, Jiang D, Xiang N. Controllable Size-Independent Three-Dimensional Inertial Focusing in High-Aspect-Ratio Asymmetric Serpentine Microchannels. Anal Chem 2022; 94:15639-15647. [DOI: 10.1021/acs.analchem.2c02361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Chen Ni
- School of Mechanical Engineering and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing211189, China
| | - Zheng Zhou
- School of Mechanical Engineering and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing211189, China
| | - Zhixian Zhu
- School of Mechanical Engineering and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing211189, China
| | - Di Jiang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing210037, China
- Jiangsu Yuyue Medical Equipment and Supply Co., Ltd., Jiangsu, Danyang212300, China
| | - Nan Xiang
- School of Mechanical Engineering and Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University, Nanjing211189, China
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Stolarek I, Samelak-Czajka A, Figlerowicz M, Jackowiak P. Dimensionality reduction by UMAP for visualizing and aiding in classification of imaging flow cytometry data. iScience 2022; 25:105142. [PMID: 36193047 PMCID: PMC9526149 DOI: 10.1016/j.isci.2022.105142] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/29/2022] [Accepted: 09/09/2022] [Indexed: 11/27/2022] Open
Abstract
Recent advances in imaging flow cytometry (IFC) have revolutionized high-throughput multiparameter analyses at single-cell resolution. Although enabling the discovery of population heterogeneities and the detection of rare events, IFC generates hyperdimensional datasets that demand innovative analytical approaches. Current methods work in a supervised manner, utilize only limited information content, or require large annotated reference datasets. Dimensionality reduction algorithms, including uniform manifold approximation and projection (UMAP), have been successfully applied to analyze the large number of parameters generated in various high-throughput techniques. Here, we apply a workflow incorporating UMAP to analyze different IFC datasets. We demonstrate that it out-competes other popular dimensionality reduction methods in speed and accuracy. Moreover, it enables fast visualization, clustering, and tagging of unannotated objects in large-scale experiments. We anticipate that our workflow will be a robust method to address complex IFC datasets, either alone or as an upstream addition to the deep learning approaches. UMAP dimensionality reduction provides fast and accurate method of IFC data analysis UMAP yields improved object clustering and tagging of the multispectral IFC data PCA decomposition allows multispectral signals merging for direct image embedding
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Affiliation(s)
- Ireneusz Stolarek
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
| | - Anna Samelak-Czajka
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
| | - Marek Figlerowicz
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
| | - Paulina Jackowiak
- Institute of Bioorganic Chemistry Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznań, Poland
- Corresponding author
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Zare Harofte S, Soltani M, Siavashy S, Raahemifar K. Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2203169. [PMID: 36026569 DOI: 10.1002/smll.202203169] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/16/2022] [Indexed: 05/14/2023]
Abstract
Nowadays, artificial intelligence (AI) creates numerous promising opportunities in the life sciences. AI methods can be significantly advantageous for analyzing the massive datasets provided by biotechnology systems for biological and biomedical applications. Microfluidics, with the developments in controlled reaction chambers, high-throughput arrays, and positioning systems, generate big data that is not necessarily analyzed successfully. Integrating AI and microfluidics can pave the way for both experimental and analytical throughputs in biotechnology research. Microfluidics enhances the experimental methods and reduces the cost and scale, while AI methods significantly improve the analysis of huge datasets obtained from high-throughput and multiplexed microfluidics. This review briefly presents a survey of the role of AI and microfluidics in biotechnology. Also, the incorporation of AI with microfluidics is comprehensively investigated. Specifically, recent studies that perform flow cytometry cell classification, cell isolation, and a combination of them by gaining from both AI methods and microfluidic techniques are covered. Despite all current challenges, various fields of biotechnology can be remarkably affected by the combination of AI and microfluidic technologies. Some of these fields include point-of-care systems, precision, personalized medicine, regenerative medicine, prognostics, diagnostics, and treatment of oncology and non-oncology-related diseases.
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Affiliation(s)
- Samaneh Zare Harofte
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Madjid Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
- Department of Electrical and Computer Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON, N2L 3G1, Canada
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran, 14176-14411, Iran
- Cancer Biology Research Center, Cancer Institute of Iran, Tehran University of Medical Sciences, Tehran, 14197-33141, Iran
| | - Saeed Siavashy
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, 19967-15433, Iran
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), Penn State University, State College, PA, 16801, USA
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
- Department of Chemical Engineering, Faculty of Engineering, University of Waterloo, 200 University Ave. W, Waterloo, ON, N2L 3G1, Canada
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Inoue A, Watanabe H, Kondo T, Katayama E, Miyazaki Y, Suehiro S, Yamashita D, Taniwaki M, Kurata M, Shigekawa S, Kitazawa R, Kunieda T. Usefulness of intraoperative rapid immunohistochemistry in the surgical treatment of brain tumors. Neuropathology 2022. [PMID: 36128673 DOI: 10.1111/neup.12864] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 08/15/2022] [Accepted: 08/16/2022] [Indexed: 11/27/2022]
Abstract
In the treatment of primary central nervous system lymphoma (PCNSL), intraoperative rapid pathological diagnosis can dramatically change the surgical strategy, and more accurate diagnostic methods are required. In April 2020, we adopted intraoperative rapid immunohistochemistry (IHC) in addition to conventional rapid intraoperative diagnosis based on morphological assessment, mainly for patients with PCNSL. Here, we investigate the usefulness and significance of intraoperative rapid IHC based on our initial experience. We performed intraoperative rapid IHC using antibodies for cluster of differentiation (CD)20, CD3, leukocyte common antigen (LCA) and glial fibrillary acidic protein (GFAP) using enzyme-labeled antibody methods in 25 patients, including PCNSL patients, from April 2020 to July 2022. We examined the utility of this approach in determining treatment strategies for brain tumors. Postoperative final pathological diagnoses from paraffin-embedded sections were as follows: diffuse large B-cell lymphoma, 16 cases; glioblastoma, six cases; pilocytic astrocytoma, one case; adenocarcinoma, one case; and inflammatory disorder, one case. The entire process took 32 min and staining for CD20, CD3, LCA, and GFAP was comparable to that using paraffin-embedded sections. In all cases, the results of intraoperative rapid IHC were consistent with final pathological diagnoses from paraffin-embedded sections. In addition, in two cases, the results of conventional intraoperative rapid pathological diagnosis based on morphological assessments using frozen sections were drastically changed by adding intraoperative rapid IHC. Intraoperative rapid IHC contributes to deciding appropriate treatment strategies and facilitating early initiation of chemotherapy for PCNSL. This may allow new therapeutic strategies not only for PCNSL but also for other brain tumors.
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Affiliation(s)
- Akihiro Inoue
- Department of Neurosurgery, Ehime University School of Medicine, Toon, Japan
| | - Hideaki Watanabe
- Department of Neurosurgery, Ehime University School of Medicine, Toon, Japan
| | - Takuya Kondo
- Division of Diagnostic Pathology, Ehime University Hospital, Toon, Japan
| | - Eiji Katayama
- Division of Diagnostic Pathology, Ehime University Hospital, Toon, Japan
| | - Yukihiro Miyazaki
- Department of Hematology, Clinical Immunology and Infectious Diseases, Ehime University Hospital, Toon, Japan
| | - Satoshi Suehiro
- Department of Neurosurgery, Ehime University School of Medicine, Toon, Japan
| | - Daisuke Yamashita
- Department of Neurosurgery, Ehime University School of Medicine, Toon, Japan
| | - Mashio Taniwaki
- Division of Diagnostic Pathology, Ehime University Hospital, Toon, Japan
| | - Mie Kurata
- Division of Diagnostic Pathology, Ehime University Hospital, Toon, Japan
| | - Seiji Shigekawa
- Department of Neurosurgery, Ehime University School of Medicine, Toon, Japan
| | - Riko Kitazawa
- Division of Diagnostic Pathology, Ehime University Hospital, Toon, Japan
| | - Takeharu Kunieda
- Department of Neurosurgery, Ehime University School of Medicine, Toon, Japan
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Kim B, Yao W, Rhie JW, Chun H. Microfluidic Potentiometric Cytometry for Size-Selective Micro Dispersion Analysis. BIOCHIP JOURNAL 2022. [DOI: 10.1007/s13206-022-00083-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ahmad A, Sala F, Paiè P, Candeo A, D'Annunzio S, Zippo A, Frindel C, Osellame R, Bragheri F, Bassi A, Rousseau D. On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy. LAB ON A CHIP 2022; 22:3453-3463. [PMID: 35946995 DOI: 10.1039/d2lc00482h] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Single-cell imaging and sorting are critical technologies in biology and clinical applications. The power of these technologies is increased when combined with microfluidics, fluorescence markers, and machine learning. However, this quest faces several challenges. One of these is the effect of the sample flow velocity on the classification performances. Indeed, cell flow speed affects the quality of image acquisition by increasing motion blur and decreasing the number of acquired frames per sample. We investigate how these visual distortions impact the final classification task in a real-world use-case of cancer cell screening, using a microfluidic platform in combination with light sheet fluorescence microscopy. We demonstrate, by analyzing both simulated and experimental data, that it is possible to achieve high flow speed and high accuracy in single-cell classification. We prove that it is possible to overcome the 3D slice variability of the acquired 3D volumes, by relying on their 2D sum z-projection transformation, to reach an efficient real time classification with an accuracy of 99.4% using a convolutional neural network with transfer learning from simulated data. Beyond this specific use-case, we provide a web platform to generate a synthetic dataset and to investigate the effect of flow speed on cell classification for any biological samples and a large variety of fluorescence microscopes (https://www.creatis.insa-lyon.fr/site7/en/MicroVIP).
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Affiliation(s)
- Ali Ahmad
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220 - INSERM U1206, Université Lyon 1, Insa de Lyon, Lyon, France
| | - Federico Sala
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Petra Paiè
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Alessia Candeo
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | | | | | - Carole Frindel
- Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), CNRS UMR 5220 - INSERM U1206, Université Lyon 1, Insa de Lyon, Lyon, France
| | - Roberto Osellame
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Francesca Bragheri
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - Andrea Bassi
- Department of Physics, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
- Istituto di Fotonica e Nanotecnologie, CNR, Piazza Leonardo da Vinci 32, 20133 Milano, Italy
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRAE IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
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Aidukas T, Konda PC, Harvey AR. High-speed multi-objective Fourier ptychographic microscopy. OPTICS EXPRESS 2022; 30:29189-29205. [PMID: 36299099 DOI: 10.1364/oe.466075] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 07/11/2022] [Indexed: 06/16/2023]
Abstract
The ability of a microscope to rapidly acquire wide-field, high-resolution images is limited by both the optical performance of the microscope objective and the bandwidth of the detector. The use of multiple detectors can increase electronic-acquisition bandwidth, but the use of multiple parallel objectives is problematic since phase coherence is required across the multiple apertures. We report a new synthetic-aperture microscopy technique based on Fourier ptychography, where both the illumination and image-space numerical apertures are synthesized, using a spherical array of low-power microscope objectives that focus images onto mutually incoherent detectors. Phase coherence across apertures is achieved by capturing diffracted fields during angular illumination and using ptychographic reconstruction to synthesize wide-field, high-resolution, amplitude and phase images. Compared to conventional Fourier ptychography, the use of multiple objectives reduces image acquisition times by increasing the area for sampling the diffracted field. We demonstrate the proposed scaleable architecture with a nine-objective microscope that generates an 89-megapixel, 1.1 µm resolution image nine-times faster than can be achieved with a single-objective Fourier-ptychographic microscope. New calibration procedures and reconstruction algorithms enable the use of low-cost 3D-printed components for longitudinal biological sample imaging. Our technique offers a route to high-speed, gigapixel microscopy, for example, imaging the dynamics of large numbers of cells at scales ranging from sub-micron to centimetre, with an enhanced possibility to capture rare phenomena.
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Meng Q, Li Y, Yu Y, Chu K, Smith ZJ. A Drop-in, Focus-Extending Phase Mask Simplifies Microscopic and Microfluidic Imaging Systems for Cost-Effective Point-of-Care Diagnostics. Anal Chem 2022; 94:11000-11007. [PMID: 35895976 DOI: 10.1021/acs.analchem.2c01421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Microscopic imaging and imaging flow cytometry have wide potential in point-of-care assays; however, their narrow depth of focus necessitates precise mechanical or fluidic focus control of a sample in order to acquire high-quality images that can be used for downstream analysis, increasing the cost and complexity of the imaging system. This complexity represents a barrier to miniaturization and translation of point-of-care assays based on microscopic imaging or imaging flow cytometry. To address this challenge, we present a simple drop-in phase mask with a physics-informed, circularly symmetric asphere phase profile that extends the depth of focus by >5-fold while largely preserving the image quality compared to other depth extending methods. We show that such a focus-extended system overcomes manufacturing tolerances in low-cost sample chambers, enlarges the useable field-of-view of low-cost objectives, and permits increased throughput and precision in flow imaging systems without the need for complex flow-focusing. As the image quality is preserved without the need for postacquisition image restoration, our solution is also highly appropriate for on-line applications such as cell sorting.
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Affiliation(s)
- Qi Meng
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Yaning Li
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Yajun Yu
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Kaiqin Chu
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu 215123, China
| | - Zachary J Smith
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230027, China
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Zhang Y, Zhao Y, Cole T, Zheng J, Bayinqiaoge, Guo J, Tang SY. Microfluidic flow cytometry for blood-based biomarker analysis. Analyst 2022; 147:2895-2917. [PMID: 35611964 DOI: 10.1039/d2an00283c] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Flow cytometry has proven its capability for rapid and quantitative analysis of individual cells and the separation of targeted biological samples from others. The emerging microfluidics technology makes it possible to develop portable microfluidic diagnostic devices for point-of-care testing (POCT) applications. Microfluidic flow cytometry (MFCM), where flow cytometry and microfluidics are combined to achieve similar or even superior functionalities on microfluidic chips, provides a powerful single-cell characterisation and sorting tool for various biological samples. In recent years, researchers have made great progress in the development of the MFCM including focusing, detecting, and sorting subsystems, and its unique capabilities have been demonstrated in various biological applications. Moreover, liquid biopsy using blood can provide various physiological and pathological information. Thus, biomarkers from blood are regarded as meaningful circulating transporters of signal molecules or particles and have great potential to be used as non (or minimally)-invasive diagnostic tools. In this review, we summarise the recent progress of the key subsystems for MFCM and its achievements in blood-based biomarker analysis. Finally, foresight is offered to highlight the research challenges faced by MFCM in expanding into blood-based POCT applications, potentially yielding commercialisation opportunities.
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Affiliation(s)
- Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Ying Zhao
- National Chengdu Centre of Safety Evaluation of Drugs, West China Hospital of Sichuan University, Chengdu, China
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Bayinqiaoge
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
| | - Jinhong Guo
- The M.O.E. Key Laboratory of Laboratory Medical Diagnostics, The College of Laboratory Medicine, Chongqing Medical University, #1 Yixueyuan Road, Yuzhong District, Chongqing, 400016, China.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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Wang M, Liang H, Chen X, Chen D, Wang J, Zhang Y, Chen J. Developments of Conventional and Microfluidic Flow Cytometry Enabling High-Throughput Characterization of Single Cells. BIOSENSORS 2022; 12:bios12070443. [PMID: 35884246 PMCID: PMC9313373 DOI: 10.3390/bios12070443] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
This article first reviews scientific meanings of single-cell analysis by highlighting two key scientific problems: landscape reconstruction of cellular identities during dynamic immune processes and mechanisms of tumor origin and evolution. Secondly, the article reviews clinical demands of single-cell analysis, which are complete blood counting enabled by optoelectronic flow cytometry and diagnosis of hematologic malignancies enabled by multicolor fluorescent flow cytometry. Then, this article focuses on the developments of optoelectronic flow cytometry for the complete blood counting by comparing conventional counterparts of hematology analyzers (e.g., DxH 900 of Beckman Coulter, XN-1000 of Sysmex, ADVIA 2120i of Siemens, and CELL-DYN Ruby of Abbott) and microfluidic counterparts (e.g., microfluidic impedance and imaging flow cytometry). Future directions of optoelectronic flow cytometry are indicated where intrinsic rather than dependent biophysical parameters of blood cells must be measured, and they can replace blood smears as the gold standard of blood analysis in the near future.
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Affiliation(s)
- Minruihong Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongyan Liang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiao Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Deyong Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junbo Wang
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (J.W.); (Y.Z.); (J.C.)
| | - Yuan Zhang
- Materials Genome Institute, Shanghai University, Shanghai 200444, China
- Correspondence: (J.W.); (Y.Z.); (J.C.)
| | - Jian Chen
- State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China; (M.W.); (H.L.); (X.C.); (D.C.)
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100049, China
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Correspondence: (J.W.); (Y.Z.); (J.C.)
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Pan J, Wang R, Shang F, Ma R, Rong Y, Zhang Y. Functional Micropeptides Encoded by Long Non-Coding RNAs: A Comprehensive Review. Front Mol Biosci 2022; 9:817517. [PMID: 35769907 PMCID: PMC9234465 DOI: 10.3389/fmolb.2022.817517] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 05/24/2022] [Indexed: 12/03/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) were originally defined as non-coding RNAs (ncRNAs) which lack protein-coding ability. However, with the emergence of technologies such as ribosome profiling sequencing and ribosome-nascent chain complex sequencing, it has been demonstrated that most lncRNAs have short open reading frames hence the potential to encode functional micropeptides. Such micropeptides have been described to be widely involved in life-sustaining activities in several organisms, such as homeostasis regulation, disease, and tumor occurrence, and development, and morphological development of animals, and plants. In this review, we focus on the latest developments in the field of lncRNA-encoded micropeptides, and describe the relevant computational tools and techniques for micropeptide prediction and identification. This review aims to serve as a reference for future research studies on lncRNA-encoded micropeptides.
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Affiliation(s)
- Jianfeng Pan
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Ruijun Wang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
- Key Laboratory of Mutton Sheep Genetics and Breeding, Ministry of Agriculture, Hohhot, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Hohhot, China
- Engineering Research Center for Goat Genetics and Breeding, Hohhot, China
| | - Fangzheng Shang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Rong Ma
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Youjun Rong
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
| | - Yanjun Zhang
- College of Animal Science, Inner Mongolia Agricultural University, Hohhot, China
- Key Laboratory of Mutton Sheep Genetics and Breeding, Ministry of Agriculture, Hohhot, China
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Hohhot, China
- Engineering Research Center for Goat Genetics and Breeding, Hohhot, China
- *Correspondence: Yanjun Zhang,
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43
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Ugawa M, Ota S. High‐Throughput Parallel Optofluidic 3D‐Imaging Flow Cytometry. SMALL SCIENCE 2022. [DOI: 10.1002/smsc.202100126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Affiliation(s)
- Masashi Ugawa
- Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba, Meguro-ku Tokyo 153-8904 Japan
| | - Sadao Ota
- Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba, Meguro-ku Tokyo 153-8904 Japan
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Ugawa M, Ota S. High-speed 3D imaging flow cytometry with optofluidic spatial transformation. BIOMEDICAL OPTICS EXPRESS 2022; 13:3647-3656. [PMID: 35781959 PMCID: PMC9208600 DOI: 10.1364/boe.455714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 06/15/2023]
Abstract
Three-dimensional (3D) fluorescence imaging is important to accurately capture and understand biological structures and phenomena. However, because of its slow acquisition speed, it was difficult to implement 3D fluorescence imaging for imaging flow cytometry. Especially, modern flow cytometers operate at a flow velocity of 1-10 m/s, and no 3D fluorescence imaging technique was able to capture cells at such high velocity. Here, we present a high-speed 3D fluorescence imaging technique in which a set of optical cross sections of a cell is captured within a single frame of a camera by combining strobe light-sheet excitation and optofluidic spatial transformation. Using this technique, we demonstrated 3D fluorescence imaging of cells flowing at a velocity of over 10 m/s, which is the fastest to our knowledge. Such technology can allow integration of 3D imaging with flow systems of common flow cytometers and cell sorters.
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Mo Y, Jiao Y. Advances and applications of single-cell omics technologies in plant research. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 110:1551-1563. [PMID: 35426954 DOI: 10.1111/tpj.15772] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 06/14/2023]
Abstract
Single-cell sequencing approaches reveal the intracellular dynamics of individual cells and answer biological questions with high-dimensional catalogs of millions of cells, including genomics, transcriptomics, chromatin accessibility, epigenomics, and proteomics data across species. These emerging yet thriving technologies have been fully embraced by the field of plant biology, with a constantly expanding portfolio of applications. Here, we introduce the current technical advances used for single-cell omics, especially single-cell genome and transcriptome sequencing. Firstly, we overview methods for protoplast and nucleus isolation and genome and transcriptome amplification. Subsequently, we use well-executed benchmarking studies to highlight advances made through the application of single-cell omics techniques. Looking forward, we offer a glimpse of additional hurdles and future opportunities that will introduce broad adoption of single-cell sequencing with revolutionary perspectives in plant biology.
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Affiliation(s)
- Yajin Mo
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Center for Quantitative Biology, School of Life Sciences, Peking University, Beijing, 100871, China
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Yuling Jiao
- State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Center for Quantitative Biology, School of Life Sciences, Peking University, Beijing, 100871, China
- State Key Laboratory of Plant Genomics and National Center for Plant Gene Research (Beijing), Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, 100101, China
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Kumar R. Materiomically Designed Polymeric Vehicles for Nucleic Acids: Quo Vadis? ACS APPLIED BIO MATERIALS 2022; 5:2507-2535. [PMID: 35642794 DOI: 10.1021/acsabm.2c00346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Despite rapid advances in molecular biology, particularly in site-specific genome editing technologies, such as CRISPR/Cas9 and base editing, financial and logistical challenges hinder a broad population from accessing and benefiting from gene therapy. To improve the affordability and scalability of gene therapy, we need to deploy chemically defined, economical, and scalable materials, such as synthetic polymers. For polymers to deliver nucleic acids efficaciously to targeted cells, they must optimally combine design attributes, such as architecture, length, composition, spatial distribution of monomers, basicity, hydrophilic-hydrophobic phase balance, or protonation degree. Designing polymeric vectors for specific nucleic acid payloads is a multivariate optimization problem wherein even minuscule deviations from the optimum are poorly tolerated. To explore the multivariate polymer design space rapidly, efficiently, and fruitfully, we must integrate parallelized polymer synthesis, high-throughput biological screening, and statistical modeling. Although materiomics approaches promise to streamline polymeric vector development, several methodological ambiguities must be resolved. For instance, establishing a flexible polymer ontology that accommodates recent synthetic advances, enforcing uniform polymer characterization and data reporting standards, and implementing multiplexed in vitro and in vivo screening studies require considerable planning, coordination, and effort. This contribution will acquaint readers with the challenges associated with materiomics approaches to polymeric gene delivery and offers guidelines for overcoming these challenges. Here, we summarize recent developments in combinatorial polymer synthesis, high-throughput screening of polymeric vectors, omics-based approaches to polymer design, barcoding schemes for pooled in vitro and in vivo screening, and identify materiomics-inspired research directions that will realize the long-unfulfilled clinical potential of polymeric carriers in gene therapy.
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Affiliation(s)
- Ramya Kumar
- Department of Chemical & Biological Engineering, Colorado School of Mines, 1613 Illinois St, Golden, Colorado 80401, United States
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Caselli F, Reale R, De Ninno A, Spencer D, Morgan H, Bisegna P. Deciphering impedance cytometry signals with neural networks. LAB ON A CHIP 2022; 22:1714-1722. [PMID: 35353108 DOI: 10.1039/d2lc00028h] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Microfluidic impedance cytometry is a label-free technique for high-throughput single-cell analysis. Multi-frequency impedance measurements provide data that allows full characterisation of cells, linking electrical phenotype to individual biophysical properties. To efficiently extract the information embedded in the electrical signals, potentially in real-time, tailored signal processing is needed. Artificial intelligence approaches provide a promising new direction. Here we demonstrate the ability of neural networks to decipher impedance cytometry signals in two challenging scenarios: (i) to determine the intrinsic dielectric properties of single cells directly from raw impedance data streams, (ii) to capture single-cell signals that are hidden in the measured signals of coincident cells. The accuracy of the results and the high processing speed (fractions of ms per cell) demonstrate that neural networks can have an important role in impedance-based single-cell analysis.
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Affiliation(s)
- Federica Caselli
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy.
| | - Riccardo Reale
- Center for Life Nano Science@Sapienza, Italian Institute of Technology (IIT), Rome, Italy
| | - Adele De Ninno
- Italian National Research Council - Institute for Photonics and Nanotechnologies (CNR - IFN), Rome, Italy
| | - Daniel Spencer
- School of Electronics and Computing Science, and, Institute for Life Sciences, University of Southampton, Highfield, Southampton, UK
| | - Hywel Morgan
- School of Electronics and Computing Science, and, Institute for Life Sciences, University of Southampton, Highfield, Southampton, UK
| | - Paolo Bisegna
- Department of Civil Engineering and Computer Science, University of Rome Tor Vergata, Rome, Italy.
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van de Velde CC, Joseph C, Biclot A, Huys GRB, Pinheiro VB, Bernaerts K, Raes J, Faust K. Fast quantification of gut bacterial species in cocultures using flow cytometry and supervised classification. ISME COMMUNICATIONS 2022; 2:40. [PMID: 37938658 PMCID: PMC9723706 DOI: 10.1038/s43705-022-00123-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 03/26/2022] [Accepted: 04/14/2022] [Indexed: 09/07/2023]
Abstract
A bottleneck for microbial community experiments with many samples and/or replicates is the fast quantification of individual taxon abundances, which is commonly achieved through sequencing marker genes such as the 16S rRNA gene. Here, we propose a new approach for high-throughput and high-quality enumeration of human gut bacteria in a defined community, combining flow cytometry and supervised classification to identify and quantify species mixed in silico and in defined communities in vitro. We identified species in a 5-species in silico community with an F1 score of 71%. In addition, we demonstrate in vitro that our method performs equally well or better than 16S rRNA gene sequencing in two-species cocultures and agrees with 16S rRNA gene sequencing data on the most abundant species in a four-species community. We found that shape and size differences alone are insufficient to distinguish species, and that it is thus necessary to exploit the multivariate nature of flow cytometry data. Finally, we observed that variability of flow cytometry data across replicates differs between gut bacterial species. In conclusion, the performance of supervised classification of gut species in flow cytometry data is species-dependent, but is for some combinations accurate enough to serve as a faster alternative to 16S rRNA gene sequencing.
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Affiliation(s)
- Charlotte C van de Velde
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, B-3000, Leuven, Belgium
| | - Clémence Joseph
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, B-3000, Leuven, Belgium
| | - Anaïs Biclot
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, B-3000, Leuven, Belgium
- VIB-KU Leuven, Center for Microbiology, B-3000, Leuven, Belgium
| | - Geert R B Huys
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, B-3000, Leuven, Belgium
- VIB-KU Leuven, Center for Microbiology, B-3000, Leuven, Belgium
| | - Vitor B Pinheiro
- KU Leuven, Department of Pharmaceutical and Pharmacological Sciences, Rega Institute for Medical Research, Medicinal Chemistry, B-3000, Leuven, Belgium
| | - Kristel Bernaerts
- KU Leuven, Department of Chemical Engineering, Chemical and Biochemical Reactor Engineering and Safety (CREaS), B-3001, Leuven, Belgium
| | - Jeroen Raes
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, B-3000, Leuven, Belgium
- VIB-KU Leuven, Center for Microbiology, B-3000, Leuven, Belgium
| | - Karoline Faust
- KU Leuven, Department of Microbiology, Immunology and Transplantation, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, B-3000, Leuven, Belgium.
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Abstract
Blood cell analysis is essential for the diagnosis and identification of hematological malignancies. The use of digital microscopy systems has been extended in clinical laboratories. Super-resolution microscopy (SRM) has attracted wide attention in the medical field due to its nanoscale spatial resolution and high sensitivity. It is considered to be a potential method of blood cell analysis that may have more advantages than traditional approaches such as conventional optical microscopy and hematology analyzers in certain examination projects. In this review, we firstly summarize several common blood cell analysis technologies in the clinic, and analyze the advantages and disadvantages of these technologies. Then, we focus on the basic principles and characteristics of three representative SRM techniques, as well as the latest advances in these techniques for blood cell analysis. Finally, we discuss the developmental trend and possible research directions of SRM, and provide some discussions on further development of technologies for blood cell analysis.
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Gao* K, Ayati* M, Koyuturk M, Calabrese JR, Ganocy SJ, Kaye NM, Lazarus HM, Christian E, Kaplan D. Protein Biomarkers in Monocytes and CD4 + Lymphocytes for Predicting Lithium Treatment Response of Bipolar Disorder: a Feasibility Study with Tyramine-Based Signal-Amplified Flow Cytometry. PSYCHOPHARMACOLOGY BULLETIN 2022; 52:8-35. [PMID: 35342205 PMCID: PMC8896753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
Abstract
Purpose To determine if enhanced flow cytometry (CellPrint™) can identify intracellular proteins of lithium responsiveness in monocytes and CD4+ lymphocytes from patients with bipolar disorder. Methods Eligible bipolar I or II patients were openly treated with lithium for 16-weeks. Baseline levels of Bcl2, BDNF, calmodulin, Fyn, phospho-Fyn/phospho-Yes, GSK3β, phospho-GSK3αβ, HMGB1, iNOS, IRS2, mTor, NLPR3, PGM1, PKA C-α, PPAR-γ, phospho-RelA, and TPH1 in monocytes and CD4+ lymphocytes of lithium responders and non-responders were measured with CellPrint™. Their utility of discriminating responders from non-responders was explored. Protein-protein network and pathway enrichment analyses were conducted. Results Of the 24 intent-to-treat patients, 12 patients completed the 16-week study. Eleven of 13 responders and 8 of 11 non-responders were available for this analysis. The levels of the majority of analytes in lithium responders were lower than non-responders in both cell types, but only the level of GSK3β in monocytes was significantly different (p = 0.034). The combination of GSK3β and phospho-GSK3αβ levels in monocytes correctly classified 11/11 responders and 5/8 non-responders. Combination of GSK3β, phospho-RelA, TPH1 and PGM1 correctly classified 10/11 responders and 6/7 non-responders, both with a likelihood of ≥ 85%. Prolactin, leptin, BDNF, neurotrophin, and epidermal growth factor/epidermal growth factor receptor signaling pathways are involved in the lithium treatment response. GSK3β and RelA genes are involved in 4 of 5 these pathways. Conclusion CellPrint™ flow cytometry was able to detect differences in multiple proteins in monocytes and CD4+ lymphocytes between lithium responders and non-responders. A large study is warranted to confirm or refute these findings.
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