In vivo molecular and single cell imaging

( Seongje Hong ),( Siyeon Rhee ),( Kyung Oh Jung ) 생화학분자생물학회 2022 BMB Reports Vol.55 No.6

Molecular imaging is used to improve the disease diagnosis, prognosis, monitoring of treatment in living subjects. Numerous molecular targets have been developed for various cellular and molecular processes in genetic, metabolic, proteomic, and cellular biologic level. Molecular imaging modalities such as Optical Imaging, Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), Single Photon Emission Computed Tomography (SPECT), and Computed Tomography (CT) can be used to visualize anatomic, genetic, biochemical, and physiologic changes in vivo. For in vivo cell imaging, certain cells such as cancer cells, immune cells, stem cells could be labeled by direct and indirect labeling methods to monitor cell migration, cell activity, and cell effects in cell-based therapy. In case of cancer, it could be used to investigate biological processes such as cancer metastasis and to analyze the drug treatment process. In addition, transplanted stem cells and immune cells in cell-based therapy could be visualized and tracked to confirm the fate, activity, and function of cells. In conventional molecular imaging, cells can be monitored in vivo in bulk non-invasively with optical imaging, MRI, PET, and SPECT imaging. However, single cell imaging in vivo has been a great challenge due to an extremely high sensitive detection of single cell. Recently, there has been great attention for in vivo single cell imaging due to the development of single cell study. In vivo single imaging could analyze the survival or death, movement direction, and characteristics of a single cell in live subjects. In this article, we reviewed basic principle of in vivo molecular imaging and introduced recent studies for in vivo single cell imaging based on the concept of in vivo molecular imaging. [BMB Reports 2022; 55(6): 267-274]

In Vivo Stem Cell Imaging Principles and Applications

Seongje Hong,Dong-Sung Lee,Geun-Woo Bae,Juhyeong Jeon,Hak Kyun Kim,Siyeon Rhee,Kyung Oh Jung Korean Society for Stem Cell Research 2023 International journal of stem cells Vol.16 No.4

Stem cells are the foundational cells for every organ and tissue in our body. Cell-based therapeutics using stem cells in regenerative medicine have received attracting attention as a possible treatment for various diseases caused by congenital defects. Stem cells such as induced pluripotent stem cells (iPSCs) as well as embryonic stem cells (ESCs), mesenchymal stem cells (MSCs), and neuroprogenitors stem cells (NSCs) have recently been studied in various ways as a cell-based therapeutic agent. When various stem cells are transplanted into a living body, they can differentiate and perform complex functions. For stem cell transplantation, it is essential to determine the suitability of the stem cell-based treatment by evaluating the origin of stem, the route of administration, in vivo bio-distribution, transplanted cell survival, function, and mobility. Currently, these various stem cells are being imaged in vivo through various molecular imaging methods. Various imaging modalities such as optical imaging, magnetic resonance imaging (MRI), ultrasound (US), positron emission tomography (PET), and single-photon emission computed tomography (SPECT) have been introduced for the application of various stem cell imaging. In this review, we discuss the principles and recent advances of in vivo molecular imaging for application of stem cell research.

Dual-Modal Nanoprobes for Imaging of Mesenchymal Stem Cell Transplant by MRI and Fluorescence Imaging

성창규,홍경아,Shunmei Lin,이유원,차진명,이진규,홍철표,한봉수,정성일,김승협,윤강섭 대한영상의학회 2009 Korean Journal of Radiology Vol.10 No.6

Objective: To determine the feasibility of labeling human mesenchymal stem cells (hMSCs) with bifunctional nanoparticles and assessing their potential as imaging probes in the monitoring of hMSC transplantation. Materials and Methods: The T1 and T2 relaxivities of the nanoparticles (MNP@SiO2[RITC]-PEG) were measured at 1.5T and 3T magnetic resonance scanner. Using hMSCs and the nanoparticles, labeling efficiency, toxicity, and proliferation were assessed. Confocal laser scanning microscopy and transmission electron microscopy were used to specify the intracellular localization of the endocytosed iron nanoparticles. We also observed in vitro and in vivo visualization of the labeled hMSCs with a 3T MR scanner and optical imaging. Results: MNP@SiO2(RITC)-PEG showed both superparamagnetic and fluorescent properties. The r1 and r2 relaxivity values of the MNP@SiO2(RITC)-PEG were 0.33 and 398 mM-1 s-1 at 1.5T, respectively, and 0.29 and 453 mM-1 s-1 at 3T, respectively. The effective internalization of MNP@SiO2(RITC)-PEG into hMSCs was observed by confocal laser scanning fluorescence microscopy. The transmission electron microscopy images showed that MNP@SiO2(RITC)-PEG was internalized into the cells and mainly resided in the cytoplasm. The viability and proliferation of MNP@SiO2(RITC)-PEG-labeled hMSCs were not significantly different from the control cells. MNP@SiO2(RITC)-PEG-labeled hMSCs were observed in vitro and in vivo with optical and MR imaging. Conclusion: MNP@SiO2(RITC)-PEG can be a useful contrast agent for stem cell imaging, which is suitable for a bimodal detection by MRI and optical imaging. Objective: To determine the feasibility of labeling human mesenchymal stem cells (hMSCs) with bifunctional nanoparticles and assessing their potential as imaging probes in the monitoring of hMSC transplantation. Materials and Methods: The T1 and T2 relaxivities of the nanoparticles (MNP@SiO2[RITC]-PEG) were measured at 1.5T and 3T magnetic resonance scanner. Using hMSCs and the nanoparticles, labeling efficiency, toxicity, and proliferation were assessed. Confocal laser scanning microscopy and transmission electron microscopy were used to specify the intracellular localization of the endocytosed iron nanoparticles. We also observed in vitro and in vivo visualization of the labeled hMSCs with a 3T MR scanner and optical imaging. Results: MNP@SiO2(RITC)-PEG showed both superparamagnetic and fluorescent properties. The r1 and r2 relaxivity values of the MNP@SiO2(RITC)-PEG were 0.33 and 398 mM-1 s-1 at 1.5T, respectively, and 0.29 and 453 mM-1 s-1 at 3T, respectively. The effective internalization of MNP@SiO2(RITC)-PEG into hMSCs was observed by confocal laser scanning fluorescence microscopy. The transmission electron microscopy images showed that MNP@SiO2(RITC)-PEG was internalized into the cells and mainly resided in the cytoplasm. The viability and proliferation of MNP@SiO2(RITC)-PEG-labeled hMSCs were not significantly different from the control cells. MNP@SiO2(RITC)-PEG-labeled hMSCs were observed in vitro and in vivo with optical and MR imaging. Conclusion: MNP@SiO2(RITC)-PEG can be a useful contrast agent for stem cell imaging, which is suitable for a bimodal detection by MRI and optical imaging.

Staining-free cell viability measurement technique using lens-free shadow imaging platform

Roy, M.,Jin, G.,Pan, J.H.,Seo, D.,Hwang, Y.,Oh, S.,Lee, M.,Kim, Y.J.,Seo, S. Elsevier Sequoia 2016 Sensors and actuators. B Chemical Vol.224 No.-

Tests for cell viability, i.e., an assay quantifying the ratio of viable cells or tissues over the total cells or tissues within an index between 0 and 1 (or 0 and 100%), play an important role in cell or tissue culturing procedures. The viability test result, varying with several biological factors such as mechanical activity, motility, contraction, or mitotic activity of cells or tissues, is a crucial indicator in cell related research protocols including toxicity and anabolic activity assays. There are several well-established methods for evaluating cell viability, such as trypan blue assay, propidium iodide assay, 7-aminoactinomycin D assay and resazurin and formazan (MTT/XTT) assay. However, most of these methods determine viability using stained cell samples, which intern affect the cells morphology eventually making it unable to keep culturing the specimen. To address this issue, we have developed a novel shadow imaging technique to capture the diffraction patterns (shadow patterns) of micro objects without the use of any staining reagent. In this paper, we introduce a shadow imaging platform that can determine cell viability of more than 3000 human cancer cells immediately with a single digital image. Our custom-built lens-free shadow imaging platform consists of a compact, cost-effective light source, i.e., a light-emitting diode, and an optoelectronic image recording device, i.e., a complementary metal-oxide semiconductor image sensor. Three types of human cancer cell lines (Caco-2, HepG2, and MCF7) were incubated in 24-well plates, and H<SUB>2</SUB>O<SUB>2</SUB> was added to track and compare the cell viability at each concentration tested. We obtained high correlation indices, with a minimum of 0.94, between the MTT assay and the shadow imaging platform. All these characterizations were done by custom developed automated detection algorithm. This algorithm analyzes the various elements of the diffraction pattern (shadow image), such as pixel intensity and connected pixel numbers, and counts the viable cells automatically, allowing the cell viability to be determined easily and immediately in a staining-free manner.

Photoacoustic Monitoring of the Viability of Mesenchymal Stem Cells Labeled with Indocyanine Green

Yoo, J.M.,Yun, C.,Bui, N.Q.,Oh, J.,Nam, S.Y. Elsevier 2019 IRBM Vol.40 No.1

<P><B>Abstract</B></P> <P><B>Background</B></P> <P>Stem cell therapy has a huge potential to enhance the recovery of damaged tissues and organs. However, it has been reported that majority of implanted stem cells cannot survive after implantation. Therefore, noninvasive monitoring of stem cell viability is essential to estimate the efficacy of stem cell therapy. However, current imaging methods have disadvantages for monitoring of stem cell viability such as cost, penetration depth, and safety. To overcome the limitations, photoacoustic imaging well known for sufficient penetration depth, relatively low cost, and non-ionizing radiation can be a novel alternative assessment method of stem cell viability.</P> <P><B>Methods</B></P> <P>In this study, indocyanine green was used as exogenous photoacoustic contrast agents to label mesenchymal stem cells. The photoacoustic signals were acquired before and after the cell death and quantified to monitor photoacoustic signal changes related to the cell viability.</P> <P><B>Results</B></P> <P>The fluorescence intensity changes of ICG labeled MSCs corresponded to decrease of PA intensity after cell death. Furthermore, the PA imaging of MSCs showed similarity between the PA intensity and the cell viability.</P> <P><B>Conclusion</B></P> <P>The experimental results imply the feasibility of noninvasive detection of stem cell viability during therapeutic procedures.</P> <P><B>Highlights</B></P> <P> <UL> <LI> ICG can be used as an MSC labeling probe for PA imaging. </LI> <LI> PA imaging of MSCs showed similarity between the PA intensity and the cell viability. </LI> <LI> PA imaging with ICG labeling is an alternative to detect MSCs viability non-invasively. </LI> </UL> </P>

In Vivo Non Invasive Molecular Imaging for Immune Cell Tracking in Small Animals

Youn, Hyewon,Hong, Kee-Jong The Korean Association of Immunobiologists 2012 Immune Network Vol.12 No.6

Clinical and preclinical in vivo immune cell imaging approaches have been used to study immune cell proliferation, apoptosis and interaction at the microscopic (intra-vital imaging) and macroscopic (whole-body imaging) level by use of ex vivo or in vivo labeling method. A series of imaging techniques ranging from non-radiation based techniques such as optical imaging, MRI, and ultrasound to radiation based CT/nuclear imaging can be used for in vivo immune cell tracking. These imaging modalities highlight the intrinsic behavior of different immune cell populations in physiological context. Fluorescent, radioactive or paramagnetic probes can be used in direct labeling protocols to monitor the specific cell population. Reporter genes can also be used for genetic, indirect labeling protocols to track the fate of a given cell subpopulation in vivo. In this review, we summarized several methods dealing with dendritic cell, macrophage, and T lymphocyte specifically labeled for different macroscopic whole-body imaging techniques both for the study of their physiological function and in the context of immunotherapy to exploit imaging-derived information and immune-based treatments.

In Vivo Non Invasive Molecular Imaging for Immune Cell Tracking in Small Animals

Hyewon Youn,홍기종 대한면역학회 2012 Immune Network Vol.12 No.6

Clinical and preclinical in vivo immune cell imaging approaches have been used to study immune cell proliferation,apoptosis and interaction at the microscopic (intra-vital imaging) and macroscopic (whole-body imaging) level by use of ex vivo or in vivo labeling method. A series of imaging techniques ranging from non-radiation based techniques such as optical imaging, MRI, and ultrasound to radiation based CT/nuclear imaging can be used for in vivo immune cell tracking. These imaging modalities highlight the intrinsic behavior of different immune cell populations in physiological context. Fluorescent, radioactive or paramagnetic probes can be used in direct labeling protocols to monitor the specific cell population. Reporter genes can also be used for genetic,indirect labeling protocols to track the fate of a given cell subpopulation in vivo. In this review, we summarized several methods dealing with dendritic cell, macrophage, and T lymphocyte specifically labeled for different macroscopic wholebody imaging techniques both for the study of their physiological function and in the context of immunotherapy to exploit imaging-derived information and immune-based treatments.

Cell Image Processing Methods for Automatic Cell Pattern Recognition and Morphological Analysis of Mesenchymal Stem Cells - An Algorithm for Cell Classification and Adaptive Brightness Correction -

임기택,박수현,선우훈,정필훈,김장호,정종훈 한국농업기계학회 2013 바이오시스템공학 Vol.38 No.1

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

Cell Image Processing Methods for Automatic Cell Pattern Recognition and Morphological Analysis of Mesenchymal Stem Cells - An Algorithm for Cell Classification and Adaptive Brightness Correction -

Lim, Kitaek,Park, Soo Hyun,Kim, Jangho,SeonWoo, Hoon,Choung, Pill-Hoon,Chung, Jong Hoon Korean Society for Agricultural Machinery 2013 바이오시스템공학 Vol.38 No.1

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

Bioprocess Engineering ; Cell Image Processing Methods for Automatic Cell Pattern Recognition and Morphological Analysis of Mesenchymal Stem Cells -An Algorithm for Cell Classification and Adaptive Brightness Correction-

( Kitaek Lim ),( Soo Hyun Park ),( Jang Ho Kim ),( Hoon Seon Woo ),( Pill Hoon Choung ),( Jong Hoon Chung ) 한국농업기계학회 2013 바이오시스템공학 Vol.38 No.1

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.