Cell identification and sorting have been hot topics recently.However,most conventional approaches can only predict the category of a single target,and lack the ability to perform multitarget tasks to provide coordina...Cell identification and sorting have been hot topics recently.However,most conventional approaches can only predict the category of a single target,and lack the ability to perform multitarget tasks to provide coordinate information of the targets.This limits the development of high-throughput cell screening technologies.Fortunately,artificial intelligence(AI)systems based on deep-learning algorithms provide the possibility to extract hidden features of cells from original image information.Here,we demonstrate an AI-assisted multitarget processing system for cell identification and sorting.With this system,each target cell can be swiftly and accurately identified in a mixture by extracting cell morphological features,whereafter accurate cell sorting is achieved through noninvasive manipulation by optical tweezers.The AI-assisted model shows promise in guiding the precise manipulation and intelligent detection of high-flux cells,thereby realizing semiautomatic cell research.展开更多
Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-...Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.展开更多
The paper presents the principles and the results of the implementation of dielectrophoresis for separation and identification of rare cells such as circulation tumor cells(CTCs)from diluted blood specimens in media a...The paper presents the principles and the results of the implementation of dielectrophoresis for separation and identification of rare cells such as circulation tumor cells(CTCs)from diluted blood specimens in media and further label-free identification of the origins of separated cells using radio-frequency(RF)imaging.The separation and the identification units use same fabrication methods which enable system integration on the same platform.The designs use the advantage of higher surface volume ratio which represents the particular feature for micro-and nanotechnologies.Diluted blood in solution of sucrose–dextrose 1–10 is used for cell separation that yields more than 95.3% efficiency.For enhanced sensitivity in identification,RF imaging is performed in 3.5–1 solution of glycerol and trypsin.Resonance cavity performance method is used to determine the constant permittivity of the cell lines.The results illustrated by the signature of specific cells subjected to RF imaging suggest a reliable label-free single cell detection method for identification of the type of CTC.展开更多
The aim of the study was to identify main metabolites of benzylisoquinoline alkaloids from Nelumbinis Plumula after biotransformation by Caco-2 cells.Caco-2 cells were seeded to a 6-well plate and cultured for a perio...The aim of the study was to identify main metabolites of benzylisoquinoline alkaloids from Nelumbinis Plumula after biotransformation by Caco-2 cells.Caco-2 cells were seeded to a 6-well plate and cultured for a period of time until 80%of each well was filled with cells.Then,cell medium was replaced and the norcoclaurine,liensinine,isoliensinine and neferine were respectively added to展开更多
基金supported by the National Natural Science Foundation of China(Nos.61975128,62175157,92150301,and 62375177)the Shenzhen Science and Technology Program(Nos.JCYJ20210324120403011 and RCJC20210609103232046)the Guangdong Major Project of Basic and Applied Basic Research(No.2020B0301030009)。
文摘Cell identification and sorting have been hot topics recently.However,most conventional approaches can only predict the category of a single target,and lack the ability to perform multitarget tasks to provide coordinate information of the targets.This limits the development of high-throughput cell screening technologies.Fortunately,artificial intelligence(AI)systems based on deep-learning algorithms provide the possibility to extract hidden features of cells from original image information.Here,we demonstrate an AI-assisted multitarget processing system for cell identification and sorting.With this system,each target cell can be swiftly and accurately identified in a mixture by extracting cell morphological features,whereafter accurate cell sorting is achieved through noninvasive manipulation by optical tweezers.The AI-assisted model shows promise in guiding the precise manipulation and intelligent detection of high-flux cells,thereby realizing semiautomatic cell research.
文摘Analysis of cellular behavior is significant for studying cell cycle and detecting anti-cancer drugs. It is a very difficult task for image processing to isolate individual cells in confocal microscopic images of non-stained live cell cultures. Because these images do not have adequate textural variations. Manual cell segmentation requires massive labor and is a time consuming process. This paper describes an automated cell segmentation method for localizing the cells of Chinese hamster ovary cell culture. Several kinds of high-dimensional feature descriptors, K-means clustering method and Chan-Vese model-based level set are used to extract the cellular regions. The region extracted are used to classify phases in cell cycle. The segmentation results were experimentally assessed. As a result, the proposed method proved to be significant for cell isolation. In the evaluation experiments, we constructed a database of Chinese Hamster Ovary Cell’s microscopic images which includes various photographing environments under the guidance of a biologist.
文摘The paper presents the principles and the results of the implementation of dielectrophoresis for separation and identification of rare cells such as circulation tumor cells(CTCs)from diluted blood specimens in media and further label-free identification of the origins of separated cells using radio-frequency(RF)imaging.The separation and the identification units use same fabrication methods which enable system integration on the same platform.The designs use the advantage of higher surface volume ratio which represents the particular feature for micro-and nanotechnologies.Diluted blood in solution of sucrose–dextrose 1–10 is used for cell separation that yields more than 95.3% efficiency.For enhanced sensitivity in identification,RF imaging is performed in 3.5–1 solution of glycerol and trypsin.Resonance cavity performance method is used to determine the constant permittivity of the cell lines.The results illustrated by the signature of specific cells subjected to RF imaging suggest a reliable label-free single cell detection method for identification of the type of CTC.
文摘The aim of the study was to identify main metabolites of benzylisoquinoline alkaloids from Nelumbinis Plumula after biotransformation by Caco-2 cells.Caco-2 cells were seeded to a 6-well plate and cultured for a period of time until 80%of each well was filled with cells.Then,cell medium was replaced and the norcoclaurine,liensinine,isoliensinine and neferine were respectively added to
