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Data for: Image-based Phenotyping of Disaggregated Cells Using Deep Learning Berryman, Samuel; Matthews, Kerryn; Lee, Jeonghyun; Ma, Hongshen

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Abstract: The ability to phenotype cells is fundamentally important in biological research and medicine. Cur-rent methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is currently unclear if this ap-proach can reliably phenotype cells that are difficult to distinguish by the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cyto-skeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an aver-age F1-score of 95.3%, tested using separately acquired images. Here we demonstrate the potential to develop an “electronic eye” to phenotype cells directly from microscopy images. Technical Info: 10X Fluorescent microscopy images of Trypsinized cells. Each Tiff image contains 6 different locations within a Greiner Sensoplate 96-well glass bottom imaging well. Channels are in order: Brightfield, Hoechst, SIR-Actin and Calcein Green. Images were taken on a Nikon TI2E with a DS-QI2 Camera.

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Title	Data for: Image-based Phenotyping of Disaggregated Cells Using Deep Learning
Creator	Berryman, Samuel; Matthews, Kerryn; Lee, Jeonghyun; Ma, Hongshen
Contributor	Berryman, Samuel
Date Issued	2020-09-21
Description	Abstract: The ability to phenotype cells is fundamentally important in biological research and medicine. Cur-rent methods rely primarily on fluorescence labeling of specific markers. However, there are many situations where this approach is unavailable or undesirable. Machine learning has been used for image cytometry but has been limited by cell agglomeration and it is currently unclear if this ap-proach can reliably phenotype cells that are difficult to distinguish by the human eye. Here, we show disaggregated single cells can be phenotyped with a high degree of accuracy using low-resolution bright-field and non-specific fluorescence images of the nucleus, cytoplasm, and cyto-skeleton. Specifically, we trained a convolutional neural network using automatically segmented images of cells from eight standard cancer cell-lines. These cells could be identified with an aver-age F1-score of 95.3%, tested using separately acquired images. Here we demonstrate the potential to develop an “electronic eye” to phenotype cells directly from microscopy images. Technical Info: 10X Fluorescent microscopy images of Trypsinized cells. Each Tiff image contains 6 different locations within a Greiner Sensoplate 96-well glass bottom imaging well. Channels are in order: Brightfield, Hoechst, SIR-Actin and Calcein Green. Images were taken on a Nikon TI2E with a DS-QI2 Camera.
Subject	Computer and Information Science; Engineering; Medicine, Health and Life Sciences; Phenotype; Deep learning; Image Cytometry; Machine Learning; Fluorescent Microscopy
Type	Dataset
Date Available	2020-02-14
Provider	University of British Columbia Library
License	CC BY-NC 4.0
DOI	10.14288/1.0394462
URI	https://doi.org/10.5683/SP2/TDULMF
Publisher DOI	https://doi.org/10.5683/SP2/TDULMF
Rights URI	http://creativecommons.org/licenses/by-nc/4.0
Aggregated Source Repository	Dataverse

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