Time-course colony tracking analysis for evaluating induced pluripotent stem cell culture processes

Yoshida, Kei; Okada, Mika; Nagasaka, Risako; Sasaki, Hiroto; Okada, Mai; Kanie, Kei; Kato, Ryuji

doi:10.1016/j.jbiosc.2019.01.011

Cited by 4 publications

(6 citation statements)

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“…A non-invasive method to support the in-process monitoring of the cellular population is needed. Sub-population monitoring is also important in other cell types, including induced pluripotent stem cells 9,18 , hematopoietic cells 39 , and programmed cell death protein 1 (PD-1)-expressing T cells 40 . Previously, we had reported the in-process quality monitoring efficacy in mesenchymal stem cells using population profiles 10,16 .…”

Section: Discussionmentioning

confidence: 99%

“…Conventional label-free image analysis utilizes two types of image data utilization concepts. The first type involves the extraction of multiple features from the cells in the image to describe the morphological features of cells (area, roundness, and peripheral features 7,[10][11][12][14][15][16] ), intensity patterns, and textures 10,11,14,[16][17][18][19] , which reflect the morphological characteristics observed in microscopy. Therefore, analysis results, especially the validity of the trained model, can be biologically interpreted.…”

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confidence: 99%

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Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells

Imai

Iida

Kanie

et al. 2022

Sci Rep

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Label-free image analysis has several advantages with respect to the development of drug screening platforms. However, the evaluation of drug-responsive cells based exclusively on morphological information is challenging, especially in cases of morphologically heterogeneous cells or a small subset of drug-responsive cells. We developed a novel label-free cell sub-population analysis method called “in silico FOCUS (in silico analysis of featured-objects concentrated by anomaly discrimination from unit space)” to enable robust phenotypic screening of morphologically heterogeneous spinal and bulbar muscular atrophy (SBMA) model cells. This method with the anomaly discrimination concept can sensitively evaluate drug-responsive cells as morphologically anomalous cells through in silico cytometric analysis. As this algorithm requires only morphological information of control cells for training, no labeling or drug administration experiments are needed. The responses of SBMA model cells to dihydrotestosterone revealed that in silico FOCUS can identify the characteristics of a small sub-population with drug-responsive phenotypes to facilitate robust drug response profiling. The phenotype classification model confirmed with high accuracy the SBMA-rescuing effect of pioglitazone using morphological information alone. In silico FOCUS enables the evaluation of delicate quality transitions in cells that are difficult to profile experimentally, including primary cells or cells with no known markers.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells

Imai

Iida

Kanie

et al. 2022

Sci Rep

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“…First, most growth quantification algorithms focus on a single type of organism, such as bacteria, [5][6][7][8][9][10] yeasts , 11,12 slime molds, [13][14][15][16] plants, 17,18 tumors 19 or stem cells 20 .…”

Section: Introductionmentioning

confidence: 99%

“…5,8,10,11,[13][14][15][16]21 Third, when equipped with a user interface, these softwares are commercial or rely on a commercial programming language. 6,7,12,20 Since they are not free and open source, commercial programs cannot be improved by their users or other developers.…”

Section: Introductionmentioning

confidence: 99%

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Cellects, a software to quantify cell expansion and motion

Boussard,

Arrufat,

Dussutour

et al. 2024

Preprint

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Automated quantification offers unique opportunities to study biological phenomena, increasing reproducibility, replicability, accuracy, and throughput, while reducing observer biases. We present Cellects, a tool to quantify growth and motion in 2D. This software operates with image sequences containing specimens growing and moving on an immobile flat surface. Its user-friendly interface makes it easy to adjust the quantification parameters to cover a wide range of species and conditions, and includes tools to validate the results and correct mistakes if necessary. The software provides the region covered by the specimens at each point of time, as well as many geometrical descriptors that characterize it. We validated Cellects with Physarum polycephalum, which is particularly difficult to detect because of its complex shape and internal heterogeneity. This validation covered five different conditions with different background and lighting, and found Cellects to be highly accurate in all cases. The main strengths of Cellects are its broad scope of action, automated computation of a variety of geometrical descriptors, easy installation and user-friendly interface. github link: https://github.com/Aurele-B/Cellects

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Shape up before you ship out: morphology as a potential critical quality attribute for cellular therapies

Daga

Priyadarshani

Larey

et al. 2021

Current Opinion in Biomedical Engineering

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Time-course colony tracking analysis for evaluating induced pluripotent stem cell culture processes

Cited by 4 publications

References 28 publications

Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells

Label-free morphological sub-population cytometry for sensitive phenotypic screening of heterogenous neural disease model cells

Cellects, a software to quantify cell expansion and motion

Shape up before you ship out: morphology as a potential critical quality attribute for cellular therapies

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