Automated detection of apoptotic versus nonapoptotic cell death using label‐free computational microscopy

Kabir, Md. Alamgir; Kharel, Ashish; Malla, Saloni; Kreis, Zachary Joseph; Nath, Peuli; Wolfe, Jared N.; Hassan, Marwa; Kaur, Devinder; Sari‐Sarraf, Hamed; Tiwari, Amit K.; Ray, Aniruddha

doi:10.1002/jbio.202100310

Cited by 15 publications

(5 citation statements)

References 94 publications

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“…We trained a MaskRCNN model ( He et al 2017 ) to detect ApoBDs in these processed images, as the model has demonstrated exceptional results in instance segmentation for medical images ( Anantharaman et al 2018 , Johnson 2020 , Jin et al 2021 , Padma et al 2022 ). The model has the ResNet50 architecture for feature extraction backbone, and the weights are pre-trained on ImageNet.…”

Section: Methodsmentioning

confidence: 99%

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks

Wu,

Martinez-Paniagua,

Reichel

et al. 2023

Bioinformatics

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Motivation Reliable label-free methods are needed for detecting and profiling apoptotic events in time-lapse cell–cell interaction assays. Prior studies relied on fluorescent markers of apoptosis, e.g. Annexin-V, that provide an inconsistent and late indication of apoptotic onset for human melanoma cells. Our motivation is to improve the detection of apoptosis by directly detecting apoptotic bodies in a label-free manner. Results Our trained ResNet50 network identified nanowells containing apoptotic bodies with 92% accuracy and predicted the onset of apoptosis with an error of one frame (5 min/frame). Our apoptotic body segmentation yielded an IoU accuracy of 75%, allowing associative identification of apoptotic cells. Our method detected apoptosis events, 70% of which were not detected by Annexin-V staining. Availability and implementation Open-source code and sample data provided at https://github.com/kwu14victor/ApoBDproject.

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Section: Methodsmentioning

confidence: 99%

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks

Wu,

Martinez-Paniagua,

Reichel

et al. 2023

Bioinformatics

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“…Doxorubicin [81,[83][84][85] and Raptinal [86] induce apoptosis [87] which results in the reduction of cell volume and membrane blebbing, among others, which can be clearly observed in figures 2(d)-(f). The concentrations of these drugs for BT-20 cells were previously optimized and their efficacy were monitored using holographic microscopy and western blot [53]. Whereas, hydrogen peroxide at high concentration induces necrosis [88,89] which is characterized by cell swelling and loss of membrane integrity which results in the leakage of cytosolic content.…”

Section: Detection Of Cell Death Process Using Microscopymentioning

confidence: 99%

“…The fluorescence-based microscopy techniques require labeling the cells with different fluorophores such as Calcein, Propidium Iodide, fluorescently labeled Annexin V [41][42][43] and others. Label-free techniques such as quantitative phase imaging (QPI), phase contrast microscopy [44][45][46][47], Raman microspectroscopy [48][49][50][51][52], and digital holography [53,54] have also been explored as alternative modalities to visualize (and quantify) some of the morphological hallmarks of cell death, such as cell shape factor, volume, blebbing, shrinkage, and membrane rupture, amongst others. In Phase contrast microscopy [55], the change in phase of light passing through and interacting with sample is visualized as a change in brightness in the image and is not quantitative.…”

Section: Introductionmentioning

confidence: 99%

Label-free identification of cell death mechanism using scattering-based microscopy and deep learning

Khoubafarin,

Kharel,

Malla

et al. 2023

J. Phys. D: Appl. Phys.

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The detection of cell death and identification of the mechanism underpins many of the biological and medical sciences. A scattering-based microscopy-based method is presented here for identifying, measuring, and quantifying cell death in breast cancer cells using a label-free approach. We identify apoptosis and necrosis pathways by analyzing the temporal changes in morphological features of the cells. Moreover, a neural network was trained to identify the cellular morphological changes and classify cell death mechanisms automatically, with an accuracy of over 95%. A pre-trained network was tested on images of cancer cells treated with different chemotherapeutic drugs, which were not used for training, and it correctly identified cell death mechanisms almost 100% of the time. This automated method will allow for quantification during the incubation steps without the need for additional steps, typically associated with conventional technique like fluorescence microscopy, western blot and ELISAs. As a result, such studies will be faster and cost effective.

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“…Recent progress in computer vision has opened the possibility of automating the detection of objects and patterns in biological images and image series ( Hallou et al, 2021 ). In particular, deep-learning approaches have been used successfully to recognise cellular events, such as cell division and cell death in yeast ( Aspert et al, 2022 ) or in mammalian cell culture ( Kabir et al, 2022 ; La Greca et al, 2021 ; Mahecic et al, 2022 ; Phan et al, 2019 ; Shkolyar et al, 2015 ). However, this was mostly applied to transmission light microscopy and these pipelines are not applicable to large samples and embryos, where imaging mostly relies on fluorescent and confocal microscopy.…”

Section: Introductionmentioning

confidence: 99%

DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo

et al. 2023

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Accurately counting and localising cellular events from movies is an important bottleneck of high content tissue/embryo live imaging. Here, we propose a new methodology based on deep learning allowing automatic detection of cellular events and their precise x-y-t localisation on live fluorescent imaging movies without segmentation. We focused on the detection of cell extrusion, the expulsion of dying cells from the epithelial layer, and devised DeXtrusion: a pipeline based on recurrent neural networks for automatic detection of cell extrusion/cell death events in large movies of epithelia marked with cell contour. The pipeline, initially trained on movies of the Drosophila pupal notum marked with fluorescent E-cadherin, is easily trainable, provides fast and accurate extrusion predictions in a large range of imaging conditions, and can also detect other cellular events such as cell division or cell differentiation. It also performs well on other epithelial tissues with reasonable retraining. Our methodology could easily be applied for other cellular events detected in live fluorescent microscopy and help to democratise the use of deep learning for automatic event detections in developing tissues.

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Automated detection of apoptotic versus nonapoptotic cell death using label‐free computational microscopy

Abstract: Identification of cell death mechanisms, par-

Cited by 15 publications

References 94 publications

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks

Automated detection of apoptotic bodies and cells in label-free time-lapse high-throughput video microscopy using deep convolutional neural networks

Label-free identification of cell death mechanism using scattering-based microscopy and deep learning

DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo

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