DynaMorph: self-supervised learning of morphodynamic states of live cells

Wu, Zhenqin; Chhun, Bryant B.; Попова, Галина; Guo, Sen; Kim, Chang N.; Yeh, Li-Hao; Nowakowski, Tomasz J.; Zou, James; Mehta, Shalin B.

doi:10.1101/2020.07.20.213074

Cited by 7 publications

(6 citation statements)

References 50 publications

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“…Notably, trajectory information, including combined motility and morphological features computed as averages over single-cell trajectories, have been used to classify cell state 24 . Our morphodynamical trajectory embedding approach differs in that we directly analyze morphological feature trajectories, rather than average morphological or cellular feature averages over time.…”

Section: Introductionmentioning

confidence: 99%

Morphodynamical cell state description via live-cell imaging trajectory embedding

Copperman

Gross

Chang

et al. 2021

Preprint

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Time-lapse imaging provides powerful insight into the dynamical response of cells to perturbation, but the quantitative analysis of morphological changes over time is a challenge. Here, we exploit the concept of "morphodynamical trajectories" to analyze cellular behavior using morphological feature trajectory histories, rather than the common practice of examining morphological feature time courses in the space of single-timepoint (snapshot) morphological features. Our morphodynamical trajectory embedding analysis yielded quantitative and descriptive models of future time points based on the extended history information of MCF10A mammary epithelial cells treated with a panel of ligands. The trajectory analysis constructs a shared morphodynamical cell-state landscape, where the response of MCF10A cells induced by various extracellular signals is characterized by ligand-specific regulation of state transitions. Additionally, we show that including trajectories in single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, and (ii) better separation of phenotypes and more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable for the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

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Section: Introductionmentioning

confidence: 99%

Morphodynamical cell state description via live-cell imaging trajectory embedding

Copperman

Gross

Chang

et al. 2021

Preprint

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“…A VAE learns a probabilistic approximation of the underlying distribution of data, meaning that the latent representation can be used as descriptive features of the system. Several recent studies have utilised autoencoders to encode complex cell shapes and other visual features in an interpretable manner [12, 13, 14, 15]. However, these studies have typically been performed on sparse, isolated cells and usually as single observations in time.…”

Section: Introductionmentioning

confidence: 99%

Learning the Rules of Cell Competition without Prior Scientific Knowledge

Soelistyo

Vallardi

Charras

et al. 2021

Preprint

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Deep learning is now a powerful tool in microscopy data analysis, and is routinely used for image processing applications such as segmentation and denoising. However, it has rarely been used to directly learn mechanistic models of a biological system, owing to the complexity of the internal representations. Here, we develop an end-to-end machine learning model capable of learning the rules of a complex biological phenomenon, cell competition, directly from a large corpus of time-lapse microscopy data. Cell competition is a quality control mechanism that eliminates unfit cells from a tissue and during which cell fate is thought to be determined by the local cellular neighborhood over time. To investigate this, we developed a new approach (τ-VAE) by coupling a variational autoencoder to a temporal convolution network to predict the fate of each cell in an epithelium. Using the τ-VAE’s latent representation of the local tissue organization and the flow of information in the network, we decode the physical parameters responsible for correct prediction of fate in cell competition. Remarkably, the model autonomously learns that cell density is the single most important factor in predicting cell fate – a conclusion that has taken over a decade of traditional experimental research to reach. Finally, to test the learned internal representation, we challenge the network with experiments performed in the presence of drugs that block signalling pathways involved in competition. We present a novel discriminator network that, using the predictions of the τ-VAE, can identify conditions which deviate from the normal behaviour, paving the way for automated, mechanism-aware drug screening.

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“…With the emergence of single-cell technologies and deep-learning tools, there has been a tremendous acceleration in the capacity to quantify and analyze specific cell states and behaviors across cell populations (49)(50)(51). Specifically, analysis of biophysical properties, such as motility and morphology, offer an efficient method to discretize functional subtypes of cells (32,52,53).…”

Section: Discussionmentioning

confidence: 99%

Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells

Engels,

Kamat,

Pafilis

et al. 2023

Preprint

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Particulate matter (PM) is a ubiquitous component of indoor and outdoor air pollution that is epidemiologically linked to many human pulmonary diseases. PM has many emission sources, making it challenging to understand the biological effects of exposure due to the high variance in chemical composition. However, the effects of compositionally unique particulate matter mixtures on cells have not been analyzed using both biophysical and biomolecular approaches. Here, we show that in a human bronchial epithelial cell model (BEAS-2B), exposure to three chemically distinct PM mixtures drives unique cell viability patterns, transcriptional remodeling, and the emergence of distinct morphological subtypes. Specifically, PM mixtures modulate cell viability and DNA damage responses and induce the remodeling of gene expression associated with cell morphology, extracellular matrix organization and structure, and cellular motility. Profiling cellular responses showed that cell morphologies change in a PM composition-dependent manner. Lastly, we observed that particulate matter mixtures with high contents of heavy metals, such as cadmium and lead, induced larger drops in viability, increased DNA damage, and drove a redistribution among morphological subtypes. Our results demonstrate that quantitative measurement of cellular morphology provides a robust approach to gauge the effects of environmental stressors on biological systems and determine cellular susceptibilities to pollution.

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DynaMorph: self-supervised learning of morphodynamic states of live cells

Cited by 7 publications

References 50 publications

Morphodynamical cell state description via live-cell imaging trajectory embedding

Morphodynamical cell state description via live-cell imaging trajectory embedding

Learning the Rules of Cell Competition without Prior Scientific Knowledge

Particulate matter composition drives differential molecular and morphological responses in lung epithelial cells

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