New open-source software for subcellular segmentation and analysis of spatiotemporal fluorescence signals using deep learning

Kamran, Sharif Amit; Hossain, Khondker Fariha; Moghnieh, Hussein; Riar, Sarah; Bartlett, Allison; Tavakkoli, Alireza; Sanders, Kenton M.; Baker, Salah A.

doi:10.1016/j.isci.2022.104277

Cited by 11 publications

(4 citation statements)

References 66 publications

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“…Raw output data may be stored and evaluated to address issues of replicability and provide opportunities for meta-analysis. In addition, recent machine learning approaches have been advanced to overcome the artifacts associated with imaging data to provide rapid and automatic signal segmentation [ 39 ]. These approaches may be integrated into the S8 image processing pipeline.…”

Section: Discussionmentioning

confidence: 99%

Algorithm for biological second messenger analysis with dynamic regions of interest

et al. 2023

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Physiological function is regulated through cellular communication that is facilitated by multiple signaling molecules such as second messengers. Analysis of signal dynamics obtained from cell and tissue imaging is difficult because of intricate spatially and temporally distinct signals. Signal analysis tools based on static region of interest analysis may under- or overestimate signals in relation to region of interest size and location. Therefore, we developed an algorithm for biological signal detection and analysis based on dynamic regions of interest, where time-dependent polygonal regions of interest are automatically assigned to the changing perimeter of detected and segmented signals. This approach allows signal profiles to be rigorously and precisely tracked over time, eliminating the signal distortion observed with static methods. Integration of our approach with state-of-the-art image processing and particle tracking pipelines enabled the isolation of dynamic cellular signaling events and characterization of biological signaling patterns with distinct combinations of parameters including amplitude, duration, and spatial spread. Our algorithm was validated using synthetically generated datasets and compared with other available methods. Application of the algorithm to volumetric time-lapse hyperspectral images of cyclic adenosine monophosphate measurements in rat microvascular endothelial cells revealed distinct signal heterogeneity with respect to cell depth, confirming the utility of our approach for analysis of 5-dimensional data. In human tibial arteries, our approach allowed the identification of distinct calcium signal patterns associated with atherosclerosis. Our algorithm for automated detection and analysis of second messenger signals enables the decoding of signaling patterns in diverse tissues and identification of pathologic cellular responses.

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Section: Discussionmentioning

confidence: 99%

Algorithm for biological second messenger analysis with dynamic regions of interest

et al. 2023

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“…Movies of dynamic fluorescence signals (i.e., cellular calcium signals) can be obtained from multiple imaging systems including: widefield, spinning disk confocal, 2 photon microscopy or macroscopy systems that can yield an image stack (movies of fluorescence signal over time). The fluorescence signals in the image stack can be plotted in 2D map (spatiotemporal map) as described in detail previously, 1 , 2 and these maps can be effectively segmented and analyzed using the 4SM software.…”

Section: Step-by-step Methods Detailsmentioning

confidence: 99%

Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks

Moghnieh¹,

Kamran²,

Hossain³

et al. 2022

STAR Protocols

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“…Utilizing pre-trained models for transfer learning tasks has shown tremendous promise in healthcare [67][68][69], physics-informed simulation [70,71], drug discovery [72], and computational biology [73,74]. Pre-trained models are architectures previously trained on an extensive data set, and then the weights of these models are transferred and trained on a downstream task.…”

Section: Pre-trained Encodermentioning

confidence: 99%

Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys

Hossain,

Kamran,

Tavakkoli

et al. 2023

J. Phys. Mater.

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Pendant drops of oxide-coated high-surface tension fluids frequently produce perturbed shapes that impede interfacial studies. Eutectic gallium indium (eGaIn) or gallium indium tin (Galinstan) are high-surface tension fluids coated with a ~5nm gallium oxide (Ga2O3) film and falls under this fluid classification, also known as liquid metals (LM). The recent emergence of LM-based applications often cannot proceed without analyzing interfacial energetics in different environments. While numerous techniques are available in the literature for interfacial studies- pendant droplet-based analyses are the simplest. However, the perturbed shape of the pendant drops due to the presence of surface oxide has been ignored frequently as a source of error. Also, exploratory investigations of surface oxide leveraging oscillatory pendant droplets have remained untapped. We address both challenges and present two contributing novelties- (a) by utilizing the machine learning technique, we predict the approximate surface tension value of perturbed pendant droplets, (ii) by leveraging the oscillation-induced bubble tensiometry method, we study the dynamic elastic modulus of the oxide-coated LM droplets. We have created our dataset from LM's pendant drop shape parameters and trained different models for comparison. We have achieved >99% accuracy with all models and added versatility to work with other fluids. The best-performing model was leveraged further to predict the approximate values of the nonaxisymmetric LM droplets. Then, we analyzed LM's elastic and viscous moduli in air, harnessing oscillation-induced pendant droplets, which provides complementary opportunities for interfacial studies alternative to expensive rheometers. We believe it will enable more fundamental studies of the oxide layer on LM, leveraging both symmetric and perturbed droplets. Our study broadens the materials science horizon, where researchers from machine learning and artificial intelligence domains can work synergistically to solve more complex problems related to surface science, interfacial studies, and other studies relevant to LM-based systems.

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New open-source software for subcellular segmentation and analysis of spatiotemporal fluorescence signals using deep learning

Cited by 11 publications

References 66 publications

Algorithm for biological second messenger analysis with dynamic regions of interest

Algorithm for biological second messenger analysis with dynamic regions of interest

Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks

Machine learning (ML)-assisted surface tension and oscillation-induced elastic modulus studies of oxide-coated liquid metal (LM) alloys

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