DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks

Rappez, Luca; Rakhlin, Alexander; Rigopoulos, Angelos; Nikolenko, Sergey I.; Alexandrov, Theodore

doi:10.15252/msb.20209474

Cited by 26 publications

(21 citation statements)

References 16 publications

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“…Residual networks allow training of very deep CNNs, by introducing residual blocks (skip connections) in the network architecture ( He et al, 2016 ). They are very effective as feature extractors and have shown great results in classification tasks ( Tegunov and Cramer, 2019 ; Rappez et al, 2020 ). DeepHEMNMA uses ResNet 34 CNN architecture, which has 34 layers ( He et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images

Hamitouche

Jonić

2022

Front. Mol. Biosci.

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Single-particle cryo-electron microscopy (cryo-EM) is a technique for biomolecular structure reconstruction from vitrified samples containing many copies of a biomolecular complex (known as single particles) at random unknown 3D orientations and positions. Cryo-EM allows reconstructing multiple conformations of the complexes from images of the same sample, which usually requires many rounds of 2D and 3D classifications to disentangle and interpret the combined conformational, orientational, and translational heterogeneity. The elucidation of different conformations is the key to understand molecular mechanisms behind the biological functions of the complexes and the key to novel drug discovery. Continuous conformational heterogeneity, due to gradual conformational transitions giving raise to many intermediate conformational states of the complexes, is both an obstacle for high-resolution 3D reconstruction of the conformational states and an opportunity to obtain information about multiple coexisting conformational states at once. HEMNMA method, specifically developed for analyzing continuous conformational heterogeneity in cryo-EM, determines the conformation, orientation, and position of the complex in each single particle image by image analysis using normal modes (the motion directions simulated for a given atomic structure or EM map), which in turn allows determining the full conformational space of the complex but at the price of high computational cost. In this article, we present a new method, referred to as DeepHEMNMA, which speeds up HEMNMA by combining it with a residual neural network (ResNet) based deep learning approach. The performance of DeepHEMNMA is shown using synthetic and experimental single particle images.

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Section: Methodsmentioning

confidence: 99%

DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images

Hamitouche

Jonić

2022

Front. Mol. Biosci.

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“…Such graph types include, for example, principle curves [170], minimum spanning trees (MST) [171], nearest neighbor (NN) graphs [172], and more complex networks. Early trajectory inference methods contemplate the trajectory structures to be non-branching (Wanderlust [173]), bifurcated (Wishbone [174]), or even cyclic (DeepCycle on single-cell imaging data [175]), and require prior biological knowledge or user-provided input. Emerging methods, some of which will be covered subsequently, allow unbiased inference of trajectory structures from transcriptomic data at the cost of increased computational complexity, which would impact their scalability and usability.…”

Section: Overview Of Trajectory Inference Methodsmentioning

confidence: 99%

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application

Wang

Song

Ming

et al. 2022

Mol Neurodegeneration

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Alzheimer’s disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

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“…(ii) Alternatively, a start and end point can be determined for each trace, and time can be scaled to allow averaging of multiple traces with different duration from start to end point. [151][152][153] Obviously, the scaling of time will distort the dynamic information. However, such an approach might be a useful tool to extract common features of how the signal changes during a biological process.…”

Section: Single Cell Analysis a Temporal Alignment And Data Normaliza...mentioning

confidence: 99%

Live cell microscopy: From image to insight

et al. 2022

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Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.

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DeepCycle reconstructs a cyclic cell cycle trajectory from unsegmented cell images using convolutional neural networks

Cited by 26 publications

References 16 publications

DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images

DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application

Live cell microscopy: From image to insight

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