scholarly journals Computational Methods for Single-Cell Imaging and Omics Data Integration

2022 ◽  
Vol 8 ◽  
Author(s):  
Ebony Rose Watson ◽  
Atefeh Taherian Fard ◽  
Jessica Cara Mar

Integrating single cell omics and single cell imaging allows for a more effective characterisation of the underlying mechanisms that drive a phenotype at the tissue level, creating a comprehensive profile at the cellular level. Although the use of imaging data is well established in biomedical research, its primary application has been to observe phenotypes at the tissue or organ level, often using medical imaging techniques such as MRI, CT, and PET. These imaging technologies complement omics-based data in biomedical research because they are helpful for identifying associations between genotype and phenotype, along with functional changes occurring at the tissue level. Single cell imaging can act as an intermediary between these levels. Meanwhile new technologies continue to arrive that can be used to interrogate the genome of single cells and its related omics datasets. As these two areas, single cell imaging and single cell omics, each advance independently with the development of novel techniques, the opportunity to integrate these data types becomes more and more attractive. This review outlines some of the technologies and methods currently available for generating, processing, and analysing single-cell omics- and imaging data, and how they could be integrated to further our understanding of complex biological phenomena like ageing. We include an emphasis on machine learning algorithms because of their ability to identify complex patterns in large multidimensional data.

2021 ◽  
Vol 12 (15) ◽  
pp. 5419-5429
Author(s):  
Yu Lin ◽  
Kui Wu ◽  
Feifei Jia ◽  
Ling Chen ◽  
Zhaoying Wang ◽  
...  

A dual-modal microscopy imaging strategy was developed to investigate in situ the interactions between transcription (co)factors with cisplatin damaged DNA in single cells, showing that cisplatin lesions disrupted the interactions of Smad3 with DNA.


Biostatistics ◽  
2015 ◽  
Vol 16 (4) ◽  
pp. 655-669 ◽  
Author(s):  
Kirsty L. Hey ◽  
Hiroshi Momiji ◽  
Karen Featherstone ◽  
Julian R.E. Davis ◽  
Michael R.H. White ◽  
...  

2019 ◽  
Author(s):  
Erick Moen ◽  
Enrico Borba ◽  
Geneva Miller ◽  
Morgan Schwartz ◽  
Dylan Bannon ◽  
...  

AbstractLive-cell imaging experiments have opened an exciting window into the behavior of living systems. While these experiments can produce rich data, the computational analysis of these datasets is challenging. Single-cell analysis requires that cells be accurately identified in each image and subsequently tracked over time. Increasingly, deep learning is being used to interpret microscopy image with single cell resolution. In this work, we apply deep learning to the problem of tracking single cells in live-cell imaging data. Using crowdsourcing and a human-in-the-loop approach to data annotation, we constructed a dataset of over 11,000 trajectories of cell nuclei that includes lineage information. Using this dataset, we successfully trained a deep learning model to perform cell tracking within a linear programming framework. Benchmarking tests demonstrate that our method achieves state-of-the-art performance on the task of cell tracking with respect to multiple accuracy metrics. Further, we show that our deep learning-based method generalizes to perform cell tracking for both fluorescent and brightfield images of the cell cytoplasm, despite having never been trained on those data types. This enables analysis of live-cell imaging data collected across imaging modalities. A persistent cloud deployment of our cell tracker is available at http://www.deepcell.org.


Micromachines ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 311 ◽  
Author(s):  
Iordania Constantinou ◽  
Michael Jendrusch ◽  
Théo Aspert ◽  
Frederik Görlitz ◽  
André Schulze ◽  
...  

Single-cell analysis commonly requires the confinement of cell suspensions in an analysis chamber or the precise positioning of single cells in small channels. Hydrodynamic flow focusing has been broadly utilized to achieve stream confinement in microchannels for such applications. As imaging flow cytometry gains popularity, the need for imaging-compatible microfluidic devices that allow for precise confinement of single cells in small volumes becomes increasingly important. At the same time, high-throughput single-cell imaging of cell populations produces vast amounts of complex data, which gives rise to the need for versatile algorithms for image analysis. In this work, we present a microfluidics-based platform for single-cell imaging in-flow and subsequent image analysis using variational autoencoders for unsupervised characterization of cellular mixtures. We use simple and robust Y-shaped microfluidic devices and demonstrate precise 3D particle confinement towards the microscope slide for high-resolution imaging. To demonstrate applicability, we use these devices to confine heterogeneous mixtures of yeast species, brightfield-image them in-flow and demonstrate fully unsupervised, as well as few-shot classification of single-cell images with 88% accuracy.


Author(s):  
Loukia G. Karacosta

In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.


Author(s):  
UKM Teichgräber ◽  
JG Pinkernelle ◽  
F Neumann ◽  
T Benter ◽  
H Bruhn ◽  
...  

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