scholarly journals From imaging a single cell to implementing precision medicine: an exciting new era

2021 ◽  
Author(s):  
Loukia G. Karacosta
Keyword(s):  
Precision Medicine ◽  
Single Cell ◽  
High Throughput ◽  
Cell Biology ◽  
Cell Imaging ◽  
Machine Learning Algorithms ◽  
Biomedical Sciences ◽  
Computational Tools ◽  
New Era ◽  
Single Cell Imaging

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.

Optofluidic time-stretch imaging – an emerging tool for high-throughput imaging flow cytometry

Lab on a Chip ◽  
2016 ◽  
Vol 16 (10) ◽  
pp. 1743-1756 ◽  
Author(s):  
Andy K. S. Lau ◽  
Ho Cheung Shum ◽  
Kenneth K. Y. Wong ◽  
Kevin K. Tsia
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
Imaging Flow Cytometry ◽  
Single Cell Imaging ◽  
Optical Time ◽  
High Throughput Imaging

Optical time-stretch imaging is now proven for ultrahigh-throughput optofluidic single-cell imaging, at least 10–100 times faster.

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
Keyword(s):  
Single Cell ◽  
Biomedical Research ◽  
Cell Imaging ◽  
New Technologies ◽  
Single Cells ◽  
Tissue Level ◽  
Machine Learning Algorithms ◽  
Multidimensional Data ◽  
Imaging Data ◽  
Single Cell Imaging

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.

scholarly journals Virus Reduction Neutralization Test: A Single-Cell Imaging High-Throughput Virus Neutralization Assay for Dengue

2018 ◽  
Vol 99 (6) ◽  
pp. 1430-1439 ◽  
Author(s):  
Melissa C. Whiteman ◽  
Leah Bogardus ◽  
Danila G. Giacone ◽  
Leonard J. Rubinstein ◽  
Joseph M. Antonello ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
Neutralization Test ◽  
Neutralization Assay ◽  
Virus Neutralization ◽  
Virus Neutralization Assay ◽  
Single Cell Imaging

scholarly journals High-throughput Automated Single Cell Imaging Analysis Reveals Dynamics Of Glioblastoma Stem Cell Population During State Transition

2018 ◽  
Author(s):  
Anastasia P. Chumakova ◽  
Masahiro Hitomi ◽  
Erik P. Sulman ◽  
Justin D. Lathia
Keyword(s):  
Stem Cell ◽  
Protein Expression ◽  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
State Transition ◽  
Imaging Analysis ◽  
Sox2 Expression ◽  
Single Cell Imaging ◽  
Stem Cell State

ABSTRACTCancer stem cells (CSCs) are a heterogeneous and dynamic population that stands at the top of tumor cellular hierarchy and is responsible for maintenance of the tumor microenvironment. As methods of CSC isolation and functional interrogation advance, there is a need for a reliable and accessible quantitative approach to assess heterogeneity and state transition dynamics in CSCs. We developed a High-throughput Automated Single Cell Imaging Analysis (HASCIA) approach for quantitative assessment of protein expression with single cell resolution and applied the method to investigate spatiotemporal factors that influence CSC state transition using glioblastoma (GBM) CSC as a model system. We were able to validate the quantitative nature of this approach through comparison of the protein expression levels determined by HASCIA to those determined by immunoblotting. A virtue of HASCIA was exemplified by detection of a subpopulation of SOX2-low cells, which expanded in fraction size during state transition. HASCIA also revealed that CSCs were committed to loose stem cell state at an earlier time point than the average SOX2 level decreased. Functional assessment of stem cell frequency in combination with quantification of SOX2 expression by HASCIA defined a stable cut-off of SOX2 expression level for stem cell state. We also developed an approach to assess local cell density and found that denser monolayer areas possess higher average levels of SOX2, higher cell diversity and a presence of a sub-population of slowly proliferating SOX2-low CSCs. HASCIA is an open source software that facilitates understanding the dynamics of heterogeneous cell population such as that of CSCs and their progeny. It is a powerful and easy-to-use image analysis and statistical analysis tool available athttps://hascia.lerner.ccf.org.

scholarly journals Dynamic analysis of MAPK signaling using a high-throughput microfluidic single-cell imaging platform

2009 ◽  
Vol 106 (10) ◽  
pp. 3758-3763 ◽  
Author(s):  
R. J. Taylor ◽  
D. Falconnet ◽  
A. Niemisto ◽  
S. A. Ramsey ◽  
S. Prinz ◽  
...  
Keyword(s):  
Dynamic Analysis ◽  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
Mapk Signaling ◽  
Single Cell Imaging

scholarly journals Review: imaging technologies for flow cytometry

Lab on a Chip ◽  
2016 ◽  
Vol 16 (24) ◽  
pp. 4639-4647 ◽  
Author(s):  
Yuanyuan Han ◽  
Yi Gu ◽  
Alex Ce Zhang ◽  
Yu-Hwa Lo
Keyword(s):  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Cell Imaging ◽  
Imaging Technologies ◽  
Imaging Flow Cytometry ◽  
Single Cell Imaging

Imaging flow cytometry combines the single-cell imaging capabilities of microscopy with the high-throughput capabilities of conventional flow cytometry. This article describes recent imaging flow cytometry technologies and their challenges.

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