Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Ušaj, Mojca Mattiazzi; Yeung, Clarence Hue Lok; Friesen, Helena; Boone, Charles; Andrews, Brenda

doi:10.1016/j.cels.2021.05.010

Cited by 29 publications

(21 citation statements)

References 133 publications

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“…Since cells cannot otherwise be tracked between observation times, data comprise single-cell snapshots and individual trajectories are not available. While measurement noise introduced by the flow cytometry electronics and background autofluorescence are not insignificant sources of variation; variability in the data is primarily biological [11,[25][26][27][28]. We demonstrate this by performing an internalisation assay with a dual-labelled fluorescent probe, finding that measurements are highly correlated, suggestive of a shared source of variability.…”

Section: Introductionmentioning

confidence: 68%

“…Since cells cannot otherwise be tracked between observation times, data comprise single-cell snapshots and individual trajectories are not available. While previous studies have shown that measurement noise introduced by the flow cytometry electronics and background autofluorescence are not insignificant; variability in the data is primarily biological [11,[25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 79%

“…Endocytosis is the primary means by which cells uptake, or internalise, drugs, viruses, and nanoparticles [1][2][3][4][5]. Single-cell in vitro analysis of internalisation and similar dynamical processes reveals significant cell-to-cell variability in otherwise isogenic cell populations [6][7][8][9][10][11][12]. Such heterogeneity is ubiquitous to biology and essential to life, allowing for robust decision making, development, and adaptation of cell populations to environmental uncertainty [13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

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Identifying cell-to-cell variability in internalisation using flow cytometry

Browning

Ansari

Drovandi

et al. 2021

Preprint

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SummaryBiological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as internalisation. Given that internalisation is the primary means by which cells absorb drugs, viruses and other material, quantifying cell-to-cell variability in internalisation is of high biological interest. Yet, it is common for studies of internalisation to neglect cell-to-cell variability. We develop a simple mathematical model of internalisation that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data collected from an experiment that probes the internalisation of antibody by transferrin receptors in C1R cells. Our model reproduces experimental observations, identifies cell-to-cell variability in the internalisation and recycling rates, and, importantly, provides information relating to inferential uncertainty. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from experiments that probe a range of dynamical processes.

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Section: Introductionmentioning

confidence: 68%

Section: Introductionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

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Identifying cell-to-cell variability in internalisation using flow cytometry

Browning

Ansari

Drovandi

et al. 2021

Preprint

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“…Advances in hardware automation, quantitative image analysis and machine learning algorithms have helped propel the field from visually studying morphology changes and a small number of cellular parameters (low throughput) to implementing high-content screening of single-cell multi-parametric data [ 15 ]. This has led to the emergence of image-based phenomic profiling ( Figure 1 ); an approach where an abundance of heterogeneous cell phenotypic traits can be simultaneously traced and cataloged from multiple samples in response to genetic variations, environmental pressures, and drug treatments [ 9 , 15 , 16 ]. Phenomics have been empowered by the development of tools such as antibodies, metabolic and oxidative stress sensors [ 17 , 18 ], and fluorescent and bioluminescent reporters [ 19 ].…”

Section: High-throughput Single-cell Microscopy and Phenomics: Phenotyping Cellular Behaviors And Responsesmentioning

confidence: 99%

“…Applications of high-throughput single-cell microscopy (whether ‘fixed, endpoint cell assays' or time-lapse imaging) address a broad range of biological queries, from genomic cell cycle studies in fission yeast [ 20 , 21 ] to drug discovery [ 22 ], classification, and quantification of the heterogeneity of cancer cell phenotypes under various experimental conditions [ 23 , 24 ]. By incorporating refined technologies such as RNAi, CRISPR, live imaging/optical clonal barcoding, and microfluidics, researchers have the tools to identify new causal relationships between genes and downstream phenotypes and processes like evolution [ 16 , 25 , 26 ]. They also have the arsenal to track and study dynamic biophysical cellular processes of each cell (e.g.…”

Section: High-throughput Single-cell Microscopy and Phenomics: Phenotyping Cellular Behaviors And Responsesmentioning

confidence: 99%

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

Karacosta

2021

Emerging Topics in Life Sciences

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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.

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Microfluidic Biochips for Single‐Cell Isolation and Single‐Cell Analysis of Multiomics and Exosomes

Wang,

Qiu,

Liu

et al. 2024

Advanced Science

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Single‐cell multiomic and exosome analyses are potent tools in various fields, such as cancer research, immunology, neuroscience, microbiology, and drug development. They facilitate the in‐depth exploration of biological systems, providing insights into disease mechanisms and aiding in treatment. Single‐cell isolation, which is crucial for single‐cell analysis, ensures reliable cell isolation and quality control for further downstream analyses. Microfluidic chips are small lightweight systems that facilitate efficient and high‐throughput single‐cell isolation and real‐time single‐cell analysis on‐ or off‐chip. Therefore, most current single‐cell isolation and analysis technologies are based on the single‐cell microfluidic technology. This review offers comprehensive guidance to researchers across different fields on the selection of appropriate microfluidic chip technologies for single‐cell isolation and analysis. This review describes the design principles, separation mechanisms, chip characteristics, and cellular effects of various microfluidic chips available for single‐cell isolation. Moreover, this review highlights the implications of using this technology for subsequent analyses, including single‐cell multiomic and exosome analyses. Finally, the current challenges and future prospects of microfluidic chip technology are outlined for multiplex single‐cell isolation and multiomic and exosome analyses.

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Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Cited by 29 publications

References 133 publications

Identifying cell-to-cell variability in internalisation using flow cytometry

Identifying cell-to-cell variability in internalisation using flow cytometry

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

Microfluidic Biochips for Single‐Cell Isolation and Single‐Cell Analysis of Multiomics and Exosomes

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