ArticleJournal of the Royal Society, Interface2022
Identifying cell-to-cell variability in internalization using flow cytometry.
Article in Journal of the Royal Society, Interface, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Discovery of Novel Glycosidase-Derived Cell-Penetrating Peptides Encoded by Human Gut Commensals.ACS synthetic biology · 2026Article
- A multilevel hierarchical framework for quantification of experimental heterogeneity in population snapshot data.PLoS computational biology · 2026Article
- Quantifying biological heterogeneity in nano-engineered particle-cell interaction experiments.Journal of the Royal Society, Interface · 2025Article
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.PLoS computational biology · 2023Article
- Efficient inference and identifiability analysis for differential equation models with random parameters.PLoS computational biology · 2022Article
- Identifying cell-to-cell variability in internalization using flow cytometry.Journal of the Royal Society, Interface · 2022Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as the internalization of material by cells. Given that internalization is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalization is of high biological interest. Yet, it is common for studies of internalization to neglect cell-to-cell variability. We develop a simple mathematical model of internalization 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 of internalization of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. This approach provides information relating to the region of the parameter space, and consequentially the nature of cell-to-cell variability, that produces model realizations consistent with the experimental data. 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 internalization assays and similar experiments that probe cellular dynamical processes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.