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Extending applications of AFM to fluidic AFM in single living cell studies.

ABSTRACT: In this article, a review of a series of applications of atomic force microscopy (AFM) and fluidic Atomic Force Microscopy (fluidic AFM, hereafter fluidFM) in single-cell studies is presented. AFM applications involving single-cell and extracellular vesicle (EV) studies, colloidal force spectroscopy, and single-cell adhesion measurements are discussed. FluidFM is an offshoot of AFM that combines a microfluidic cantilever with AFM and has enabled the research community to conduct biological, pathological, and pharmacological studies on cells at the single-cell level in a liquid environment. In this review, capacities of fluidFM are discussed to illustrate (1) the speed with which sequential measurements of adhesion using coated colloid beads can be done, (2) the ability to assess lateral binding forces of endothelial or epithelial cells in a confluent cell monolayer in an appropriate physiological environment, and (3) the ease of measurement of vertical binding forces of intercellular adhesion between heterogeneous cells. Furthermore, key applications of fluidFM are reviewed regarding to EV absorption, manipulation of a single living cell by intracellular injection, sampling of cellular fluid from a single living cell, patch clamping, and mass measurements of a single living cell.

SUBMITTER: Qiu Y

PROVIDER: S-EPMC9378449 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Extending applications of AFM to fluidic AFM in single living cell studies.

Qiu Yuan Y Chien Chen-Chi CC Maroulis Basile B Bei Jiani J Gaitas Angelo A Gong Bin B

Journal of cellular physiology 20220613 8

In this article, a review of a series of applications of atomic force microscopy (AFM) and fluidic Atomic Force Microscopy (fluidic AFM, hereafter fluidFM) in single-cell studies is presented. AFM applications involving single-cell and extracellular vesicle (EV) studies, colloidal force spectroscopy, and single-cell adhesion measurements are discussed. FluidFM is an offshoot of AFM that combines a microfluidic cantilever with AFM and has enabled the research community to conduct biological, path ...[more]

PMID: 35696489

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Project description:Blood and tissue are composed of many functionally distinct cell subsets. In immunological studies, these can be measured accurately only using single-cell assays. The characterization of these small cell subsets is crucial to decipher system-level biological changes. For this reason, an increasing number of studies rely on assays that provide single-cell measurements of multiple genes and proteins from bulk cell samples. A common problem in the analysis of such data is to identify biomarkers (or combinations of biomarkers) that are differentially expressed between two biological conditions (e.g. before/after stimulation), where expression is defined as the proportion of cells expressing that biomarker (or biomarker combination) in the cell subset(s) of interest. Here, we present a Bayesian hierarchical framework based on a beta-binomial mixture model for testing for differential biomarker expression using single-cell assays. Our model allows the inference to be subject specific, as is typically required when assessing vaccine responses, while borrowing strength across subjects through common prior distributions. We propose two approaches for parameter estimation: an empirical-Bayes approach using an Expectation-Maximization algorithm and a fully Bayesian one based on a Markov chain Monte Carlo algorithm. We compare our method against classical approaches for single-cell assays including Fisher's exact test, a likelihood ratio test, and basic log-fold changes. Using several experimental assays measuring proteins or genes at single-cell level and simulations, we show that our method has higher sensitivity and specificity than alternative methods. Additional simulations show that our framework is also robust to model misspecification. Finally, we demonstrate how our approach can be extended to testing multivariate differential expression across multiple biomarker combinations using a Dirichlet-multinomial model and illustrate this approach using single-cell gene expression data and simulations.

Dataset Information

Extending applications of AFM to fluidic AFM in single living cell studies.

Publications

Extending applications of AFM to fluidic AFM in single living cell studies.

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