Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 670

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Particle-Tracking Microrheology of Living Cells: Principles and Applications

Wirtz, Denis·2009·Annual Review of Biophysics

Abstract

A multitude of cellular and subcellular processes depend critically on the mechanical deformability of the cytoplasm. We have recently introduced the method of particle-tracking microrheology, which measures the viscoelastic properties of the cytoplasm locally and with high spatiotemporal resolution. Here we establish the basic principles of particle-tracking microrheology, describing the advantages of this approach over more conventional approaches to cell mechanics. We present basic concepts of molecular mechanics and polymer physics relevant to the microrheological response of cells. Particle-tracking microrheology can probe the mechanical properties of live cells in experimentally difficult, yet more physiological, environments, including cells embedded inside a 3D matrix, adherent cells subjected to shear flows, and cells inside a developing embryo. Particle-tracking microrheology can readily reveal the lost ability of diseased cells to resist shear forces.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1146/annurev.biophys.050708.133724

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Cited by

See all 670

Metadata

Authors
Wirtz, D.
Venue
Annual Review of Biophysics
Published
2009-06-01
Volume
38
Issue
1
Pages
301-326
ISSN
  • 1936-122X (ISSN-L)
  • 1936-1238
OpenAlex
W2161346554
Cited by
670
References
4
FWCI
1.61
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Particle-Tracking Microrheology of Living Cells: Principles and Applications. https://app.cochat.ai/papers/10.1146/annurev.biophys.050708.133724
The paper
Wirtz, D. (2009). Particle-Tracking Microrheology of Living Cells: Principles and Applications. Annual Review of Biophysics. https://doi.org/10.1146/annurev.biophys.050708.133724

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.