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Record W2945095433 · doi:10.1101/641084

High-throughput microfluidic micropipette aspiration device to probe time-scale dependent nuclear mechanics in intact cells

2019· preprint· en· W2945095433 on OpenAlexaff
Patricia M. Davidson, Gregory R. Fedorchak, Solenne Mondésert-Deveraux, Emily Bell, Philipp Isermann, Denis Aubry, Rachèle Allena, Jan Lammerding

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsCanadian Nautical Research Society
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsLigue Contre le CancerU.S. Department of DefenseNational Science FoundationNational Institutes of HealthFondation ARC pour la Recherche sur le Cancer
KeywordsPipetteLaminViscoelasticityMicrofluidicsNucleusMaterials scienceThroughputBiomedical engineeringNanotechnologyBiological systemMechanicsChemistryComputer sciencePhysicsBiologyCell biologyEngineeringComposite material

Abstract

fetched live from OpenAlex

Abstract The mechanical properties of the cell nucleus are increasingly recognized as critical in many biological processes. The deformability of the nucleus determines the ability of immune and cancer cells to migrate through tissues and across endothelial cell layers, and changes to the mechanical properties of the nucleus can serve as novel biomarkers in processes such as cancer progression and stem cell differentiation. However, current techniques to measure the viscoelastic nuclear mechanical properties are often time consuming, limited to probing one cell at a time, or require expensive, highly specialized equipment. Furthermore, many current assays do not measure time-dependent properties, which are characteristic of viscoelastic materials. Here, we present an easy-to-use microfluidic device that applies the well-established approach of micropipette aspiration, adapted to measure many cells in parallel. The device design allows rapid loading and purging of cells for measurements, and minimizes clogging by large particles or clusters of cells. Combined with a semi-automated image analysis pipeline, the microfluidic device approach enables significantly increased experimental throughput. We validated the experimental platform by comparing computational models of the fluid mechanics in the device with experimental measurements of fluid flow. In addition, we conducted experiments on cells lacking the nuclear envelope protein lamin A/C and wild-type controls, which have well-characterized nuclear mechanical properties. Fitting time-dependent nuclear deformation data to power law and different viscoelastic models revealed that loss of lamin A/C significantly altered the elastic and viscous properties of the nucleus, resulting in substantially increased nuclear deformability. Lastly, to demonstrate the versatility of the devices, we characterized the viscoelastic nuclear mechanical properties in a variety of cell lines and experimental model systems, including human skin fibroblasts from an individual with a mutation in the lamin gene associated with dilated cardiomyopathy, healthy control fibroblasts, induced pluripotent stem cells (iPSCs), and human tumor cells. Taken together, these experiments demonstrate the ability of the microfluidic device and automated image analysis platform to provide robust, high throughput measurements of nuclear mechanical properties, including time-dependent elastic and viscous behavior, in a broad range of applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.190
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

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