DryMass: handling and analyzing quantitative phase microscopy images of spherical, cell-sized objects.

Cell analysis Cell characterization Digital holography Marker-free imaging Quantitative phase imaging Refractive index Rytov approximation

Journal

BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194

Informations de publication

Date de publication:
03 Jun 2020
Historique:
received: 17 01 2020
accepted: 20 05 2020
entrez: 5 6 2020
pubmed: 5 6 2020
medline: 15 7 2020
Statut: epublish

Résumé

Quantitative phase imaging (QPI) is an established tool for the marker-free classification and quantitative characterization of biological samples. For spherical objects, such as cells in suspension, microgel beads, or liquid droplets, a single QPI image is sufficient to extract the radius and the average refractive index. This technique is invaluable, as it allows the characterization of large sample populations at high measurement rates. However, until now, no universal software existed that could perform this type of analysis. Besides the choice of imaging modality and the variety in imaging software, the main difficulty has been to automate the entire analysis pipeline from raw data to ensemble statistics. We present DryMass, a powerful tool for QPI that covers all relevant steps from loading experimental data (multiple file formats supported), computing the phase data (built-in, automated hologram analysis), performing phase background corrections (offset, tilt, second order polynomial) to fitting scattering models (light projection, Rytov approximation, Mie simulations) to spherical phase objects for the extraction of dry mass, radius, and average refractive index. The major contribution of DryMass is a user-convenient, reliable, reproducible, and automated analysis pipeline for an arbitrary number of QPI datasets of arbitrary sizes. DryMass is a leap forward for data analysis in QPI, as it not only makes it easier to visualize raw QPI data and reproduce previous results in the field, but it also opens up QPI analysis to users without a background in programming or phase imaging.

Sections du résumé

BACKGROUND BACKGROUND
Quantitative phase imaging (QPI) is an established tool for the marker-free classification and quantitative characterization of biological samples. For spherical objects, such as cells in suspension, microgel beads, or liquid droplets, a single QPI image is sufficient to extract the radius and the average refractive index. This technique is invaluable, as it allows the characterization of large sample populations at high measurement rates. However, until now, no universal software existed that could perform this type of analysis. Besides the choice of imaging modality and the variety in imaging software, the main difficulty has been to automate the entire analysis pipeline from raw data to ensemble statistics.
RESULTS RESULTS
We present DryMass, a powerful tool for QPI that covers all relevant steps from loading experimental data (multiple file formats supported), computing the phase data (built-in, automated hologram analysis), performing phase background corrections (offset, tilt, second order polynomial) to fitting scattering models (light projection, Rytov approximation, Mie simulations) to spherical phase objects for the extraction of dry mass, radius, and average refractive index. The major contribution of DryMass is a user-convenient, reliable, reproducible, and automated analysis pipeline for an arbitrary number of QPI datasets of arbitrary sizes.
CONCLUSION CONCLUSIONS
DryMass is a leap forward for data analysis in QPI, as it not only makes it easier to visualize raw QPI data and reproduce previous results in the field, but it also opens up QPI analysis to users without a background in programming or phase imaging.

Identifiants

pubmed: 32493205
doi: 10.1186/s12859-020-03553-y
pii: 10.1186/s12859-020-03553-y
pmc: PMC7268593
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

226

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Auteurs

Paul Müller (P)

Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Tatzberg 47/49, Dresden, 01307, Germany. paul.mueller@mpl.mpg.de.
Max Planck Institute for the Science of Light, Max-Planck-Zentrum für Physik und Medizin, Staudtstr. 2, Erlangen, 91058, Germany. paul.mueller@mpl.mpg.de.

Gheorghe Cojoc (G)

Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Tatzberg 47/49, Dresden, 01307, Germany.

Jochen Guck (J)

Biotechnology Center, Center for Molecular and Cellular Bioengineering, Technische Universität Dresden, Tatzberg 47/49, Dresden, 01307, Germany.
Max Planck Institute for the Science of Light, Max-Planck-Zentrum für Physik und Medizin, Staudtstr. 2, Erlangen, 91058, Germany.

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Classifications MeSH