Dataset Information

Single cell dynamic phenotyping.

ABSTRACT: Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking, and data filtering to remove erroneous cell tracks using the novel Tracking Aberration Measure (TrAM). Our workflow is broadly applicable across imaging platforms and analysis software. By applying this workflow to cancer cell assays, we reduced aberrant cell track prevalence from 17% to 2%. The cost of this improvement was removing 15% of the well-tracked cells. This enabled detection of significant motility differences between cell lines. Similarly, we avoided detecting a false change in translocation kinetics by eliminating the true cause: varied proportions of unresponsive cells. Finally, by systematically seeking heterogeneous behaviors, we detected subpopulations that otherwise could have been missed, including early apoptotic events and pre-mitotic cells. We provide optimized protocols for specific applications and step-by-step guidelines for adapting them to a variety of biological systems.

SUBMITTER: Patsch K

PROVIDER: S-EPMC5052535 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Publications

Single cell dynamic phenotyping.

Patsch Katherin K Chiu Chi-Li CL Engeln Mark M Agus David B DB Mallick Parag P Mumenthaler Shannon M SM Ruderman Daniel D

Scientific reports 20161006

Live cell imaging has improved our ability to measure phenotypic heterogeneity. However, bottlenecks in imaging and image processing often make it difficult to differentiate interesting biological behavior from technical artifact. Thus there is a need for new methods that improve data quality without sacrificing throughput. Here we present a 3-step workflow to improve dynamic phenotype measurements of heterogeneous cell populations. We provide guidelines for image acquisition, phenotype tracking ...[more]

PMID: 27708391

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Project description:Electroactive organisms contribute to metal cycling, pollutant removal, and other redox-driven environmental processes via extracellular electron transfer (EET). Unfortunately, developing genotype-phenotype relationships for electroactive organisms is challenging because EET is necessarily removed from the cell of origin. Microdroplet emulsions, which encapsulate individual cells in aqueous droplets, have been used to study a variety of extracellular phenotypes but have not been applied to investigate EET. Here, we describe the development of a microdroplet emulsion system to sort and enrich EET-capable organisms from complex populations. We validated our system using the model electrogen Shewanella oneidensis and described the tooling of a benchtop microfluidic system for oxygen-limited conditions. We demonstrated the enrichment of strains exhibiting electroactive phenotypes from mixed wild-type and EET-deficient populations. As a proof-of-concept application, we collected samples from iron sedimentation in Town Lake (Austin, TX) and subjected them to microdroplet enrichment. We measured an increase in electroactive organisms in the sorted population that was distinct compared to a population growing in bulk culture with Fe(III) as the sole electron acceptor. Finally, two bacterial species not previously shown to be EET-capable, Cronobacter sakazakii and Vagococcus fessus, were further cultured and characterized for electroactivity. Our results demonstrate the utility of microdroplet emulsions for isolating and identifying EET-capable bacteria.IMPORTANCEThis work outlines a new high-throughput method for identifying electroactive bacteria from mixed populations. Electroactive bacteria play key roles in iron trafficking, soil remediation, and pollutant degradation. Many existing methods for identifying electroactive bacteria are coupled to microbial growth and fitness-as a result, the contributions from weak or poor-growing electrogens are often muted. However, extracellular electron transfer (EET) has historically been difficult to study in high-throughput in a mixed population since extracellular reduction is challenging to trace back to the parent cell and there are no suitable fluorescent readouts for EET. Our method circumvents these challenges by utilizing an aqueous microdroplet emulsion wherein a single cell is statistically isolated in a pico- to nano-liter-sized droplet. Then, via fluorescence obtained from copper reduction, the mixed population can be fluorescently sorted and gated by performance. Utilizing our technique, we characterize two previously unrecognized weak electrogens Vagococcus fessus and Cronobacter sakazakii.

Dataset Information

Single cell dynamic phenotyping.

Publications

Single cell dynamic phenotyping.

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