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Statistical modeling of single target cell encapsulation.

ABSTRACT: High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and enable reliable and repeatable control over the encapsulation of cells in droplets during the isolation process with high confidence level. We have modeled and experimentally verified a microdroplet-based cell encapsulation process for various combinations of cell loading and target cell concentrations. Here, we explain theoretically and validate experimentally a model to isolate and pattern single target cells from heterogeneous mixtures without using complex peripheral systems.

SUBMITTER: Moon S

PROVIDER: S-EPMC3140975 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Statistical modeling of single target cell encapsulation.

Moon SangJun S Ceyhan Elvan E Gurkan Umut Atakan UA Demirci Utkan U

PloS one 20110721 7

High throughput drop-on-demand systems for separation and encapsulation of individual target cells from heterogeneous mixtures of multiple cell types is an emerging method in biotechnology that has broad applications in tissue engineering and regenerative medicine, genomics, and cryobiology. However, cell encapsulation in droplets is a random process that is hard to control. Statistical models can provide an understanding of the underlying processes and estimation of the relevant parameters, and ...[more]

PMID: 21814548

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Project description:BackgroundSensitive detection of low-frequency single nucleotide variants carries great significance in many applications. In cancer genetics research, tumor biopsies are a mixture of normal and tumor cells from various subpopulations due to tumor heterogeneity. Thus the frequencies of somatic variants from a subpopulation tend to be low. Liquid biopsies, which monitor circulating tumor DNA in blood to detect metastatic potential, also face the challenge of detecting low-frequency variants due to the small percentage of the circulating tumor DNA in blood. Moreover, in population genetics research, although pooled sequencing of a large number of individuals is cost-effective, pooling dilutes the signals of variants from any individual. Detection of low frequency variants is difficult and can be cofounded by sequencing artifacts. Existing methods are limited in sensitivity and mainly focus on frequencies around 2 % to 5 %; most fail to consider differential sequencing artifacts.ResultsWe aimed to push down the frequency detection limit close to the position specific sequencing error rates by modeling the observed erroneous read counts with respect to genomic sequence contexts. 4 distributions suitable for count data modeling (using generalized linear models) were extensively characterized in terms of their goodness-of-fit as well as the performances on real sequencing data benchmarks, which were specifically designed for testing detection of low-frequency variants; two sequencing technologies with significantly different chemistry mechanisms were used to explore systematic errors. We found the zero-inflated negative binomial distribution generalized linear mode is superior to the other models tested, and the advantage is most evident at 0.5 % to 1 % range. This method is also generalizable to different sequencing technologies. Under standard sequencing protocols and depth given in the testing benchmarks, 95.3 % recall and 79.9 % precision for Ion Proton data, 95.6 % recall and 97.0 % precision for Illumina MiSeq data were achieved for SNVs with frequency > = 1 %, while the detection limit is around 0.5 %.ConclusionsOur method enables sensitive detection of low-frequency single nucleotide variants across different sequencing platforms and will facilitate research and clinical applications such as pooled sequencing, cancer early detection, prognostic assessment, metastatic monitoring, and relapses or acquired resistance identification.

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.

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Statistical modeling of single target cell encapsulation.

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Statistical modeling of single target cell encapsulation.

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