A microfluidic device and computational platform for high throughput live imaging of gene expression

1] Department of Biology, Duke University, Durham, North Carolina, USA. [2] Gregor Mendel Institute of Molecular Plant Biology, Austrian Academy of Sciences, Vienna, Austria. [3] Duke Center for Systems Biology, Duke University, Durham, North Carolina, USA. [4].
Nature Methods (Impact Factor: 25.95). 09/2012; 9(11). DOI: 10.1038/nmeth.2185
Source: PubMed

ABSTRACT To fully describe gene expression dynamics requires the ability to quantitatively capture expression in individual cells over time. Automated systems for acquiring and analyzing real-time images are needed to obtain unbiased data across many samples and conditions. We developed a microfluidics device, the RootArray, in which 64 Arabidopsis thaliana seedlings can be grown and their roots imaged by confocal microscopy over several days without manual intervention. To achieve high throughput, we decoupled acquisition from analysis. In the acquisition phase, we obtain images at low resolution and segment to identify regions of interest. Coordinates are communicated to the microscope to record the regions of interest at high resolution. In the analysis phase, we reconstruct three-dimensional objects from stitched high-resolution images and extract quantitative measurements from a virtual medial section of the root. We tracked hundreds of roots to capture detailed expression patterns of 12 transgenic reporter lines under different conditions.

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    • "The ability to identify unique cell types from 3D images creates the possibility to perform virtual cell-type-specific analyses. This can be accomplished through the introduction of distinct cell lineage markers (Long et al., 2009; Federici et al., 2012) or the creation of user-defined cellular reference lookup (Busch et al., 2012; Schmidt et al., 2014). The ability to extend these approaches to different organs and contexts relies upon additional user input to define either novel reference atlases or to introduce lineage markers into additional genetic backgrounds. "
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    ABSTRACT: Diverse molecular networks underlying plant growth and development are rapidly being uncovered. Integrating these data into the spatial and temporal context of dynamic organ growth remains a technical challenge. We developed 3DCellAtlas, an integrative computational pipeline that semiautomatically identifies cell types and quantifies both 3D cellular anisotropy and reporter abundance at single-cell resolution across whole plant organs. Cell identification is no less than 97.8% accurate and does not require transgenic lineage markers or reference atlases. Cell positions within organs are defined using an internal indexing system generating cellular level organ atlases where data from multiple samples can be integrated. Using this approach, we quantified the organ-wide cell-type-specific 3D cellular anisotropy driving Arabidopsis thaliana hypocotyl elongation. The impact ethylene has on hypocotyl 3D cell anisotropy identified the preferential growth of endodermis in response to this hormone. The spatiotemporal dynamics of the endogenous DELLA protein RGA, expansin gene EXPA3, and cell expansion was quantified within distinct cell types of Arabidopsis roots. A significant regulatory relationship between RGA, EXPA3, and growth was present in the epidermis and endodermis. The use of single-cell analyses of plant development enables the dynamics of diverse regulatory networks to be integrated with 3D organ growth. © 2015 American Society of Plant Biologists. All rights reserved.
    The Plant Cell 04/2015; 27(4). DOI:10.1105/tpc.15.00175 · 9.58 Impact Factor
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    • "Confocal laser microscopy has been used to assess dynamic gene expression of root initiation and cell growth within the root tissues (Busch et al., 2012; Vermeer et al., 2014). Linked studies of gene regulation, growth regulators, intercellular communication and tissue development have led to advances in mechanistic multiscale modeling that can be used to predict root phenotypes (Band et al., 2012). "
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    ABSTRACT: I. II. III. IV. References Summary There is wide breadth of root function within ecosystems that should be considered when modeling the terrestrial biosphere. Root structure and function are closely associated with control of plant water and nutrient uptake from the soil, plant carbon (C) assimilation, partitioning and release to the soils, and control of biogeochemical cycles through interactions within the rhizosphere. Root function is extremely dynamic and dependent on internal plant signals, root traits and morphology, and the physical, chemical and biotic soil environment. While plant roots have significant structural and functional plasticity to changing environmental conditions, their dynamics are noticeably absent from the land component of process-based Earth system models used to simulate global biogeochemical cycling. Their dynamic representation in large-scale models should improve model veracity. Here, we describe current root inclusion in models across scales, ranging from mechanistic processes of single roots to parameterized root processes operating at the landscape scale. With this foundation we discuss how existing and future root functional knowledge, new data compilation efforts, and novel modeling platforms can be leveraged to enhance root functionality in large-scale terrestrial biosphere models by improving parameterization within models, and introducing new components such as dynamic root distribution and root functional traits linked to resource extraction.
    New Phytologist 09/2014; DOI:10.1111/nph.13034 · 7.67 Impact Factor
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    • "The use of high-throughput image acquisition, such as in phenotypic screens, has been quickly increasing and thus provides a new source of data for computational biologists. Microscopy of colored or fluorescent probes, followed by imaging, is able to deliver spatial and temporal quantitative phenotype information such as gene expression at high resolution (Busch et al., 2012; Ljosa et al., 2009; Walter et al., 2010). In addition, expression patterns can be documented and distributed over the internet as a valuable resource to the research community. "
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    ABSTRACT: Motivation: Computational approaches for the annotation of phenotypes from image data have shown promising results across many applications, and provide rich and valuable information for studying gene function and interactions. While data are often available both at high spatial resolution and across multiple time points, phenotypes are frequently annotated independently, for individual time points only. In particular, for the analysis of developmental gene expression patterns, it is biologically sensible when images across multiple time points are jointly accounted for, such that spatial and temporal dependencies are captured simultaneously. Methods: We describe a discriminative undirected graphical model to label gene-expression time-series image data, with an efficient training and decoding method based on the junction tree algorithm. The approach is based on an effective feature selection technique, consisting of a non-parametric sparse Bayesian factor analysis model. The result is a flexible framework, which can handle large-scale data with noisy incomplete samples, i.e. it can tolerate data missing from individual time points. Results: Using the annotation of gene expression patterns across stages of Drosophila embryonic development as an example, we demonstrate that our method achieves superior accuracy, gained by jointly annotating phenotype sequences, when compared with previous models that annotate each stage in isolation. The experimental results on missing data indicate that our joint learning method successfully annotates genes for which no expression data are available for one or more stages. Contact:
    Bioinformatics 07/2013; 29(13):i27-i35. DOI:10.1093/bioinformatics/btt206 · 4.62 Impact Factor
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