Tracking Gene Expression via Light Sheet Microscopy and Computer Vision in Living Organisms

Buckner, Eli; Ottley, Chanae; Williams, Cranos M.; Balaguer, Angels de Luis; Melvin, Charles; Sozzani, Rosangela

doi:10.1109/embc.2018.8512416

Cited by 3 publications

(6 citation statements)

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“…Confocal images of the TCX2:TCX2-YFP, CYCB1;1:CYCB1;1-GFP, and CYCB1;1:CYCB1;1-GFP x tcx2 lines were obtained by imaging roots submerged in agar every 2 hours. A MATLAB-based image analysis software (https://github.com/edbuckne/BioVision_Tracker) was used to detect, segment, and track individual cells expressing YFP/GFP in 3D time-course fluorescence microscopy images 46 . The average voxel intensity, which is a proxy for YFP/GFP expression, was measured as the average voxel value within the set of voxels describing a segmented cell.…”

Section: Methodsmentioning

confidence: 99%

Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks

Clark

Buckner

Fisher

et al. 2019

Preprint

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Section: Methodsmentioning

confidence: 99%

Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks

Clark

Buckner

Fisher

et al. 2019

Preprint

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“…We chose to observe CYCB1;1:GFP every 20 min because CYCB1;1 expression, on average, has a duration of 3 h, which gave us about nine samples per occurrence (Yin et al, 2014). Using the digital images from both the fluorescent and brightfield channels of the light sheet microscope, we segmented, processed, and tracked CYCB1;1:GFP Regions of Interest (ROI) within the root with our custom automated image analysis software as described below in Development of an Automated Image Analysis Software to Extract Quantitative Spatiotemporal Metrics of CYCB1;1:GFP (adapted from Buckner et al, 2018). We utilized the tracking portion of our pipeline to assess root growth by quantifying the global movement of the root between sequential images.…”

Section: Resultsmentioning

confidence: 99%

“…To further assess the characteristics that this combination of stresses induced at the molecular level, we modified our existing computational pipeline (Buckner et al, 2018) to provide an automated process for collecting temporal characteristics of cells newly expressing CYCB1;1:GFP as well as the perdurance of CYCB1;1:GFP signal. It also collected spatial information of where in the root the CYCB1;1:GFP signal was detected.…”

Section: Resultsmentioning

confidence: 99%

“…We developed the BioVision Tracker (BVT) image analysis software to track ROIs in the time course microscopy images using a method that we adapted from our previously developed algorithm (Buckner et al, 2018). BVT first processes 3D fluorescence microscopy images ( Figure 3A ) by selecting ROIs using an image intensity threshold and segmenting each ROI using a watershed algorithm for each image in the time-course ( Figures 3C–E ).…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, BVT is able to define a root-specific coordinate system that localizes the ROIs within specific portions of the root by processing the 3D images from the Brightfield channel ( Figure 3B ). This is done by using an unsupervised clustering algorithm on the Brightfield image’s gradient data to estimate which voxels in the image contain root tissue and which do not (Buckner et al, 2018).…”

Section: Resultsmentioning

confidence: 99%

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Automated Imaging, Tracking, and Analytics Pipeline for Differentiating Environmental Effects on Root Meristematic Cell Division

et al. 2019

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Exposure of plants to abiotic stresses, whether individually or in combination, triggers dynamic changes to gene regulation. These responses induce distinct changes in phenotypic characteristics, enabling the plant to adapt to changing environments. For example, iron deficiency and heat stress have been shown to alter root development by reducing primary root growth and reducing cell proliferation, respectively. Currently, identifying the dynamic temporal coordination of genetic responses to combined abiotic stresses remains a bottleneck. This is, in part, due to an inability to isolate specific intervals in developmental time where differential activity in plant stress responses plays a critical role. Here, we observed that iron deficiency, in combination with temporary heat stress, suppresses the expression of iron deficiency-responsive pPYE::LUC (POPEYE::luciferase) and pBTS::LUC (BRUTUS::luciferase) reporter genes. Moreover, root growth was suppressed less under combined iron deficiency and heat stress than under either single stress condition. To further explore the interaction between pathways, we also created a computer vision pipeline to extract, analyze, and compare high-dimensional dynamic spatial and temporal cellular data in response to heat and iron deficiency stress conditions at high temporal resolution. Specifically, we used fluorescence light sheet microscopy to image Arabidopsis thaliana roots expressing CYCB1;1:GFP, a marker for cell entry into mitosis, every 20 min for 24 h exposed to either iron sufficiency, iron deficiency, heat stress, or combined iron deficiency and heat stress. Our pipeline extracted spatiotemporal metrics from these time-course data. These metrics showed that the persistency and timing of CYCB1;1:GFP signal were uniquely different under combined iron deficiency and heat stress conditions versus the single stress conditions. These metrics also indicated that the spatiotemporal characteristics of the CYCB1;1:GFP signal under combined stress were more dissimilar to the control response than the response seen under iron deficiency for the majority of the 24-h experiment. Moreover, the combined stress response was less dissimilar to the control than the response seen under heat stress. This indicated that pathways activated when the plant is exposed to both iron deficiency and heat stress affected CYCB1;1:GFP spatiotemporal function antagonistically

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Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks

et al. 2019

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Stem cells are responsible for generating all of the differentiated cells, tissues, and organs in a multicellular organism and, thus, play a crucial role in cell renewal, regeneration, and organization. A number of stem cell type-specific genes have a known role in stem cell maintenance, identity, and/or division. Yet, how genes expressed across different stem cell types, referred to here as stem-cell-ubiquitous genes, contribute to stem cell regulation is less understood. Here, we find that, in the Arabidopsis root, a stem-cell-ubiquitous gene, TESMIN-LIKE CXC2 (TCX2), controls stem cell division by regulating stem cell-type specific networks. Development of a mathematical model of TCX2 expression allows us to show that TCX2 orchestrates the coordinated division of different stem cell types. Our results highlight that genes expressed across different stem cell types ensure cross-communication among cells, allowing them to divide and develop harmonically together.

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Tracking Gene Expression via Light Sheet Microscopy and Computer Vision in Living Organisms

Cited by 3 publications

References 12 publications

Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks

Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks

Automated Imaging, Tracking, and Analytics Pipeline for Differentiating Environmental Effects on Root Meristematic Cell Division

Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks

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