Biology subjects

Parker, S. M.

Publications and source records attributed to Parker, S. M..

3 recordsLinked to original sources

bioRxiv2023

Analysis of intracellular transport dynamics using quantitative phase imaging and FRET-based calcium sensors

Understanding cellular responses to mechanical environmental stimuli is an essential goal of cellular mechanotransduction studies and remains a target for microscopy technique development. While fluorescence microscopy has been advanced for this purpose - its molecular sensitivity, the ability of quantitative phase imaging to visualize subcellular structure has yet to be widely applied, perhaps due to its limited specificity. Here we seek to combine Quantitative Phase Imaging (QPI) with a molecularly sensitive Forster resonance energy transfer (FRET) construct for cell mechanotransduction studies. The multimodal imaging instrument is applied to examine cellular response to hypo-osmotic stimulus by observing the influx of calcium ions using a FRET based sensor coupled with mapping of the redistribution of intracellular mass using QPI. The combined imaging modality enables discrimination of cell response by localized region and reveals distinct behavior for each. The analysis shows cell flattening and oscillatory mass transport in response to the stimulus. With the broad array of FRET sensors under development, the combination with QPI offers new avenues for studying cell response to environmental stimuli.

biophysics↗

bioRxiv2023

Guiding the design of well-powered Hi-C experiments to detect differential loops

3D chromatin structure plays an important role in gene regulation by connecting regulatory regions and gene promoters. The ability to detect the formation and loss of these loops in various cell types and conditions provides valuable information on the mechanisms driving these cell states and is critical for understanding how long-range gene regulation works. Hi-C is a powerful technique used to characterize three-dimensional chromatin structure; however, Hi-C can quickly become a costly and labor-intensive endeavor, and proper planning is required to determine how to best use time and resources while maintaining experimental rigor and well-powered results. To facilitate better planning and interpretation of Hi-C experiments, we have conducted a detailed evaluation of statistical power using publicly available Hi-C datasets paying particular attention to the impact of loop size on Hi-C contacts and fold change compression. In addition, we have developed Hi-C Poweraid, a publicly-hosted web application to investigate these findings (http://phanstiel-lab.med.unc.edu/poweraid/). For experiments involving well-replicated cell lines, we recommend a total sequencing depth of at least 6 billion contacts per condition, split between at least 2 replicates in order to achieve the power to detect the majority of differential loops. For experiments with higher variation, more replicates and deeper sequencing depths are required. Exact values and recommendations for specific cases can be determined through the use of Hi-C Poweraid. This tool simplifies the complexities behind calculating power for Hi-C data and will provide useful information on the amount of well-powered loops an experiment will be able to detect given a specific set of experimental parameters, such as sequencing depth, replicates, and the sizes of the loops of interest. This will allow for more efficient use of time and resources and more accurate interpretation of experimental results. HIGHLIGHTSO_LIDistance-dependent Hi-C contacts and fold change compression influence power C_LIO_LIIntegrative modeling of Hi-C power provides guidance for experimental design C_LIO_LIAn interactive web app aids the design of differential Hi-C experiments C_LI

bioinformatics↗

bioRxiv2021

Plotgardener: Cultivating precise multi-panel figures in R

The R programming language is one of the most widely used programming languages for transforming raw genomic data sets into meaningful biological conclusions through analysis and visualization, which has been largely facilitated by infrastructure and tools developed by the Bioconductor project. However, existing plotting packages rely on relative positioning and sizing of plots, which is often sufficient for exploratory analysis but is poorly suited for the creation of publication-quality multi-panel images inherent to scientific manuscript preparation. We present plotgardener, a coordinate-based genomic data visualization package that offers a new paradigm for multi-plot figure generation in R. Plotgardener allows precise, programmatic control over the placement, aesthetics, and arrangements of plots while maximizing user experience through fast and memory-efficient data access, support for a wide variety of data and file types, and tight integration with the Bioconductor environment. Plotgardener also allows precise placement and sizing of ggplot2 plots, making it an invaluable tool for R users and data scientists from virtually any discipline. AvailabilityPackage: https://bioconductor.org/packages/plotgardener Code: https://github.com/PhanstielLab/plotgardener Documentation: https://phanstiellab.github.io/plotgardener/

bioinformatics↗