Project description:The goal of this dataset was to transcriptionally profile the Arabidopsis root stem cells over developmental time. To accomplish this, we protoplasted roots expressing PET111:GFP every 8 hours from 4 days to 6 days old. We then collected GFP negative cells from the PET111:GFP line using cell sorting, as these cells represent the stem cell population. RNA was extracted from cells, libraries were prepared, and RNA-seq was performed. FPKM values were calulcated using Cufflinks.
Project description:A major limitation in quantitative time-course proteomics is the tradeoff between depth-of-analysis and speed-of-analysis. In high complexity, high dynamic samples such as plant extracts, this is tradeoff is especially apparent. To address this, we evaluate multiple composition voltage (CV) High Field Asymetric Waveform Ion Mobility Spectrometry (FAIMSpro) settings using the latest label-free, single-shot Orbitrap-based DIA acquisition workflows for their ability to deeply-quantify the Arabidopsis thaliana seedling proteome at microliter per minute flow rates. Using a micro-flow BoxCar DIA acquisition workflow with -30, -50, -70 CV FAIMSpro settings we are able to consistently quantify >5000 Arabidopsis seedling proteins over a 21-minute gradient, facilitating the analysis of ~48 samples per day. Utilizing this acquisition approach, we next performed an abiotic stress time-course experiment, whereby we quantified proteome-level changes occurring in Arabidopsis seedling shoots and roots over 24 h of salt and osmotic stress. Here, we successfully quantify over >6400 shoot and >8500 root protein groups, respectively, quantifying nearly 9700 unique protein groups total across the study. Collectively, we pioneer a fast-flow, multi-CV FAIMSpro BoxCar DIA acquisition workflow that represents an exciting new analysis approach for quantitative time-course proteomics experimentation in plants.
Project description:A major limitation in quantitative time-course proteomics is the tradeoff between depth-of-analysis and speed-of-analysis. In high complexity, high dynamic samples such as plant extracts, this is tradeoff is especially apparent. To address this, we evaluate multiple composition voltage (CV) High Field Asymetric Waveform Ion Mobility Spectrometry (FAIMSpro) settings using the latest label-free, single-shot Orbitrap-based DIA acquisition workflows for their ability to deeply-quantify the Arabidopsis thaliana seedling proteome at microliter per minute flow rates. Using a micro-flow BoxCar DIA acquisition workflow with -30, -50, -70 CV FAIMSpro settings we are able to consistently quantify >5000 Arabidopsis seedling proteins over a 21-minute gradient, facilitating the analysis of ~48 samples per day. Utilizing this acquisition approach, we next performed an abiotic stress time-course experiment, whereby we quantified proteome-level changes occurring in Arabidopsis seedling shoots and roots over 24 h of salt and osmotic stress. Here, we successfully quantify over >6400 shoot and >8500 root protein groups, respectively, quantifying nearly 9700 unique protein groups total across the study. Collectively, we pioneer a fast-flow, multi-CV FAIMSpro BoxCar DIA acquisition workflow that represents an exciting new analysis approach for quantitative time-course proteomics experimentation in plants.
Project description:We preformed at time-course of the expression of whole Arabidopsis roots for 3H, 12H, 24H, 48H and 72H after transfer to media lacking sulfur. We combined these data with 13 other datasests and performed a meta-analysis to ask whether a universal stress response exists in Arabidopsis roots. Stress responses in plants are tightly coordinated with developmental processes, but the interaction between these pathways is poorly understood. Here we use genome-wide assays at high spatial and temporal resolution to understand the processes that lnk development and stress in the Arabidopsis root. Our meta-analysis finds little evidence for a universal stress response. Common stress responses appear to exists and, analagous to animal systems, many of them show cell-type specificity, suggesting a convergent evolutionary theme in multicellular organisms. Common stress responses may be mediated by cell identity regulators, as mutations in these genes resulted in altered responses to stress. Our results reveal surprising linkages between stress and development at cellular resolution, and show the power of multiple genome-wide datasets to elucidate biological processes.