More Like this

1. Data from: Regulation of vacuole fusion in stomata by dephosphorylation of the HOPS subunit VPS39

   https://doi.org/10.5061/dryad.905qftv05  

   Pullen, Anne-Marie; Billings, Grant; White, Giselle; Hodgens, Charles; Akpa, Belinda; Rojas-Pierce, Marcela (January 2025, Dryad)

   {"Abstract":["Understanding how plants regulate water loss is important for improving\n crop productivity. Tight control of stomatal opening and closing is\n essential for the uptake of CO2 while mitigating water vapor loss. The\n opening of stomata is regulated in part by homotypic vacuole fusion, which\n is mediated by conserved homotypic vacuole protein sorting (HOPS) and\n vacuolar SNARE (soluble N-ethylmaleimide sensitive factor attachment\n protein receptors) complexes. HOPS tethers apposing vacuole membranes and\n promotes the formation of trans-SNARE complexes to mediate fusion. In\n yeast, HOPS dissociates from the assembled SNARE complex to complete\n vacuole fusion, but little is known about this process in plants.\n HOPS-specific subunits VACUOLE PROTEIN SORTING39 (VPS39) and VPS41 are\n required for homotypic plant vacuole fusion, and a computational model\n predicted that post-translational modifications of HOPS may be needed for\n plant stomatal vacuole fusion. Here, we characterized a viable T-DNA\n insertion allele of VPS39 which demonstrated a critical role of VPS39 in\n stomatal vacuole fusion. We found that VPS39 has increased levels of\n phosphorylation at S413 when stomata are closed versus open, and that\n VPS39 function in stomata and embryonic development requires dynamic\n changes in phosphorylation. Among all HOPS and vacuolar SNARE subunits,\n only VPS39 showed differential levels of phosphorylation between open and\n closed stomata. Moreover, regions containing S413 are not conserved\n between plants and other organisms, suggesting plant-specific mechanisms.\n  Our data are consistent with VPS39 phosphorylation altering\n vacuole dynamics in response to environmental cues, similar to\n well-established phosphorylation cascades that regulate ion transport\n during stomatal opening."],"TechnicalInfo":["# Data from: Regulation of vacuole fusion in stomata by dephosphorylation\n of the HOPS subunit VPS39 --- The methods for this dataset are described\n in detail in our manuscript. These compressed files contain: Raw images\n (.czi) for vacuoles from roots (Root_vacuole_data.zip) used for Figure 1C.\n Raw images (.czi) for stomata vacuoles (Stomata_Vacuole_Data.zip) used for\n Figure 1D-E and Figure 3D-E. Images (.jpg) of siliques used for\n quantification of Figure 3A-C (Siliques_Data.tar). Genotypes associated\n with each plant number on each slide are listed in an Excel file. qRT-PCR\n data (.xlsx) from seedlings corresponding to Figure 1B\n (Seedling_qRT_PCR_vps39-2.xlsx). qRT-PCR data (.xlsx) from guard\n cell-enriched tissue corresponding to Figure 1F\n (Guard_Cell_enriched_RT_qPCR.xlsx). ## Description of the data and file\n structure ### **Root Vacuole image data files** This includes confocal raw\n image files captured with a Zeiss LSM980 with Airy scan microscope. Images\n are organized in folders by date of image acquisition (set 1 to set 6).\n Within each set, images are organized by genotype (WT,\n *vps39-2* or *vps39-2* VPS39-RFP/+). Each image includes green channel for\n BCECF fluorescence detection and red channel for VPS39-RFP detection. ###\n Stomata vacuole image data files This includes confocal raw image files\n captured with a Zeiss LSM980 with an Airy scan microscope. Data is\n organized in folders based on data of image acquisition. Each folder is\n subdivided by genotype: wild type (WT), *vps39-2*\n mutant, or *vps39-2* mutant complemented with VPS39-S-A-GFP (v*ps39-2*\n VPS39-S-A-GFP) or VPS39-S-D-GFP (v*ps39-2* VPS39-S-A-GFP). Within each\n genotype, images are sorted by box numbers, where each box corresponds to\n a leaf fragment from a different plant. ### **Silique image data** This\n contains all the images from siliques as captured with a Leica Thunder for\n Model Organisms dissecting scope. Images are organized in folders by date\n of data acquisition. Within each date, data is sorted by genotype. Within\n each genotype, each image includes multiple siliques from 1 or more\n plants. Each silique is marked with a genotype number as part of the\n image. An Excel sheet is included to match a plant number to a specific\n plant genotype for each image. ### **qRT-PCR files** These files contain\n raw data from gene expression studies. **Date (when included):** Date when\n qRT-PCR run was performed. **Well:** The plate position of the reaction on\n the qRT-PCR plate. **Fluor:** The fluorescence channel used for detection\n (SYBR GREEN was always used). **Target:** Specific gene transcript\n amplified for that reaction. **Content:** The reaction type as designated\n in the run file (e.g., Unknown sample, Standard, NTC, etc.). This labels\n the functional role of the well in the experiment, including NTC (no DNA\n Template Control) or NRT (no RT reaction) controls. This column was not\n specified for wells containing samples, and therefore, these were marked\n as "Unkn" by the qPCR machine. All other empty wells (not used)\n are marked as "Unkn". **Sample:** The sample ID corresponding to\n the biological sample loaded in the well. Genotypes used were either wild\n type (WT) or vps39-2 mutant (39). For each of these, biological replicates\n are indicated as A, B, C, and D, and technical replicates with numbers\n (1-4).  **Cq:** The quantification cycle (Ct) value is automatically\n calculated by the instrument. Empty cells indicate that no Ct value was\n generated due to an unused well. "N/A" indicates that no\n fluorescence was detected, and these cells correspond to non-template\n controls. Other cells left blank correspond to wells intentionally not\n used in the plate layout (no-template, no-primer, or unassigned wells).\n These were left blank because the instrument does not output data for\n unused wells."]} 

   more » « less
2. 3D printing of low-cost, high-resolution open capillary microfluidics towards self-sustaining, long-term hydration of engineered living materials

   https://doi.org/10.1007/s40964-025-01081-w  

   Sun, Aileen Y; Lin, Carrie H; Tatka, John A; Drake, Kinsey; Daily, Shannon; Castellanos, Megan; Nelson, Alshakim; Olanrewaju, Ayokunle O (April 2025, Progress in Additive Manufacturing)

   Engineered living materials (ELMs) are an emerging class of biohybrid materials that have shown great promise with advanced capabilities unachievable by conventional materials. However, application of ELMs outside of the laboratory has been limited due to the need for periodic media replenishment or complete media immersion. We herein demonstrated the integration of capillary microfluidics for the autonomous and pump-free hydration of ELM hydrogels. We optimized 3D printing parameters, including exposure time and build plate lift and retract distances, to obtain microchannel dimensions capable of spontaneous capillary flow using a low-cost liquid crystal display stereolithographic apparatus (LCD-SLA) 3D printer and two hydrogel resins that are suitable for ELMs. Microchannel dimensions were accurate with ≤ 10% deviation between designed and measured widths and precise with coefficients of variation (CVs) <5% for microchannels ≥ 206.4 µm. We demonstrated proof-of-concept spontaneous capillary flow in 3D printed microfluidic devices using dye-incorporated lysogeny broth (LB). Snapshots of the devices captured up to 24 hours showed the diffusion of dye-incorporated LB throughout the bulk material. Through this proof-of-concept study, we have showcased the feasibility of integrating capillary microfluidics with ELMs for the autonomous and pump-free flow of fluids towards self-sustaining and long-term hydration. 

   more » « less
3. Replication Data for: Multi-Point Nanoindentation Method to Determine Mechanical Anisotropy in Nanofibrillar Thin Films

   https://doi.org/10.7910/DVN/LUEV5J  

   Perera, Dinidu; Wang, Qijue; Schniepp, Hannes C. (January 2022, Harvard Dataverse)

   Raw data of scanning electron microscopy (SEM), atomic force microscopy (AFM), force spectroscopy, data analysis and plotting, optical microscopy, and finite element simulations (FEA) for our manuscript. File Formats AFM raw data is provided in Gwyddion format, which can be viewed using the Gwyddion AFM viewer, which has been released under the GNU public software licence GPLv3 and can be downloaded free of charge at http://gwyddion.net/ Optical microscopy data is provided in JPEG format SEM raw data is provided in TIFF format Data analysis codes were written in MATLAB (https://www.mathworks.com/products/matlab) and stored as *.m files Imported raw data to MATLAB and saved MATLAB data were stored as MATLAB multidimensional arrays (MATLAB “struct” data format, *.mat files) FEA results were saved as text files, .txt files) Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. Read me file.docx More Explanations of analysis in docx format. Figure 1 Figure 1 - panel b.jpg (5.5 MB) Optical micrograph (JPEG format) Figure 1 - panel c - AFM Raw Data.gwy (8.0 MB) AFM raw data (Gwyddion format) Figure 1 - panel e - P0_Force-curve_raw_data.txt (3 KB) Raw force-displacement data at P0 (text format) Figure 1 - panel e - Px_Force-curve_raw_data.txt (3 KB) Raw force-displacement data at Px (text format) Figure 1 - panel e - Py_Force-curve_raw_data.txt (3 KB) Raw force-displacement data at Py (text format) Figure 1 - panel e - P0_simulation_raw_data.txt (12 KB) FEA simulated force-distance data at P0 (text format) Figure 1 - panel e - Px_simulation_raw_data.txt (12 KB) FEA simulated force-distance data at Px (text format) Figure 1 - panel e - Py_simulation_raw_data.txt (12 KB) FEA simulated force-distance data at Py (text format) Figure 1 - panel e - FCfindc.m (2 KB) MATLAB code to calculate inverse optical lever sensitivity (InverseOLS) of AFM cantelever (matlab .m format) Figure 1 - panel e - FreqFindANoise_new.m (2 KB) MATLAB code to calculate white noise constant, A (Explained in the Read me file) (matlab .m format) Figure 1 - panel e - FreqFindQ_new.m (4 KB) MATLAB code to calculate Q factor of the AFM cantelever (matlab .m format) Figure 1 - panel e - FCkeff.m (2 KB) MATLAB code to calculate the effective spring constant k of the AFM cantelever (matlab .m format) Figure 1 - panel e - FCimport.m (7 KB) MATLAB code to import raw force-displacement data into MATLAB (matlab .m format) Figure 1 - panel e - FCForceDist.m (2 KB) MATLAB code to convert raw force-displacement data into force-distance data (matlab .m format) Figure 1 - panel e - Figure 1- Panel e - data.mat (6 KB) MATALB struct data file for calibrated force-distance data at all indentation points (matlab .mat format) Figure 1 - panel e - Panel_e_MatlabCode.m (6 KB) MATALB code for plotting experimental and simulated force curves in panel e (matlab .m format) Figure 1 - panel e - Read me file - force curve calibration.docx (14 KB) Explains force curve calibration (.docx format) Figure 1 - panel e - Read me file - lever spring constant calibration.docx (14 KB) Explains AFM lever spring constant calibration (.docx format) Figure 2 Figure 2 - panel a - MATLAB data.mat (2.6 KB) MATALB data file for simulated data (matlab .mat format) Figure 2 - panel b - MATLAB data.mat (2.4 KB) MATALB data file for simulated data (matlab .mat format) Figure 2 - panel a - simulation raw data.txt (5.0 KB) Raw simulation data: xyz coordinates of the nodes of deformed FEA mesh (text format) Figure 2 - panel b - simulation raw data.txt (5.0 KB) Raw simulation data: xyz coordinates of the nodes of deformed FEA mesh (text format) Figure 2 - panel ab - MATLABcode.m (1.0 KB) MATALB code for plotting panel a b figures (matlab .m format) Figure 2 - panel c - Degree of Anisotropy datacode.m (1.0 KB) MATALB code for plotting panel c graph (matlab .m format) Figure 3 Figure 3 - panel a - App_curve_1_raw_data.txt (35 KB) Raw force-displacement data approach curve 1 (text format) Figure 3 - panel a - App_curve_2_raw_data.txt (34 KB) Raw force-displacement data approach curve 2 (text format) Figure 3 - panel a - App_curve_3_raw_data.txt (34 KB) Raw force-displacement data approach curve 3 (text format) Figure 3 - panel a - App_curve_4_raw_data.txt (34 KB) Raw force-displacement data approach curve 4 (text format) Figure 3 - panel a - Ret_curve_1_raw_data.txt (35 KB) Raw force-displacement data of retract curve 1 (text format) Figure 3 - panel a - Ret_curve_2_raw_data.txt (35 KB) Raw force-displacement data of retract curve 2 (text format) Figure 3 - panel a - Ret_curve_3_raw_data.txt (35 KB) Raw force-displacement data of retract curve 3 (text format) Figure 3 - panel a - Simulation_raw_data-part 1.txt (43 KB) simulated force-displacement data of -part 1 (text format) Figure 3 - panel a - Simulation_raw_data-part 2.txt (43 KB) simulated force-displacement data of -part 2 (text format) Figure 3 - panel a - FCfindc.m (2 KB) MATLAB code to calculate inverse optical lever sensitivity (InverseOLS) of AFM cantelever (matlab .m format) Figure 3 - panel a - FreqFindANoise_new.m (2 KB) MATLAB code to calculate white noise constant, A (Explained in the Read me file) (matlab .m format) Figure 3 - panel a - FreqFindQ_new.m (4 KB) MATLAB code to calculate Q factor of the AFM cantelever (matlab .m format) Figure 3 - panel a - FCkeff.m (2 KB) MATLAB code to calculate the effective spring constant k of the AFM cantelever (matlab .m format) Figure 3 - panel a - FCimport.m (7 KB) MATLAB code to import raw force-displacement data into MATLAB (matlab .m format) Figure 3 - panel a - FCForceDist.m (2 KB) MATLAB code to convert raw force-displacement data into force-distance data (matlab .m format) Figure 1 - panel e - Read me file - force curve calibration.docx (14 KB) Explains force curve calibration (.docx format) Figure 1 - panel e - Read me file - lever spring constant calibration.docx (14 KB) Explains AFM lever spring constant calibration (.docx format) Figure 3 - panel b - SEM Raw Data.tiff (9 KB) SEM raw image of broken silk membrane due to extreme indentation (.tiff format) 

   more » « less
4. Replication Data for: Self-Strengthening Tape Junctions Inspired by Recluse Spider Webs

   https://doi.org/10.7910/DVN/Q7LTLW  

   Skopic, Ben H.; Koebley, Sean R.; Schniepp, Hannes C. (January 2022, Harvard Dataverse)

   The raw data for the associated manuscript is organized here into three categories: 1) relating to the measurement and analysis of the native recluse spiders loop junctions, 2) raw images found in the figures throughout the manuscript, and 3) relating to the experiments testing the effect that junction angle has on the strength of two intersecting tapes. It is recommended to browse the data files in Tree mode, which will make the files appear in folders reflecting this organization. 1) Loxosceles Loop Junction Images and Analysis The folder titled, SEM Raw Images, has all of the scanning electron microscopy (SEM) images taken of the native recluse loop junctions. Some images are close-ups of individual junctions and others take a broader perspective (macro) of many loop junctions in series. Where possible several close-up images of the individual junctions are accompanied with a macro image. These images were imported into ImageJ where the junction angle was measured. The measurements for all 41 loop junctions observed are in the folder titled, Raw Data Files in the file titled, Loxosceles Loop Junction Angle Measurements.txt. The folder titled, Raw Data Files contains, in addition to the angle measurements, the raw data for analyzing the strength of individual loop junctions. The data is in native MATLAB data format. These datasets include the complete tensile data and the cross-sectional area data for each spiders silk. The MATLAB code titled, Figure_2A_2B_code, processes the raw tensile data from the natural recluse spiders loop junctions. This data is plotted as two representative curves in Figure 2A and as a complete set as a histogram in Figure 2B. The MATLAB code titled, Figure_7_code, processes and plots the loop junction data found in, Loxosceles Loop Junction Angle Measurements.txt and executed the model of a random set of recluse loops. This code can be executed to generate Figure 7. The folder titled, Raw Data Files, must be open in MATLAB to run this code! This code uses the MATLAB function, areacalculation, to calculate the junction area for a given junction angle. 2) Raw Images This folder is organized by the respective figure in the manuscript where each image can be found. Additional metadata for each image can be found accompanying each image. 3) Tensile Data and Analysis This folder contains all of the raw tensile data for all tape-tape junction experiments conducted. All of the tensile data is in the folder titled, Raw Data Test Files. Within this folder is a .txt file for each sample tested. The file names are critical to the figure codes working properly because they contain the information for the junction angle and iterations. The file names are in the format year-month-day_trialnumber_junctionangle.txt. Also in the Raw Data Test Files folder are two functions used within some of the figure codes: fbfill and areacalculation. These functions will be used in the figure codes to properly analyze the data. To generate any figure using the MATLAB code in this folder, first open the code in MATLAB. Then within MATLAB, open the folder Raw Data Test Files. Only with this folder open in MATLAB will the code be able to find the correct raw data .txt files. The rest of the contents of this folder are MATLAB codes for specific figures in the manuscript. The only exception to this is the code titled, surfaceenergy_code, which is executed to calculate the phenomenological surface energy for the tapes used in these experiments. 

   more » « less
5. Replication Data for: Natural Spider Silk Nanofibrils Produced by Assembling Molecules or Disassembling Fibers

   https://doi.org/10.7910/DVN/P91YZ8  

   Perera, Dinidu; Li, Linxuan; Walsh, Chloe; Wang, Qijue; Schniepp, Hannes (January 2022, Harvard Dataverse)

   Raw data of optical microscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and diameter measurements of the exfoliated and self-assembled nanofibrils for our manuscript. File Formats AFM raw data is provided in Gwyddion format, which can be viewed using the Gwyddion AFM viewer, which has been released under the GNU public software licence GPLv3 and can be downloaded free of charge at http://gwyddion.net/ Optical microscopy data is provided in JPEG format SEM raw data is provided in TIFF format Data analysis codes were written in MATLAB (https://www.mathworks.com/products/matlab) and stored as *.m files Data analysis results were stored as MATLAB multidimensional arrays (MATLAB “struct” data format, *.mat files) Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. ReadMe.md This description in Markdown format. Figure 2 - Microscopy Raw Data Figure 2 - panel a.jpg (7.2 MB) Optical micrograph (JPEG format) Figure 2 - panel b.jpg (6.1 MB) Optical micrograph (JPEG format) Figure 2 - panel c f.tif (1.2 MB) SEM raw data (TIFF format) Figure 2 - panel d.tif (1.2 MB) SEM raw data (TIFF format) Figure 2 - panel e - Exfoliated Fibrils.gwy (32.0 MB) AFM raw data (Gwyddion format) Figure 3 - AFM Raw Data Figure 3 - Panel a - Exfoliated fibrils.gwy (81.5 MB) AFM raw data (Gwyddion format) Figure 3 - Panel c - Self-assembled fibrils.gwy (24.0 MB) AFM raw data (Gwyddion format) Figure 3 - Diameter Measurements Figure 3a and Figure 3c show the AFM images of exfoliated and self-assembled nanofibrils, respectively. However, due to the AFM tip-induced broadening of lateral dimensions of small features (such as nanofibrils), the diameters of nanofibrils are generally overestimated in AFM images. Hence, the diameters of the nanofibrils were estimated as the full width at half maximum (FWHM) value of line scans taken over nanofibrils perpendicular to their axial direction. Line profiles were taken at multiple locations using Gwyddion, and the raw data were stored in MATLAB struct files (lineProfileData_Exfoliated.mat and lineProfileData_Self-Assembled.mat). These data files can be directly imported into MATLAB and will appear as “DataExf” and “DataSA” in MATLAB workspace. For instance, “DataExf.x{i}” contains the x-axis data of i-th line profile, and “DataExf.y{i}” contains the y-axis data of i-th line profile. The MATLAB codes MainCode_Exf.m and MainCode_SA.m are used to fit Gaussian curves for each line profile and calculate the FWHM. The *.m files for functions gaussian.m and createFit.m must be in the same folder as the file for the main code. The main code generates figures for each line profile containing raw line profile, related Gaussian fit, and FWHM. These FWHM values are considered as the diameters of the fibrils and stored in variables called “Exf_Dia” and “SA_Dia”. Finally, these values are plotted in a histogram and calculate the statistics such as the mean and the standard deviation. Exfoliated createFit.m (1.1 KB) MATLAB code file (see above) gaussian.m (134 B) MATLAB code file (see above) lineProfileData_Exfoliated.mat (11.7 KB) Line profiles for exfoliated nanofibrils (MATLAB struct format) MainCode_Exf.m (1.8 KB) MATLAB code file (see above) Line profile raw data - Exfoliated Folder with all corresponding cross section raw data in ASCII format Self Assembled createFit.m (1.1 KB) MATLAB code file (see above) gaussian.m (134 B) MATLAB code file (see above) lineProfileData_Self-Assembled.mat (9.9 KB) Line profiles for self-assembled nanofibrils (MATLAB struct format) MainCode_SA.m (1.8 KB) MATLAB code file (see above) Line profile raw data - SelfAssembled Folder with all corresponding cross section raw data in ASCII format Figure 4 - AFM Raw Data Figure 4 - Panal a.gwy (73.4 MB) AFM raw data (Gwyddion format) Figure 4 - Panel e.gwy (42.0 MB) AFM raw data (Gwyddion format) 

   more » « less