Find research datasets worth reusing

opencc-zeroJul 2019View details →

Data from: Tissue-specific carbon concentration, carbon stock, and distribution in Cunninghamia lanceolata (Lamb.) Hookplantations at various developmental stages in subtropical China

Key message Carbon (C) concentrations in Cunninghamia lanceolata (Lamb.) Hook plantations differed significantly among tissue types and were greater for aboveground than belowground tissues. Plantation C stock increased with developmental stage from young to mature to overmature, but at all stages the majority occurred as soil organic carbon (SOC) and was more influenced by belowground fine roots than by aboveground litterfall. Context Failing to account for tissue-specific variation in the C concentration can result in inaccurate forest C stock estimates. Aims We aimed to quantify the relative magnitudes of C stock for Chinese fir plantations at different developmental stages. Specifically, we focused on assessing tissue-specific C concentrations and C dynamics return of above- and belowground litterfall. Methods Carbon traits (C concentration, C flux, C stock and distribution at tree and ecosystem scales) were quantified in a chronosequence of Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) monoculture plantation stands at young (10), mature (22), and overmature (34 years old) developmental stages. Results Carbon concentrations differed significantly among tissue types, with mean values of 48.5 ± 0.1% and 42.5 ± 0.2% for above- and belowground biomass, respectively. The aboveground tissue C concentration, tree- and plantation-scale C stock, and SOC stock depended on developmental stage. Carbon return in litterfall, tree C stock, and SOC increased from the young to the overmature stage. SOC stock accounted for the majority of plantation C stock at all developmental stages (78.3, 59.6 and 55.7% in the young, mature and overmature stages, respectively) and was more highly influenced by belowground fine roots than aboveground litterfall. Carbon stocks in Chinese fir plantations were 86, 129, and 153 t ha-2 at the young, mature, and overmature stages. Conclusion Prolonging Chinese fir rotation increases C sequestration potential and should be the focus of forest management strategies. The tissue-specific C concentrations provide detailed information for more accurate biomass C stock estimates for Chinese fir plantations and other subtropical coniferous forest. They indicate that current guidelines result in an overestimation of belowground biomass C stocks. Using the standard 0.47 biomass to C conversion factor, the belowground C stock would have been overestimated by 7.6-13.0% for the Chinese fir developmental stages investigated, while tree C stock would be underestimated by 0.08-3.24%. Therefore, developing species- and tissue-specific conversion factors are required for supporting C plantation and forest C accounting strategies.

opencc-by-4.0Dec 2016View details →

Expression Data from "A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis, pulmonary fibrosis, and pulmonary arterial hypertension"

Normalized expression data (PCL files) and labeled PCL files (LPCL) that contain the WGCNA coexpression module assignment from Taroni, et al. A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis, pulmonary fibrosis, and pulmonary arterial hypertension. bioRxiv. doi: 10.1101/038950 See also Gene Expression Omnibus under the following accession numbers: GSE9285, GSE32413, GSE45485, GSE59785, GSE76806, GSE76807, GSE76808, GSE48149, GSE68698, GSE19617, and GSE22356.

opencc-by-4.0Mar 2022View details →

Data from: Evidence for tissue-specific defense-offense interactions between milkweed and its community of specialized herbivores

Input data used to generate the results for the manuscript under publication. It contains: 1) the residual enzymatic activity (absorbance) of 3 cardenolide-tolerant insects and the non tolerant fruit fly to 5 tissue extracts from Asclepias curassavica (roots, leaves, flower buds, seeds and latex); 2) The dilution level required to inhibit the enzymes by 50% (IC50); 3) The amount of tissue used to prepare the extracts for the enzymatic assay, as well as their cardenolide concentration to standardize the IC50 estimates by tissue mass and plant defense concentration; 4) The dataset of the chemical characterization by HPLC of the different plant parts of Asclepias curassavica; 5) The latex data used to estimate dry/wet mass ratio to correct for our latex samples.

opencc-by-4.0May 2024View details →

Transposon DNA sequences facilitate the tissue-specific gene transfer of circulating tumor DNA between human cells

nuc_ctDNA_process ImageJ 1.x macros and Matlab code for processing 3D nuclear classification and quantification. This repo is designed to help you recreate the methods use in the associated publication. Please don't hesitate to contact if you have questions. Happy to debug, update, etc if there's need. Lif files: Use ImageJ 1.x macro in fiji folder to process lif files for subsequent ilastik and Matlab processing. Works with 3 channel data (DAPI, DIC, Rh-Red-X) and 4 channel data (DAPI, Cy5, Rh-Red-X, DIC). Generates .h5 or .tif files for ilastik raining, .jpgs for visualization and ROI overlays, and raw tif files for Matlab analysis. Macro Usage Drag and drop; click Run and select .lif of interest. Only 3D data will be included, single layer images will be noted in output. A table of dimensions and max intensities is also created. Save .csv image info, and .txt output log for reference. Organize Folder Structure Folders:  <ul> <li>Ilastik output</li> <li>Nuc</li> <li>Raw</li> <li>Roi</li> </ul>  ------------ <ul> <li>Place .h5 nuclear, or .tif nuclear and DIC, and .jpg thumbnail data in subfolder called “nuc”</li> <li>Place .tif raw data export into subfolder called “raw”</li> <li>Create subfolders “ilastik output” and “roi”</li> <li>Ilastik (version 1.3.2post1) trained with ~10-20% of datasets </li> <ul> <li>Ilastik side note: currently don't know how to share Ilastik projects without getting errors on loading for the given files and filepaths present during creation. You will need to train your own models. See NoPhotonLeftBehind for Ilastik series that includes training tips and details of features used for these data. <a href="https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ">https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ</a></li> </ul> <li>Models trained as Pixel Classifications – two classes, background and nucleus</li> <li>Ilsatik model trained to classify nuclear vs non nuclear – classical thresholding methods found to be less effective due to varying amounts on cytoplasmic DNA stain present.</li> <li>Single match and mismatch trained using nuclear channel only; double mismatch trained using nuclear and DIC channels together</li> <li>Data separated and models trained for each cell type due to distinct morphologies, e.g. MM1S model, HCT116 model, etc etc</li> <li>Probability density files </li> <ul> <li>Matlab looks for “*_nrmNuc.tiff“ in relative folder “.\ilastik output”, and this is the suffix added in the Fiji macro</li> <li>In ilastik, set output format to multipage tiff, and select path to .{nickname}.tiff. Note, use path of .{nickname}_nrmNuc.tiff if _nrmNuc is not added during your file collation and logistics to this point. Also note .tiff not .tif</li> <li>Leave image export settings as default; shape here is, for example, 16, 512, 512, 1, with axis order zyxc and data type float32</li> <li>In Batch Processing section, select all of the .h5 or .tif files in the “nuc” folder and Process all files</li> </ul> <li>Matlab UI </li> <ul> <li>Files Tab: </li> <ul> <li>Set Root – select folder containing “ilastik output”, “raw”, “roi”, and “nuc”</li> <li>Filename list will propagate, and Overview text at the top will highlight red if the correct number of files are not present in all folders. (TODO: - run test on error scenario to get instructions)</li> <li>Sig Num Chns – the total number of channels in the raw data tif files</li> <li>Rh/Cy5 Sig Chn – the 1 to N based index of the channel to measure inside the nucleus</li> <li>Rh/Cy5 Bkgd – the number of counts considered as background/cell autoflourescene/non-specific signal during measurements; only voxels with counts above this level will be included in the measurements</li> <li>ROI Num Chns – total number of channels in the ilastik probability density tiff files</li> <li>ROI Chn – 1 to N based index of channel to use for generating nuclear 3D ROIs</li> <li>Thumbnails on/off toggle when selecting images in list</li> <li>Currently only single or double channel analyses available (signal is measured inside and outside of nucleus 3D ROI)</li> <li>Click on files to view the nuc jpgs. Click Processing tab to experiment with settings. Note, above channel totals and indices do not currently have error checking. Check correct combinations if you receive tif read errors. Jpgs are loaded on each click, and raw is loaded on switching to Processing tab; expect short delay depending on file size and available disk read speeds.</li> <li>Open in Explorer button – no prizes for guessing that it opens the selected file in explorer. It defaults to the raw data.</li> <li>Process All button runs all the files using the settings in place in the Processing Tab. </li> <ul> <li>A dated folder in roi is created. Inside this folder there are four different types of output file:</li> </ul> </ul> <li>.bin – a binary mask of the 3D ROI</li> <li>_dims.bin – the dimensions of the binary mask</li> <li>.jpg – a thumbnail of ROI overlays</li> <li>.mat – parameters used for generating the ROIs (open .mat files, and click on the params variable in the Import Wizard to quickly view the relevant parameters) </li> <ul> <li>Use Masks dropdown: </li> <ul> <li>For faster re-processing of data with differing minimum number of voxels existing binary masks can be used</li> <li>Note, resulting .mat file in subsequent output will not reflect the parameters used to generate the binary masks – refer to the original folder (this is noted and will be added to newer versions)</li> </ul> <li> </li> </ul> <li>Processing tab: </li> <ul> <li>FFT % is the amount of Fourier space to keep; lower values retain low frequencies only – empirically determined for best resulting nuclear shape</li> <li>FFT Smooth value is Gaussian smoothing value in pixels applied to the ellipsoid mask used to retain the central region of Fourier space. Ringing can be seen for values close to 0, increase as needed.</li> <li>Gauss Smooth is the Gaussian smoothing applied to the raw prob data prior to Otsu thresholding. In noisy classifications thresholding leads to multiple fragmented regions; some smoothing prior to thresholding helps to ‘fuse’ these fragmented regions, prior to 3D FFT spatial filtering to smooth based on size.</li> <li>FFT xz factor is used to avoid smoothing nuclei in the z direction more than x and y. This value affects the ratio of xy and z of the 3D ellipsoid used to mask Fourier space. Set empirically; Click Run and then View Volume to inspect the z ‘stretch’.</li> <li>Button group options to apply different combinations of smoothing and FFT spatial filters: </li> <ul> <li>Gauss – uses Gauss Smooth value above; applied to raw prob data</li> <li>Otsu – Otsu binary threshold</li> <li>Fill – Binary fill applied after smooth and binarization</li> <li>FFT – 3D spatial filtering based on % of Fourier space</li> </ul> <li>Run, well, runs the analysis</li> <li>View Volume displays 3D viewer for resulting data set</li> <li>Min volume slider and value are used to exclude all 3D ROIs smaller than specified value; in voxels. Note slider is linear and plot is log.</li> </ul> <li>Notes: </li> <ul> <li>Requires Matlab 2018a or newer</li> <li>Requires Parallel Computing Toolbox for parfor loop in function ProcessAllButtonPushed. Change parfor to for if not available.</li> <li> </li> </ul> </ul> <li>Matlab filelist: </li> <ul> <li>*.mlapp</li> <li>import_tif.m</li> <li>bw_outline_p.m</li> <li>smth_otsu_fill_p.m</li> <li>LPFFT3D_p.m</li> <li>otsu_bw.m</li> <li>makepsd3.m</li> <li>ellipsoid_mask.m</li> <li>bin_load_mask.m</li> <li>process_ctDNA_table.m</li> <li>_p refers to passed param struct: </li> <ul> <li>wid = 3; % width of dilation in outline overlay</li> <li>pc; % percent of Fourier space to keep - smaller numbers -> more blurred out larger images</li> <li>pad = 1; % pad Fourier space to the next power of 2</li> <li>umpx = 0.09; % image pix size</li> <li>umpz = 0.3; % again in z</li> <li>fft_smth; % smoothing of the eliptical Fourier space mask</li> <li>gauss_smth; % sigma of Guass smooth for Guass, Otsu, Fill, BW</li> <li>scl = [1 1 1/0.3]; % scale ratios for volume viewer</li> <li>fft_xz_factor; % factor to increase or decrease the amount of z FFT smoothing compared to xy</li> <li>minvol = 0;</li> </ul> </ul> </ul>

opencc-by-4.0Jun 2023View details →

Transposon DNA sequences facilitate the tissue-specific horizontal transfer: te expression supplementary datasets

These datasets contain data on analyses of TE expression stability. Raw RNA seq counts were processed using DESeq2 R package. Low count genes and TEs were removed. Counts across samples were normalized for library sizes and log-transformed using 'regularized log' transformation. Batch normalization was performed on log-transformed data with ComBat function from sva R package.  Expression variability (EV) of TEs and genes (probes) was estimated using the previously described method [1, 2]. 1. Bashkeel, N., Perkins, T.J., Kærn, M. et al. Human gene expression variability and its dependence on methylation and aging. BMC Genomics 20, 941 (2019). https://doi.org/10.1186/s12864-019-6308-7 2. Alemu EY, Carl JW Jr, Corrada Bravo H, Hannenhalli S. Determinants of expression variability. Nucleic Acids Res. 2014;42(6):3503-3514. doi:10.1093/nar/gkt1364   PC.zip - the results of TE expression and stability in prostate cancer. <ul> <li>0.PC.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.PC.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.PC.TE.rlogcpm.mad.xls -stability measures according MAD (median absolute deviance) criteria </li> <li>0.PC.TE.rlogcpm.sd.xls - stability measures according SD criteria </li> <li>0.PC_TE_bootstrap.pdf - TE expression stability</li> <li>PC.deseq.logCPM.csv - TE log transformed expression matrix </li> <li>PC.TE_count_table.csv - TE raw count matrix </li> <li>PC_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul>   MM.zip - the results of TE expression and stability in multiple myeloma. <ul> <li>0.MM.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.MM.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.MM_TE_bootstrap.pdf - TE expression stability</li> <li>MM.deseq.logCPM.csv - TE raw count matrix </li> <li>MM.rlog.mad.xlsx- stability measures according MAD (median absolute deviance) criteria</li> <li>MM.rlog.sd.xlsx - stability measures according MAD (median absolute deviance) criteria </li> <li>MM.TE_count_table.csv - TE raw count matrix </li> <li>Myeloma_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul>

openother-openDec 2022View details →

Datasets and code for "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits"

This archive contains the code and datasets generated in the associated manuscript: "Phylogenetic modeling of enhancer shifts in mole-rats reveals regulatory changes associated with tissue-specific traits". We provide instructions, environments and scripts to reproduce the presented analyses.

Fig. 7 in Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum

Fig. 7. Schematic representation of the mechanisms underlying camphor and eugenol partitioning in O. kilimandscharicum revealed by integration of transcriptomics and metabolomics data. Partitioning of camphor to the aerial tissues was attributed to differential expression of camphor biosynthesis pathway genes (Mechanism 1), while partitioning of eugenol to the roots was attributed to transport from source (aerial tissues) to the sink (roots) (Mechanism 2) potentially mediated by ABC and MATE transporter proteins [geranyl diphosphate synthase (gpps), borneol dehydrogenase (bdh), eugenol synthase (egs), ATP-binding cassette (ABC) transporters, multidrug and toxic compound extrusion (MATE) transporters].

Fig. 6 in Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum

Fig. 6. Gene co-expression analysis. A whole genome co-expression network was constructed using our RNA-Seq data in CoExpNetViz to identify candidate genes putatively involved in eugenol biosynthesis and transportation. egs1 and 4cl4 were used as the bait genes (indicated using red font on the heat map representing digital gene expression of genes involved in phenylpropanoid pathway across different tissues). For heatmap analysis, the color scale for fold-change values is shown. The network was filtered to hold correlations with r-value cut-off corresponding to the top 95th percentile and bottom 5th percentile of all correlations measured (rvalue> 0.8 and r-value <−0.2, Pearson correlation coefficient). Red edges indicate negative correlation while green edges indicate positive correlation between genes. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3 in Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum

Fig. 3. Agrobacterium-mediated transient silencing and overexpression of genes putatively involved in camphor biosynthesis in O. kilimandscharicum. GC-MS based metabolic profiling of (A) bdh silenced (RNAi_BDH) (B) gpps silenced (RNAi_GPPS) (C) bdh overexpressing (OE_BDH), and (D) gpps overexpressing (OE_GPPS) plants (where, "control" indicates uninduced plants). All experiments were repeated twice independently (number of plants per treatment, n = 6). Error bars represent the mean ± standard deviation. Data were statistically significant at p <0.0001 (indicated by "****"), p <0.001 (indicated by "***"), p <0.01 (indicated by "**"), and p <0.05 (indicated by "*").

Fig. 5 in Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum

Fig. 5. Integration of transcriptomics and metabolomics data for understanding eugenol biosynthesis in O. kilimandscharicum (Ok). (A) Pathway genes and intermediate metabolites in eugenol biosynthesis in O. kilimandscharicum. Transcripts for the genes marked in red were present in the Ok transcriptome. Metabolites marked in black were putatively identified; however, those marked in blue could not be detected on the LC-Orbitrap [phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), p-coumarate 3-hydroxylase (C3H), caffeic acid O-methyltransferse (COMT), 4-coumarate:CoA ligase (4CL), cinnamoyl-CoA reductase (CCR), cinnamyl alcohol dehydrogenase (CAD), alcohol acyltransferase (AAT), eugenol synthase (EGS)]. (B) Heat map analysis showing putatively identified intermediates detected on the LC-Orbitrap across different tissues [anther + pistil (A + P)]. All values represent an average of three biological replicates. The color scale for foldchange values is shown. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 2 in Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum

Fig. 2. Putative camphor biosynthesis pathway. Schematic representation of the putative camphor biosynthesis pathway in O. kilimandscharicum. Genes highlighted in red were cloned and functionally characterized [isopentynyl diphosphate (IPP), dimethylallyl pyrophosphate (DMAPP), geranyl diphosphate (GPP), bornyl diphosphate (BPP), geranyl diphosphate synthase (gpps), bornyl diphosphate synthase (bpps), bornyl diphosphate diphosphatase (bppd), borneol dehydrogenase (bdh)]. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 4 in Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum

Fig. 4. Integration of transcriptomics and metabolomics data for analyzing camphor biosynthesis in O. kilimandscharicum. (A) Heat map analysis showing the accumulation of camphor biosynthesis pathway intermediates and metabolites across different tissues using GC-MS [inflorescence (Infl)]. All values represent an average of three biological replicates. The color scale for fold-change values is shown. (B) Relative expression analysis of camphor biosynthesis pathway genes performed using real-time PCR [geranyl diphosphate synthase (gpps), borneol dehydrogenase (bdh)]. Error bars represent the mean ± standard deviation. Unpaired t-test suggested significant difference between data at p <0.001 (indicated by "***"), p <0.01 (indicated by "**"), and p <0.05 (indicated by "*"). NS indicates non-significant difference in fold change (x). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 1 in Biosynthesis and tissue-specific partitioning of camphor and eugenol in Ocimum kilimandscharicum

Fig. 1. Specialized metabolite partitioning across Ocimum species. Partitioning of metabolites observed between the aerial tissues [young leaves (YL), mature leaves (ML), inflorescence (I), flowers (F)], and roots (R) in O. kilimandscharicum (Ok), O. tenuiflorum (Ot), O. gratissimum (Og), O. basilicum chemotype I (ObaI), O. basilicum chemotype II (ObaII), and O. americanum (Oa). Numbers represent the metabolites observed in GC-MS analysis [camphor (1), eugenol methyl ether (2), eugenol (3), methyl cinnamate (4), germacrene A (5), borneol (6), and estragole (7)]. Heat map analysis was performed using MeV [Scale: green (low), black (medium), red (high) content]. All values represent an average of three biological replicates. The color scale for fold-change values is shown. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

VRC 900: Evaluation of Tissue-Specific Immune Responses in Adults 18 Years of Age and Older

ClinicalTrials.gov study NCT01132859. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.

publicNov 2021View details →

Sex and tissue-specific evolution of developmental plasticity in Drosophila melanogaster

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publicApr 2017View details →

Data from: Tissue-specific transcriptome characterization for developing tadpoles of the northern leopard frog (Lithobates pipiens)

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