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opencc-by-4.0Dec 2020View details →

[Data from:] Genetic Analysis Reveals Three Novel QTLs Underpinning a Butterfly Egg-Induced Hypersensitive Response-Like Cell Death in Brassica Rapa

Background Cabbage white butterflies (Pieris spp.) can be severe pests of Brassica crops such as Chinese cabbage, Pak choi (Brassica rapa) or cabbages (B. oleracea). Eggs of Pieris spp. can induce a hypersensitive response-like (HR-like) cell death which reduces egg survival in the wild black mustard (B. nigra). Unravelling the genetic basis of this egg-killing trait in Brassica crops could improve crop resistance to herbivory, reducing major crop losses and pesticides use. Here we investigated the genetic architecture of a HR-like cell death induced by P. brassicae eggs in B. rapa. Results A germplasm screening of B. rapa 56 accessions, representing the genetic and geographical diversity of a B. rapa core collection, showed phenotypic variation for cell death. An image-based phenotyping protocol was developed to accurately measure size of HR-like cell death and was then used to identify two accessions that consistently showed weak (R-o-18) or strong cell death response (L58). Screening of 160 RILs derived from these two accessions resulted in three novel QTLs for Pieris brassicae-induced cell death on chromosomes A02 (Pbc1), A03 (Pbc2), and A06 (Pbc3). The three QTLs Pbc1-3 contain cell surface receptors, intracellular receptors and other genes involved in plant immunity processes, such as ROS accumulation and cell death formation. Synteny analysis with A. thaliana suggested that Pbc1 and Pbc2 are novel QTLs associated with this trait, while Pbc3 contains also LecRK-I.1, a gene of A. thaliana previously associated with cell death induced by a P. brassicae egg extract. Conclusions This study provides the first genomic regions associated with the Pieris egg-induced HR-like cell death in a Brassica crop species. It is a step closer towards unravelling the genetic basis of an egg-killing crop resistance trait, paving the way for breeders to further fine-map and validate candidate genes.

Digital Phase Contrast on Primary Dermal Human Fibroblasts cells

Name: Digital Phase Contrast on Primary Dermal Human Fibroblasts cells  Data type: Paired microscopy images (Digital Phase Contrast, square rooted) and corresponding labels/masks used for cellpose training (the corresponding Brightfield images are also present), organized as recommended by <a href="https://cellpose.readthedocs.io/en/latest/train.html">cellpose documentation</a>. Microscopy data type: Light microscopy (Digital Phase Contrast and Brighfield ) Manual annotations: Labels/masks obtained via manual segmentation. For each region, all cells were annotated manually. Uncertain objects (Dust, fused cells) were left unannotated, so that the cellpose model (10.5281/zenodo.6023317) may mimic the same user bias during prediction. This was particularly necessary due to the accumulation of floating debris in the center of the well. Microscope: Perkin Elmer Operetta microscope with a 10x 0.35 NA objective Cell type: Primary Dermal Human Fibroblasts cells File format: .tif (16-bit for DPC and 16-bit for the masks) Image size: 1024x1024 (Pixel size: 634 nm) NOTE : This dataset was used to train cellpose model ( 10.5281/zenodo.6023317 )

opencc-by-4.0Nov 2013View details →

Fig. 2. – Species prediction for a grid cell. A in Geographical patterns of woody plants' functional traits in Burkina Faso

Fig. 2. – Species prediction for a grid cell. A. Average of maximal plant size; B. Percentage of spinescent species; C. Percentage of species containing latex; D. Percentage of species with compound leaves.

opencc-by-4.0Oct 2021View details →

Annotated images from yeast cell lifespans - Training & Test sets - DetecDiv (id01)

This dataset has been generated by manual annotation from timelapse images of yeast cells dividing using the DetecDiv software (see below). It contains ~250 000 images from 250 cellular lifespans (each lifespan is made of between 700 and 1000 images). Each image is classified between 6 classes: "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog", according to the subfolder of the image. Besides, this folder also contains a .mat file containing 250 timeseries of classes corresponding to the lifespan of the 200 cells.   The dataset used for training (200 cellular lifespans) is in the folder /trainingset, while the dataset used for testing (50cellular lifespans) is in the folder /testset It has been used to train the network <a href="https://doi.org/10.5281/zenodo.5553862">doi.org/10.5281/zenodo.5553862</a> from the software DetecDiv: <a href="https://github.com/gcharvin/DetecDiv">github.com/gcharvin/DetecDiv</a> <a href="https://biorxiv.org/content/10.1101/2021.10.05.463175v1">biorxiv.org/content/10.1101/2021.10.05.463175v1</a>   Data type: 3D microscopy images (3 stacks brightfield) (.tif) + annotation (.mat) Microscopy data type: Brightfield images with 3 stacks, each stack representing a color of RGB. Imaging: 20x 0.45 NA brightfield, 6.5µm*6.5µm sCMOS Cell type: Budding yeast wild type cell (BY4742) File format: .tif (16-bit RGB, 1 color per z-stack) + .mat Image size: 60x60x1 (Pixel size: x,y: 325 nm, 3*z: 3*1325 nm)   Author(s): Théo, ASPERT Contact email: theo.aspert@gmail.com Affiliation: IGBMC, Université de Strasbourg Funding bodies: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.

A Multiplexed Cell-Free Assay to Screen for Antimicrobial Peptides in Double Emulsion Droplets

Data underlying the figures in the publication “A Multiplexed Cell-Free Assay to Screen for Antimicrobial Peptides in Double Emulsion Droplets”, published in Angew. Chem. Int. Ed., 2022, e202114632. <a href="https://onlinelibrary.wiley.com/doi/10.1002/anie.202114632">https://onlinelibrary.wiley.com/doi/10.1002/anie.202114632</a>   Table of contents: 1. Figure 1b: Bright-field image of the double emulsions droplets produced on the microfluidic chip (scale bar 40 μm). 2. Figure 1c: Source video of the image in Figure 1c. Overlaid fluorescence and bright-field image of a double emulsion in a hydrodynamic trap, containing LUVs loaded with a self-quenching concentration of SRB in the cell-free extract, showing background fluorescence (scale bar 20 μm). 3. Figure 2a: Excel file containing the experimental data for Figure 2a. Cell-free protein production. Cell-free production of sfGFP in double emulsion (DE) droplets. The expression and folding of sfGFP was confirmed by the increase of fluorescence at 516 nm (ex. 488 nm). The dashed ribbon represents standard deviation (n=150). 4. Figure 2c: Excel files containing the experimental data for Figure 2c. Mean fluorescence intensities of b) after incubation at room temperature for 16 hours. no DNA: DEs without any alpha-hemolys in plasmid DNA(n=107), α-HL:DEs with the alpha-hemolys in plasmid DNA(n=258), SDS: double emulsions without any alpha-hemolys in plasmid DNA, exposed to a solution of 0.5% SDS in buffer throughout the incubation (n=204). 5. Figure 2d: Excel file containing the experimental data for Figure 2d. Fluorophore leakage kinetics from mammalian-like LUVs with SRB and from bacteria-like LUVs with 6-FAM, induced by the cell-free expression of pneumolysin in a 384 well-plate, starting at time 0. Fractional fluorescence (fF) is calculated by setting the zero level to the vesicle fluorescence in the absence of DNA, and the maximum level of fluorescence, scaled to a value of 1, to the value obtained by lysing the vesicles with 0.5% SDS. Solid lines represent the average of three independent reactions visible below. 6. Figures 2e and 2f: FACS data for Figures 2e and 2f. 7. Figure 3a: Excel file containing the experimental data for Figure 3a.   Fluorophore leakage kinetics from mammalian-like LUVs with SRB and bacteria-like LUVs with 6-FAM, induced by the cell-free expression of meucin-25 in a 384 well-plate. Each well contained 8 nM of plasmid (Supporting Information Table 1). Solid lines represent the average of three technical replicates displayed as well (the lines are overlapping, thus not visible). 8. Figure 3c: Excel file containing the experimental data for Figure 3c.   Bacterial viability assay with increasing meucin-25 concentrations, measured by flow cytometry. Propidium iodide (PI) cannot pass intact bacterial membranes and only intercalates the DNA of permeabilized dead bacteria (“PI positive”). Constitutively expressed sfGFP proteins normally efficiently retained in intact bacterial cells (“GFPpositive”) but lost in suitably permeabilized cells. Error bars indicate standard deviation (n=10000). 9. Figure SI_2: Excel files containing the experimental data for Supplementary Figure 2. 10. Figure SI_3: Excel file containing the experimental data for Supplementary Figure 3. 11. Figure SI_4a: Excel files containing the experimental data for Supplementary Figure 4a. 12. Figure SI_4b: Excel files containing the experimental data for Supplementary Figure 4b. 13. Figure SI_5: Excel files containing the experimental data for Supplementary Figure 5. 14. Figure SI_6: Excel files containing the experimental data for Supplementary Figure 6.

Integrated Data of Single cell RNA sequencing for Human Pancreatic Adenocarcinoma

These data are collected and integrated from five available deposit data and one original data of single cell RNA sequencing from human pancreatic adenocarcinoma. Further analyses data for bulk transcriptomics (such as TCGA )using scRNAseq data and re-clustering for ductal epithelial cells and fibroblasts are also stored in step by step. Moreover, all R code is uploaded.

Bevacizumab plus erlotinib versus erlotinib alone as first line treatment of patients with EGFR-mutated advanced nonsquamous non-small cell lung cancer. BEVacizumab plus ERLotinib studY (BEVERLY): an academic, multicenter, randomised phase III trial.

Background. Adding bevacizumab to erlotinib prolonged PFS of patients with EGFR-mutated advanced NSCLC in the Japanese NEJ026 trial, but limited data were available in non-Asian patients. BEVERLY is an Italian, multicenter, randomized phase III trial of bevacizumab plus erlotinib versus erlotinib alone as first-line treatment of advanced EGFR-mutated NSCLC. Methods. Eligible patients were randomized 1:1 to erlotinib (150mg daily) plus bevacizumab (15mg/kg iv q3w) or erlotinib alone, until disease progression or unacceptable toxicity. Center, ECOG PS and type of mutation (ex19 deletion vs ex21 L858R vs others) were stratification variables. Investigator-assessed PFS (IA-PFS) and blinded-independent centrally-reviewed PFS (BICR-PFS) were co-primary endpoints. With 80% power in detecting a 0·60 HR and 2–sided α error 0·05, 126 events out of 160 patients were needed. The trial was registered as NCT02633189 and EudraCT 2015-002235-17. Findings. From Apr 11, 2016 to Feb 27, 2019, 160 pts were randomized to erlotinib pus bevacizumab (80) or erlotinib alone (80). Baseline characteristics were balanced between arms; 34 (42·5%) patients in erlotinib plus bevacizumab arm and 43 (53·8%) in erlotinib arm were former or current smokers. At a median follow-up of 36·3 months, 140 PFS events (87·5%) were reported, 68 with erlotinib plus bevacizumab and 72 with erlotinib. Median IA-PFS was 15·4 months (95% CI 12·2–18·6) with erlotinib plus bevacizumab and 9·6 months (95% CI 8·2–10·6) with erlotinib (HR 0·66; 95%CI: 0·47–0·92). BICR-PFS analysis confirmed this result. A significant interaction with treatment effect was found for smoking habit (P=0·0323): former or current smokers receiving erlotinib plus bevacizumab had a longer PFS (16·9 months [95% CI 10·2–21·8] versus 8·8 months [95% CI 5·6–9·6]) than those receiving erlotinib alone. Hypertension (grade≥3: 24% vs 5%), skin rash (grade≥3: 31% vs 14%), thromboembolic events (any grade: 11% vs 4%), and proteinuria (any grade: 23% vs 6%) were more frequent with the combination treatment. Interpretation. The addition of bevacizumab to first-line erlotinib significantly prolonged PFS in Italian patients with EGFR-mutated NSCLC, without unexpected safety issues.

opencc-by-4.0Nov 2020View details →

Datasets for chromatin hub prediction in six cell lines based on multiple genomic features

Tables with features and classes for machine learning prediction of chromatin hubs. Genomic features include CTCF, EP300, H3K27me3, H3K36me3, H3K4me1, H3K4me2, H3K4me3, H3K9ac, H3K9me3, RAD21, RNAPol2, and RNA.Seq, while the classes are Hubs and Non-Hubs. The cell lines featured here are A549, H1ESC, HeLa, IMR90, K562, and MCF7. They happen to be the 6 cell lines out of 8 existing in our integrative database, GREG (https://doi.org/10.1093/database/baz162). The normalized read-coverages from features (variables) are mapped through genomic intervals of 2 Kbs, genome-wide. Such genomic intervals (bins), are classified as Hubs or Non-Hubs. Hubs are those bins with multiple chromatin interactions, including at least one long-range interaction (larger than 1Mb) or an inter-chromosomal interaction (tagged as Inf). Columns per table: chr    start    end    CTCF    EP300    H3K27me3    H3K36me3    H3K4me1    H3K4me2    H3K4me3    H3K9ac    H3K9me3    RAD21    RNA.Seq    RNAPol2    Class Note that features may be inconsistent across different cell types, due to the availability of data. The BAM files have been sourced from ENCODE and NCBI repositories. The analysis following this data can be found at https://github.com/mora-lab/GREG-Hubs.

Molecular Signatures of Tumour and its Microenvironment for Precise Quantitative Diagnosis of Oral Squamous Cell Carcinoma: An Interna-tional Multi-cohort Diagnostic Validation Study

Supplementary Materials: The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, Table ST1 – qMIDSV2 Gene panel primer sequences; Figure S1 – qMIDSV1 vs qMIDSV2 384-well assay format and protocols; Figure S2. Individual target gene expression pattern in 1761 samples; Figure S3. Various statistical methods used for gene selection analysis on 1761 clinical samples; Figure S4. Diagnostic performance comparison between qMIDSV2 vs qMIDSV2* (with 4 less effective genes removed from the panel of 14 target genes of qMIDSV2); Figure S5. Effect of removing individual genes from the 14-target gene panel qMIDSV2 (qV2) on diagnostic test performance based on the UK patient cohort data.

Raw Data for the article: Changes in the Transcriptome Profiles of Human Amnion-Derived Mesenchymal Stromal/Stem Cells Induced by Three-Dimensional Culture: A Potential Priming Strategy to Improve Their Properties

Mesenchymal stromal/stem cells (MSCs) are believed to function in vivo as a homeostatic tool that shows therapeutic properties for tissue repair/regeneration. Conventionally, these cells are expanded in two-dimensional (2D) cultures, and, in that case, MSCs undergo genotypic/phenotypic changes resulting in a loss of their therapeutic capabilities. Moreover, several clinical trials using MSCs have shown controversial results with moderate/insufficient therapeutic responses. Different priming methods were tested to improve MSC effects, and three-dimensional (3D) culturing techniques were also examined. MSC spheroids display increased therapeutic properties, and, in this context, it is crucial to understand molecular changes underlying spheroid generation. To address these limitations, we performed RNA-seq on human amnion-derived MSCs (hAMSCs) cultured in both 2D and 3D conditions and examined the transcriptome changes associated with hAMSC spheroid formation. We found a large number of 3D culture-sensitive genes and identified selected genes related to 3D hAMSC therapeutic effects. In particular, we observed that these genes can regulate proliferation/differentiation, as well as immunomodulatory and angiogenic processes. We validated RNA-seq results by qRT-PCR and methylome analysis and investigation of secreted factors. Overall, our results showed that hAMSC spheroid culture represents a promising approach to cell-based therapy that could significantly impact hAMSC application in the field of regenerative medicine.

opencc-by-4.0Dec 2021View details →

Proteomics data of mitochondrial fraction of CRL-2097 cancer cell line model

The cancer cell line model developed using human dermal fibroblasts CRL-2097 was used in these experiments: Sample 1 - CRL2097 + hTERT Sample 2 - CRL2097 + hTERT + LT Sample 2 - CRL2097 + hTERT + LT + Ras The mitochondrial fraction was prepared from each of these cell lines and analysed via mass spec for their proteomics. The experiment was done in duplicates.

Natural Killer cells demonstrate distinct eQTL and transcriptome-wide disease associations, highlighting their role in autoimmunity.

Abstract  Natural Killer (NK) cells are innate lymphocytes with central roles in immunosurveillance and are implicated in autoimmune pathogenesis. The degree to which regulatory variants affect NK gene expression is poorly understood. We performed expression quantitative trait locus (eQTL) mapping of negatively selected NK cells from a population of healthy Europeans (n=245). We find a significant subset of genes demonstrate eQTL specific to NK cells and these are highly informative of human disease, in particular autoimmunity. An NK cell transcriptome-wide association study (TWAS) across five common autoimmune diseases identified further novel associations at 27 genes. In addition to these cis observations, we find novel master-regulatory regions impacting expression of trans gene networks at regions including 19q13.4, the Killer cell Immunoglobulin-like Receptor (KIR) Region, GNLY, MC1R and UVSSA. Our findings provide new insights into the unique biology of NK cells, demonstrating markedly different eQTL from other immune cells, with implications for disease mechanisms. Preprint https://www.biorxiv.org/content/10.1101/2021.05.10.443088v1 Dataset nk_raw_for_zenodo.txt: Matrix of raw gene expression at 47,209 probes in primary human NK cells from 245 healthy individuals of European ancestry. Gene expression is quantified using the Illumina HumanHT-12 v4 BeadChip gene expression array platform. Column names represent Array Address ID for each probe, and row names represent pseudonymised sample identifiers, which can be matched to sample genotypes. Sample genotypes are available at the European Genome-Phenome Archive with accession ID EGAS00000000109). probes_passing_QC.txt: List of probes passing quality control; probe sequences mapping to a unique genomic locus, and probe sequences not containing common genomic variation (minor allele frequency >1%), n=29,002. Column names are Array Address ID (probeID), Ensembl ID (ensembl), Gene ID (gene), and Illumina probe ID (ilmn).

Multiple Nuclei HeLa cell ground truth images with four labels (nuclear envelope, nucleus, rest of the cell, and background) for deep learning architecture training.

This is a data set that contains labelled HeLa cell images, indicating the four different classes - nuclear envelope, nucleus, rest of the cell, and background. Similar ground truth have been published for this data set, but in this case, multiple nuclei have been labelled, whilst previous ones only focused on the central cell (https://doi.org/10.5281/zenodo.3874949) Details of the imaging, preparation and segmentation have been published in: <ul> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Segmentation and Modelling of the Nuclear Envelope of HeLa Cells Imaged with Serial Block Face Scanning Electron Microscopy. J. Imaging 2019, 5(9), 75; <a href="https://doi.org/10.3390/jimaging5090075">https://doi.org/10.3390/jimaging5090075</a></li> <li>Cefa Karabağ, Martin L. Jones, Christopher J. Peddie, Anne E. Weston, Lucy M. Collinson, Constantino Carlos Reyes-Aldasoro. Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures, PLOS ONE, 2020;  <a href="https://doi.org/10.1371/journal.pone.0230605">https://doi.org/10.1371/journal.pone.0230605</a></li> <li> Cefa Karabağ, Martin L. Jones, Constantino Carlos Reyes-Aldasoro, Segmentation of the Plasma Membrane of HeLa Cells, J. Imaging 2021, 7(6), 93; <a href="https://doi.org/10.3390/jimaging7060093">https://doi.org/10.3390/jimaging7060093</a> </li> </ul> <ul> <li>The data sets are freely available through EMPIAR: http://dx.doi.org/10.6019/EMPIAR-10094 EMPIAR.</li> </ul>

opencc-zeroMar 2022View details →

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Modulation of fracture healing by the transient accumulation of senescent cells

Senescent cells have detrimental effects across tissues with aging but may have beneficial effects on tissue repair, specifically on skin wound healing. However, the potential role of senescent cells in fracture healing has not been defined. Here, we performed an in silico analysis of public mRNAseq data and found that senescence and senescence-associated secretory phenotype (SASP) markers increased during fracture healing. We next directly established that the expression of senescence biomarkers increased markedly during murine fracture healing. We also identified cells in the fracture callus that displayed hallmarks of senescence, including distension of satellite heterochromatin and telomeric DNA damage; the specific identity of these cells, however, requires further characterization. Then, using a genetic mouse model (Cdkn2aLUC) containing a Cdkn2aInk4a-driven luciferase reporter, we demonstrated transient in vivo senescent cell accumulation during callus formation. Finally, we intermittently treated young adult mice following fracture with drugs that selectively eliminate senescent cells ('senolytics', Dasatinib plus Quercetin), and showed that this regimen both decreased senescence and SASP markers in the fracture callus and significantly accelerated the time course of fracture healing. Our findings thus demonstrate that senescent cells accumulate transiently in the murine fracture callus and, in contrast to the skin, their clearance does not impair but rather improves fracture healing.

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A role for myosin II cluster and membrane energy in cortex rupture for Dictyostelium discoideum cells

Blebs, pressure driven protrusions of the cell membrane, facilitate the movement of eukaryotic cells such as the soil amoeba Dictyostelium discoideum, white blood cells and cancer cells. Blebs initiate when the cell membrane separates from the underlying cortex. A local rupture of the cortex, has been suggested as a mechanism by which blebs are initiated. However, much clarity is still needed about how cells inherently regulate rupture of the cortex in locations where blebs are expected to form. In this work, we examine the role of membrane energy and the motor protein myosin II (myosin) in facilitating the cell driven rupture of the cortex. We perform under-agarose chemotaxis experiments, using Dictyostelium discoideum cells, to visualize the dynamics of myosin and calculate changes in membrane energy in the blebbing region. To facilitate a rapid detection of blebs and analysis of the energy and myosin distribution at the cell front, we introduce an autonomous bleb detection algorithm that takes in discrete cell boundaries and returns the coordinate location of blebs with its shape characteristics. We are able to identify by microscopy naturally occurring gaps in the cortex prior to membrane detachment at sites of bleb nucleation. These gaps form at positions calculated to have high membrane energy, and are associated with areas of myosin enrichment. Myosin is also shown to accumulate in the cortex prior to bleb initiation and just before the complete disassembly of the cortex. Together our findings provide direct spatial and temporal evidence to support cortex rupture as an intrinsic bleb initiation mechanism and suggests that myosin clusters are associated with regions of high membrane energy where its contractile activity leads to a rupture of the cortex at points of maximal energy.

opencc-zeroMar 2022View details →

opencc-by-4.0Jan 2022View details →

Mutations in the telomerase reverse transcriptase promoter and PIK3CA gene are common events in penile squamous cell carcinoma of Italian and Ugandan patients

Somatic mutations in the promoter region of TERT (TERTp) gene are highly frequent in penile carcinoma in Italian and Ugandan patients, predominantly in human papillomavirus (HPV) negative cancers (67.6%). TERTp and PIK3CA hotspot changes coexist in 15.8% of cases. The higher mutant allele frequencies (MAFs) of TERT -124A/-146A compared to PIK3CA E545K MAFs suggest an asynchronous mutation timing. The coexistence of TERTp and PIK3CA mutations may represent a novel co-actionable therapeutic target in penile carcinoma patients.

Data and Software for "Complex Dynamics in a Synchronized Cell-Free Genetic Clock"

Contains raw data, analysis scripts, simulation scripts, device operation software for the publication: "Complex Dynamics in a Synchronized Cell-Free Genetic Clock".

SEACells: Inference of transcriptional and epigenomic cellular states from single-cell genomics data

Processed data for the manuscript "" available on bioRxiv at "" Data is available for the following samples <ol> <li>CD34+ Multiome data : 2 replicates </li> <li>T-cell depleted bone marrow Multiome data: 2 replicates </li> </ol>   The following counts and fragments files are available for each replicate  <ol> <li><sample>_filtered_feature_bc_matrix.h5: Feature counts from CellRanger ARC</li> <li><sample>_atac_fragments.tsv.gz: ATAC fragments file from Cellranger ARC</li> <li><sample>_atac_fragments.tsv.gz.tbi: Index files for ATAC fragments file from Cellranger ARC</li> </ol>   The following scanpy anndata objects are also available <ol> <li>cd34_multiome_rna.h5ad: Anndata object with normalized data, cell type annotations and clusters for the RNA modality of CD34+ hematopoietic stem and progenitor cells </li> <li>cd34_multiome_atac.h5ad: Anndata object with peak counts, cell type annotations and clusters for the ATAC modality of of CD34+ hematopoietic stem and progenitor cells.</li> <li>cd34_multiome_rna.h5ad: Anndata object with normalized data, cell type annotations and clusters for the RNA modality of T-cell depleted bone marrow dataset.</li> <li>bm_multiome_atac.h5ad: Anndata object with peak counts, cell type annotations and clusters for the ATAC modality of T-cell depleted bone marrow dataset.</li> </ol>

Computational Analysis of Two-dimensional High-throughput Data from Large-scale RNAi Screens and Single-cell Transcriptomics

This publication provides a singularity definition file to reproduce the computational environment along with the scripts to reproduce every figure or table in the revised manuscript using ZetaSuite Perl module and R package. First, generate a new folder and then download all the files into the folder. Then, uncompressed the files DataSets_part1.tar.gz,DataSets_part2.tar.gz,DataSets_part3.tar.gz,DataSets_part4.tar.gz, and scripts.tar.gz. within the folder. Next, move all the files in DataSets_part1 folder, DataSets_part2 folder,DataSets_part3 folder and DataSets_part4 folder to a new folder called DataSets. Finally, run the following scripts to generate the figures and tables in our manuscript. Regeneration of Figure2 and S2: singularity exec ZetaSuite.sif sh Figure2andS2.sh   Regeneration of Figure3 and S3: singularity exec ZetaSuite.sif sh Figure3andS3.sh   Regeneration of Figure4 and S4: singularity exec ZetaSuite.sif sh Figure4andS4.sh   Regeneration of Figure5 and S5: singularity exec ZetaSuite.sif sh Figure5andS5.sh   Regeneration of Figure6 and S6: singularity exec ZetaSuite.sif sh Figure6andS6.sh   Regeneration of Figure7 and S7: singularity exec ZetaSuite.sif sh Figure7andS7.sh

Sphingolipids are involved in Pieris brassicae egg-induced cell death in Arabidopsis thaliana

This table contains mean + SEM values of sphingolipid levels by LC-MS analysis in Arabidopsis thaliana (wild-type and mutant lines) and Brassica nigra (wild-type) in response to egg extract of Pieris brassicae, as well as P-values for selected comparisons by Welsch t-test. These data were used for Fig. 7 and Fig. 8 of Groux et al. 2022