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Tumor and Blood B Cell Abundance Outperforms Established ICB Response Prediction Signatures in Head and Neck Cancer
<div> <div> <div> <div> <p>This dataset contains processed flow cytometry data and clinical information for deidentified patients from Cohort 11, as well as deconvoluted cell abundances and clinical data for deidentified patients from Cohort 10, associated with the study titled <em>"Tumor and Blood B Cell Abundance Outperforms Established Immune Checkpoint Blockade Response Prediction Signatures in Head and Neck Cancer"</em> published in <strong>Annals of Oncology (2024)</strong>. <a href="https://doi.org/10.1016/j.annonc.2024.11.008" target="_new" rel="noopener">DOI: https://doi.org/10.1016/j.annonc.2024.11.008</a>.</p> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
scGeneAI mouse eye cells dataset
<p>The input mouse eye cell dataset used in the full-size examples in scGenAI is uploaded here</p>
An integrated transcriptomic cell atlas of human neural organoids: Full Dataset
<p>This deposition includes the full HNOCA dataset for the following pre-print: </p> <blockquote> <p>He, Z., Dony, L., Fleck, J.S. <em>et al.</em> An integrated transcriptomic cell atlas of human neural organoids. <em>Nature</em> <strong>635</strong>, 690–698 (2024). https://doi.org/10.1038/s41586-024-08172-8</p> </blockquote> <p><strong>This file contains additional data representations and metadata from intermediate processing steps of the HNOCA. For day-to-day use of the HNOCA as a resource, we recommend using the cleaned-up HNOCA object that can be found together with the disease atlas and the extended version of HNOCA in the <a href="https://doi.org/10.5281/zenodo.11203684" target="_blank" rel="noopener">original Zenodo deposition</a>.</strong></p> <p> </p> <p>Abstract:</p> <p>Neural tissues generated from human pluripotent stem cells in vitro (known as neural organoids) are becoming useful tools to study human brain development, evolution and disease. The characterization of neural organoids using single-cell genomic methods has revealed a large diversity of neural cell types with molecular signatures similar to those observed in primary human brain tissue. However, it is unclear which domains of the human nervous system are covered by existing protocols. It is also difficult to quantitatively assess variation between protocols and the specific cell states in organoids as compared to primary counterparts. Single-cell transcriptome data from primary tissue and neural organoids derived with guided or unguided approaches and under diverse conditions combined with large-scale integrative analyses make it now possible to address these challenges. Recent advances in computational methodology enable the generation of integrated atlases across many data sets. Here, we integrated 36 single-cell transcriptomics data sets spanning 26 protocols into one integrated human neural organoid cell atlas (HNOCA) totaling over 1.7 million cells. We harmonize cell type annotations by incorporating reference data sets from the developing human brain. By mapping to the developing human brain reference, we reveal which primary cell states have been generated in vitro, and which are under-represented. We further compare transcriptomic profiles of neuronal populations in organoids to their counterparts in the developing human brain. To support rapid organoid phenotyping and quantitative assessment of new protocols, we provide a programmatic interface to browse the atlas and query new data sets, and showcase the power of the atlas to annotate new query data sets and evaluate new organoid protocols. Taken together, the HNOCA will be useful to assess the fidelity of organoids, characterize perturbed and diseased states and facilitate protocol development in the future.</p>
An integrated transcriptomic cell atlas of human neural organoids: Cleaned datasets
<p>This deposition includes the datasets in h5ad format for the following publication: </p> <blockquote> <p>He, Z., Dony, L., Fleck, J.S. <em>et al.</em> An integrated transcriptomic cell atlas of human neural organoids. <em>Nature</em> <strong>635</strong>, 690–698 (2024). https://doi.org/10.1038/s41586-024-08172-8</p> </blockquote> <p><strong>The file `hnoca_cleanedmeta.h5ad` is a cleaned up version of the HNOCA dataset. While it contains all cells, some metadata and representations originating from intermediate processing steps are removed. You can find the full (and significantly larger) object in a <a href="https://doi.org/10.5281/zenodo.12536006" target="_blank" rel="noopener">second Zenodo deposition</a>.</strong></p> <p><strong>You can find the minimal HNOCA and primary reference h5ad files for query-to-reference-mapping in a <a href="https://doi.org/10.5281/zenodo.15004817">third Zenodo deposition</a>.</strong></p> <p> </p> <p>Abstract:</p> <p>Neural tissues generated from human pluripotent stem cells in vitro (known as neural organoids) are becoming useful tools to study human brain development, evolution and disease. The characterization of neural organoids using single-cell genomic methods has revealed a large diversity of neural cell types with molecular signatures similar to those observed in primary human brain tissue. However, it is unclear which domains of the human nervous system are covered by existing protocols. It is also difficult to quantitatively assess variation between protocols and the specific cell states in organoids as compared to primary counterparts. Single-cell transcriptome data from primary tissue and neural organoids derived with guided or unguided approaches and under diverse conditions combined with large-scale integrative analyses make it now possible to address these challenges. Recent advances in computational methodology enable the generation of integrated atlases across many data sets. Here, we integrated 36 single-cell transcriptomics data sets spanning 26 protocols into one integrated human neural organoid cell atlas (HNOCA) totaling over 1.7 million cells. We harmonize cell type annotations by incorporating reference data sets from the developing human brain. By mapping to the developing human brain reference, we reveal which primary cell states have been generated in vitro, and which are under-represented. We further compare transcriptomic profiles of neuronal populations in organoids to their counterparts in the developing human brain. To support rapid organoid phenotyping and quantitative assessment of new protocols, we provide a programmatic interface to browse the atlas and query new data sets, and showcase the power of the atlas to annotate new query data sets and evaluate new organoid protocols. Taken together, the HNOCA will be useful to assess the fidelity of organoids, characterize perturbed and diseased states and facilitate protocol development in the future.</p>
YY1 mutations disrupt corticogenesis through a cell type-specific rewiring of cell-autonomous and non-cell-autonomous transcriptional programs
<p>This supplementary data includes counts from bulk and pseudobulk omic experiments, h5ad for single-cell experiments, and outputs of differential expression and enrichments performed on different omics assays.</p>
Distinct tissue-dependent composition and gene expression of human fetal innate lymphoid cells
<p>Countmatrix and metadata for gene expression in bulk NK cells and CD304+ ILC3s isolated from human fetal liver, lung, intestine, and skin. </p>
Datasets for: Integrative Mapping of Human CD8+ T Cell in Inflammation and Cancer
<p>Files for the integrated pan-disease CD8+ T cell atlas: </p> <ul> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.all_genes.h5ad.gz?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.all_genes.h5ad.gz</a> : 19957 genes raw matrix </li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.X_gex.npy?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.X_gex.npy</a> : scatlasavae model embedding</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.clone_subtype.csv?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.clone_subtype.csv</a> : clone type definition</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.supervised.model</a>. scatlasavae model checkpoint</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.h5ad?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.h5ad</a> : Tex subset of 4000 highly variable genes raw matrix</li> <li><a href="https://zenodo.org/records/13382785/files/huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model?download=1" target="_blank" rel="noopener">huARdb_v2_GEX.CD8.hvg4k.Tex.supervised.model</a>. scatlasavae model checkpoint for the Tex subset</li> </ul> <p>Files for the TILs CD8+ T cell atlas</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_chu.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_chu.h5ad</a>. the Chu <em>et al.</em>, 2023 Dataset</li> <li><a href="https://zenodo.org/records/13382785/files/adata_cd8_zheng.h5ad?download=1" target="_blank" rel="noopener">adata_cd8_zheng.h5ad.</a> The Zheng <em>et al.</em>, 2021 Dataset</li> </ul> <p>Files for the transfer (query) datasets:</p> <ul> <li><a href="https://zenodo.org/records/13382785/files/Zhang_LC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Zhang_LC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_TIL.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_TIL.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Luoma_HNSCC_PBMC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Luoma_HNSCC_PBMC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Watson_MELA.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Watson_MELA.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bassez_BC.cohort1.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bassez_BC.cohort1.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Bi_RCC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Bi_RCC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Caushi_NSCLC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Caushi_NSCLC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Liu_TNBC.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Liu_TNBC.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Borra%CC%80s_2024_Colorectal_cancer.CD8_T.h5ad?download=1" target="_blank" rel="noopener">Borràs_2024_Colorectal_cancer.CD8_T.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Garner_2023_MAIT.h5ad" target="_blank" rel="noopener">Garner_2023.h5ad</a></li> <li><a href="https://zenodo.org/records/13382785/files/Vorkas_2022_MAIT.h5ad?download=1" target="_blank" rel="noopener">Vorkas_2022_MAIT.h5ad</a></li> </ul>
Memory B Cells and Their Transcriptomic Profiles Associated with Belimumab Resistance in Systemic Lupus Erythematosus in the Maintenance Phase
<p>Normalized count of RNA sequences from systemic lupus erythematosus patients treated with belimumab.</p> <p>We collected blood samples from patients with systemic lupus erythematosus (n=44) before and approximately 3 and 6 months after treatment by belimumab, respectively. We also included healthy individuals (n=17). There were no missing samples in the before-treatment specimen for 44 patients; however, five samples were missing at the three-month time, and four were missing at the six-month time after treatment. Whole blood samples were stored in PAXgene tubes (QUIAGEN). RNA was extracted by PAXgene Blood RNA Kit (QUIAGEN). Library preparation was performed using TruSeq stranded Total RNA Library PrepKit with Ribo-Zero Globin Human. Sequencing was conducted by NovaSeq6000 in the 100-bp paired-end mode. Sequencing reads were trimmed using Trimmomatic ver. 0.36 (leading: 20, trailing: 20, slidingwindow: 4:15, minlen: 36) and aligned to hg38 reference genome using STAR (ver. 2.7.3a). Gene counts were generated by RSEM (ver. 1.3.1) using Homo_sapiens.GRCh38.95.gtf from the Ensembl database. Gene counts were normalized by size factor implemented in DESeq2 and converted to count per million (CPM), and log<sub>2</sub>(CPM+1) was calculated. </p> <p>Sample names starting with 'R_' indicate responders, while those ending with '_NR' indicate non-responders. '0M' represents before treatment, '3M' represents three months after treatment, and '6M' represents six months after treatment. The numeric ID, excluding the last two characters, represents individual patients.</p> <p> </p> <p>If you use this data, please cite Iwasaki et al. Front. Immunol., 04 February 2025 <a href="https://doi.org/10.3389/fimmu.2025.1506298">https://doi.org/10.3389/fimmu.2025.1506298</a></p>
The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability
<p>This dataset contains data and Python scripts in "The spreading of magnetic reconnection X-line in particle-in-cell simulations– mechanism and the effect of drift-kink instability" prepared to submit to the Journal of Geophysical Research. </p>
Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance
<p>The below data are associated with our paper entitled "Linking regulatory variants to target genes by integrating single-cell multiome methods and genomic distance."</p> <p>1) SNP-gene link predictions generated by pgBoost and existing methods SCENT (Sakaue et al. 2024 <em>Nat Genet</em>), Signac (Stuart et al. 2021 <em>Nat Methods</em>), ArchR (Granja et al. 2021 <em>Nat Genet</em>), and Cicero (Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><strong>pgBoost_scores.tsv.gz </strong>contains linking predictions made by pgBoost.</p> <p><strong>constituent_method_scores.tsv.gz</strong> contains linking predictions made by constituent methods.</p> <p><em><span>**NOTE: promoters (+/- 1kb from TSS) and candidate links >500kb are excluded from linking predictions (see manuscript)**</span></em></p> <p>Linking scores and percentiles are reported for each method (pgBoost score, SCENT FDR, Signac correlation, ArchR correlation, Cicero co-accessibility). Rank percentiles are computed as: 1 - (rank / n). When multiple links receive the same score, they are assigned the percentile of the top rank. Links unscored by each method (denoted by zeros* in the linking score column) are assigned a percentile equivalent to the percent of links unscored by the focal method. See the Methods section of the paper for further details on computing linking scores and summarizing scores across cell types and data sets.</p> <p>*Candidate links tested and assigned a co-accessibility of zero by the Cicero method are given a score of 1e-100 in the "Cicero" column to distinguish between unscored candidate links and candidate links assigned a partial correlation of zero (see Pliner et al. 2018 <em>Mol Cell</em>).</p> <p><em>NOTE: The predictions associated with this release (version 2) were generated using an expanded set of data sets, an expanded training set, and corrected TSS coordinates.</em></p> <p>2) GWAS-derived evaluation SNP-gene link evaluation set.</p> <p><strong>gwas_evaluation.tsv</strong>: GWAS-derived evaluation SNP-gene link evaluation set. Column 1 provides SNP coordinates in the format <chr-start-end>. This evaluation framework was proposed by Weeks et al. 2024 <em>Nature Genetics</em> based on fine-mapping results from Kanai et al. <em>medrxiv</em> (see Methods: <em>Evaluation data sets</em> of Dorans et al.). "True" links (gold = 1) are non-coding variants fine-mapped to a focal trait (PIP > 0.1) with a coding variant for exactly one candidate gene within 1 Mb attaining PIP > 0.5 for the same trait. "False" links (gold = 0) are candidate SNP-gene pairs involving a SNP with a "true" link. This file of SNP-gene links was adapted from credible set-gene links <a href="https://github.com/Deylab999/GWAS_benchmark_IGVF/blob/bb91d08cc02d59cdd829eb1430569057ac26c5fe/V2G/ENCODE_E2G_2023/UKBiobank.ABCGene.anyabc.tsv">here</a> (the "truth" column defines true/false links) by identifying SNPs with PIP > 0.1 within each credible set-gene link.</p>
Erythropoietin directly remodels the clonal composition of murine hematopoietic multipotent progenitor cells
<p>## In version 1 of the repository the scRNAseq data was corrupted -- in version 2 this has been corrected and the bam files of both scRNAseq runs have been uploaded ##</p> <p>This dataset consists of the raw sequencing files of 9 independent barcoding experiments and of one 10X Genomics scRNAseq experiment. These are the source files for the main figures of the associated publication.</p> <p>HSPCs (C-Kit+ Sca1+ CD150+ Flt3-) or MPP2 (C-Kit+ Sca1+ Flt3- CD150+ CD48+) were isolated form mouse bone marrow through flushing, MACS-enrichment and sorting. The cells were barcoded by spin infection for 6h, and cultured in StemSpanMedium SFEM with 50 ng/ml mSCF (STEMCELL Technologies) for 16h with or without human recombinant EPO (Eprex, erythropoietin alpha, Janssen) at 1000 ng/ml or 160 ng/ml. At this stage scRNAseq was perfromed on the 10X Chromium platfom (10X Genomics), or cells were transplanted by tail vein injection into 6Gy irradiated recipient mice. When appropirate an additional injection of EPO 133ug/kg was given at the moment of transplantation. Different mature hematopoietic cells were isolated form the transplanted mice after 4 weeks or 16 weeks.</p> <p>For barcoding experiments, a three-step PCR was performed to amplify barcode sequences, to add Read1 and Read2 Illumina sequencing adapters, P5 and P3 flow cell attachment sites as well as plate and sample indices. Libraries were sequenced on an Illumina HiSeq SR65 with 10% of PhiX spike-in. For scRNAseq experiment, libraries were made using the Chromium SIngle Cell 3' v2 kit and sequencing was performed on a HiSeq PE26-98.</p> <p>The repository encompasses the following datasets:</p> <ul> <li>A1006.tar.gz -- AE05Low -- HSPCs EPO 160 ng/ml + injection 4 weeks -- main figure 2</li> <li>A1007.tar.gz -- AE05High -- HSPCs EPO 1000 ng/ml + injection 4 weeks -- main figure 2</li> <li>A984.tar.gz -- AE03Low -- HSPCs EPO 160 ng/ml 4 weeks -- main figure 2 and HSPC part of main figure 4</li> <li>A1008.tar.gz -- AE03High -- HSPCs EPO 1000 ng/ml 4 weeks -- main figure 1, 2 and DC part of main figure 3</li> <li>A1012.tar.gz -- AE04Low -- HSPCs EPO 160 ng/ml 4 months -- main figure 8</li> <li>A1013.tar.gz -- AE04High -- HSPCs EPO 1000 ng/ml 4 months -- main figure 8</li> <li>A1105.tar.gz -- AE07part1 -- HSPCs EPO 1000 ng/ml 4 weeks part 1 -- MkP part of main figure 3</li> <li>A1107.tar.gz -- AE07part2 -- HSPCs EPO 1000 ng/ml 4 weeks part 2 -- MkP part of main figure 3</li> <li>A1166.tar.gz -- AE13part1 -- HSPCs EPO 1000 ng/ml 4 weeks part 1 -- HSPC part of main figure 4</li> <li>A1166.tar.gz -- AE13part2 -- HSPCs EPO 1000 ng/ml 4 weeks part 2 -- HSPC part of main figure 4</li> <li>D757_3697.tar.gz -- LP26MPP2 -- MPP2 EPO 1000 ng/ml 4 weeks -- main figure 7</li> </ul> <p>In version two of the repository the scRNAseq data is changed into:</p> <ul> <li>possorted_genome_bam_T1.bam -- 10X of EPO exposed (T1) HSPCs -- main figure 5 and 6</li> <li>possorted_genome_bam_T2.bam -- 10X of control (T2) HSPCs -- main figure 5 and 6</li> </ul> <p>The folder for each barcode experiment encompasses fastq files for each sample index used in the experiment. These have to be further de-multiplexed by plate index and barcode reads have to be called. These, and all subsequent processing steps till the making of the main figures are described in the github folder accompanying the associated publication.</p> <p> </p> <p> </p> <p> </p> <p> </p>
GC-MS raw data_Figure 6E_Lysophosphatidic Acid Shifts Metabolic and Transcriptional Landscapes to Induce a Distinct Cellular State in Human Pluripotent Stem Cells
<p><strong>Sample name</strong></p> <p>hESCs (H1 cells) were given treatments for two days and then collected for GC-MS analysis.</p> <p>E8: E8 medium</p> <p>AX: E8 + 1.6% AlbuMAX;</p> <p>BSA: E8 + 1% albumin;</p> <p>BSA+hCDL: E8 + 1% albumin + 0.1% hCDL;</p> <p>LPA+BSA: E8 + 1 μM LPA + 1% albumin;</p> <p>LPA+BSA+hCDL: E8 + 1 μM LPA + 1% albumin + 0.1% hCDL</p> <p>STD: standard lipids mixture used as reference</p> <p><strong>Extraction and Methylation</strong></p> <p>Sample preparation was conducted according to the previously reported method (Araujo et al., 2008) with the modification. Briefly, spent medium was removed, and cells were rinsed with 1 mL/well 0.9% (w/v) saline twice. Then 0.5 mL/well -80°C 80% methanol was added to quench the metabolism. Five wells of cells (from 6-well plate) were scrapped off into a glass screw-cap tube. Then 4 mL heptadecanoate containing chloroform (4 μg/mL, internal standard for fatty acids) was added into the tube. Vortex, and then centrifuge at 2000 rpm for 5 min. Cellular debris was carefully removed, and nitrogen blow the solution till dry. Add 1.5 mL hexane and 1.5 mL 14% boron trifluoride (BF<sub>3</sub>)/methanol solution. Seal the tube with nitrogen gas, heat it at 100°C for 1 h using MK200-2 dry bath incubator (Aosheng), and then cool down to room temperature. Add 1 mL water into the tube, vortex and then centrifuge at 3000 rpm for 10 min. The upper layer was transferred into a new 1.5-mL eppendorf tube and evaporated by nitrogen gas. The residue was re-dissolved in 100 μL hexane for GC-MS analysis.</p> <p><strong>GC-MS method</strong></p> <p>Samples were analyzed using an Agilent GC-MS system (Agilent) consisting of a 6890 gas chromatography and a 5973 mass spectrometer. Fatty acid methyl esters were separated by an Omegawax™ 250 fused silica capillary column (30 m × 0.25 mm i.d., 0.25 μm film thickness, Supelco, Bellefonte, PA). The optimized oven temperature program was: initial temperature set at 180°C and held for 3 min; ramped to 206°C at 2°C/min and held at 206°C for 25 min, then, ramped to 240°C at 10°C/min and held for 5 min. Overall, the total run time was 50 min. Carrier gas was high-purity helium at a flow rate of 1.5 mL/min. Injector temperature was set at 250°C. Injection volume was 2 μL with a split ratio of 1:15. The mass spectrometer was operated in electron-impact (EI) mode at 70 eV ionization energy. The temperatures of quadrupole and ionization source were set at 150°C and 280°C, respectively. The spectra from 3 to 50 min were acquired with the <em>m/z</em> range of 35–550 at a scan rate of 0.34 s per scan.</p>
LC-MS raw data_Lysophosphatidic Acid Shifts Metabolic and Transcriptional Landscapes to Induce a Distinct Cellular State in Human Pluripotent Stem Cells
<p><strong>LC-MS/MS analysis</strong></p> <p><strong>Metabolite extraction</strong></p> <p>For LC-MS/MS quantification, cell sample preparation was conducted as described in the previous literatures (Ying, Kimmelman et al. 2012, Zhang, Badur et al. 2016). Briefly, the spent medium was removed, and cells were rinsed with 1 mL/well 0.9% (w/v) saline twice. Then 0.5 mL/well -80°C 0.2 μg/mL norvaline containing 80% methanol was added to quench the metabolism. Cells were scraped off into 1.5-mL eppendorf tube and stored in -80℃ overnight. The mixtures were vortexed and then centrifuged 12500 × <em>g </em>for 15 min at 4℃. The supernatant was used for LC-MS analysis.</p> <p><strong>LC-MS/MS method</strong></p> <p>Waters Xevo TQD coupled with Waters Acquity UPLC system was used for quantification. Acquity UPLC BEH HILIC column (2.1 × 100 mm, 1.7 μm), Acquity UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm), and Acquity UPLC BEH amide column (2.1 × 100 mm, 1.7 μm) were used for the separation of metabolites. Column temperature was set at 40 °C.</p> <p>For the quantification of norvaline, amino acids, GSH, GSSG, SAH, SAM, ascorbic acid and myo-inositol, amide column was used for the separation. Acetonitrile with 0.1% formic acid (A) and water with 0.1% formic acid (B) were used as mobile phases. The gradient setting is: 0-4 min, 99% A to 90% A; 4-10 min, 90% A to 67% A; 10-13 min, 67% A to 1% A; 13-15 min, 1% A; 15-16.5 min, 1% A to 99% A; 16.5-20 min, 99% A. Flowrate was set as 0.4 mL/min.</p> <p>For the quantification of metabolites involved in TCA cycle, energy related and ribonucleotides, an amide column was used for the separation. Acetonitrile with 0.1% formic acid (A) and water with 0.1% formic acid (B) were used as mobile phases. The gradient setting is: 0-2 min, 80% A; 2-3 min, 80% A to 20% A; 3-5 min, 20% A; 5-6 min, 20% A to 80% A; 6-10 min, 80% A. Flowrate was set as 0.4 mL/min.</p> <p>For the quantification of acetate, acetyl-CoA and metabolites involved in glycolysis and pentose phosphate pathway, HILIC column was used for the separation. Acetonitrile (A) and 10 mM ammonium bicarbonate were used as mobile phases. The gradient setting is: 0-2 min, 10% A; 2-5 min, 10% A to 5% A; 5-6 min, 5% A to 10% A; 6-10 min, 10% A. Flowrate was set as 0.2 mL/min.</p> <p>For the quantification of LPA, LPC and PC, HILIC column was used for the separation. Acetonitrile (A) and 10 mM ammonium bicarbonate aqueous solution (B) were used as mobile phases. The gradient setting is: 0-2 min, 95% A; 2-4 min, 95% A to 10% A; 4-7 min, 10% A; 7-9 min, 10% A to 95% A; 9-15 min, 95% A. Flowrate was set as 0.2 mL/min.</p> <p>For the quantification of CDL lipids, C18 column was used for the separation. 98% Acetonitrile aqueous solution (A) and 10 mM ammonium acetate 90% acetonitrile aqueous solution (B) were used as mobile phases. The gradient setting is: 0-5 min, 0.1% A; 5-6 min, 0.1% A to 99.9% A; 6-11 min, 99.9% A; 11-12 min, 99.9% A to 0.1% A; 12-15 min, 0.1% A. Flowrate was set as 0.4 mL/min.</p> <p>Argon was used as source gas, capillary voltage was 3500 V, and desolvation temperature was 500 °C. Multiple reaction monitoring (MRM) was conducted, and the ion transitions are listed in the supplemental Table S2. Selected ion recording (SIR) was conducted for the detection of CDL-related lipids, and the setting is listed in the supplemental Table S3.</p> <p>Standard solutions of TCA metabolites (100 μg/mL) and intermediates of glycolysis and pentose phosphate pathway (10 μg/mL) were prepared to confirm the retention time. Peak intensity of product ion was used for the quantification. Data analysis was performed by TargetLynx software (Waters) with statistical analysis in Graphpad Prism (version 8.4.0) and R.</p>
Data and analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui"
<p>Butterfly scale data and data analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in <em>Vanessa cardui</em>."</p> <p> </p> <p>It is recommended to download all files and folders into a single root folder for use in MATLAB.<br> This code was prepared for use in MATLAB R2019b, and some scripts or functions require the Image Processing Toolbox.</p>
Datasets for CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning
<p>This repository contains datasets used in the project CASSL: A cell-type annotation method for single cell transcriptomics data using semi-supervised learning. This project aims at learning cell annotations for missing cell labels via NMF and recursive k-Means clustering.</p>
Miscible viscous fingering in a quarter-spot Hele-Shaw cell: Experimental dataset
<p>This is the experimental dataset associated with the paper 'The effect of viscosity ratio and Peclet number on miscible viscous fingering in a Hele-Shaw cell: A combined numerical and experimental study' by Keable et al. 2021 - preprint available at https://arxiv.org/abs/2111.06548. This repository includes image and volumetric data described in the main paper. The images are used in the processing code available at https://github.com/sci-sjj/MiscibleViscousFingering.</p> <p>Images are included for M2, M5, M10 and M20 mobility ratios, performed at flowrate Q = 1ml/min. For M=20, we also include Q=0.1ml/min (filename M20_0_1mlpm), Q=0.5ml/min (filename M20_0_5mlpm), Q=5ml/min (filename M20_5mlpm) and Q=10ml/min (filename M20_10mlpm). Multiple repeat experiments are given filename extension v2, v3 etc (some experiments did not work so there may be missing v1 etc, extensions). The experiments at each flow rate and mobility ratio then have the following zipped folders containing images:</p> <ol> <li>_exp_images_time_lapse_raw.zip. These are the raw image files.</li> <li>_exp_images_time_lapse_processed.zip. These are cropped images from 1. </li> <li>_exp_images_time_lapse_final.zip. These are geometric transforms of 2, to be centered and aligned. They represent the final processed images.</li> <li>_exp_images_selected_PV.zip. These are selected images from 3 at different pore-volumes injection and times as a fraction of the breakthrough time.</li> <li>_exp_images_time_lapse_segmented.zip. These are segmented images used in interfacial calculations. </li> </ol> <p>Each image file in the above zipped folders follows the same prefix naming convention (e.g. M2_V2) followed by a number, e.g. (1000). Time sequences start at number 1000. Image capture times can be found in the image properties of the raw details to give aquisition rates. Full experimental details are listed in the paper, and also in the excel summary files on the github repository above. <br> <br> We include simulation image results, txt based macroscopic experimental results and literature results in zipped folders. These can be run using the github processing codes to produce the figures/analysis in the main paper. </p>
Classification of blood cells dynamics with convolutional and recurrent neural networks: a sickle cell disease case study
<p>The fraction of red blood cells (RBC) adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease (SCD). Its high-throughput measurement relies on the video analysis of thousands of cell motions for each blood sample to eliminate a large majority of unreliable samples(out of focus or overlapping cells) and discriminate between tank-treading and flipping motion, characterizing highly and poorly deformable cells respectively. These videos are of different durations (from 6 to more than 100 frames).</p> <p>This dataset contains four adult patients with SCD. They were enrolled in the study Drepaforme (approved by the institutional review board CPP Ouest 6 under the reference n°2018A00679-46) and were sampled weekly for several months. The movies were processed using in-house routines in Matlab (Matlab, R2016a) and RBC were detected individually and tracked over time. The database provided in this repository are already pre-processed sequences of tracked and centered RBC over time, each time step image being normalized to 31x31 pixels. Within the 32 experiments, the total number of sequences (or samples) is nearly 150 000. All sequences were semi-automatically labelled into 3 classes, depending on the dynamic of the cell: tank-treading, flipping and unreliable (140 000 are unreliable). The percentage of tank-treading cells with respect to all reliable cells (tank-treading+flipping) in every experiment is the final goal of this study.</p> <p>This dataset is very interesting to the community as it is a large database for cell dynamics classification: the class depends on the movement of the cell.</p> <p>An automatic processing of the database using a 2-stage deep learning model is available here https://github.com/icannos/redbloodcells_disease_classification</p> <p>For opening the data in python:</p> <p> from scipy.io import loadmat<br> x=loadmat('BG20191003shear10s01_Export.mat')</p> <p> * x['Norm_Tab'] is of size nb_samples x max_len_sequences x 31 x 31, where max_len_sequences is the length of the longest sequence of the series, typically ~150 to 180. The other sequences are padded with 31x31 zero matrices at the end in order to fill this maximal length.</p> <p> * x['Labels_Num'] is the corresponding label of each sequence, of size nb_samples. Label can be:<br> - 0 : "tank-treading" (or healthy)<br> - 1 : "flipping" (or tumbling, i.e. related to a SCD)<br> - 2 : "unreliable"</p>
Whole cell-based catalyst for enzymatic production of the osmolyte 2-O-α-glucosylglycerol
<p>We provide here the underlying data of the publication "Whole cell-based catalyst for enzymatic production of the osmolyte 2-O-α-glucosylglycerol". Please find the abstract below.</p> <p><strong>Background: </strong>Glucosylglycerol (2‑O‑α‑D‑glucosyl‑sn‑glycerol; GG) is a natural osmolyte from bacteria and plants. It has promising applications as cosmetic and food‑and‑feed ingredient. Due to its natural scarcity, GG must be prepared through dedicated synthesis, and an industrial bioprocess for GG production has been implemented. This process uses sucrose phosphorylase (SucP)‑catalyzed glycosylation of glycerol from sucrose, applying the isolated enzyme in immobilized form. A whole cell‑based enzyme formulation might constitute an advanced catalyst for GG production. Here, recombinant production in <em>Escherichia coli</em> BL21(DE3) was compared systematically for the SucPs from <em>Leuconostoc mesenteroides</em> (LmSucP) and <em>Bifidobacterium adolescentis</em> (BaSucP) with the purpose of whole cell catalyst development.<br> <strong>Results:</strong> Expression from pQE30 and pET21 plasmids in <em>E. coli</em> BL21(DE3) gave recombinant protein at 40–50% share of total intracellular protein, with the monomeric LmSucP mostly soluble (≥80%) and the homodimeric BaSucP more prominently insoluble (~40%). The cell lysate specific activity of LmSucP was 2.8‑fold (pET21; 70±24 U/mg; N=5) and 1.4‑fold (pQE30; 54±9 U/mg, N=5) higher than that of BaSucP. Synthesis reactions revealed LmSucP was more regioselective for glycerol glycosylation (~88%; position O2 compared to O1) than BaSucP (~66%), thus identifying LmSucP as the enzyme of choice for GG production. Fed‑batch bioreactor cultivations at controlled low specific growth rate (µ=0.05 h<sup>−1</sup>; 28°C) for LmSucP production (pET21) yielded ~40 g cell dry mass (CDM)/L with an activity of 2.0×10<sup>4</sup> U/g CDM, corresponding to 39 U/mg protein. The same production from the pQE30 plasmid gave a lower yield of 6.5×10<sup>3</sup> U/g CDM, equivalent to 13 U/mg. A single freeze–thaw cycle exposed ~70% of the intracellular enzyme activity for GG production (~65 g/L, ~90% yield from sucrose), without releasing it from the cells during the reaction.<br> <strong>Conclusions:</strong> Compared to BaSucP, LmSucP is preferred for regioselective GG production. Expression from pET21 and pQE30 plasmids enables high‑yield bioreactor production of the enzyme as a whole cell catalyst. The freeze– thaw treated cells represent a highly active, solid formulation of the LmSucP for practical synthesis.</p>
Metadata of "Incorporating a molecular antenna in diatom microalgae cells enhances photosynthesis"
<p>Metadata of "Incorporating a molecular antenna in diatom microalgae cells enhances photosynthesis"</p>
Pre-vaccination and early B cell signatures of the antibody response to SARS-CoV-2 mRNA vaccine
<p>The data presented in Code repository for Kardava, L., Rachmaninoff, N., Lau, W. W., Buckner, C. M., Trihemasava, K., Blazkova, J., ... & Moir, S. (2022). Early human B cell signatures of the primary antibody response to mRNA vaccination. Proceedings of the National Academy of Sciences, 119(28), e2204607119.<a href="https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119">https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119</a> are made available here.</p> <p>All code to reproduce the figures can be found here: https://github.com/niaid/COVID_Vaccine_Bcells</p> <p><a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/all_subjects_cd19_positive_and_keys.zip">all_subjects_cd19_positive_and_keys.zip</a> contains a CSV file of all CD19+ cells with flowSOM clusters shown. Accompanying files allow for matching of timepoint and subject information.</p> <p><a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/All_subjects_FCS_files_deidentified.zip">All_subjects_FCS_files_deidentified.zip</a> contains the raw fcs files and is organized by timepoint and subject.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.