Find research datasets worth reusing

UPF3A and UPF3B are redundant and modular activators of nonsense-mediated mRNA decay in human cells

Source data for the publication: UPF3A and UPF3B are redundant and modular activators of nonsense-mediated mRNA decay in human cells. Includes raw image data (e.g. agarose gels, western blots, northern blots), quantifications, qPCR raw Ct values and other supporting material.

opencc-by-4.0Nov 2021View details →

Raw data for Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications

In the following the raw data lying the foundation of the paper “Ultrathin wide-bandgap a-Si:H based solar cells for transparent photovoltaic applications” (Lopez-Garcia et al.) published in Solar Rapid Research Letters, DOI: 10.1002/solr.202100909 (2021), are described. They were obtained under the funding provided by the European Union H2020 Framework Programme under Grant Agreement no. 826002 (Tech4Win) and by the Mater-One (Refs. PID 2020-116719RB-C42 and PID 2020-116719RB-C41) and SCALED (Ref. PID 2019-109215RB-C4) projects funded by the Spanish MCIN/AEI/10.13039/5011000110033. UV–vis measurements were acquired with a dual-beam spectrophotometer setup (Perkin Elmer Lambda L35) in transmittance mode (light source and detector normal to sample’s surface (i.e., 0o)) and in reflectance mode (with an Integrating sphere) scanning from 300 to 800 nm. J–V measurements under illumination were carried out using a homemade setup consisting on a AAA solar simulator calibrated using a NREL-certified Si reference solar cell (Abet Technologies, Model 15150). Electrical measurements were carried out with a source-measure unit (Keithley 2400) in four-wire sense mode, controlled by the software Tracer (ReRa solutions) using a IEEE 488 GPIB Instrument Control Device (National Instruments GPIB-USB-HS).

opencc-by-4.0Nov 2021View details →

Cell type specificity of glucocorticoid signaling in the adult mouse hippocampus

The present study is based on the 10X scRNA-seq dataset published by the Allen Institute for Brain Science and publicly available at: <a href="https://portal.brain-map.org/atlases-and-data/RNA-seq/mouse-whole-cortex-and-hippocampus-10x">https://portal.brain-map.org/atlases-and-data/RNA-seq/mouse-whole-cortex-and-hippocampus-10x</a>. The cells from the hippocampus region were selected from the gene count expression matrix and pre-processed in R v3.6.1 according to the Seurat v3.1.5 standard pre-processing workflow for quality control, normalization, and analysis of scRNA-seq data. Here we make the final seurat object and other datasets further used in the code (<a href="https://github.com/eviho/10XHip2021_VihoEMG">https://github.com/eviho/10XHip2021_VihoEMG</a>) available for download.

opencc-by-4.0Nov 2021View details →

Associated code and data for "Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization (doi: 10.3389/fcell.2021.74635)"

This deposit contains the data, code, and analysis to reproduce the results in the manuscript - Lai X, Keller C, Santos-Rosales G, Schaft N, Dörrie J, Vera J. Multi-level computational modeling of anti-cancer dendritic cell vaccination utilized to select molecular targets for therapy optimization. Frontiers in Cell and Developmental Biolology. 2022; 9:746359; <a href="https://www.researchgate.net/publication/358461035_Multi-Level_Computational_Modeling_of_Anti-Cancer_Dendritic_Cell_Vaccination_Utilized_to_Select_Molecular_Targets_for_Therapy_Optimization">doi:10.3389/fcell.2021.746359</a>. If you have used the code for your research, please cite the original publication. Thank you very much.

opencc-by-4.0Jun 2021View details →

Stem cell hierarchies and developmental origins of rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood and is comprised of two major molecular subtypes. Despite sharing features with skeletal muscle, the conservation of underlying cellular hierarchy with human muscle development and the identification of molecularly-defined tumor-propagating cells have not been reported. Using single-cell RNA sequencing of patient-derived RMS, DNA-barcode cell fate mapping, and antibody enrichment and functional stem cell assays using in vitro culture and mouse xenografts, we have uncovered tumor cell hierarchies in Fusion-negative (FN-) RMS that are shared with normal human muscle development.  We also identified common developmental stages at which tumor cells become arrested.  FN-RMS resemble early muscle found in embryonic and larval development, while fusion-positive (FP-)RMS express a highly specific developmental gene program found in muscle cells transiting from embryonic to fetal development at 7-7.75 weeks of age.  FP-RMS also have neural-pathway enriched cell states, suggesting less-rigid adherence to muscle development hierarchies in this disease.  Finally, we identify a molecularly-defined tumor-propagating cell in FN-RMS that shares remarkable similarity to the newly described bi-potent, muscle mesenchyme stem/progenitor cell that makes both muscle and osteogenic cells.

SBF-SEM datasets related to the manuscript "Trans-cellular tunnels induced by the fungal pathogen Candida albicans facilitate invasion through successive epithelial cells without host damage" by Lachat et al, 2022.

11 serial block face- scanning electron microscopy (SBF-SEM) datasets described in the manuscript "Trans-cellular tunnels induced by the fungal pathogen Candida albicans facilitate invasion through successive epithelial cells without host damage" by Lachat et al, 2022. Resolution: 10 nm x,y, 100 nm Z. Datasets description and quantification can be found in the Supplementary information.

Data and code from: "A transcriptional rheostat couples past activity to future sensory responses" (Tsukahara, Brann, et al. 2021 Cell)

# A transcriptional rheostat couples past activity to future sensory responses Code and data to replicate analyses in Tsukahara, Brann et al. 2021 Cell <a href="https://doi.org/10.1016/j.cell.2021.11.022">https://doi.org/10.1016/j.cell.2021.11.022</a> ## Summary Animals traversing different environments encounter both stable background stimuli and novel cues, which are thought to be detected by primary sensory neurons and then distinguished by downstream brain circuits. Here we show that each of the ~1000 olfactory sensory neuron (OSN) subtypes in the mouse harbors a distinct transcriptome whose content is precisely determined by interactions between its odorant receptor and the environment. This transcriptional variation is systematically organized to support sensory adaptation: expression levels of more than 70 genes relevant to transforming odors into spikes continuously vary across OSN subtypes, dynamically adjust to new environments over hours, and accurately predict acute OSN-specific odor responses. The sensory periphery therefore separates salient signals from predictable background via a transcriptional rheostat whose moment-to-moment state reflects the past and constrains the future; these findings suggest a general model in which structured transcriptional variation within a cell type reflects individual experience. ## Manuscript For more details, please see our Open Access manuscript: <a href="https://www.cell.com/cell/fulltext/S0092-8674(21)01337-4">https://www.cell.com/cell/fulltext/S0092-8674(21)01337-4</a> # Code 1. The code here is a copy of that on GitHub: <a href="https://github.com/dattalab/Tsukahara_Brann_OSN">https://github.com/dattalab/Tsukahara_Brann_OSN</a>. Instructions for how to download and install it can be found in the README.md file. 2. Data is available on the NCBI GEO (accession <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE173947">GSE173947</a>) and raw fastq files are available from the SRA (accession SRP318630). 3. Supplementary data (imaging traces and example preprocessed AnnData object for the home-cage dataset) can be found in the data folders of the attached Tsukahara_Brann_OSN-zenodo.zip file.

Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: discovery cohort meta data and parsed TCR repertoire data

Meta data corresponding the the discovery cohort for the paper, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include:  (1) a file mapping the SNP data subject IDs to the TCR repertoire data subject IDs (gwas_id_mapping.tsv) (2) a file including the PCAir PCs, self-reported ancestry, and genomic ancestry for each subject (all_pc_air.txt) (3) a file including the PCAir variance explained by each PC (all_pc_air_variance.txt) (3) a file including the SNP ID, chromosome, hg19 position, allele, rsid, and quality control metrics for each SNP in the SNP array (emerson_snp_rs_data.tsv) (4) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv) (5) a file including predicted TRBD2 allele genotypes for each subject (emerson_trbd2_alleles.tsv) (6) Parsed TCRB repertoire data. These raw data were first published in Emerson et. al, Nature Genetics 2017. (emerson_parsed_tcrb.tgz) Corresponding discovery cohort raw TCR repertoire data is available here: https: //doi.org/10.21417/B7001Z (ImmuneACCESS database) Corresponding discovery cohort SNP data is available here: https: //www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001918.v1.p1 (The database of Genotypes and Phenotypes, accession number: phs001918) Software tools designed to work with these data are available here: https://github.com/phbradley/tcr-gwas

Harnessing single cell RNA sequencing to identify dendritic cell types, characterize their biological states and infer their activation trajectory

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how. To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.   Data: 1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis 2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis

# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.

Donor Age and Red Cell Age Contribute to the Variance in Lorrca Indices in Healthy Donors for Next Generation Ektacytometry: A Pilot Study

This dataset refers to the article "Donor Age and Red Cell Age Contribute to the Variance in Lorrca Indices in Healthy Donors for Next Generation Ektacytometry: A Pilot Study" Front. Physiol. 12:639722 2021 DOI: 10.3389/fphys.2021.639722

opencc-by-4.0Sep 2021View details →

Data for Universal prediction of cell cycle position using transfer learning

The repo contains the data for Universal prediction of cell cycle position using transfer learning (https://www.biorxiv.org/content/10.1101/2021.04.06.438463v2). The scripts to analyze and generate all figures could be found at https://github.com/hansenlab/tricycle_paper_figs v1.1 update: add neurosphere_scvelo.qs - an R SingleCellExperiment object saved as qs file that has spliced counts, unspliced counts, and all outputs from scvelo.

Terminal density reversal and the role of Ca2+ in red blood cells clearance of healthy individuals

Density reversal of senescent red blood cells (RBCs) has been known for more than ten years, yet the identity of the candidate protein(s) is still elusive. While performing Percoll density gradient separation of RBCs from healthy individuals and their subsequent characterization, we identified a fraction of cells in the low-density fraction (~0.025% compared to total RBCs population) which shows reversal in their densities along with the characteristics of cellular senescence such as loss of membrane Band 3 protein and the phosphatidylserine exposure to the outer membrane leaflet. Our subsequent analysis showed that these cells are overloaded with Ca2+. We further measured intracellular [Na+] in individual RBCs by flow cytometry utilizing the dye CoroNa Green-AM.  Our findings showed that the cells with senescent characteristics lost their transmembrane Na+ gradient despite maintaining the membrane integrity. Consequently, these findings lead us to designate these cells as “senescent-like” cells. Our data further demonstrated altered activities of nonselective cation channels and pumps in these cells. In addition to a facilitated Na+ extrusion by Na+, K+-ATPase, our findings indicated altered ion transport via Piezo1 in these cells. Pharmacological modulation of Piezo1 with Yoda1/GsMTx4 showed that Piezo1 and, possibly, other nonselective cation channels by promiscuously transporting Na+ and Ca2+ play an important role in producing these low density “senescent like” cells.

opencc-zeroJan 2022View details →

dryad40/100

In vitro cell cycle oscillations exhibit a robust and hysteretic response to changes in cytoplasmic density

Cells control the properties of the cytoplasm to ensure proper functioning of biochemical processes. Recent studies showed that cytoplasmic density varies in both physiological and pathological states of cells undergoing growth, division, differentiation, apoptosis, senescence, and metabolic starvation. Little is known about how cellular processes cope with these cytoplasmic variations. Here, we study how a cell cycle oscillator comprising cyclin-dependent kinase (Cdk1) responds to changes in cytoplasmic density by systematically diluting or concentrating cycling Xenopus egg extracts in cell-like microfluidic droplets. We found that the cell cycle maintains robust oscillations over a wide range of deviations from the endogenous density: as low as 0.2× to more than 1.22× relative cytoplasmic density (RCD). A further dilution or concentration from these values arrested the system in a low or high steady state of Cdk1 activity, respectively. Interestingly, diluting an arrested cytoplasm of 1.22× RCD recovers oscillations at lower than 1× RCD. Thus, the cell cycle switches reversibly between oscillatory and stable steady states at distinct thresholds depending on the direction of tuning, forming a hysteresis loop. We propose a mathematical model which recapitulates these observations and predicts that the Cdk1/Wee1/Cdc25 positive feedback loops do not contribute to the observed robustness, supported by experiments. Our system can be applied to study how cytoplasmic density affects other cellular processes.

dryad40/100

Immuno-proteomic profiling reveals aberrant immune cell regulation in the airways of individuals with ongoing post-COVID-19 respiratory disease

Some patients hospitalized with acute COVID-19 suffer respiratory symptoms that persist for many months. We delineated the immune-proteomic landscape in the airway and peripheral blood of healthy controls and post-COVID-19 patients 3 to 6 months after hospital discharge. Post-COVID-19 patients showed abnormal airway (but not plasma) proteomes, with elevated concentration of proteins associated with apoptosis, tissue repair and epithelial injury versus healthy individuals. Increased numbers of cytotoxic lymphocytes were observed in individuals with greater airway dysfunction, while increased B cell numbers and altered monocyte subsets were associated with more widespread lung abnormalities. 1 year follow-up of some post-COVID-19 patients indicated that these abnormalities resolved over time. In summary, COVID-19 causes a prolonged change to the airway immune landscape in those with persistent lung disease, with evidence of cell death and tissue repair linked to ongoing activation of cytotoxic T cells.

opencc-zeroJan 2022View details →

Identifying strengths and weaknesses of methods for computational network inference from single cell RNA-seq data

These data files contain single-cell RNA-sequencing expression data (expression_data.zip) and pseudotime files (pseudotime.zip) used to conduct comparisons of network inference methods on six published single-cell RNA-sequencing datasets. The resulting networks generated from the network inference methods are also uploaded here (normalized_inferred_networks.zip and imputed_inferred_networks.zip). Finally, the gold standard networks we used as ground truth to measure accuracy of the inferred networks are uploaded here (gold_standard_datasets.zip).

Immune disease variants modulate gene expression in regulatory CD4+ T cells

We mapped genetic regulation (QTL) of gene expression and chromatin activity in Tregs and we identified 133 colocalizing loci with immune disease variants. For the time being, the preprint DOI: <a href="https://doi.org/10.1101/654632">10.1101/654632</a>

Germinal centre-driven maturation of B cell response to SARS-CoV-2 mRNA vaccination

These are the processed BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., Nature, 2022</a>. The raw sequencing data new to this study are available on SRA under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA777934">PRJNA777934</a>. This study also used BCR repertoire data from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a>) and <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>).   Code Code along with Docker containers for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2022">found on GitHub</a>.   Metadata File: WU368_kim_et_al_nature_2022_meta.tsv Notes: <ul> <li>Sample breakdown by `sequence_type` (132 total) <ul> <li>73 bulk BCR sequencing samples (`bulk`) <ul> <li>57 new</li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a></li> <li>11 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a>.</li> </ul> </li> <li>56 10x Genomics single-cell VDJ + 5' gene expression samples (`tgx`)</li> <li>3 samples from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (`mab`) corresponding to a total of 37 S-binding mAbs previously reported. These are not the same as the 2099 recombinant mAbs generated in this study (see below).</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Timepoints were referenced in weeks in the manuscript, as shown in the `timepoint_ms` column.</li> <li>`bio_rep` and `tech_rep` = biological replicate and technical replicate respectively.</li> </ul> Abbreviations: <ul> <li>LN = lymph node</li> <li>BM = bone marrow</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>LLPC = long-lived plasma cell</li> <li>NS = no sorting</li> <li>mAb = monoclonal antibody</li> </ul>   Information on the 2099 recombinant mAbs generated in this study File: WU368_kim_et_al_nature_2022_mabs.tsv Notes on columns: <ul> <li>`h_sequence_id` and `l_sequence_id`: Sequence IDs of the heavy and light chains respectively.</li> <li>`elisa`: ELISA results for binding to SARS-CoV-2 S (`TRUE` = positive).</li> </ul>   Processed BCR data - heavy chains File: WU368_kim_et_al_nature_2022_bcr_heavy.tsv Analysis was based on heavy chain-based clonal inference. Notes on columns: The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories such as <a href="https://gateway.ireceptor.org/login">iReceptor</a> use these. Non-standard columns are noted below. <ul> </ul> <ul> <li>`cell_id`: Only sequences from single-cell samples and the 37 mAbs from Turner & O'Halloran et al., Nature, 2021 have cell IDs following the format `[donor]_[sample]@[id]`. `NA` for bulk sequences.</li> <li>`sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`.</li> <li>`v_call_genotyped`: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a>.</li> <li>`germline_[vdj]_call`: Clonal consensus germline calls after corresponding clonal consensus sequence were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from Change-O</a>.</li> <li>`isotype`: IGH[ADEGM].</li> <li>`cdr3`: CDR3 nucleotide sequence.</li> <li>`cdr3_length`: CDR3 nucleotide sequence length.</li> <li>`cdr3_aa`: CDR3 amino acid sequence.</li> <li>`collapse_count`: Number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a>.</li> <li>`donor`, `timepoint`, `tissue`, `sorting`, `seq_type`: Propagated as is from the metadata file. <ul> <li>In `seq_type`, `tgx` corresponds to 10x Genomics data; `mab` corresponds specifically to the 37 S-binding mAbs from Turner & O'Halloran et al., Nature, 2021.</li> </ul> </li> <li>`timepoint_2`: Same as `timepoint`, except that `d28+d35` and `d201+d208` were treated as `d28` (week 4) and `d201` (week 29) respectively as described in Materials & Methods.</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.18` from WU368_kim_et_al_nature_2022_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment. <ul> <li>ABC = activated B cell. LNPC = lymph node plasma cell. RMB = resting memory B cell.</li> <li>Minor differences in terminology <ul> <li>The manuscript refers to the memory compartment as MBCs, whereas the terminology used in the data is RMB. As described in Materials & Methods, analysis involving the memory compartment used specifically d201 bulk-sequenced memory sorts from blood. To get these sequences, subset `s_pos_clone`, `seq_type`, `compartment`, and `timepoint_2` to, respectively, `TRUE`, `bulk`, `RMB`, and `d201`. </li> <li>The manuscript uses the term BMPC (bone marrow plasma cell), whereas the data uses the term LLPC.</li> </ul> </li> </ul> </li> <li>`clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`.</li> <li>`s_pos_clone`: `TRUE` if a sequence belonged to a B cell clone that was designated as S-binding by virtue of containing one of the recombinant mAbs that tested positive via ELISA or one of the S-binding mAbs from Turner & O'Halloran et al., Nature, 2021.</li> <li>`expressed_id`: mAb IDs for the 2099 recombinant mAbs generated in this study (mapped from `mab_id` from WU368_kim_et_al_nature_2022_mabs.tsv) and the 37 mAbs from Turner & O'Halloran et al., Nature, 2021. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive. `NA` if not tested.</li> <li>`nuc_RS_19_312`: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a>.</li> <li>`nuc_denom_19_312`: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".").</li> <li>`nuc_RS_freq_19_312`: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312).</li> </ul>   Processed BCR data - light chains File: WU368_kim_et_al_nature_2022_bcr_light.tsv Light chains were not used for heavy chain-based clonal inference or analysis.   Processed transcriptomics data Files: <ul> <li>WU368_kim_et_al_nature_2022_gex_all_cells.h5ad (clustering all cells)</li> <li>WU368_kim_et_al_nature_2022_gex_b_cells.h5ad (re-clustering only the B cells)</li> </ul> Notes: <ul> <li>The `h5ad` files can be imported into <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> as an <a href="https://scanpy.readthedocs.io/en/stable/usage-principles.html#anndata">AnnData object</a>.</li> <li>Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. <ul> <li>`raw_counts`: Imported from `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` output by `scanpy.read_10x_mtx`.</li> <li>`log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`.</li> <li>`scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. </li> </ul> </li> <li>The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF.</li> <li>Columns in `.obs` (each row corresponds to a cell) <ul> <li>`n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes.</li> <li>`n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell.</li> <li>`pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes.</li> <li>`n_hkg`: The number of housekeeping genes for which expression was detected.</li> <li>`n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes.</li> <li>`pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). </li> <li>`leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`.</li> <li>`anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data.</li> </ul> </li> <li>UMAP coordinates can be found in `.obsm["X_umap"]`.</li> <li>`.X` has been set to `None` in order to reduce file size.</li> </ul> In addition, the preprocessed count matrix outputted by `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` is available from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195673">GEO under BioProject PRJNA777934</a>.

A Comparison of Different Textured and Non-Textured Anti-Reflective-Coatings for Planar Monolithic Silicon-Perovskite Tandem Solar Cells

Figure data for the paper: A Comparison of Different Textured and Non-Textured Anti-Reflective-Coatings for Planar Monolithic Silicon-Perovskite Tandem Solar Cells. Submitted to ACS Applied Energy Materials.

Cell Instance Segmentation Dataset

This project contains the data for the paper: A. Bouyssoux, R. Fezzani and J. -C. Olivo-Marin, "Cell Instance Segmentation Using Z-Stacks In Digital Cytopathology," 2022 IEEE International Symposium on Biomedical Imaging (ISBI), 2022. The code associated with this project is available at: <a href="https://gitlab.com/vitadx/articles/zstacks_cell_instance_segmentation">https://gitlab.com/vitadx/articles/zstacks_cell_instance_segmentation</a> A new large Cell Instance Segmentation Dataset (CISD) is introduced. It comprises 3911 samples containing at least two touching or overlapping urothelial cells. Cell instances were manually annotated by trained cytotechnicians. All samples are extracted from 30 digital cytology slides stained with nine variations of Papanicolaou staining. The cytology slides are prepared from urine samples from healthy patients, using a Hologic ThinPrep®5000 processor, and routinely stained with the Agilent Dako CoverStainer®. The slides are finally digitized using a Hamamatsu NanoZoomer®S360 with 21 focal planes and centered on the best focus plane determined by the scanner autofocus. Note that cell instances with a bounding box width/height smaller than 10% of the sample width/height, as well as red blood cells and neutrophil cells were automatically filtered out because under-represented in the available data. Each sample is considered in three different manners in the CISD, allowing experimentation with different methods for handling Z-stack data and comparison with simple 2D acquisition: <ul> <li>Center slice: the best focus plane only as determined by the scanner, which is equivalent to a 2D slide acquisition.</li> <li>Extended Depth of Field (EDF): the 21 planes merged in an image where all textured parts appear in focus.</li> <li>Raw Z-stack: the volume composed of the 21 focal planes.</li> </ul> Once extracted, the dataset folder contains three subfolders, one for each of the three types of samples described here above, containing the images and stack of images. A JSON file contains the instance masks, encoded in RLE format.