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openmit-licenseMar 2023View details →

Celluloepidemiology: a novel paradigm for quantifying infectious disease dynamics through T-cell modelling on a population level

T-cell receptor sequencing (TCR-seq) was performed on enriched CD8+ T-cells. TCR clonotype annotation was performed using MiXCR v.3.0.13 with the default input parameters. Full origin and method description available in: Celluloepidemiology: a novel paradigm for quantifying infectious disease dynamics through T-cell modelling on a population level

SARS-CoV-2 Omicron Boosting Induces De Novo B Cell Response in Humans

These are the processed BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-023-06025-4">Alsoussi & Malladi et al., Nature, 2023</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=PRJNA800176">PRJNA800176</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>), <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>), and <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., Nature, 2022</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA777934/">PRJNA777934</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_2023">found on GitHub</a>.   Metadata File: WU382_alsoussi_et_al_nature_2023_meta.tsv.gz Notes: <ul> <li>181 samples in total, including: <ul> <li>78 new</li> <li>90 from <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., Nature, 2022</a></li> <li>8 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a></li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a></li> </ul> </li> <li>Participant IDs: 6 participants who were in previous studies and who continued in the new study were referenced by new participant IDs. Correspondence with previous participant IDs is as follows: <ul> <li>382-01 = 368-22</li> <li>382-02 = 368-20</li> <li>382-07 = 368-02a</li> <li>382-08 = 368-04</li> <li>382-13 = 368-01a</li> <li>382-15 = 368-10</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Values in parentheses indicate variations in which the BCR data was coded. Timepoints were mainly referenced in weeks in the manuscript, as shown in the `timepoint_ms` column. </li> <li>Pre-3rd dose ("pre-boost") samples were coded `b0` in the `booster_num` column; post-3rd dose ("post-boost") samples were coded `b1`.</li> <li>The `booster_type` column records the 3rd dose ("booster") variant. <ul> <li>`regular` = mRNA-1273 (WA1/2020)</li> <li>`beta_delta` = mRNA-1273.213 (Beta & Delta)</li> <li>`v1.1.529` = mRNA-1273.529 (Omicron)</li> </ul> </li> <li>382-02/07/08 received mRNA-1273; 382-01/13/15 received mRNA-1273.213; 382-53/54/55 received mRNA-1273.529.</li> <li>The `seq_type` column indicates the platform from which sequences originated. <ul> <li>`bulk` = bulk BCR sequencing</li> <li>`tgx` = 10x Genomics single-cell VDJ + 5' gene expression</li> <li>`mab`, `mab_1`, `mab_2`: single-cell sorted mAb synthesis. The suffixes were purely for the convenience of distinguishing originating studies.</li> </ul> </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>   [Beta & Delta booster] Processed BCR data - heavy chains File: WU382_alsoussi_et_al_nature_2023_betaDelta_bcr_heavy.tsv.gz 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> <li>`cell_id`:  Only sequences from single-cell samples and synthesized mAbs have cell IDs. 10x sequences follow 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 sequences were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from Change-O</a>.</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>`timepoint`: Timepoints follow the format `b[01]_d*`, where `b0` and `b1` correspond to pre-3rd dose ("pre-boost") and post-3rd dose ("post-boost") respectively, and `d*` indicates the timepoint in days. There's one exception: `b0_m6or9` for pre-3rd dose d201 or d280 (m6or9 = 6 or 9 months).</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.35` from WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment</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.</li> <li>`expressed_id`: mAb IDs of mAbs from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> and the current study; and of recombinant mAbs generated based on 10x BCRs from <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., Nature, 2022</a>. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive (WA1+); `FALSE` if negaive; `NA` if not tested or test failed.</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>   [Beta & Delta booster] Processed BCR data - light chains File: WU382_alsoussi_et_al_nature_2023_betaDelta_bcr_light.tsv.gz Light chains were not used for heavy chain-based clonal inference or analysis.   [Beta & Delta booster] Processed transcriptomics data Files:  <ul> <li>WU382_alsoussi_et_al_nature_2023_betaDelta_gex_all_cells.h5ad</li> <li>WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cells.h5ad</li> <li>WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cell_umap.tsv.gz</li> </ul> Notes on the `h5ad` files: <ul> <li>These 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> Note on the `tsv.gz` file: This file was derived from WU382_alsoussi_et_al_nature_2023_betaDelta_gex_b_cells.h5ad. It contains UMAP coordinates and select attributes of the cells, including their log-normalized expression values of XBP1 (`ln_XBP1`). For analysis and visualization in conjunction with BCR data. 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=GSE227562">GEO under BioProject PRJNA800176</a>.   [Omicron booster] Processed BCR data - heavy chains File: WU382_alsoussi_et_al_nature_2023_omicron_bcr_heavy.tsv.gz Notes on columns: <ul> <li>`elisa`: ELISA results for mAbs, with values being one of `WA1+`, `BA1+WA1-`, or `negative`. `NA` for bulk sequences.</li> <li>`clone_type`: If a sequence was in an S-binding B cell clone (`TRUE` for `s_pos_clone`), its `clone_type` was based on the `elisa` value of the S-binding mAb in that clone -- either `WA1+` or `BA1+WA1-`; otherwise `NA`.</li> </ul>

Red Blood Cell RedTell Dataset

The dataset contains microscopic images of red blood cells (RBCs) and consists of two parts: 1. dse_data.zip This dataset includes brightfield images of healthy control individuals obtained in the scope of the CoMMiTMenT study. The images are annotated on a single cell level for segmentation and classification (discocyte-stomatocyte-echinocyte sequence) purposes.  2. anemia_data.zip This dataset includes brightfield and fluorescent images of RBCs of sickle cell disease (SCD) patients from the MemSID study as well as of thalassemia patients and healthy controls from the CoMMiTMenT study. The CoMMiTMenT study was funded by the European Seventh Framework Program under grant agreement number 602121 (CoMMiTMenT) and from the European Union’s FP7 Programme. The study protocols were approved by the Medical Ethical Research Board of the University Medical Center Utrecht, the Netherlands, under reference code 15/426M and by the Ethical Committee of Clinical Investigations of Hospital Clinic, Spain (IDIBAPS) under reference code 2013/8436. The MemSID study was performed at the University hospital Zurich (#NCT02615847 at https://clinicaltrials.gov/). The trial protocol was approved by the Ethics committee of Canton Zurich (KEK-ZH 2015-0297).

opencc-by-4.0Mar 2022View details →

Oncogenic signalling is coupled to colorectal cancer cell differentiation state

Mass cytometry and single-cell RNA-sequencing data as well as R Markdown reports to reproduce the figures of our publication. Raw MC data were saved post de-convolution, spillover-compensation, and removal of calibration bead events. Gates for singlets and non-dead cells (low_Pt) are included as logical columns and should be applied prior to usage. As we performed random sampling to equalise cell numbers across conditions, batch normalisation, and used non-linear dimensionality reduction techniques (UMAP and Diffusion Maps), resulting plots may differ slightly from the published figures, yet still support the drawn conclusions. Already normalised and/or sampled data as well as pre-computed UMAP and Diffusion Map coordinates are included in this data set to reproduce the manuscript figures exactly, as shown in the included report “figures_only”. For all details on the batch normalisation and data analysis steps performed, please consult the report “data_analysis” instead.

dryad40/100

Active cell divisions generate fourfold orientationally ordered phase in living tissue

Morphogenesis, the process through which genes generate form, establishes tissue scale order as a template for constructing the complex shapes of the body plan. The extensive growth required to build these ordered substrates is fuelled by cell proliferation, which, naively, should destroy order. Understanding how active morphogenetic mechanisms couple cellular and mechanical processes to generate order — rather than annihilate it — remains an outstanding question in animal development. We show that cell divisions are the primary drivers of tissue flow leading to a fourfold orientationally ordered phase. Waves of anisotropic cell proliferation propagate across the embryo with precise patterning. Defects introduced into the nascent lattice by cell divisions are moved out of the tissue bulk towards the boundary by subsequent divisions. Specific cell proliferation rates and orientations enable cell divisions to organize rather than fluidize the tissue. We observe this using live imaging and tissue cartography to analyse the dynamics of fourfold tissue ordering in the trunk segmental ectoderm of the crustacean Parhyale hawaiensis beginning 72 hours after egg laying. The result is a robust, active mechanism for generating global orientational order in a non-equilibrium system that sets the stage for the subsequent development of shape and form.

opencc-zeroApr 2023View details →

opencc-by-4.0Dec 2022View details →

Supplementary material - Non-invasive Multimodal Imaging Reveals Early Therapy-Induced Senescence in Human Cancer Cells

The repository features .xls and .txt worksheets including all the data used through this work and reported in the manuscript figures and graphs. More precisely, we included the following:   <ul> <li>Figure 2. Raw pixel-wise signals detected in NLO images of TIS cells control cells that were used to perform the colocalization graphs and analyses reported. We describe the average colocalization of SRS and F-CARS signals, and TPEF and E-CARS signals, in both phenotypes.</li> <li>Figure 4. Raw data from image analyses of TPEF and SRS channels of multimodal NLO images, divided in 5 different time points over the therapy follow-up period. The data describe the early rearrangement of mitochondria (TPEF) and lipid vesicles (SRS) in TIS cells, with respect to control counterparts.</li> <li>Figure 6. Raw data from image analyses of QPI images, divided in 4 different time points over the therapy follow-up period. The data describe the early morphological modifications of TIS cells, with respect to control counterparts.</li> </ul>

Supplementary material - Full-spectrum CARS Microscopy Of Cells And Tissues With Ultrashort White-light Continuum Pulses

Supplementary material containing the raw Broadband CARS data presented in the manuscript entitled "Full-spectrum CARS Microscopy Of Cells And Tissues With Ultrashort White-light Continuum Pulses".

Training material for the mapping and quantification of single-cell ATAC-seq 10X Datasets

The data provided here is part of the Galaxy Training Network tutorial that analyses 10x genomics single-cell ATAC-seq data from the 10x platform. The original data is from 1k Peripheral Blood Mononuclear Cells (PBMCs) from a Healthy Donor. Due to time constraints during training, the datasets were subsampled to reads that map to chromosome 21 only. The 10x Genomics Datasets follow the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution</a> license. There is an additional count matrix in Anndata format created from full datasets.

Data for "Influences of Glyphosate Contaminations and Concentrate Feed on Performance, Blood Parameters, Blood Cell Functionality and DNA Damage Properties in Fattening Bulls"

The deposited data consist of three data tables and three tables containing legends for the data: <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/oneTimepoint.txt">oneTimepoint.txt</a> contains data from statistical tests within one timepoint; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/oneTimepoint_legend.txt">oneTimepoint_legend.txt</a> contains the corresponding legend. <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/threeTimepoints.txt">threeTimepoints.txt</a> contains data from statistical tests inclduing three distinct timepoints; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/threeTimepoints_legend.txt">threeTimepoints_legend.txt</a> contains the corresponding legend. <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/Performance.txt">Performance.txt</a> contains data from statistical tests inclduing two time periods in fattening; <a href="https://zenodo.org/api/files/afb9373a-d44c-4441-ba97-052c0b6ec1b6/Performance_legend.txt">Performance_legend.txt</a> contains the corresponding legend.

Peripheral T-cell profiling reveals downregulated exhaustion marker and increased diversity in lymphedema post-lymphatic venous anastomosis

In this study, we compared T cell receptor (TCR) repertoire diversity between patients with lymphedema, post-lymphatic venous anastomosis and healthy controls (HCs). TCR diversity was decreased in lymphedema compared with HCs; such TCR skewing was drastically improved in post-LVA.

Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.

Microscopy data for the paper: Analysis and design of single-cell experiments to harvest fluctuation information while rejecting measurement noise.   List of files used for each dataset.   Dataset 0 : MS2-CY5_Cyto543_560_woStim     Images in the dataset :         ROI001_XY1657814108_Z00_T0_merged.tif   - Image Id Number:  0         ROI002_XY1657815441_Z00_T0_merged.tif   - Image Id Number:  1         ROI003_XY1657814110_Z00_T0_merged.tif   - Image Id Number:  2         ROI004_XY1657814111_Z00_T0_merged.tif   - Image Id Number:  3         ROI005_XY1657814112_Z00_T0_merged.tif   - Image Id Number:  4         ROI006_XY1657814113_Z00_T0_merged.tif   - Image Id Number:  5         ROI007_XY1657814114_Z00_T0_merged.tif   - Image Id Number:  6         ROI008_XY1657814115_Z00_T0_merged.tif   - Image Id Number:  7         ROI009_XY1657814116_Z00_T0_merged.tif   - Image Id Number:  8         ROI010_XY1657814117_Z00_T0_merged.tif   - Image Id Number:  9         ROI011_XY1657814118_Z00_T0_merged.tif   - Image Id Number:  10         ROI012_XY1657814119_Z00_T0_merged.tif   - Image Id Number:  11   Datset 1 : MS2-CY5_Cyto543_560_18minTPL_5uM     Images in the dataset :         ROI001 - Position 1_XY1657818948_Z00_T0_merged.tif   - Image Id Number:  0         ROI001 - Position 2_XY1657818949_Z00_T0_merged.tif   - Image Id Number:  1         ROI001 - Position 4_XY1657818951_Z00_T0_merged.tif   - Image Id Number:  2         ROI001 - Position 5_XY1657818952_Z00_T0_merged.tif   - Image Id Number:  3         ROI001 - Position 6_XY1657818953_Z00_T0_merged.tif   - Image Id Number:  4         ROI001 - Position 7_XY1657818954_Z00_T0_merged.tif   - Image Id Number:  5         ROI001 - Position 8_XY1657818955_Z00_T0_merged.tif   - Image Id Number:  6         ROI001 - Position 9_XY1657818956_Z00_T0_merged.tif   - Image Id Number:  7         ROI001 - Position 10_XY1657818957_Z00_T0_merged.tif   - Image Id Number:  8         ROI001 - Position 11_XY1657818958_Z00_T0_merged.tif   - Image Id Number:  9         ROI001 - Position 12_XY1657818959_Z00_T0_merged.tif   - Image Id Number:  10   Dataset 2: MS2-CY5_Cyto543_560_5hTPL_5uM     Images in the datset :         ROI001_XY1657822809_Z00_T0_merged.tif   - Image Id Number:  0         ROI002_XY1657822933_Z00_T0_merged.tif   - Image Id Number:  1         ROI003_XY1657822934_Z00_T0_merged.tif   - Image Id Number:  2         ROI005_XY1657822936_Z00_T0_merged.tif   - Image Id Number:  3         ROI006_XY1657822937_Z00_T0_merged.tif   - Image Id Number:  4         ROI007_XY1657822938_Z00_T0_merged.tif   - Image Id Number:  5         ROI008_XY1657822939_Z00_T0_merged.tif   - Image Id Number:  6         ROI010_XY1657822941_Z00_T0_merged.tif   - Image Id Number:  7         ROI013_XY1657822944_Z00_T0_merged.tif   - Image Id Number:  8         ROI014_XY1657822945_Z00_T0_merged.tif   - Image Id Number:  9         ROI015_XY1657822946_Z00_T0_merged.tif   - Image Id Number:  10         ROI016_XY1657822947_Z00_T0_merged.tif   - Image Id Number:  11         ROI017_XY1657822948_Z00_T0_merged.tif   - Image Id Number:  12         ROI018_XY1657822949_Z00_T0_merged.tif   - Image Id Number:  13

opencc-by-4.0Jan 2023View details →

Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor-infiltrating CD8+ T cells_Supplemental_Data

Processed Datasets for: EGA Study ID: EGAS00001006141 EGA Dataset ID: EGAD00001008662   Find processed files and arrow files   Abstract: Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying the heterogeneous functional and dysfunctional states of human tumor-infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify gene-regulatory landscapes in a wide breadth of functional and dysfunctional CD8+ TIL states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor-entity-matching samples and prioritize cell-state-specific genes by super-enhancer analysis. Besides revealing entity-specific chromatin remodeling in exhausted TILs, our analyses identify a common chromatin trajectory to TIL dysfunction and determine key enhancers, transcriptional regulators, and deregulated genes involved in this process. Finally, we validate enhancer regulation at immunotherapeutically relevant loci by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study provides a framework for understanding and manipulating cell-state-specific gene-regulatory cues from human tumor-infiltrating lymphocytes.

Database of Ca2+ transients from single cell E. coli measurements

Database configuration To efficiently compare conditions from over 7 Tb of image data, we constructed an HDF5 database with the extracted fluorescent traces for each experiment.  The database was configured such that the fluorophore specifies the first group (e.g., '/gcamp'). The second group is the unique experimental identifier generated at the time of the experiment (e.g., '/20210322_5 Hz_gcamp_90pts'). The third group specifies the variable name (See Table below for description).  The database is also available from https://bitbucket.org/meyerct6/soc_bacteria_repo/src/master/ in (Database/soc_bacteria_database.h5) and can be used to recreate most figures in the manuscript. <table> <caption>Description of HDF5 database variables for each experiment</caption> <tbody> <tr> <td>/meanintMatG</td> <td>Matrix of mean intensity within each segmented cell (rows) at all time points (columns).</td> </tr> <tr> <td>/corrIntMatG</td> <td>Matrix of mean intensity corrected for photobleaching.</td> </tr> <tr> <td>/fitRsqr</td> <td>Vector of R-squared fit values for photobleach equation.</td> </tr> <tr> <td> /timeMat</td> <td>Matrix of acquisition times for each cell (rows) at every timepoint (columns).</td> </tr> <tr> <td>/heat_kill</td> <td>Boolean vector denoting whether the cell was heat killed</td> </tr> </tbody> </table>

"PROGNOSTIC ROLE OF TUMOR BUDDING IN ORAL SQUAMOUS CELL CARCINOMA"

Master Data Sheet

Basaloid Squamous Cell Carcinoma: A Case Report

Background: The upper aerodigestive tract is where BSCC (basaloid squamous cell carcinoma), a rare variation of conventional SCC, is most frequently found. The hypopharynx, tonsil, supraglottic larynx, tongue (base), and head-neck regions are particularly susceptible to BSCC. Clinically, the presentation of BSCC is similar to that of conventional SCC, but it has a poor prognosis than traditional SCC. BSCC is distinguished histopathologically by a dimorphic-pattern, a distinctive basal cell component paired with a squamous component, and a squamous component. Compared to traditional SCC, the prognosis for BSCC is worse. However, clinically it shows similar features like conventional SCC which makes it difficult to diagnose. Therefore, histopathology and immunohistochemistry have a crucial role in diagnosing such tumors. We here present a case of a seventy-year male diagnosed with BSCC involving the tongue. Keywords: Basaloid squamous cell carcinoma, dimorphic pattern, basaloid cells, comedo necrosis.

Dataset for the paper "Low-Cost Three-Quadrant Single Solar Cell I-V Tracer"

Dataset related to the publication "Low-Cost Three-Quadrant Single Solar Cell I-V Tracer" in Applied Sciences 2022, 12(13), 6623; <a href="https://doi.org/10.3390/app12136623">https://doi.org/10.3390/app12136623</a>

Flow cytometry of smooth muscle cells (SMCs)

"SMC phenotype assessment by flow cytometry for positive SMC intracellular markers. SMC were isolated from bladder tissue samples and cultured in vitro in xenogeneic-free culture medium"

Memory or naive T cell origin of VZV gE-specific T cell clonotypes boosted by zoster vaccination.

Subjects were vaccinated with either the live attenuated zoster vaccine (n=5) or recombinant zoster vaccine (n=10). Peripheral blood mononuclear cells were obtained before vaccination (baseline), at peak response approximately one month after vaccination, and after 5 years. The TRB sequences for VZV gE-specific T cell clonotypes were identified from CD4 T cells that proliferated in response to overlapping peptides spanning the VZV gE protein. To assess if the vaccines recruited responses from existing memory or from naive T cells, VZV gE-specific clonotypes were cross-matched to naive and memory CD4 T cell subsets isolated from baseline PBMC. This dataset contains the raw TRB sequences obtained using the Adaptive Biotechnologies Immunoseq platform for: 1) peak gE clonotypes 2) year 5 gE clonotypes 3) baseline memory CD4 T cell subsets 4) baseline naive CD4 T cell subsets.

Polygenic risk scores validated in patient-derived cells stratify for mitochondrial subtypes of Parkinson's disease

Background Parkinson’s disease (PD) is the fastest growing neurodegenerative disorder, with affected individuals expected to double during the next 20 years. This raises the urgent need to better understand the genetic architecture and downstream cellular alterations underlying PD pathogenesis, in order to identify more focused therapeutic targets. While only &sim;10% of PD cases can be clearly attributed to monogenic causes, there is mounting evidence that additional genetic factors could play a role in idiopathic PD (iPD). In particular, common variants with low to moderate effect size in multiple genes regulating key neuroprotective activities may act as risk factors for PD. In light of the well-established involvement of mitochondrial dysfunction in PD, we hypothesized that a fraction of iPD cases may harbour a pathogenic combination of common variants in nuclear-encoded mitochondrial genes, ultimately resulting in neurodegeneration. Methods to capture this mitochondria-related “missing heritability”, we leveraged on existing data from previous genome-wide association studies (GWAS) – i.e., the large PD GWAS from Nalls and colleagues. We then used computational approaches based on mitochondria-specific polygenic risk scores (mitoPRSs) for imputing the genotype data obtained from different iPD case-control datasets worldwide, including the Luxembourg Parkinson’s Study (412 iPD patients and 576 healthy controls) and the COURAGE-PD cohorts (7270 iPD cases and 6819 healthy controls). Results applying this approach to gene sets controlling mitochondrial pathways potentially relevant for neurodegeneration in PD, we demonstrated that common variants in genes regulating Oxidative Phosphorylation (OXPHOS-PRS) were significantly associated with a higher PD risk both in the Luxembourg Parkinson’s Study (odds ratio, OR=1.31[1.14-1.50], p=5.4e-04) and in COURAGE-PD (OR=1.23[1.18-1.27], p=1.5e-29). Functional analyses in primary skin fibroblasts and in the corresponding induced pluripotent stem cells-derived neuronal progenitor cells from Luxembourg Parkinson’s Study iPD patients stratified according to the OXPHOS-PRS, revealed significant differences in mitochondrial respiration between high and low risk groups (p < 0.05). Finally, we also demonstrated that iPD patients with high OXPHOS-PRS have a significantly earlier age at disease onset compared to low-risk patients. Conclusions our findings suggest that OXPHOS-PRS may represent a promising strategy to stratify iPD patients into pathogenic subgroups – in which the underlying neurodegeneration is due to a genetically defined mitochondrial burden – potentially eligible for future, more tailored mitochondrially targeted treatments.

Processed data and scripts supporting the manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma"

This submission contains the compiled count table, processed R objects and various scripts and output files accompanying our manuscript "Single-cell transcriptomics reveals immune suppression and cell states predictive of patient outcomes in rhabdomyosarcoma" (Nature Communications, 2023, https://doi.org/10.1038/s41467-023-38886-8)