We analyzed the field of expression profiling by high throughput sequencing, or HT-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository.

Archived dataset contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of NCBI GEO series

- output/publications.csv, publication info of NCBI GEO series

- output/scopus_citedbycount.csv, Scopus citation info of NCBI GEO series

- output/single-cell.csv, single cell experiments

- spots.csv, NCBI SRA sequencing run metadata

- suppfilenames.txt, list of all supplementary file names of NCBI GEO submissions. One filename per row.

- suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.

Functional genomics data repository supporting MIAME-compliant data submissions. Includes microarray-based experiments measuring the abundance of mRNA, genomic DNA, and protein molecules, as well as non-array-based technologies such as serial analysis of gene expression (SAGE) and mass spectrometry proteomic technology. Array- and sequence-based data are accepted. Collection of curated gene expression DataSets, as well as original Series and Platform records. The database can be searched using keywords, organism, DataSet type and authors. DataSet records contain additional resources including cluster tools and differential expression queries.

Table of Contents

Main Description File Descriptions Linked Files Installation and Instructions

1. Main Description

This is the Zenodo repository for the manuscript titled "A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity.". The code included in the file titled marengo_code_for_paper_jan_2023.R was used to generate the figures from the single-cell RNA sequencing data. The following libraries are required for script execution:

Seurat scReportoire ggplot2 stringr dplyr ggridges ggrepel ComplexHeatmap

File Descriptions

The code can be downloaded and opened in RStudios. The "marengo_code_for_paper_jan_2023.R" contains all the code needed to reproduce the figues in the paper The "Marengo_newID_March242023.rds" file is available at the following address: https://zenodo.org/badge/DOI/10.5281/zenodo.7566113.svg (Zenodo DOI: 10.5281/zenodo.7566113). The "all_res_deg_for_heat_updated_march2023.txt" file contains the unfiltered results from DGE anlaysis, also used to create the heatmap with DGE and volcano plots. The "genes_for_heatmap_fig5F.xlsx" contains the genes included in the heatmap in figure 5F.

Linked Files

This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. The "Rdata" or "Rds" file was deposited in Zenodo. Provided below are descriptions of the linked datasets:

Gene Expression Omnibus (GEO) ID: GSE223311(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223311)

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the "matrix.mtx", "barcodes.tsv", and "genes.tsv" files for each replicate and condition, corresponding to the aligned files for single cell sequencing data. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Sequence read archive (SRA) repository ID: SRX19088718 and SRX19088719

Title: Gene expression profile at single cell level of CD4+ and CD8+ tumor infiltrating lymphocytes (TIL) originating from the EMT6 tumor model from mSTAR1302 treatment. Description: This submission contains the raw sequencing or .fastq.gz files, which are tab delimited text files. Submission type: Private. In order to gain access to the repository, you must use a reviewer token (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html).

Zenodo DOI: 10.5281/zenodo.7566113(https://zenodo.org/record/7566113#.ZCcmvC2cbrJ)

Title: A TCR β chain-directed antibody-fusion molecule that activates and expands subsets of T cells and promotes antitumor activity. Description: This submission contains the "Rdata" or ".Rds" file, which is an R object file. This is a necessary file to use the code. Submission type: Restricted Acess. In order to gain access to the repository, you must contact the author.

Installation and Instructions

The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation:

Ensure you have R version 4.1.2 or higher for compatibility.

Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code.

1. Download the *"Rdata" or ".Rds" file from Zenodo (https://zenodo.org/record/7566113#.ZCcmvC2cbrJ) (Zenodo DOI: 10.5281/zenodo.7566113).
2. Open R-Studios (https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R.
3. Set your working directory to where the following files are located:

marengo_code_for_paper_jan_2023.R Install_Packages.R Marengo_newID_March242023.rds genes_for_heatmap_fig5F.xlsx all_res_deg_for_heat_updated_march2023.txt

You can use the following code to set the working directory in R:

setwd(directory)

1. Open the file titled "Install_Packages.R" and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies in order to set up an environment where the code in "marengo_code_for_paper_jan_2023.R" can be executed.
2. Once the "Install_Packages.R" script has been successfully executed, re-start R-Studios or your IDE of choice.
3. Open the file "marengo_code_for_paper_jan_2023.R" file in R-studios or your IDE of choice.
4. Execute commands in the file titled "marengo_code_for_paper_jan_2023.R" in R-Studios or your IDE of choice to generate the plots.

This dataset contains data files and identifiers for original data sources for 39 gene expression datasets from over 7,000 individuals with estrogen receptor positive (ER-positive) Breast Cancer (BC).BackgroundThe related study developed a novel in silico approach to assess activation of different signalling pathways. The phosphatidylinositol 3-kinase (PI3K)/AKT/mTOR signalling pathway mediates key cellular functions, including growth, proliferation and survival and is frequently involved in carcinogenesis, tumor progression and metastases. This research seeks to target relative contribution of AKT and mTOR (downstream of PI3K) in BC outcomes using the in silico approach via integrated reverse phase protein array (RPPA) and matched gene expression.Methods and sample sizeThe methodology includes the development of gene signatures that reflect level of expression of pAKT and p-mTOR separately. Pooled analysis of gene expression data from over 7,000 patients with ER-positive BC was then performed. This data record holds links to the repositories holding these data, as well as the R-data files for each data record used in the analysis. All gene signatures developed are captured in Supplementary Data Sonnenblick.pdf.xlsxData sourcesThe dataset name, relevant DOI, accession number or access requirements are listed alongside the file type and repository name or other source where applicable.GEO=Gene Expression OmnibusEGA=European Genome-phenome ArchiveThis data table is available to download as NPJBCANCER-00304R1-data-sources.xlsx including more detailed information and web urls to each data source. data_db.tab contains more detailed technical metadata for each data source.

Dataset Data location Permanent identifier/url

NKI CCB NKI http://ccb.nki.nl/data/van-t-Veer_Nature_2002/

UCSF GEO GSE123833

STNO2 GEO GSE4335

NCI Research Article (Supplementary files) 10.1073/pnas.1732912100

UNC4 GEO GSE18229

CAL Array Express E-TABM-158

MDA4 GEO GSE123832

KOO GEO GSE123831

HLP Array Express E-TABM-543

EXPO GEO GSE2109

VDX GEO GSE2034/GSE5327

MSK GEO GSE2603

UPP GEO GSE3494

STK GEO GSE1456

UNT GEO GSE2990

DUKE GEO GSE3143

TRANSBIG GEO GSE7390

DUKE2 GEO GSE6961

MAINZ GEO GSE11121

LUND2 GEO GSE5325

LUND GEO GSE5325

FNCLCC GEO GSE7017

EMC2 GEO GSE12276

MUG GEO GSE10510

NCCS GEO GSE5364

MCCC GEO GSE19177

EORTC10994 GEO GSE1561

DFHCC GEO GSE19615

DFHCC2 GEO GSE18864

DFHCC3 GEO GSE3744

DFHCC4 GEO GSE5460

MAQC2 GEO GSE20194

TAM GEO GSE6532/GSE9195

MDA5 GEO GSE17705

VDX3 GEO GSE12093

METABRIC EGA EGAS00000000083

TCGA TCGA https://tcga-data.nci.nih.gov/docs/publications/brca_2012/

DNA methylation (Dedeurwaerder et al. 2011) GEO https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20713

Context

No matter how much you wash your hands, you are still susceptible to flu airborne viruses or cold viruses in close proximity to others who have a cold or flu. The flu vaccine is a treatment many folks get in hopes of not getting sick that cold/flu season. The flu vaccine is somewhat of a math cheat sheet for your body preparing for a math course final without having to know all of the formulas off hand, but only the ones that are on the exam. If you have a crooked teacher/TA that decided not to allow the cheat sheet to be a good representation of what the content of the final exam is, then you could assume that is how your body will be with a flu vaccine that doesn't have the strand(s) of flu your body is likely to encounter that flu season. I found this data set munging the GEO database sets of NCBI while searching for 'flu vaccines' and wanted some microarray gene expression data sets that I could also compare those values to other blood micro array samples from separate studies on females using EGCG for obesity, and males who do/don't have heart disease. This data can be blended with the other data sets here or in my github repositories at janjanjan2018.

Content

Blood gene expressions of microarray samples.

Acknowledgements

NCBI and the GEO grant funded data repositories of gene expression data.

Inspiration

Sick people.

This is the GitHub repository for the single cell RNA sequencing data analysis for the human manuscript. The following essential libraries are required for script execution: Seurat scReportoire ggplot2 dplyr ggridges ggrepel ComplexHeatmap Linked File: -------------------------------------- This repository contains code for the analysis of single cell RNA-seq dataset. The dataset contains raw FASTQ files, as well as, the aligned files that were deposited in GEO. Provided below are descriptions of the linked datasets: 1. Gene Expression Omnibus (GEO) ID: GSE229626 - Title: Gene expression profile at single cell level of human T cells stimulated via antibodies against the T Cell Receptor (TCR) - Description: This submission contains the matrix.mtx, barcodes.tsv, and genes.tsv files for each replicate and condition, corresponding to the aligned files for single cell sequencing data. - Submission type: Private. In order to gain access to the repository, you must use a "reviewer token"(https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html). 2. Sequence read archive (SRA) repository - Title: Gene expression profile at single cell level of human T cells stimulated via antibodies against the T Cell Receptor (TCR) - Description: This submission contains the "raw sequencing" or .fastq.gz files, which are tab delimited text files. - Submission type: Private. In order to gain access to the repository, you must use a "reviewer token" (https://www.ncbi.nlm.nih.gov/geo/info/reviewer.html). Please note that since the GSE submission is private, the raw data deposited at SRA may not be accessible until the embargo on GSE229626 has been lifted. Installation and Instructions -------------------------------------- The code included in this submission requires several essential packages, as listed above. Please follow these instructions for installation: > Ensure you have R version 4.1.2 or higher for compatibility. > Although it is not essential, you can use R-Studios (Version 2022.12.0+353 (2022.12.0+353)) for accessing and executing the code. The following code can be used to set working directory in R: > setwd(directory) Steps: 1. Download the "Human_code_April2023.R" and "Install_Packages.R" R scripts, and the processed data from GSE229626. 2. Open "R-Studios"(https://www.rstudio.com/tags/rstudio-ide/) or a similar integrated development environment (IDE) for R. 3. Set your working directory to where the following files are located: - Human_code_April2023.R - Install_Packages.R 4. Open the file titled Install_Packages.R and execute it in R IDE. This script will attempt to install all the necessary pacakges, and its dependencies. 5. Open the Human_code_April2023.R R script and execute commands as necessary.

Secretory factors in animals play an important role in communication between different cells, tissues and organs. Especially, the secretory factors with specific expression in one tissue may reflect important functions and unique status of that tissue in an organism. In this study, we identified potential tissue-specific secretory factors in the fat, muscle, heart, lung, kidney and liver in the mouse by analyzing microarray data from NCBI’s Gene Expression Omnibus (GEO) public repository and searching and predicting their subcellular location in GeneCards and WoLF PSORT, and then confirmed tissue-specific expression of the genes using semi-quantitative PCR reactions. With this approach, we confirmed 11 lung, 7 liver, 2 heart, 1 heart and muscle, 7 kidney and 2 adipose and liver-specific secretory factors. Among these genes, 1 lung-specific gene - CTLA2A (cytotoxic T lymphocyte-associated protein 2 alpha), 3 kidney-specific genes - SERPINA1F (serpin peptidase inhibitor, Clade A, member 1F), WFDC15B (WAP four-disulfide core domain 15B) and DEFB29 (defensin beta 29) and 1 liver-specific gene - MUP19 (major urinary protein 19) have not been reported as secretory factors. These genes were tagged with hemagglutinin at the 3’end and then transiently transfected to HEK293 cells. Through protein detection in cell lysate and media using Western blotting, we verified secretion of the 5 genes and predicted the potential pathways in which they may participate in the specific tissue through data analysis of GEO profiles. In addition, alternative splicing was detected in transcripts of CTLA2A and SERPINA1F and the corresponding proteins were found not to be secreted in cell culture media. Identification of novel secretory factors through the current study provides a new platform to explore novel secretory factors and a general direction for further study of these genes in the future.

(a) BP terms, which differ between rearing laboratories at TP1_one to one (oto) comparison; (b) BP terms, which differ between rearing laboratories at TP2_one to one (oto) comparison; (c) CC; terms that differ between rearing laboratories at TP1_one-to-one (oto) comparison; (d) CC; terms that differ between rearing laboratories at TP2_one-to-one (oto) comparison; (e) MF; terms that differ between rearing laboratories at TP1_one-to-one (oto) comparison; (f) MF; terms that differ between rearing laboratories at TP2_one-to-one (oto) comparison. The ATAC-seq data are available from the NCBI GEO database under accession number GSE191125. The analysis script is available at the GitHub repository https://github.com/MWSchmid/Jaric-et-al.-2022. The supporting data are available at the Figshare repository https://doi.org/10.6084/m9.figshare.21088504. ATAC-seq, assay for transposase-accessible chromatin using sequencing; BP, Biological Process; CC, cellular components; GEO, Gene Expression Omnibus; MF, molecular function. (XLSX)

This study represents the first in vivo genome wide analysis of gene epxression of Group A Streptococcus (GAS) in humans with pharyngitis. The micorarray used is a custom microarray that relies on an electrochemical reaction as the measured signal rather than flourescence. Distinct clusters of gene expression were discovered and analyzed. A functional analysis examining differences and similarities between the clusters was performed. Samples were taken from pediatric patients who had received a throat swab as part of their clinical care for evaluating pharyngitis. Eleven samples that were culture postive for GAS were used along with 3 samples from subjects who were GAS culture negative. The microarray was a composite comprised of 12,000 probes, representing 2724 GAS open reading frames belonging to serotypes M1, M3, M4, M12, and M28. There were 1671 probe sets that were homologous for the M1 serotype and represented over 95% of the total predicted coding region. Probe sets were 30-40mers in length and were selected using a Combimatrix probe selection algorithm taking into account gene (>90% BLAST score) and serotype specificity, Tm, hairpins and GC content. In addition, 70 Combimatrix built in probes were used as negative controls for background subtraction.

Affymetrix Bovine GeneChip® Gene 1.0 ST Array RNA expression analysis was performed on four somatic ovarian cell types: the granulosa cells (GCs) and theca cells (TCs) of the dominant follicle and the large luteal cells (LLCs) and small luteal cells (SLCs) of the corpus luteum. The normalized linear microarray data was deposited to the NCBI GEO repository (GSE83524). Subsequent ANOVA determined genes that were enriched (≥2 fold more) or decreased (≤−2 fold less) in one cell type compared to all three other cell types, and these analyzed and filtered datasets are presented as tables. Genes that were shared in enriched expression in both follicular cell types (GCs and TCs) or in both luteal cells types (LLCs and SLCs) are also reported in tables. The standard deviation of the analyzed array data in relation to the log of the expression values is shown as a figure. These data have been further analyzed and interpreted in the companion article "Gene expression profiling of ovarian follicular and luteal cells provides insight into cellular identities and functions", Romereim et al., (2017) Mol. Cell. Endocrinol. 439:379-394. https://doi.org/10.1016/j.mce.2016.09.029 Resources in this dataset: Resource Title: RNA Expression Data from Four Isolated Bovine Ovarian Somatic Cell Types. File Name: Web Page, url: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83524 NCBI Gene Expression Omnibus (GEO) Accession Display. Analysis of the RNA present in each bovine cell type using Affymetrix microarrays yielded new cell-specific genetic markers, functional insight into the behavior of each cell type via Gene Ontology Annotations and Ingenuity Pathway Analysis, and evidence of small and large luteal cell lineages using Principle Component Analysis. Enriched expression of select genes for each cell type was validated by qPCR. This expression analysis offers insight into the lineage and differentiation process that transforms somatic follicular cells into luteal cells. The orignal Affymetrix .CEL files and the normalized linear expression data are included in this submission.

We analyzed the field of expression profiling by high throughput sequencing, or RNA-seq, in terms of replicability and reproducibility, using data from the NCBI GEO (Gene Expression Omnibus) repository. Our work puts an upper bound of 56% to field-wide reproducibility, based on the types of files submitted to GEO.

Archived dataset contains following files:

- output/parsed_suppfiles.csv, p-value histograms, histogram classes, estimated number of true null hypotheses (pi0).

- output/document_summaries.csv, document summaries of GEO series

- output/publications.csv, publication info of GEO series

- output/scopus_citedbycount.csv, Scopus citation info of GEO series

- output/single-cell.csv, single cell experiments

- spots.csv, sequencing run metadata: number of spots and bases

- suppfilenames.txt, list of all supplementary file names of GEO submissions. One filename per row.

- suppfilenames_filtered.txt, list of supplementary file names used for downloading files from NCBI GEO. One filename per row.

The chicken chorioallantoic membrane (CAM) is an extraembryonic structure that exhibits many vital functions to support the development of the chicken embryo (gaseous exchange, innate defence, calcium transport from the eggshell to the embryo skeleton, homeostasis). Developing from day 6 of incubation, the CAM progressively differentiates into three functional layers (the chorionic epithelium in contact with the inner eggshell, the highly vascularized mesoderm, and the allantoic epithelium), between 11 and 15 days of incubation. This article describes the RNASeq dataset and the analyses performed on total CAMs collected from male and female embryos after 11 and 15 days of incubation. The datasets are available at the NCBI Gene Expression Omnibus (GEO) repository (http://www.ncbi.nlm.nih.gov/geo) using GSE199780 as the accession number. The statistical analysis of the data allowed identifying genes differentially expressed depending on the sex of the embryo at two time points of CAM differentiation. Knowing that the CAM is widely used as a model to study tumour growth, metastasis or wound healing, the resulting analysis highlights the necessity to include this sex variable in experimental assays to avoid any bias of interpretation. Indeed, the functional annotation of genes that are differentially expressed between male and female CAMs revealed an enrichment of activities and functions related to lipid metabolism, bone formation, and morphogenesis suggesting that the response of the CAM to external and experimental stimuli might be different depending on the sex of the embryo.

This dataset contains the raw data associated with and published in the following academic manuscript - McAuliffe et al. (2021), JITC: Heterologous prime-boost vaccination targeting MAGE-type antigens promotes tumor T-cell infiltration and improves checkpoint blockade therapy.

Details on the research hypothesis of this project, descriptions of data acquired and notable findings and interpretation of the data have been described in full within the text of the published manuscript.

This dataset contains raw data for all of the figures contained within the manuscript (organized and broken down as such within this dataset). This is with the exception of the high-throughput sequencing data comprising Fig. 4H, Fig. 5, Fig. S5 and Fig. S7 which has been uploaded to and made publicly available in the NCBI Gene Expression Omnibus (GEO) repository. GEO accession numbers to access these datasets have been included in the manuscript.

Full descriptions of the data acquired for each figure / figure sub-part are provided in the text and figure legends of the manuscript, and information on how it was gathered and how to interpret it are contained in the materials & methods and results sections of the manuscript.

Stroke is a leading cause of adult disability and death. Inflammation plays an important role in stroke pathology, but the factors which promote brain inflammation in this setting remain to be fully defined. Here we investigate the meninges, the membranes that envelop the brain, for a potential role in modulating immune cell trafficking to the brain. We also investigate the potential of mast cells (MCs) to modulate this response as MCs are often considered as 'first responders' playing a critical role in the initiation and development of inflammation in many disease settings. We find that stroke increases expression of inflammatory and immune response genes in the meninges in mice consistent with a potential role in modulating immune cell trafficking. Moreover, genetic and cell transfer approaches identify MCs as important modulators of this response. Three categories of male mice were used: wild-type (WT) mice, mast cell-deficient (KO) mice, and mast cell-engrafted mice (EN), which are mast cell-deficient mice repaired of their mast cell deficiency by engraftment of mast cells i.v. 8-10 weeks prior to experimentation. The mouse strain was WBB6F1-Kit+/+ (wild-type ) and WBB6F1-KitW/W-v (mast cell-deficient ). Each mouse category was subdivided into two groups, naïve (N) and stroke (S), with n=3 per group. The stroke model was 30 minute filament occlusion of the middle cerebral artery. The dura were removed from the mouse brains at 2d post-stroke and from aged-matched naïve mice for microarray analysis. Dura were not pooled but run on separate arrays.

In this study, the authors analysed primary human Langerhans cells (LCs), using bulk and single-cell RNAseq as well as H3K4Me3 and H4K27Ac chromatin immunoprecipitation sequencing (ChIPseq), and coupled these genomic and epigenomic profiles with LC phenotypic and functional characteristics.Data access: RNA-seq, scRNA-seq and ChiP-seq data generated during the current study, are publicly available in the NCBI Gene Expression Omnibus repository at: https://identifiers.org/geo:GSE120386. Data supporting figures 1-6 and supplementary figures 1-4 are publicly available in the figshare repository at: https://doi.org/10.6084/m9.figshare.11372013.Dataset description:This data record consists of one data set, Source_data.xlsx, in .xlsx file format. The dataset contains a total of 19 sheets labelled Figure 1d, Figure 1e, Figure 2a, Figure 2c, Figure 2d, Figure 2e, Figure 3c, Figure 4a, Figure 4d, Figure 5b, Figure 5c, Figure 6b, Figure 6d, Sup Fig 1c, Sup Fig 2b, Sup Fig 2d, Sup Fig 2f, Sup Fig 4a and Sup Fig 4d.Sheet “Figure 1d” contains the raw data used to generate figure 1d. Data contain interferon gamma (IFN-γ) secretion levels of steady-state and migrated LCs, that were either pulsed (with 30 amino acid peptide containing the Epstein-Barr virus (EBV) epitope) or unpulsed.Sheet “Figure 1e” contains the raw data used to generate figure 1e. Data contain IFN-γ secretion by EBV-specific CD8 T cell line stimulated by pulsed migrated LCs. IFN-γ secretion was measured with or without (Medium) TNF stimulation.Sheet “Figure 2a” contains the raw data used to generate figure 2a. Data contain the results of Gene ontology analysis for each expression level interval determined by RNA-seq. Analysis was performed using ToppGene on-line tool.Sheet “Figure 2c” contains the raw data used to generate figure 2c. Data contain the intracellular expression of SQSTM1 and TRIM21 measured by flow cytometry in steady-state and migrated LCs.Sheet “Figure 2d” contains the raw data used to generate figure 2d. Data contain the gene expression levels of 13 genes in tumor necrosis factor (TNF)-stimulated (T24) LCs. Expression levels of 3 biological replicates for each gene are shown. Sheet “Figure 2e” contains the raw data used to generate figure 2e. Data contain the gene expression levels of 10 genes in LCs stimulated with TNF for 24 hours (T24), for 2 hours (T2) and 0 hours (T0). Expression levels of 3 biological replicates for each gene are shown. Sheet “Figure 3c” contains the raw data used to generate figure 3c. Data contain the results of Gene ontology analysis for marker genes (n=100) representative of indicated cluster, performed using ToppGene. –log(10) Benjamini Hochberg corrected p values are shown for cluster-specific processes.Sheet “Figure 4a” contains the raw data used to generate figure 4a. Data contain the number of differentially expressed genes (DEGs) and the number of DEGs with the H3K27Ac mark in LCs following stimulation with TNF. Results are shown for genes upregulated early (2h), and for genes upregulated late (24h).Sheet “Figure 4d” contains the raw data used to generate figure 4d. Transcripts with the H3K4Me3 and H3K27Ac marks were identified. This sheet shows the biological processes enriched in those genes, detected using ToppGene based on false discover rate (FDR) corrected p-values for gene Ontology (GO) categories.Sheet “Figure 5b” contains the raw data used to generate figure 5b. Data show the IRF4 protein expression (expressed as %) in steady state vs migrated LCs. IRF4+ LCs (%) was measured by flow cytometry.Sheet “Figure 5c” contains the raw data used to generate figure 5c. Data show the expression levels of the key transcription factors in migrated LCs before (0h), and after TNF stimulation (2h, 24h). Results of three biological donors are shown. Sheet “Figure 6b” contains the raw data used to generate figure 6b. Data show Mean fluorescence intensity (MFI) of IRF4 expressing CD207+ CD1a+ live LCs (in CRISPR-Cas9 edited (KD) and control (WT) migrated LCs).Sheet “Figure 6d” contains the raw data used to generate figure 6d. Data show the results of the GO processes and pathways differentially enriched in control LC (WT) versus IRF4 CRISPR-Cas9 edited LCs (IRF4 KD).Sheet “Sup Fig 1c” contains the raw data used to generate supplementary figure 1c. Data show IFN-γ secretion levels of pulsed (with 9 amino acid peptide GLC) or unpulsed LCs that were stimulated with TNF.Sheet “Sup Fig 2b” contains the raw data used to generate supplementary figure 2b. Data show log(2) FPKM gene expression levels for genes involved in antigen processing and presentation in LCs. Results of three biological donors are shown (LC1, LC2, LC3).Sheet “Sup Fig 2e” contains the raw data used to generate supplementary figure 2e. Data show gene expression of PSME2 and CAV1 in migrated LC assessed by qPCR before (medium) and following stimulation with TNF.Sheet “Sup Fig 2f” contains the raw data used to generate supplementary figure 2f. Data show Log (2) Fragments Per Kilobase of transcript per Million mapped reads (FPKM) median expression values for each gene included in the antigen presentation class I signature from Reactome database. Expression levels of each gene are shown before (T0) and after (T2, T24) stimulation with TNF.Sheet “Sup Fig 4a” contains the raw data used to generate supplementary figure 4a. Data show the number of DAGs and the number of DAGs with the H3K27Ac mark at 2h and 24h in clusters of coexpressed genes up-regulated early (2h, clusters 3) and late (24h, cluster 2) duringstimulation with TNF.Sheet “Sup Fig 4d” contains the raw data used to generate supplementary figure 4a. Peaks H3K4Me3 and H3K27Ac T0 datasets were scanned for ISRE/AICE/EICE bindingmotifs. 1193 consensus genes (present in all 3 biological replicates with both chromatin marks) were identified. Biological processes enriched in those genes (shown in this sheet) were detected using ToppGene (FDR corrected p-values for GO categories).Study aims and methodology: LC are highly specialised antigen presenting cells, priming protective immune responses against pathogens encountered via the skin, such as viruses, bacteria and fungi. The authors used transcriptional and chromatin profiling and showed that migratory LCs are robustly programmed for MHC-I and MHC-II antigen presentation as well as mitochondrial oxidative phosphorylation.The following methods are described in more detail in the published article: cell isolation (from skin specimens and blood samples of healthy individuals) and stimulation with TNF, antigen cross-presentation assay, flow cytometry, RNA-seq, quantitative reverse transcription polymerase chain reaction (qRT PCR), ChIP-seq, Drop-seq, bioinformatics analysis of sequencing data and CRISPR-Cas9 gene editing.

In this study, the authors identified a new class of compounds effective in suppressing FOXM1 activity in breast cancers, and displaying good potency for antitumor efficacy.Data access: Datasets supporting figures 1-6, supplementary table 1 and supplementary figures 1, 2, 4 and 5 are publicly available in the figshare repository as part of this data record (https://doi.org/10.6084/m9.figshare.10052219). RNA-Seq data of the effects of compounds and of siFOXM1 on global FOXM1 gene regulation, are publicly available in the NCBI Gene Expression Omnibus (GEO) repository at https://identifiers.org/geo:GSE132343.Uncropped Western blots are available as part of the supplementary information (supplementary figure 8).Study approval: All experiments involving animals were conducted in accordance with National Institutes of Health (NIH) standards for the care and use of animals, with protocols approved by the University of Illinois IACUC.Study aims and methodology:The transcription factor FOXM1 is up-regulated and overexpressed in aggressive, therapy resistant forms of hormone receptor-positive and triple negative breast cancers, and is associated with less good patient survival. FOXM1 signalling is also a key driver in many other cancers. This study aimed to investigate the suppressive activity of a new class of compounds on FOXM1 activity in breast cancers, and hence determine, whether these may be suitable for further clinical evaluation in targeting aggressive breast cancers driven by FOXM1. The authors used a panel of human breast cancer cell lines and the non-tumorigenic MCF10A breast cell line that differed in their FOXM1 protein content (high to intermediate levels, DT22, MCF7, T47D, BT474, MDA-MB-453, MDA-MB-468, and MDA-MB-231 cells; and low level, MCF10A cells) to examine the effects of potential FOXM1 inhibitor compounds on cell proliferation.The chemical synthesis of compounds and their spectroscopic characterisation is described in detail in the Supplementary Information.The following procedures were carried out during the study and are described in detail in the published article: Cell viability (WST-1) assay, Western blot and immunofluorescence assays, Fluorescence binding assays with FOXM1, Drug affinity responsive target stability (DARTS) assay (to examine the effect of compounds on the stability of FOXM1 to proteolysis by exogenous pronase), Cell cycle analysis, Apoptosis analysis, Cytoplasmic and Nuclear extract preparation, RNA isolation and real-time PCR, RNA-Seq transcriptional profiling and gene ontology analysis, Pharmacokinetic studies, and in vivo breast cancer xenograft studies.Datasets supporting figures, and supplementary figures and tables: Table Ziegler et. al.xlsx is in .xlsx file format and describes all the datasets (names, dataset format and links to datasets) supporting the figures and supplementary figures and tables in the published article.

Data associated with the manuscript entitled "Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis".

Files:

table_A_LCM_TPM_values.tsv: Gene expression levels of microdissected villus quintiles. First column is the ensemble gene id. Next 15 columns are the raw Kallisto TPM values for villus segments 1 (bottom) to 5 (top) for three different mice (a to c). Additional columns include the external gene name, description, and gene biotype.

table_B_scRNAseq_UMI_counts.tsv: Raw UMI counts of cells that were utilized in this study. Analysis is based on raw data from the NCBI GEO datasets GSM2644349 and GSM2644350. Each of the columns represents a single cell, column headers are the corresponding cell barcodes and enable retrieval of tSNE coordinates from table_C_scRNAseq_tsne_coordinates_zones.tsv. Values represent raw UMI counts.

table_C_scRNAseq_tsne_coordinates_zones.tsv: tSNE coordinates and reconstructed zones of cells that were utilized in this study. Tab separated text file. Analysis is based on raw data from the NCBI GEO datasets GSM2644349 and GSM2644350. Columns: cell_id: cell barcode, corresponds to column header of Table S2. Seurat tSNE coordinate 1 and tSNE coordinate 2. Last column is the inferred zone (Crypt, V1..V6).

table_D_zonation_reconstruction.tsv: Zonation table of reconstructed scRNAseq data. Tab separated text file. Columns: Gene names: gene id, mean expression in each of the crypt zone and 6 villus zones, standard error of the means in the Crypt zone and 6 villus zones, p-value and q-value for the zonation profiles.

raw_data.zip: The raw and intermediary data for runnning the scripts in https://github.com/aemoor/Code_spatial_reconstruction_enterocytes

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". Resources in this dataset:Resource Title: Supporting information as Excel spreadsheets and tables. File Name: Web Page, url: http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070

We hypothesized that exposure to fatty acids in metabolic dysfunction-associated steatohepatitis-like environment will profoundly affect gene expression of hepatocytes. More precisely, we wish to investigate expression of genes related to ferroptosis, i.e. an iron-catalyzed form of cell death through lethal lipid peroxidation. We aimed to study the effect of fatty acid supplementation in a metabolic dysfunction-associated steatohepatitis-like environment on gene expression of HepG2 cells profiled with bulk mRNA-sequencing. HepG2 cells were exposed for 48 hours to oleic acid (100microM) and palmitic acid (50microM), as well as hyperglycemia (4.5 mg/mL), hyperinsulinemia (100 nM), tumor necrosis factor alpha (50 ng/mL), interleukin 1-beta (25 ng/mL) and transforming growth factor-beta (8ng/mL). Control samples were exposed to solvents needed to dissolve oleic acid and palmitic acid in medium.

MicroRNA (miRNA) and other types of small regulatory RNAs play a crucial role in the regulation of gene expression in eukaryotes. Several distinct classes of small regulatory RNAs have been discovered in recent years. To extend the repertoire of small RNAs characterized in mammals and to examine relationship between host miRNA expression and viral infection we used Illumina's ultrahigh throughput sequencing approach. We sequenced three small RNA libraries prepared from cell line derived from the adult bovine kidney under normal conditions and upon infection of the cell line with Bovine herpesvirus 1. We used a bioinformatics approach to distinguish authentic mature miRNA sequences from other classes of small RNAs and short RNA fragments represented in the sequencing data. Using this approach we detected 219 out of 356 known bovine miRNAs and 115 respective miRNA* sequences. In addition we identified five new bovine orthologs of known mammalian miRNAs and discovered 268 new cow miRNAs many of which are not identifiable in other mammalian genomes and thus might be specific to the ruminant lineage. In addition we found seven new bovine mirtron candidates. We also discovered 10 small nucleolar RNA (snoRNA) loci that give rise to small RNA with possible miRNA-like function. Results presented in this study extend our knowledge of the biology and evolution of small regulatory RNAs in mammals and illuminate mechanisms of small RNA biogenesis and function. New miRNA sequences and the original sequencing data have been submitted to miRNA repository (miRBase) and NCBI GEO archive respectively. We envisage that these resources will facilitate functional annotation of the bovine genome and promote further functional and comparative genomics studies of small regulatory RNA in mammals.