This dataset contains files reconstructing single-cell data presented in 'Reference transcriptomics of porcine peripheral immune cells created through bulk and single-cell RNA sequencing' by Herrera-Uribe & Wiarda et al. 2021. Samples of peripheral blood mononuclear cells (PBMCs) were collected from seven pigs and processed for single-cell RNA sequencing (scRNA-seq) in order to provide a reference annotation of porcine immune cell transcriptomics at enhanced, single-cell resolution. Analysis of single-cell data allowed identification of 36 cell clusters that were further classified into 13 cell types, including monocytes, dendritic cells, B cells, antibody-secreting cells, numerous populations of T cells, NK cells, and erythrocytes. Files may be used to reconstruct the data as presented in the manuscript, allowing for individual query by other users. Scripts for original data analysis are available at https://github.com/USDA-FSEPRU/PorcinePBMCs_bulkRNAseq_scRNAseq. Raw data are available at https://www.ebi.ac.uk/ena/browser/view/PRJEB43826. Funding for this dataset was also provided by NRSP8: National Animal Genome Research Program (https://www.nimss.org/projects/view/mrp/outline/18464). Resources in this dataset:Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells 10X Format. File Name: PBMC7_AllCells.zipResource Description: Zipped folder containing PBMC counts matrix, gene names, and cell IDs. Files are as follows: matrix of gene counts* (matrix.mtx.gx) gene names (features.tsv.gz) cell IDs (barcodes.tsv.gz) *The ‘raw’ count matrix is actually gene counts obtained following ambient RNA removal. During ambient RNA removal, we specified to calculate non-integer count estimations, so most gene counts are actually non-integer values in this matrix but should still be treated as raw/unnormalized data that requires further normalization/transformation. Data can be read into R using the function Read10X().Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells Metadata. File Name: PBMC7_AllCells_meta.csvResource Description: .csv file containing metadata for cells included in the final dataset. Metadata columns include: nCount_RNA = the number of transcripts detected in a cell nFeature_RNA = the number of genes detected in a cell Loupe = cell barcodes; correspond to the cell IDs found in the .h5Seurat and 10X formatted objects for all cells prcntMito = percent mitochondrial reads in a cell Scrublet = doublet probability score assigned to a cell seurat_clusters = cluster ID assigned to a cell PaperIDs = sample ID for a cell celltypes = cell type ID assigned to a cellResource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells PCA Coordinates. File Name: PBMC7_AllCells_PCAcoord.csvResource Description: .csv file containing first 100 PCA coordinates for cells. Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells t-SNE Coordinates. File Name: PBMC7_AllCells_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells UMAP Coordinates. File Name: PBMC7_AllCells_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for all cells.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells t-SNE Coordinates. File Name: PBMC7_CD4only_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - CD4 T Cells UMAP Coordinates. File Name: PBMC7_CD4only_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only CD4 T cells (clusters 0, 3, 4, 28). A dataset of only CD4 T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells UMAP Coordinates. File Name: PBMC7_GDonly_UMAPcoord.csvResource Description: .csv file containing UMAP coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and UMAP coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gamma Delta T Cells t-SNE Coordinates. File Name: PBMC7_GDonly_tSNEcoord.csvResource Description: .csv file containing t-SNE coordinates for only gamma delta T cells (clusters 6, 21, 24, 31). A dataset of only gamma delta T cells can be re-created from the PBMC7_AllCells.h5Seurat, and t-SNE coordinates used in publication can be re-assigned using this .csv file.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - Gene Annotation Information. File Name: UnfilteredGeneInfo.txtResource Description: .txt file containing gene nomenclature information used to assign gene names in the dataset. 'Name' column corresponds to the name assigned to a feature in the dataset.Resource Title: Herrera-Uribe & Wiarda et al. PBMCs - All Cells H5Seurat. File Name: PBMC7.tarResource Description: .h5Seurat object of all cells in PBMC dataset. File needs to be untarred, then read into R using function LoadH5Seurat().

Raw (unnormalized ) count data from Ampliseq Whole Transcriptome analyses of neuroblastoma samples, before and after chemotherapy. RNA was extracted from formalin fixed, paraffin embedded tissue blocks with standard methods and sequenced with Ion Torrent technology using the Ampliseq Whole Transcriptome assay. The raw data was processed through the standard Ion Torrent pipeline to generate raw counts for each transcript included in the Whole Transcriptome Assay.

All source data is provided as MAT-files, which can be opened in Matlab. In total, the source data contain 270 core data files and 540 processed data files. The data for each individual promoter is stored in a different directory and the 9 promoters are:

• ALD3

• DCS2

• DDR2

• HXK1

• RTN2

• TKL2

• SIP18

• pSIP18_mut6 (also referred to as mutant A4)

• pSIP18_mut21 (also referred to as mutant D6)

The data for the first 7 promoters was previously reported in (Hansen and O’Shea, 2013), though in an unnormalized form. That is, it was previously reported as a concentration per cell in arbitrary fluorescence units (AU). In the present manuscript, we have calibrated the data to obtain absolute abundances, such that the MAT-files now contain both the old AU concentration as well as absolute abundances, i.e. number of YFP molecules per cell. The calibration was performed as described in (Huang et al., 2016). Similarly, the data for the last 2 promoters (A4 and D6) was previously reported in its unnormalized form in (Hansen and O’Shea, 2015) and it is here also reported in the form of absolute abundances.

The MAT-files containing the raw data have the suffix “_size.mat”. The name of the MAT files describes the experiment. If the file name contains “DM”, then it is a single pulse. Thus, “SIP18_DM_40min_275nM_size.mat” refers to a single 40 min pulse with 275 nM 1-NM-PP1 for the SIP18 promoter. Similarly if the file name contains “FM”, e.g. “RTN2_FM_8_5min_690nM_size.mat” then it refers to eight 5 min pulses separated by 5 min intervals at 690 nM for the RTN2 promoter. Finally, if the file name contains “FM4”, e.g. “TKL2_FM4_15minINT_690nM_size.mat” then the experiment was four 5 min pulses separated by 15 min intervals at 690 nM for the TKL2 promoter. The concentration is the concentration of 1-NM-PP1 that was used and 100 nM, 275 nM, 690 nM and 3mM refers to approximately, 25%, 50%, 75% and 100% Msn2 activation. For full experimental details please see (Hansen and O’Shea, 2013; Hansen et al., 2015).

The “_size.mat” MAT-files contain the following variables:

• cell_size_pixels
• CFP
• CFP_molecules
• CFP_raw
• inhibitor_conc
• MSN2_raw
• MSN2_RFP
• pulse_parameters • time
• YFP
• YFP_molecules
• YFP_raw

CFP, CFP_molecules, CFP_raw and YFP, YFP_molecules, YFP_raw are Nx64 matrices, where each row N correspond to a different cell and the 64 columns correspond to the 64 experimentally measured timepoints corresponding to the “time” vector running from -5 min to 152.5 min in increments of 2.5 min and the 1NM-PP1 inhibitor was added at time 0. “CFP_raw” and “YFP_raw” contains raw, uncorrected data, so without photobleaching correction and background subtraction. “CFP” and “YFP” contain corrected data in arbitrary fluorescence units (AU) and report on the concentration (i.e. size normalized). Finally, “CFP_molecules” and “YFP_molecules” contains the total number of CFP and YFP molecules per cell (i.e. this is not a concentration, but the absolute abundance). The area of each cell at each timepoint can be found in the matrix “cell_size_pixels”. Since the cells are live and growing, this will tend to increase during the experiments. Occasionally large fluctuations can occur due to errors in cell segmentation or due to division. For full details on the image analysis and cell segmentation, please see (Hansen and O’Shea, 2013; Hansen et al., 2015).

The variables “inhibitor_conc” and “pulse_parameters” refer to the type of experiment and is also given by the name. “inhibitor_conc” gives the 1NMPP1 concentration: 100 nM, 275 nM, 690 nM or 3000 nM. “pulse_parameters” contains either 2 or 3 elements and given the dynamical pulse sequence parameters. Column 1 contains the number of pulses and column 2 the duration of the pulses. Column 3 gives the interval between the pulses if more than one pulse is used – otherwise column 3 is zero.

Moreover, on a more technical note it should be noted that the signal-to-noise of the CFP reporter is worse than the YFP reporter. Therefore, we always use the YFP reporter for quantitative analysis. Furthermore, the two other MAT-files “…MSN2.mat” and “…YFP.mat” contain processed data. Please see the ReadMe file on the code for a full description and how these were derived.

Finally, Supplementary Table 1 contains the model-inferred parameters for each promoter and condition.

References

Hansen, A.S., and O’Shea, E.K. (2013). Promoter decoding of transcription factor dynamics involves a trade-off between noise and control of gene expression. Mol. Syst. Biol.

Hansen, A.S., and O’Shea, E.K. (2015). Cis Determinants of Promoter Threshold and Activation Timescale. Cell Rep.

Hansen, A.S., Hao, N., and OShea, E.K. (2015). High-throughput microfluidics to control and measure signaling dynamics in single yeast cells. Nat. Protoc.

Huang, L., Pauleve, L., Zechner, C., Unger, M., Hansen, A.S., and Koeppl, H. (2016). Reconstructing dynamic molecular states from single-cell time series. J. R. Soc. Interface.

Raw, unnormalized scRNA-seq data from Uhlitz et al. colorectal cancer patient cohort.

format: h5ad saved under scanpy version 1.9.1, anndata version 0.8.0 gzip compressed internally (may be slower to load) counts were soupx-corrected for ambient mRNA contains clinical metadata annotations in obs and gene annotations in var dataframes.

Originally published in: Uhlitz, F., Bischoff, P., Peidli, S., Sieber, A., Trinks, A., Lüthen, M., ... & Morkel, M. (2021). Mitogen‐activated protein kinase activity drives cell trajectories in colorectal cancer. EMBO molecular medicine, 13(10), e14123.

Supplementary raw datasets for the manuscript "Circa-SCOPE: high-throughput, live single-cell imaging method for analysis of circadian clock resetting"

The datasets contain the raw, unfiltered, unnormalized fluorescence measurements from the Rev-VNP reporter per cell. Data was extracted by the Circa-SCOPE CellProfiler pipeline and then rearranged with the Circa-SCOPE MATLAB script.

In each of the files, columns correspond to individual cells, and rows correspond to timepoints in 1 hour intervals. Each condition/concentration is in a separate sheet.

The correspondence of the files to the manuscript's figures is as the following:

• RawData_1 is related to Fig 1, S1
• RawData_2 is related to Fig 2, S3, S4
• RawData_3 is related to Fig 3, S5
• RawData_4 is related to Fig 4, S6
• RawData_5 is related to Fig 5, S7
• RawData_6 is related to Fig S8
• RawData_7 is related to Fig 6
• RawData_8 is related to Fig S9

RNA-seq data from ILC samples. This dataset includes raw, unnormalized counts per gene, per sample.