Find research datasets worth reusing

opencc-by-4.0Mar 2020View details →

Detection of HER2+ Breast Cancer Cells using Bioinspired DNA-Based Signal Amplification

<p>Circulating tumor cells (CTC) are promising biomarkers for metastatic cancer detection and monitoring progression. However, CTC detection remains challenging due to their low frequency and heterogeneity. Herein, we report a bioinspired approach to detect individual cancer cells, based on a signal amplification cascade using a programmable DNA hybridization chain reaction (HCR) circuits. We applied this approach to detect HER2+ cancer cells using the anti-HER2 antibody (trastuzumab) coupled to initiator DNA eliciting a HCR cascade that leads to a fluorescent signal at the cell surface. At 4°C, this HCR detection scheme resulted in highly efficient, specific and sensitive signal amplification of the DNA hairpins specifically on the membrane of the HER2+ cells in a background of HER2- cells and peripheral blood leukocytes, which remained almost non-fluorescent. The results indicate that this system offers a new strategy that may be further developed toward an in vitro diagnostic platform for the sensitive and efficient detection of CTC.</p>

opencc-by-4.0May 2020View details →

Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S1 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"

<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S1 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S1. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S1 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>

opencc-by-4.0May 2020View details →

Raw read counts and phased SNP counts for every single cell in the sequencing datasets of the breast cancer patient S0 from "Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL"

<p>This dataset contains the raw read counts and phased SNP counts for every single cell in the sequencing datasets of breast cancer patient S0 from “Characterizing allele- and haplotype-specific copy numbers in single cells with CHISEL” [Zaccaria & Raphael, 2020]. These data enable the full reproduction of all the results in the related manuscript for breast cancer patient S0. Specifically, the data are provided in two files for every dataset <em>DAT</em> of patient S0 with the following format:</p> <ol> <li><em>DAT.raw</em>_<em>read</em>_<em>counts.bed.gz </em>is a multi-cell BED file containing the raw read counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>START: the starting genomic position of a genomic bin in the chromosome</li> <li>END: the ending genomic position of the genomic bin in the chromosome</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>NORMAL: the raw read count for the specified bin from a matched-normal sample</li> <li>COUNT: the raw read count for the specified bin in the specified cell</li> <li>RDR: the estimated read-depth ratio for the specified bin in the specified cell</li> </ul> </li> <li><em>DAT.phased</em>_<em>snps</em>_<em>counts.pos.gz </em>is a multi-cell POS file containing the phased SNP counts in the following fields: <ul> <li>CHROMOSOME: the name of a human chromosome</li> <li>POS: the genomic position in the chromosome of a germline SNP</li> <li>CELL: the cell barcode that uniquely identifies a cell</li> <li>COUNT_HAPLOTYPE_A: the count of reads that cover the SNP and that belong to haplotype A in the specified cell</li> <li>COUNT_HAPLOTYPE_B: the count of reads that cover the SNP and that belong to haplotype B in the specified cell</li> </ul> </li> </ol> <p>All the files have been compressed using standard <em>gzip</em>.</p>

opencc-by-4.0Oct 2023View details →

2 million histological images of breast cancer tumors with her2 labels

<p><strong>Data Description</strong><br> This is a 2 million set of non-overlapping image patches from hematoxylin & eosin (H&E) stained histological images of human breast cancer tumor tissue.</p> <p>The anonymized dataset comes from a cohort of BC patients from the A. C. Camargo Cancer Center (ACCCC, N = 504). All patients were treated for breast cancer at the ACCCC between 2019 and 2021. As part of their diagnosis, in HER2 IHC score 2+ cases, patients' HER2 status was determined following the ASCO guidelines updated in 2018, with visual evaluation of IHC assay and either a FISH or DDISH test. All cases with metastasis or neoadjuvant treatment were excluded.</p> <p>A total of 426 H&E stained high resolution images (40x magnification) were scanned from biopsy and resection tissue samples with a Leica Aperio AT2 scanner. Ethical approval of the ACCCC study was given by the ethics committee of the Fundação Antônio Prudente. We divided the cases into the following 3 groups according to the results of the IHC and ISH tests: HER2-negative, HER2-low and HER2-high.</p> <p>The slides were divided into 256 px x 256 px tiles at 0.5 um/pixel magnification. Then, we used a custom trained ConvNext-tiny neural network to only include tiles from the tumor region and its environment, generating a total of 2051877 image patches.</p> <p>A sample is considered her2-negative with an IHC score of 0; her2-low with an IHC score of 1+ or an IHC score of 2+ with a negative ISH-based test result, and her2-high with an IHC score of 2+ with a positive ISH-based test or an IHC score of 3+.</p> <p>The accompanying code used for training the models is available at https://github.com/tojallab/wsi-mil</p>

opencc-by-4.0Jan 2022View details →

Multi-omic machine learning predictor of breast cancer therapy response

<p>H&E slides used in the training dataset described in "Multi-omic machine learning predictor of breast cancer therapy response" published in <em>Nature</em>: <a href="https://www.nature.com/articles/s41586-021-04278-5">https://www.nature.com/articles/s41586-021-04278-5</a></p> <p>Metadata associated with these images also included in file Slide metadata.xlsx</p>

opencc-by-4.0May 2024View details →

Evaluation of transcription factor knockout impact on paclitaxel response for Triple Negative Breast Cancer

<div>Data and code related to Zenodo repository: 10.5281/zenodo.11238552</div> <div> </div> <div>Two experimental formats included:</div> <div>'fixed' prefix: data from terminal time point of siRNA screen applied to HCC1143, HCC1806, and MDA-MB-468 Triple Negative Breast Cancer cell lines.</div> <div>'live' prefix: data from live-cell imaging of cell cycle reporter (HDHB-mClover/NLS-mCherry) HCC1143 Triple Negative Breast Cancer cell line.</div> <div>Note: 'live' level 1 data is available upon request (heiserl@ohsu.edu, calistri@ohsu.edu).</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate whether siRNA knockdown of transcription factors elevated during paclitaxel response impact cell count, cell morphology or cycling dynamics.</div> <div> </div> <div>Methods:</div> <div>siRNA Knockdown: Cells were plated in 90ul of serum free media per well of a 96 well plate. 24 hours later, siRNA knockdown mixture was prepared using a cell-line optimized concentration of Lipofectamine RNAiMAX (cat 13778075-075, Invitrogen) and siRNA (Horizon Discovery ON-TARGETplus) following RNAiMAX recommended protocol. The final concentration of siRNA per well was 1pmol and the final volume of RNAiMAX per well was 75nL for HCC1143, and 37.5nL for HCC1806 or MDA-MB-468 in 100uL of cell containing volume. 24 hours after siRNA transfection cells were treated with an addition of 100uL complete media containing either DMSO vehicle control or paclitaxel. </div> <div> </div> <div>Fixed-cell assays: Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask Green in PBS (H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 50, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>HDHB reporter live-cell assays: siRNA knockdown and drug treatment was performed as described above, and then the plate was loaded on an Incucyte S3 (Sartorious) and cells imaged every 15 minutes for 72 hours post drug treatment. At each timepoint 4 fields of view were captured at 20x magnificantion in each well using the phase, red and green channels. A cytoplasmic mask was computed from the mean of normalized red/green channel (cellpose parameters: diameter = 57, chan = mean(normalized(red), normalized(green)), and a nuclear mask was computed from the red channel (cellpose parameters: diameter = 30, chan = DAPI) using custom trained Cellpose models. Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0). An additional perinuclear ring mask was computed as the 11 pixel dilation from the nuclear mask, but still bound by the cytoplasmic mask. To determine mClover localization thresholds for cell cycle assignment, 250 cell images were randomly selected and manually assigned to the G1, S/G2 or M cell cycle state based on mClover localization. The mClover intensity ratios were then used to determine thresholds for automated cell cycle phase calling which was applied to the rest of the data set (Supplemental Figure 5A). Mononuclear cells with a Perinuclear:Nuclear mean intensity ratio greater than 0.8 and Nuclear:Cytoplasmic total intensity less than 0.5 were assigned to the S/G2 phase. Mononuclear and Multinuclear cells with a Nuclear:Cytoplasmic total intensity ratio greater than 0.8 and Perinuclear:Nuclear mean intensity ratio less than 0.8 were assigned to the ‘M’ phase. The remainder of mononuclear cells were assigned ‘G1’, and the remainder of multinucleated cells were assigned ‘Multinucleated’. </div> <div> </div> <div>Included files:</div> <div>fixed_level_1-plate_#.zip : Six .zip archives containing the raw images (DAPI/CellMask/Brightfield) from fixed-cell experiments.</div> <div>plate 1: HCC1143 cells treated with plate A schema</div> <div>plate 2: HCC1143 cells treated with plate B schema</div> <div>plate 3: HCC1806 cells treated with plate A schema</div> <div>plate 4: HCC1806 cells treated with plate B schema</div> <div>plate 5: MDA-MB-468 cells treated with plate A schema</div> <div>plate 6: MDA-MB-468 cells treated with plate B schema</div> <div>fixed_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>fixed_level_3: Data from 'fixed_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>fixed_incell_to_cellpose.rmd: R markdown code for converting original incell files (fixed_level_1) to RGB images for cellpose segmentation</div> <div>fixed_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (fixed_level_1)</div> <div>fixed_cellpose_models.zip: Archive including cellpose models used for fixed experiment</div> <div>live_level_2: Data quantified from cellpose masks at the single-nuclei level (redundant cytoplasm information)</div> <div>live_level_3: Data from 'live_level_2.csv' collapsed to the single cell level, including staining intensity and aggregate nuclear information</div> <div>live_level_4: Data from 'live_level_3.csv' collapsed to the single condition level summarizing the number, multinucleation status and phase of cells at each time point.</div> <div>live_image_quantification.rmd: R markdown code for quantifying images using cellpose segmentation masks and original images (live_level_1).</div> <div>l ive_incu_archive2rgb.rmd: R markdown code for converting incucyte archive formatted data into RGB images, where the blue channel is the arithmetic mean of the min-max (0-1) normalized red and green channels.</div> <div>live_cellpose_models.zip: Archive including cellpose models used for live experiment.</div> <div> </div> <div> </div>

opencc-by-4.0May 2024View details →

Impact of paclitaxel treatment on the Triple Negative Breast Cancer Cell line HCC1143

<div>Data and code related to Zenodo repository: 10.5281/zenodo.11237850</div> <div> </div> <div>Experimental goal:</div> <div>Evaluate the impact of escalating paclitaxel dose on cell count, nuclear morphology and cellular outcome.</div> <div> </div> <div>Methods:</div> <div>Cells were plated at 3000 cells in 100ul of complete media per well in a 96 well plate (#08-772-225, FisherScientific). After 24 hours, an additional 100ul of either vehicle (0.1% DMSO) or paclitaxel containing complete media was added. After 72 hours cells were fixed with 4% Formaldehyde (#28908, ThermoFisher Scientific) for 15 minutes at room temperature, then permeabilized with 0.3% Triton X-100 (#X100-100ML, Sigma Aldrich) for 10 minutes at room temperature, then washed twice with PBS. Fixed cells were blocked with 1% BSA (A7906-100G, Millipore Sigma) in PBS for 1 hour at room temperature and then stained overnight with 1:100 anti-CDKN2A/p16INK4A+CDKN2B/p15INK4B-AF644 (#ab199756, Abcam), and 1:100 anti-cPARP-AF647 (#6987S, Cell Signaling Technology) or 1:500 anti-TUBB3-AF647 (#ab190575, Abcam) overnight at 4C. Each well was washed twice with room temp PBS then stained with 0.5ug/mL DAPI (4083S, Cell Signaling Technology) in PBS for 15 minutes at room temperature. Following DAPI staining, wells were washed once with PBS, then stained with 1:20,000 HCS CellMask in PBS (Orange: #H32713, Green: #H32714, Invitrogen) for 15 minutes at room temperature. Wells were washed twice with room temperature PBS and then 4 fields of view per well imaged on an InCell 6000 (GE Healthcare). Images were segmented with two custom Cellpose models to segment the nucleus (using parameters: diameter = 45, chan = DAPI, chan2 = Cellmask Orange) and cytoplasm (using parameters: diameter = 90, chan = Cellmask Orange, chan2 = DAPI). Image quantification was performed in R (v4.3.1) using EBImage (v4.42.0), and cells were annotated based on the number of distinct nuclei segmented within each cytoplasmic mask. </div> <div> </div> <div>Included files:</div> <div>row_#_level_1.zip : 6 zip file containing original images from InCell 6000, one zip per row</div> <div>level_2.csv : Data quantified to the nuclear level (cytoplasmic quantification is duplicates across multiplet nuclei)</div> <div>level_3.csv: Data quantified at the cellular level including number of nuclei and stain intensities for segmented compartments</div> <div>platemap.csv: Description of each well from the stained plate</div> <div>cellpose_modelz.zip: Zip file containing the two CellPose models used for segmentation</div> <div>image_quantification.rmd : R markdown file containing code for extracting and quantifying image intensities using the raw images (level_1) and segmentation masks created from cellpose.</div>

opencc-by-4.0Feb 2024View details →

Quality of Life of Breast Cancer Patients in Albania

<p>Quality of Life of Breast Cancer Patients in Albania. <span>For this study, the European Organization for Research and Treatment of Cancer Quality of Life (EORTC QLQ – C30) questionnaire was used</span></p>

opencc-by-4.0Jun 2018View details →

Single-cell RNA-seq of breast cancer infiltrating T cells (case 1)

<p>Single cell suspensions were generated from two individual TNBC primary tumor samples (this entry contains case 2) and the viable cells were FACS sorted for CD3<sup>+</sup> T cells. Sorted cells were then counted and assessed for viability. Single cell library preparation was carried out as per the 10X Genomics Chromium Single cell protocol for the v2 reagent kit (10X Genomics, Pleasanton, CA, USA). Cell suspensions were loaded onto a Chromium Single Cell Chip along with the reverse transcription (RT) mastermix and single cell 3’ gel beads. Following generation of single cell gel bead-in-emulsions (GEMs), reverse transcription was performed using a C1000 Touch Thermal Cycler with a Deep Well Reaction Module (Bio-Rad Laboratories, Hercules, CA, USA). Amplified cDNA was purified using SPRIselect beads (Beckman Coulter, Lane Cove, NSW, Australia) and sheared to approximately 200bp with a Covaris S2 instrument (Covaris, Woburn, MA, USA) using the manufacturer’s recommended parameters. Sequencing libraries were generated with unique sample indices (SI) for each sample. Libraries were sequenced on an Illumina HiSeq 2500 High Output Mode using V4 clustering and sequencing chemistry.</p> <p>This dataset contains the raw .bcl files.</p>

opencc-by-4.0Jun 2018View details →

Single-cell RNA-seq of breast cancer infiltrating T cells (case 2)

<p>Single cell suspensions were generated from two individual TNBC primary tumor samples (this entry contains case 1) and the viable cells were FACS sorted for CD3<sup>+</sup> T cells. Sorted cells were then counted and assessed for viability. Single cell library preparation was carried out as per the 10X Genomics Chromium Single cell protocol for the v2 reagent kit (10X Genomics, Pleasanton, CA, USA). Cell suspensions were loaded onto a Chromium Single Cell Chip along with the reverse transcription (RT) mastermix and single cell 3’ gel beads (this sample was divided into two channels). Following generation of single cell gel bead-in-emulsions (GEMs), reverse transcription was performed using a C1000 Touch Thermal Cycler with a Deep Well Reaction Module (Bio-Rad Laboratories, Hercules, CA, USA). Amplified cDNA was purified using SPRIselect beads (Beckman Coulter, Lane Cove, NSW, Australia) and sheared to approximately 200bp with a Covaris S2 instrument (Covaris, Woburn, MA, USA) using the manufacturer’s recommended parameters. Sequencing libraries were generated with unique sample indices (SI) for each sample. Libraries were sequenced on an Illumina HiSeq 2500 High Output Mode using V4 clustering and sequencing chemistry.</p> <p>This dataset contains the raw .bcl files.</p>

opencc-by-4.0Mar 2023View details →

Clonal heterogeneity of endocrine therapy resistance in breast cancer

<p>We barcoded endocrine therapy sensitive cell lines (MCF7 and T47D) and rendered them resistant to commonly applied first line endocrine therapeutics (Tamoxifen and estrogen deprivation). Next, we isolated single cell clones of endocrine therapy resistant populations and subjected clonal cell lines to RNA-Seq and Phosphoproteomics profiling.</p>

opencc-by-4.0Jan 2020View details →

Breast Cancer

<p>Dataset of Breast Cancer characteristics.</p>

Data_Figure 2_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

<p>Data of figure 2 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration</p> <p>Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1007_s00018-019-03227-w_CMLS_Fig2). Corresponding raw data obtained from a) Migration potential as four files in CSV format (31003A-179400_ date_examiner_17BHSD12_16_1_1-4. b) mRNA content analyzed by RT-PCR provided as ten files in CSV format (31003A-179400_date_examiner_17BHSD12_1_1-2_1-6) and proliferation investigation on xCELLigence provided as six files in CSV format (31003A-179400_date_examiner_17BHSD12_9_2_1-6). All further experiment related information and subsequent data analysis provided as four meta-data-files (31003A-179400_ date_examiner_17BHSD12_16/1/9_dataset_M_1) as TXT format.</p>

Data_Figure 6_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

<p>Data of figure 6 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration</p> <p>Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 6). Corresponding raw data obtained from a1/2) cellomics HTC array scan analysis provided as six files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_6_1-6), b1/2) oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) provided as 10 files in CSV format (31003A-179400_20190521_MT_17BHSD12_10_3-4_1-5); c 1/2 ), cellomics HTC array scan analysis provided as 12 files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_7-8_1-8). d) Western blot and densitometry provided as eight files in CSV format (31003A-179400_Date_examiner_17BHSD12_2_3-4_1-5). All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_8/10/2_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.</p>

Data_Figure 7_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

<p>Data of figure 7 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration</p> <p>Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 7). Corresponding raw data obtained from a1/2) Western blot and densitometry provided as eight files in CSV format (31003A-179400_date_examiner_17BHSD12_2_5-6_1-6); b) mRNA content analyzed by RT-PCR provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_1_6_1-4); c 1/2) cellomics HTC array scan analysis provided as 11 files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_9-10_1-6); d1/2); Western blot and densitometry provided as eight files in CSV format (31003A-179400_date_examiner_17BHSD12_2_7-8_1-6). All further experiment related information protocols and subsequent data analysis provided as meta-data-files (31003A-179400_date_examiner_17BHSD12_2/1/8_dataset_M_1) as TXT format and (31003A-179400_date_examiner_17BHSD12_2_dataset_M_2-3) as PNG format.</p>

Data_Figure 5_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

<p>Data of figure 5 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration</p> <p>Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 5). Corresponding raw data obtained from oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) provided as 10 files in CSV format (31003A-179400_20190521_MT_17BHSD12_10_1-2_1-5). All further experiment related information and subsequent data analysis provided as two meta-data-file: (31003A-179400_20190521_MT_17BHSD12_10_1-2_1) as TXT format.</p>

Data_Figure 4_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

<p>Data of figure 4 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration</p> <p>Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF-format (10.1194_jlr.M092908_Fig. 4). Corresponding raw data obtained from: a1/2) Migration potential as five files in CSV format (31003A-179400_date_examiner_17BHSD12_16_3_1-5); b 1/2) Migration potential as four files in CSV format (31003A-179400_date_examiner_17BHSD12_16_4_1-4); c1/2/3) mRNA content analyzed by RT-PCR provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_1_4_1-4); cellomics HTC array scan analysis provided as three files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_3-4_1-4); d) Migration potential as five files in CSV format (31003A-179400_ date_examiner_17BHSD12_16_5_1-5); e) mRNA content analyzed by RT-PCR provided as six files in CSV format (31003A-179400_date_examiner_17BHSD12_1_5_1-6); f) Migration potential as four files in CSV format (31003A-179400_date_examiner_17BHSD12_16_6_1-4), cellomics HTC array scan analysis provided as three files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_5_1-5); g) ELISA measurement provided as four files in CSV format (31003A-179400_date_examiner_17BHSD12_20_1_1-4). All further experiment related information protocols and subsequent data analysis provided as 10 meta-data-files (31003A-179400_date_examiner_17BHSD12_8/16/1/20_dataset_M_1) as TXT format.</p>

Data_supplemental figure 2_Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration

<p>Data of supplemental figure 2 from Impact of 17β‑HSD12, the 3‑ketoacyl‑CoA reductase of long‑chain fatty acid synthesis, on breast cancer cell proliferation and migration</p> <p>Dataset (doi: 10.1007/s00018-019-03227-w) contains the original figure as TIF format (10.1194_jlr.M092908_Fig. S2). Corresponding raw data obtained from cellomics HTC array scan analysis provided as seven files in CSV format (31003A-179400_Date_examiner_17BHSD12_8_11-12_1-4) All further experiment related information protocols as meta-data-files (31003A-179400_date_examiner_17BHSD12_8_11-12_M_1) as TXT format.</p>