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18,200 results for “breast”
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>
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>
Cape Sable Seaside Sparrow (Ammospiza maritima mirabilis) breast feather total mercury concentrations from the Florida Everglades, Florida, USA: breeding seasons 2016 - 2018
This dataset was used to determine hydrologic parameters influencing Cape Sable Seaside Sparrow (CSSS) mercury exposure and potential mercury effects on their reproductive success in the Florida Everglades. We collected breast feathers for total mercury determination from juvenile and adult CSSS during (or shortly after) three breeding seasons (March 1 to July 31) and monitored the same individuals' breeding performance (mate status, number of nest attempts, number of successful nest attempts, total productivity of nests, clutch size, total count of eggs, and hatch success). Hydrologic parameters (average water depths, drought length, water recession rate, and hydroperiod) were estimated using the Everglades Depth Estimation Network and in situ depth measurements. Data collection is complete.
Measurements of height, diameter at breast height and basal diameter for Prestoea acuminata at the Luquillo Forest Dynamics Plot (LFDP), Puerto Rico in January 2020.
Data were collected in January 2020 in Puerto Rico, within and outside the boundaries of LFDP. For each of these palms, we measured stem height from the ground to the base of the crown (Hbc; height of the youngest internode), diameter at 130 cm above ground (D130), and basal diameter (DB; just above the top of the roots). These are the data which accompany the publication: Height-diameter allometry for a dominant palm to improve understanding of carbon and forest dynamics in forests of Puerto Rico.
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>
Full summary statistics of mixQTL for GTEx v8 Breast_Mammary_Tissue
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
FTIR Breast Cancer
<p>Two breast cancer subtypes FTIR spectroscopy imaging</p> <p>A = LA breast cancer</p> <p>B = HER2 breast cancer</p>
TCGA Breast Cancer 450K Methylation Data
<p>The archived folder holds level 1 450K DNA methylation IDAT files for the TCGA Breast Cancer dataset. The data is used in the analysis pipeline associated with the GitHub repository: https://github.com/gwaygenomics/brca_lowstage_DMGRs</p>
Raw BRCA1/2 variants in breast cancer patients and healthy relatives produced with GATK.
<p>Aligned sequencing data is available in the NCBI Sequence Read Archive (SRA, https://www.ncbi.nlm.nih.gov/sra/) under accession SRP095082. Variants were called using GATK HaplotypeCaller (version 3.6). After joint performing joint genotyping multi-sample vcf file was generated. Next, SNPs and indels were extracted into two different vcf files and specific set of filters were applied for each case.</p> <p> </p> <p><strong>File descriptions</strong></p> <p><strong><em>Datasets</em></strong></p> <p><strong>BRCA_SNVs.vcf</strong> - this file contains SNPs called with GATK and hard filters applied. Following filtering options were applied: "QD < 2.0", "FS > 60.0", "MQ < 40.0", "MQRankSum < -12.5", "ReadPosRankSum < -8.0", "SB < -0.10" , "DP < 10" , "GQ < 30" , and "SOR > 3.0"</p> <p><strong>BRCA_indels.vcf</strong> - This file contains indels called with GATK and hard filters applied. Following filtering options were applied: "QD < 2.0", "FS > 200.0", "ReadPosRankSum < -20.0", "InbreedingCoeff < -0.8", "SOR > 10.0".</p> <p> </p> <p><strong><em>Scripts package (scritps.zip)</em></strong></p> <p>Scripts.zip file contains scripts and supporting files for genotype calling and filtering. </p> <p><strong>raw.variant.caling.sh </strong>– bam files preprocessing, alignment refining and raw genotype calling with HaplotypeCaller.</p> <p><strong>genotyping_and_filtering.sh </strong>– joint genotyping, variant hard filtering and callset refinement.</p> <p><strong>LIST.txt</strong> – supporting file that contains bam filenames containing aligned reads.</p> <p><strong>sample_order.txt</strong> – supporting file for sample renaming.</p> <p> </p> <p><strong><em>Reference files (hg19) used in variant calling scripts</em></strong></p> <p>Reference files can be downloaded from GATK bundle web-site at https://software.broadinstitute.org/gatk/download/bundle. </p> <p><strong>ucsc.hg19.fasta</strong> - human genome assembly;</p> <p><strong>Mills_and_1000G_gold_standard.indels.hg19.sites.vcf.gz</strong> – set of known indels to be used for local realignment;</p> <p><strong>1000G_phase1.indels.hg19.sites.vcf.gz</strong> – set of known indels to be used for local realignment;</p> <p><strong>dbsnp_138.hg19.vcf.gz</strong> – a recent dbSNP release (build 138); </p> <p><strong>1000G_phase3_v4_20130502.hg19.lifted.sites.vcf</strong> – the latest set from 1000G phase 3 (v4) for genotype refinement.</p> <p> </p>
Quantum Cascade Laser Spectral Histopathology: Breast Cancer Diagnostics Using High Throughput Chemical Imaging
<p>Fourier transform infrared (FT-IR) microscopy, coupled with machine learning approaches, has been demonstrated to be a powerful technique for identifying abnormalities in human tissue. The ability to objectively identify the prediseased state, and diagnose cancer with high levels of accuracy, has the potential to revolutionise current histopathological practice. Despite recent technological advances in FT-IR microscopy, sample throughput and speed of acquisition are key barriers to clinical translation. Wide-field quantum cascade laser (QCL) infrared imaging systems with large focal plane array detectors utilising discrete frequency imaging, have demonstrated that large tissue microarrays (TMA) can be imaged in a matter of minutes. However this ground breaking technology is still in its infancy and its applicability for routine disease diagnosis is, as yet, unproven. In light of this we report on a large study utilising a breast cancer TMA comprised of 207 different patients. We show that by using QCL imaging with continuous spectra acquired between 912 and 1800 cm<sup>-1</sup>, we can accurately differentiate between 4 different histological classes. We demonstrate that we can discriminate between malignant and non-malignant stroma spectra with high sensitivity (93.56%) and specificity (85.64%) for an independent test set. Finally, we classify each core in the TMA and achieve high diagnostic accuracy on a patient basis with 100% sensitivity and 86.67% specificity. The absence of false negatives reported here opens up the possibility of utilising high throughput chemical imaging for cancer screening, thereby reducing pathologist workload and improving patient care.</p>
Single-cell datasets for cell cycle plasticity underlies fractional resistance to palbociclib in ER+/HER2- breast tumor cells
<p>There are 7 files uploaded in the data.</p><p>tumor_preprocessed.h5ad: Full primary tumor dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>T47D_preprocssed.h5ad: Full dataset of main text T47D dataset post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). AnnData object format. 14 cell cycle features, phase labels and other cell metadata, and two PHATE dimensions for manifold visualization.</p><p>sketched_integrated.h5ad: After downsample 6,000 (2,000 per condition) from T47D_preprocessed and tumor_preprocessed, we integrate the two datasets into one joint latent space using TRANSACT. Now included in the data are the consensus component columns ('0',..,'13'). AnnData object.</p><p>sketched_integrated_df.csv: sketched_integrated.h5ad in .csv format.</p><p>T47D_replicate_preprocessed: Replicate experimental dataset of T47D for supplementary analysis post-feature selection and standardization across three treatment conditions (0, 10, and 100 nM palbociclib). 15 cell cycle features (same 14 but with CDK6).</p><p>sketched_rep.h5ad: Representative downsample of the T47D_replicate_preprocessed. Selecting 6,000 cells (2,000 for each of the three treatment conditions) using kernel herding sketching. AnnData object.</p><p>sketched_rep_df.csv: Same data as sketched_rep.h5ad in csv format.</p><p>T47D_triplicate_preprocessed.h5ad: T47D biological replicate sample collected in triplicate form (three wells for 0, 10, and 100 nM of palbociclib). Wells were joined and the data were sketched down to 20,000 per condition.</p><p>T47D_triplicate_preprocessed.h5ad: T47D triplicate in .csv form.</p><p>tumor_2_preprocessed.h5ad: An additional tumor sample from a new patient with the same treatment conditions of palbociclib. Sketched down to 2,000 cells per condition.</p><p>tumor_2_preprocessed.csv: The additional tumor sample in .csv form.</p><p> </p><p>Further description of sketched_integrated: This is the joint dataset between the T47D and primary tumor, after subsampling using kernel herding sketching. This is a dataset consisting of T47D and primary tumor cells resected from a consented patient. The samples were imaged using iterative indirect immunofluorescent imaging (4i) to get proteomic measurements on a single-cell level. The T47D and tumor samples were gathered, cultured, and imaged separately. Each sample was treated with three conditions of CDK4/6 inhibitor palbociclib (control, 10 nM, and 100 nM). Then, we used kernel sketching to representatively downsample each dataset, selecting 2,000 from each of the three treatment conditions (6,000 cells from each of the two sources). We used an integration method called TRANSACT to integrate the two datasets into one shared, latent space. The dataset here is consisting of these 12,000 cells. The columns ('0','1',...'13') are the principal vectors of the joint latent space. After that, there are the columns of the standardized proteomic measurements of different cell cycle effectors, and biological annotations of interest. The standardization is done for each data source separately. Well refers to the treatment condition. 'prb_ratio' is a marker of if a cell is still proliferating or arrested, found by selecting the upper modality of pRB/RB values. 'phase' are cell cycle phase labels found by unsupervised clustering done on a handful of known cell cycle markers.</p>
Supplementary File 7 from: Rapier-Sharman N et. al., Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers, 2024
<p>Supplementary Materials File 7. Please note that though the order of the supplementary materials has changed since initial upload (File S7 was previously File S9 or S10), the contents of this zipped folder remain the same.</p>
Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics (codes and data files)
<p>This dataset includes all the relevant codes and data files associated with the paper ("Molecular features of luminal breast cancer defined through spatial and single-cell transcriptomics") in Clinical and Translational Medicine journal.</p>
DrugToleranceHallmarks_data_breast_seurat
<p>A repository of data for analysis/processing generated for the study of Transcriptional Hallmarks of Drug Tolerance in Hormone-Dependent Cancers.</p> <p>The respective github repository with a description of the data and all the performed analyses is available under https://github.com/barozziLab/DrugToleranceHallmarks.</p> <p>For reproducing the analyses, download the code from the github repository and the two zenodo repositories (10.5281/zenodo.14179561 and 10.5281/zenodo.14179578) into the same directory. The folder structure is similar between them and combining them will allow to load all the intermediate files and annotations necessary to run the scripts.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.