Find research datasets worth reusing

Data from: External validation of prognostic and predictive gene signatures in 1097 European head and neck squamous cell carcinoma patients

Anonymized data containing survival endpoints and gene signature scores for head and neck cancer patients. File data_os_gs.csv : data linking overall survival and gene signature scores File data_dfs_gs.csv : data linking disease-free survival and gene signature scores Variables: <ul> <li>supertreat_id: patient ID</li> <li>GS_score_172GS: gene signature score for the 172-GS signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>GS_score_3clustersHPV: gene signature score for the 3 clusters HPV signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>GS_score_RSI: gene signature score for the radiosenstivity index (RSI) signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>GS_score_pancancerCisplatin: gene signature score for the pancancer-cisplatin signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>GS_score_cl3Hypoxia: gene signature score for the Cl3-hypoxia signature. The score is Z-score normalized with a mean of 0 and SD of 1. </li> <li>Variables only available in data_os_gs.csv:  <ul> <li>overall_survival_days_2years: Overall survival censored at 2 years since diagnosis. Number of days from diagnosis to death or censoring.</li> <li>overall_survival_days_5years: Overall survival censored at 5 years since diagnosis. Number of days from diagnosis to death or censoring.</li> <li>overall_survival_status_2years: Overall survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if dead. </li> <li>overall_survival_status_5years:  Overall survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if dead. </li> </ul> </li> </ul> <ul> <li>Variables only available in data_dfs_gs.csv: <ul> <li>disease_free_survival_days_2years: Disease-free survival censored at 2 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</li> <li>disease_free_survival_days_5years: Disease-free survival censored at 5 years since diagnosis. Number of days from diagnosis to an event (death or cancer recurrence) or censoring.</li> <li>disease_free_survival_status_2years: Disease-free survival status when censored at 2 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </li> <li>disease_free_survival_status_5years:  Disease-free survival status when censored at 5 years since diagnosis. Coded as 0 if censored, and 1 if an event (death or recurrence). </li> </ul> </li> </ul>

TMEM206 contributes to cancer hallmark functions in colorectal cancer cells and is regulated by p53 in a p21-dependent manner

Acid-induced ion flux plays a role in pathologies where tissue acidification is prevalent, including cancer. In 2019, TMEM206 was identified as the molecular component of acid-induced chloride flux. Localizing to the plasma membrane, TMEM206 contributes to cellular processes like acid-induced cell death. Since over 50% of human cancers carry loss of function mutations in the p53 gene, we aimed to analyze how TMEM206 is regulated by p53 and its role in cancer hallmark function and acid-induced cell death in HCT116 colorectal cancer (CRC) cells. We generated p53-deficient HCT116 cells and assessed TMEM206-mediated Cl- currents and transcriptional regulation using the patch-clamp and a dual-luciferase reporter assay, respectively. To investigate the contribution of TMEM206 to cancer hallmark functions we performed migration and metabolic activity assays. The role of TMEM206 in p53-mediated acid-induced cell death has been assessed with cell death assays. TMEM206 mRNA level is significantly elevated in human primary CRC tumors. TMEM206 knockout increased acid-induced cell death and reduced proliferation and migration, indicating a role for TMEM206 in these cancer hallmark functions. Furthermore, we observed increased TMEM206 mRNA levels and currents in HCT116 p53 knockout cells. This phenotype can be rescued by transient overexpression of p53, but not by overexpression of dysfunctional p53. In addition, our data suggests that TMEM206 may mediate cancer hallmark functions within p53-associated pathways. TMEM206 promoter activity is not altered by p53 overexpression. Conversely, knockout of p21, a major target gene of p53, increased TMEM206-mediated currents suggesting expression control of TMEM206 by p21 downstream signaling. Our results show that in colorectal cancer cells, TMEM206 expression is elevated, contributes to cancer hallmark functions and its regulation is dependent on p53 through a p21-dependent mechanism.

VeloCycle-estimated cell cycle phases of single cells from a genome-scale perturb-seq performed in K562

Continuous cell cycle phase position between 0 and 2π estimated by VeloCycle (Lederer et al., Nature Methods 2024) for single cells of the perturb-seq performed in the K562 CML cell line by Replogle et al., Cell 2022.  Underlies the cell cycle imbalances inferred in Pulver & Forey et al., 2024. Method manuscript exerpt: "For analysis on the genome-wide perturb-seq dataset of K562 cells (Replogle et al., 2022), a transfer learning approach was applied. Condition-independent estimation of the periodic Fourier series components would be especially challenging on Perturb-seq knockdown conditions containing either (1) very few cells or (2) cells belonging to just one phase of the cell cycle. To infer accurate cell cycle phases for these cells, we first performed manifold-learning for 5,000 training steps to estimate the gene harmonic coefficients (ν0, ν1sin, ν1cos) on a larger set of non-targeting control (NT) K562 cells (75,328 cells), which are more evenly distributed throughout the various phases of the cell cycle. Next, we ran manifold-learning again for 5,000 training steps, but on the entire perturb-seq dataset of 1,971,608 cells and 4,127 gene knockdown conditions (with at least 75 cells per condition). This time, we conditioned VeloCycle on the gene harmonic coefficients learned in the first step. This allowed cells belonging to each stratified gene knockdown condition to be assigned to a position on the cell cycle manifold, while restricting those assignments such that they were based on gene expression patterns earned on a larger and more informative dataset (allowing for batch effect expression differences)."

opengpl-3.0-or-laterOct 2024View details →

Characterizing cell-type spatial relationships across length scales in spatially resolved omics data: data repository

<h1>CRAWDAD</h1> Spatially resolved omics (SRO) technologies enable the identification of cell types while preserving their organization within tissues. Application of such technologies offers the opportunity to delineate cell-type spatial relationships, particularly across different length scales, and enhance our understanding of tissue organization and function. To quantify such multi-scale cell-type spatial relationships, we develop CRAWDAD, Cell-type Relationship Analysis Workflow Done Across Distances, as an open-source R package with source code and additional documentation at https://jef.works/CRAWDAD/. During CRAWDAD's development, we generated simulated datasets and new cell-type annotations for human spleen data, provided here. The external datasets such as the mouse cerebellum, mouse embryo, mouse brain, and human breast cancer data used in the paper can be found in their original publication. See more information in CRAWDAD's data availability statement. <h2>Simulated Datasets</h2> <ul> <li>sim.csv: the simulated data. Used in Figure 1 b-g, Supplementary Figure 1 a-c, and Supplementary Figure 9 a-b.</li> <li>ext_sim.csv: the extended simulated data. Used in Supplementary Figure 1 d-f.</li> <li>null_sim_visualization.csv: the null simulated data. Used to generate the plots Supplementary Figure 2 a-d.</li> <li>null_sim_1.csv - null_sim_10.csv: the 10 null simulated datasets. Used to quantitatively compare CRAWDAD, Squidpy’s co-occurrence implementation, and Ripley’s K Cross.</li> </ul> <h2>HuBMAP Datasets</h2> <ul> <li>pkhl.csv: annotated cell types and positions of sample HBM389.PKHL.936 from donor HBM966.VNKN.965. Used in Figure 5 a-h, Supplementary Figure 5 a, Supplementary Figure 7 a-c, and Supplementary Figure 8 c. doi:10.35079/HBM389.PKHL.936</li> <li>xxcd.csv: annotated cell types and positions of sample HBM772.XXCD.697 from donor HBM966.VNKN.965. Used in Figure 5 d-h, Supplementary Figure 5 a-c, and Supplementary Figure 7 a-c. doi:10.35079/HBM772.XXCD.697</li> <li>fsld.csv: annotated cell types and positions of sample HBM342.FSLD.938 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM342.FSLD.938</li> <li>pbvn.csv: annotated cell types and positions of sample HBM825.PBVN.284 from donor HBM245.ZWNT.288. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM825.PBVN.284</li> <li>ksfb.csv: annotated cell types and positions of sample HBM556.KSFB.592 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM556.KSFB.592</li> <li>ngpl.csv: annotated cell types and positions of sample HBM568.NGPL.345 from donor HBM298.KGNJ.374. Used in Figure 5 e-f, h, Supplementary Figure 5 a-c, Supplementary Figure 6 a-b, and Supplementary Figure 7 a-c. doi:10.35079/HBM568.NGPL.345</li> </ul> <h2>External Datasets</h2> <ul> <li>Mouse cerebellum: Used in Figure 2 a-e, Supplementary Figure 3 a-b, Supplementary Figure 4 a-d, and Supplementary Figure 8 a.</li> <li>Mouse embryo: Used in Figure 2 f-j, Supplementary Figure 3 c-d, Supplementary Figure 4 e-h, and Supplementary Figure 8 b.</li> <li>Human breast cancer: Used in Figure 3 a-c.</li> <li>Mouse brains: Used in Figure 4 a-e.</li> </ul>

Data from: Polymorphic tandem repeats shape single-cell gene expression across the immune landscape

This dataset contains the association summary statistics (v0.1) for genome-wide tandem repeat (TR) expression quantitative trait (eQTL) analysis of TenK10K Phase 1 (https://doi.org/10.1101/2024.11.02.621562).  Please access the README for a detailed description of file contents.

Process-structure-property map for organic solar cells

This archive contains: - 1708 morphologies generated using Cahn-Hilliard equation for two parameters: blend ratio in range (0.5-0.63) and chi/interaction parameter (2.3-4.0). Blend ratio and chi are the processing conditions. Morphologies are given in two formats: plt files (srcdata folder) - raw data from simulations, and txt file (data folder) in the row-wise format for Graspi. For quick visualization morphologies are visualized and stored in folder figs. The dataset contains: Five folders: - srcdata: the source data with all plt files (1708 files) generated by Cahn Hilliard equation solver - data: the data used by graspi to compute descriptors (these are txt files stored as row-wise array, the volume fraction has been segmented using tools of graspi) - logs: 1708 log files with the descriptors generated by graspi (native C++ version) - figs: 1708 png files with visualized morphologies Two tables (in comma separated values format): - Combined PSP.csv (combined data on process-structure-property maps), where process information consists of three variables: PHI, CHI, NN (volume fraction, interaction parameter and time step index) - AllPropertiesCurated.csv - File with results from EDD - see reference below for more details One shell script: extractDesc.sh to build CombinedPSP.csv table from three sources: filename (contains info about PHI, CHI, NN), descriptors (graspi and logs), and Jsc (from AllPropertiesCurated.csv) More details on the dataset: Wodo, O., J. Zola, . Pokuri, P. Du, and B. Ganapathysubramanian. "Automated, high throughput exploration of process–structure–property relationships using the mapreduce paradigm." Materials discovery 1 (2015): 21-28.

Datasets for "Quantifying nanodiamonds biodistribution in whole cells with correlative iono-nanoscopy"

Readme: These are datasets that were generated during experiments, simulations, and data processing. [1] Data saved in .xlsx files are data points for plots and can be opened with Microsoft Excel. [2] Data saved in .txt files are image files recorded in 1024 x 1024 pixel arrays. A custom software IonDAQ-lite (ref: Bettiol, A. A. et al. Nucl. Instrum. Methods Phys. Res. B 267, 2069-2072, 2009) was used to open and process these image files, however some commonly used image processing programs (e.g. Matlab) can also be used to open these files. [3] The .lsm and .czi files are 2D or 3D confocal image files and can be viewed with ZEN 3.4 (ZEN lite, Carl Zeiss).

opencc-by-4.0May 2021View details →

GWAS to single cell: Intersecting single-cell transcriptomics and genome wide association studies identifies crucial cell-populations and candidate genes for atherosclerosis.

Background Genome-wide association studies (GWAS) have discovered hundreds of common genetic variants for atherosclerotic disease and cardiovascular risk factors. The translation of susceptibility loci into biological mechanisms and targets for drug discovery remains challenging. Intersecting genetic and gene expression data has led to identification of candidate genes. However, the assayed tissues are often non-diseased and heterogeneous in cell composition confounding the candidate prioritization. We collected single-cell transcriptomics (scRNA-seq) from atherosclerotic plaques and aimed to identify cell-type-specific expression of disease-associated genes.    Methods and Results To identify disease-associated candidate genes, we applied gene-based analyses using GWAS summary statistics from 46 atherosclerotic, cardiometabolic, and other traits. Next we intersected these candidates with single-cell transcriptomics (scRNA-seq) to identify those genes that are specifically expressed in individual cell (sub)populations of atherosclerotic plaques. We derive an enrichment score and show that loci that associated with coronary artery disease demonstrated a prominent substrate in plaque smooth muscle cells (SKI, KANK2, SORT1), endothelial cells (SLC44A1, ATP2B1), and macrophages (APOE, HNRNPUL1). Further sub clustering of SMC-subtypes revealed genes in risk loci for coronary calcification specifically enriched in a synthetic cluster of SMCs. To verify the robustness of our approach, we used liver-derived scRNAseq-data and showed enrichment of circulating lipids-associated loci in hepatocytes. Conclusion We confirm known gene-cell pairs relevant for atherosclerotic disease, and discovered novel pairs pointing to new biological mechanisms amenable for therapy. We present an intuitive single-cell transcriptomics driven workflow rooted in human large-scale genetic studies to identify putative candidate genes and affected cells associated with cardiovascular traits.

Data_Fig1_Monitoring Changes in the Oxidizing Milieu in the Endoplasmic Reticulum of Mammalian Cells Using HyPerER

Data of JB_10.21769_BioProtoc.4076_Figure 1, The Dataset (Original publication Doi:10.21769/BioProtoc.4076) contains the original figure as PNG-format (JB_10.21769_BioProtoc.4076_Fig1.PNG). Corresponding raw data from confocal microscopy measurements provided as two files (31003A_179400_10.21769_BioProtoc.4076_JB_Hyper_14_1/2.avi). Related information provided as one meta-data-file (31003A_179400_10.21769_BioProtoc.4076_JB_Hyper_14_M1.txt) in txt format and two files in pdf format (31003A_179400_10.21769_BioProtoc.4076_JB_Hyper_14_M2/3. pdf).

Dataset for chirality-enhanced optical solitons in planar unwound cholesteric cells

This dataset includes all the XML VTK files (*.vti) of simulated optical solitons in unwound planar cholesteric cells as described in [G. Poy & S. Zumer, "Chirality-enhanced nonlinear optical response of frustrated liquid crystals", Proceedings of conference SPIE Optics+Photonics, Liquid Crystals XXV, 2021]. It also includes the postprocessing scripts that were used to generate the figures of the aforementioned paper, which correspond to the 00_*, 01_* and 02_* files. *.nb files were run without any problem with Mathematica 12.2.0.0. The *.py file can be run with a recent version of python3 with numpy and matplotlib installed. As for the *.vti raw data files, they can be visualized with the open-source software Paraview.

Symphony pre-built single-cell reference atlases

Pre-built Symphony reference objects that can be downloaded and used to map new query datasets. The Symphony algorithm is used to perform reference mapping to these atlases.  <ul> <li>Preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2">https://www.biorxiv.org/content/10.1101/2020.11.18.389189v2</a></li> <li>Usage: <a href="https://github.com/immunogenomics/symphony">https://github.com/immunogenomics/symphony</a></li> </ul> References available for download: <ol> <li>10x PBMCs Atlas (pbmcs_10x_reference.rds)</li> <li>Pancreatic Islet Cells Atlas (pancreas_plate-based_reference.rds)</li> <li>Fetal Liver Hematopoiesis Atlas (fetal_liver_reference_3p.rds)</li> <li>Healthy Fetal Kidney Atlas (kidney_healthy_fetal_reference.rds)</li> <li>T cell CITE-seq atlas (tbru_ref.rds)</li> <li>Cross-tissue Fibroblast Atlas (see <a href="https://sandbox.zenodo.org/record/772596#.YOqMiBNKhTY">here</a>)</li> <li>Cross-tissue Inflammatory Immune Atlas (<a href="https://sandbox.zenodo.org/record/888445#.YV9In2ZKijB">here</a>)</li> <li>Tabula Muris Senis (FACS) Atlas (TMS_facs_reference.rds)</li> </ol> To read in a reference into R, one may simply execute: reference = readRDS('path/to/reference_name.rds') Note: To be able to map query datasets into the reference UMAP coordinates, you must also download the corresponding 'uwot_model' file and set the reference$save_uwot_path.

opencc-by-3.0-usJul 2021View details →

ZIRFs: zero-inflated random forests for estimating gene regulatory networks from single cell RNA-seq data (assessment of predictive accuracy and VIM stability)

We developed a zero-inflated random forests (ZIRFs) algorithm to produce a metric of connection strength between regulator genes and target genes. This file contains SCENIC results for the aorta and diaphragm tissue data sets from the Tabula Muris Consortium results. SCENIC is a genetic regulatory network analysis published by Aibar et al. (2017). The purpose of the data sets and R source code are described by README files in each directory.

opencc-by-4.0Jun 2021View details →

Continuous process technology for glucoside production from sucrose using a whole cell-derived solid catalyst of sucrose phosphorylase

We provide here the underlying data of the publication "Continuous process technology for glucoside production from sucrose using a whole cell-derived solid catalyst of sucrose phosphorylase". Please find the abstract below. Advanced biotransformation processes typically involve the upstream processing part performed continuously and interlinked tightly with the product isolation. Key in their development is a catalyst that is highly active, operationally robust, conveniently produced and recyclable. A promising strategy to obtain such catalyst is to encapsulate enzymes as permeabilized whole cells in porous polymer materials. Here, we show immobilization of the sucrose phosphorylase from Bifidobacterium adolescentis (P134Q-variant) by encapsulating the corresponding E. coli cells into polyacrylamide. Applying the solid catalyst, we demonstrate continuous production of the commercial extremolyte 2-α-D-glucosyl-glycerol (2-GG) from sucrose and glycerol. The solid catalyst exhibited similar activity (≥70%) as the cell free extract (~800 U g-1 cell wet weight) and showed excellent in-operando stability (40 °C) over 6 weeks in a packed-bed reactor. Systematic study of immobilization parameters related to catalyst activity led to the identification of cell loading and catalyst particle size as important factors of process optimization. Using glycerol in excess (1.8 M), we analyzed sucrose conversion dependent on space velocity (0.075 – 0.750 h-1) and revealed conditions for full conversion of up to 900 mM sucrose. The maximum 2-GG space-time yield reached was 45 g L-1 h-1 for a product concentration of 120 g L-1. Collectively, our study establishes a step-economic route towards a practical whole cell-derived solid catalyst of sucrose phosphorylase, enabling continuous production of glucosides from sucrose. This strengthens the current biomanufacturing of 2-GG, but also has significant replication potential for other sucrose-derived glucosides, promoting their industrial scale production using sucrose phosphorylase.

opencc-by-4.0Aug 2021View details →

Data Set for 'Self-Supervised Machine Learning for Live Cell Imagery Segmentation'

Self-supervised machine learning code and data for segmenting live cell imagery (Matlab) Running the Code SSL_Demo_2.m : main program for self-supervised machine learning segmentation SSL_Declumping_2.m : main program for declumping application (applied to output of SSL_Demo_2.m) This Matlab code is designed to be used with time-resolved live cell microscopy images (tiffs) for the automated segmentation of cells from background. It is recommended you first run this code with its accompanying demo data (included in this package), keeping the current directory structure. Simply open SSL_Demo_2.m or SSL_Declumping_2.m in Matlab and hit Run. Code Methodology The principle of self-supervised machine learning is that you simply load your images and Run - no parameter tuning needed, no training imagery required. Run from start to finish, the SSL_Demo_2.m code uses consecutive pairs of images to generate training data of 'cells' and 'background' via dynamic feature vectors based on optical flow (unsupervised). These self-labeled pixels are then used to generate static feature vectors (entropy, gradient), which in turn are used to train a classifier model. The training data is updated every image in order to automatically adapt to temporal changes in cell morphologies or background illumination. The code was tested for high fidelity segmentation using five different modes of light microscopy: transmitted light, DIC, phase contrast, fluorescence and interference reflection microscopy. Six different cell lines were imaged to cover a range of morphologies and phenotypic dynamics using three cameras of differing resolutions. The associated manuscript for this work can be found here (although the latest version is under peer review as of this writing):  <a href="https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1">https://www.biorxiv.org/content/10.1101/2021.01.07.425773v1</a> This code was tested on Matlab v2020a and v2021a using commercially available laptop computers running the Windows 10 operating system.

opencc-by-4.0Aug 2021View details →

Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death

This dataset contains the raw data that were used for the publication entitled, "Membrane-Interacting DNA Nanotubes Induce Cancer Cell Death" published in Nanomaterials on 4 August 2021. Abstract: DNA nanotechnology offers to build nanoscale structures with defined chemistries to precisely position biomolecules or drugs for selective cell targeting and drug delivery. Owing to the negatively charged nature of DNA, for delivery purposes DNA is frequently conjugated with hydrophobic moieties, positively charged polymers/peptides, cell surface receptor recognizing molecules or antibodies. Here, we designed and assembled cholesterol-modified DNA nanotubes to interact with cancer cells and conjugated them with cytochrome c to induce cancer cell apoptosis. By flow cytometry and confocal microscopy, we observed that DNA nanotubes efficiently bound to the plasma membrane as a function of the number of conjugated cholesterol moieties. The complex was taken up by the cells and localized to the endosomal compartment. Cholesterol-modified DNA nanotubes, but not unmodified ones, induced increased membrane permeability, caspase activation and cell death. Irreversible inhibition of caspase activity, with Z-VAD-FMK, however, only partially prevented cell death. Cytochrome c conjugated DNA nanotubes were also efficiently taken up but did not increased the rate of cell death. These results demonstrate that cholesterol-modified DNA nanotubes induce cancer cell death associated with increased cell membrane permeability and only partially dependent on caspase activity, consistent with a combined form of apoptotic and necrotic cell death. DNA nanotubes may be further developed as primary cytotoxic agents, or drug delivery vehicles, through cholesterol mediated cellular membrane interactions and uptake.

opencc-by-4.0Dec 2020View details →

Sparsification of AP firing in adult-born hippocampal granule cells via voltage-dependent alpha5-GABAA receptors

The ZIP file consists of folders, containing RAW data used for analysis and used to prepare the Figures 1-6 of the paper published in Cell Reports 2021, with the title mentioned above. All recordings were made from acute hippocampal brain slices, obtained from adult C57BL6 mice. Whole-cell voltage-clamp and current-clamp recordings of mature and adult-born young hippocampal granule cells were performed as outlined in methods and recordings were digitized using a power1401 interface from CED (Cambridge Electronic Design, UK), and saved to file using the CFS library support from CED. The contents of the different files is described in File_Description.pdf.

opencc-by-4.0Aug 2021View details →

Dataset and Code for Manuscript "Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification"

Dataset of measurements for cell cycle analysis with description: <ul> <li>ReadMe file with explanations on the data set and analysis</li> <li>exemplary Matlab script file for analysis</li> <li>binary data files conatining the pulse shapes in all channels</li> <li>FCS data files containing common flow cytometry parameters in each channel</li> </ul> Data on unsorted HEK cells, HEK cells sorted for cell cycle phases, and unsorted Jurkat cell are included.

opencc-by-4.0Sep 2021View details →

Mammary single-cell RNA-seq analysis and prostate cancer survival as a function of H2AFJ expression for the paper entitled: The histone variant H2A.J is enriched in luminal epithelial cells

H2A.J is a poorly studied mammalian-specific variant of histone H2A. We used immunohistochemistry to study its localization in various human and mouse tissues. H2A.J showed cell-type specific expression with a striking enrichment in luminal epithelial cells of multiple glands including those of breast, prostate, pancreas, thyroid, stomach, and salivary glands. H2A.J was also highly expressed in many carcinoma cell lines and in particular, those derived from luminal breast and prostate cancer. H2A.J thus appears to be a novel marker for luminal epithelial cancers. Knocking-out the H2AFJ gene in T47D luminal breast cancer cells reduced the expression of several estrogen-responsive genes which may explain its putative tumorigenic role in luminal-B breast cancer.

opencc-by-4.0Oct 2021View details →

Vectra Polatis image of human colorectal cancer (CRC1) from: A SIMPLI (Single-cell Identification from MultiPLexed Images) approach for spatially resolved tissue phenotyping at single-cell resolution.

Two 4 µm thick serial sections were cut from CRC1 FFPE block using a microtome. The first slide was dewaxed and rehydrated before carrying out HIER with Antigen Retrieval Reagent-Basic (R&D Systems). The tissue was then blocked and incubated with the anti-CD3 antibody (Dako, Supplementary Table 2) followed by horseradish peroxidase (HRP) conjugated anti-rabbit antibody (Dako) and stained with 3,3' diaminobenzidine (DAB) substrate (Abcam) and haematoxylin. Areas with CD3+ infiltration in the proximity of the tumour invasive margin were identified by a clinical pathologist (M. R-J.) The second slide was stained with a panel of six antibodies (CD8, PD1, Ki67, PDL1, CD68, GzB, Supplementary Table 2), Opal fluorophores and 4’,6-diamidino-2-phenylindole (DAPI) on a Ventana Discovery Ultra automated staining platform (Roche). Expected expression and cellular localisation of each marker as well as fluorophore brightness were used to minimise fluorescence spillage upon antibody-Opal pairing. Following a one-hour incubation at a 60°C, the slide was subjected to an automated staining protocol on an autostainer. The protocol involved deparaffinisation (EZ-Prep solution, Roche), HIER (DISC. CC1 solution, Roche) and seven sequential rounds of: one hour incubation with the primary antibody, 12 minutes incubation with the HRP-conjugated secondary antibody (DISC. Omnimap anti-Ms HRP RUO or DISC. Omnimap anti-Rb HRP RUO, Roche) and 16 minute incubation with the Opal reactive fluorophore (Akoya Biosciences). For the last round of staining, the slide was incubated with Opal TSA-DIG reagent (Akoya Biosciences) for 12 minutes followed by Opal 780 reactive fluorophore for our hour (Akoya Biosciences). A denaturation step (100°C for 8 minutes) was introduced between each staining round in order to remove the primary and secondary antibodies from the previous cycle without disrupting the fluorescent signal. The slide was counterstained with DAPI (Akoya Biosciences) and coverslipped using ProLong Gold antifade mounting media (Thermo Fisher Scientific). The Vectra Polaris automated quantitative pathology imaging system (Akoya Biosciences) was used to scan the labelled slide. Six fields of view, within the area selected by the pathologist, were scanned at 20x and 40x magnification using appropriate exposure times and loaded into inForm{Kramer, 2018 #23} for spectral unmixing and autofluorescence isolation using the spectral libraries. After spectral unmixing and merging of six 20x fields of view for a total of >5mm2 ROI (Table 2), one single-tiff image was extracted for each marker and its intensity was rescaled from 0 to 1 with custom R scripts.