Find research datasets worth reusing

Characterising neutrophil subtypes in cancer using human and murine single-cell RNA sequencing datasets

Single cell RNA sequencing data generated by 10xGenomics for Neutrophils derived from colorectal cancer (CRC) KPN tumours (CRC_KPN_counts.csv) and normalised counts (CRC_KPN_NormalisedCounts.csv) as well as from other mouse models of CRC carrying AKPT, BPN, BP and KP mutations (CRC_other_counts.csv and CRC_other_NormalisedCounts.csv), together with the relevant metadata (CRC_KPN_metadata.csv and CRC_other_metadata.csv).

Single-Cell Mapping Reveals Several Immune Subsets Associated with Liver Metastasis of Pancreatic Ductal Adenocarcinoma

Identifying a metastasis-correlated immune cell composition within the tumor microenvironment (TME) of pancreatic ductal adenocarcinoma (PDAC) will help to develop promising and innovative therapeutic strategies. Twenty-six samples from 11 patients (including 11 primary tumor tissues, 10 blood, and 5 lymph nodes) with different stages were used to develop a multiscale immune profile. High-dimensional single-cell analysis with mass cytometry was performed to search for metastasis-correlated immune changes in the microenvironment. The details about the files uploaded are as follows: 1. panelA_Blood.zip includes 10 .fcs files from blood samples in Panel A; 2. panelA_LN.zip includes 5 .fcs files from lymph node samples in Panel A; 3. panelA_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel A; 4. panelB_Tumor.zip includes 11 .fcs files from tumor tissue samples in Panel B; 5. panel_metadata.xlsx describes marker used in Panel A and B; 6. sample_metadata.xlsx describes detailed sample information.

opencc-by-4.0Feb 2023View details →

A Single-cell Perturbation Landscape of Colonic Stem Cell Polarisation

Cancer cells are regulated by oncogenic mutations and microenvironmental signals, yet these processes are often studied separately. To functionally map how cell-intrinsic and cell-extrinsic cues co-regulate cell-fate in colorectal cancer (CRC), we performed a systematic single-cell analysis of 1,107 colonic organoid cultures regulated by 1) CRC oncogenic mutations, 2) microenvironmental fibroblasts and macrophages, 3) stromal ligands, and 4) signalling inhibitors. Multiplexed single-cell analysis revealed a stepwise epithelial differentiation landscape dictated by combinations of oncogenes and stromal ligands, spanning from fibroblast-induced Clusterin (CLU)+ revival colonic stem cells (revCSC) to oncogene-driven LRIG1+ hyper-proliferative CSC (proCSC). The transition from revCSC to proCSC is regulated by decreasing WNT3A and TGF-β-driven YAP signalling and increasing KRASG12D or stromal EGF/Epiregulin-activated MAPK/PI3K flux. We find APC-loss and KRASG12D collaboratively limit access to revCSC and disrupt stromal-epithelial communication -- trapping epithelia in the proCSC fate. These results reveal that oncogenic mutations dominate homeostatic differentiation by obstructing cell-extrinsic regulation of cell-fate plasticity.

dryad36/100

Causal identification of single-cell experimental perturbation effects with CINEMA-OT

Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. In this work, we present a causal inference-based approach to single-cell perturbation analysis, termed CINEMA-OT (Causal INdependent Effect Module Attribution + Optimal Transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly-generated datasets: (1) rhinovirus and cigarette smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.

opencc-zeroJul 2023View details →

Trellis Single-Cell Screening Reveals Stromal Regulation of Patient-Derived Organoid Drug Responses

Patient-derived organoids (PDOs) can model personalized therapy responses, however current screening technologies cannot reveal drug response mechanisms or how tumor microenvironment cells alter therapeutic performance. To address this, we developed a highly-multiplexed mass cytometry platform to measure post translational modification (PTM) signaling, DNA-damage, cell-cycle activity, and apoptosis in >2,500 colorectal cancer (CRC) PDOs and cancer associated fibroblasts (CAFs) in response to clinical therapies at single-cell resolution. To compare patient- and microenvironment-specific drug responses in thousands of single-cell datasets, we developed Trellis — a highly-scalable, hierarchical tree-based treatment effect analysis method. Trellis single-cell screening revealed that on-target cell-cycle blockage and DNA-damage drug effects are common, even in chemorefractory PDOs. However, drug-induced apoptosis is rare, patient-specific, and aligns with cancer cell PTM signaling. We find that CAFs can regulate cancer cell plasticity — shifting proliferative stem cells to slow-cycling revival stem cells via YAP to protect cancer cells from chemotherapy.   This repo contains the processed scRNA-seq Scanpy AnnData objects generated from the study. More information describing the data can be found at: https://github.com/TAPE-Lab/Ramos-et-al-Trellis

In silico spatial transcriptomic editing at single-cell resolution

The data for training the GAN (Inversion) model and reproduce the results reported in the paper

opengpl-3.0-or-laterDec 2023View details →

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios

All total 152 datasets are collected in the benchmarking study. Every dataset contains two parts: the gene expression matrix (or well-established model by dynwrap for trajectory) and the data information including the data id, repository, accession number, URL, technology platform, species, organ (source), cell number, gene number, data type, ERCC spike-in, dilution factor, volume, group condition, treatment, batch information and cluster labels. There are 23 datasets (data79-data101) for evaluating the simulation ability for cell trajectories which are derived from another Zenodo repository (https://zenodo.org/record/1443566).

BSSE_QGF_61757 L010-L016 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)

Fastq files from sequencing of sample BSSE_QGF_61757 L010 to L016 Publication: https://doi.org/10.15252/msb.20188266

BSSE_QGF_61757 L001-L009 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)

Fastq files from sequencing of sample BSSE_QGF_61757 L001 to L009 Publication: https://doi.org/10.15252/msb.20188266

BSSE_QGF_61756 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)

Fastq files from sequencing of sample BSSE_QGF_61756 Publication: https://doi.org/10.15252/msb.20188266

BSSE_QGF_61759 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)

Fastq files from sequencing of sample BSSE_QGF_61759 Publication: https://doi.org/10.15252/msb.20188266

BSSE_QGF_61758 (Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction)

Fastq files from sequencing of sample BSSE_QGF_61758 Publication: https://doi.org/10.15252/msb.20188266

opencc-by-nc-nd-4.0Sep 2023View details →

Models and Data associated with: Single-cell gene expression prediction from DNA sequence at large contexts

This archive holds trained models and associated data for the <a href="https://www.biorxiv.org/content/10.1101/2023.07.26.550634v1">manuscript</a>: "Single-cell gene expression prediction from DNA sequence at large contexts" Structure: <ul> <li>configs - example configs for the workflows to produce publication data </li> <li>data_* - pre-processed single cell data used for publication</li> <li>models_* - model checkpoints, hyperparameters and training progress in tensorboard logs</li> <li>preprocessing - additional data required to reproduce the pre-processing workflow</li> </ul>   "Copyright 2023 GlaxoSmithKline Research & Development Limited. All rights reserved."

opencc-by-4.0Sep 2023View details →

Spatially-resolved single-cell atlas of ascidian endostyle provides insights into the origin of vertebrate pharyngeal organs

This repository contains the data and code needed to recreate figures in the main and supplementary text of the paper "Spatially-resolved single-cell atlas of ascidian endostyle provides insights into the origin of vertebrate pharyngeal organs" by the same authors. Instructions for running the code are provided in the README.md file. See also <a href="https://github.com/lskfs/ascidian-endostyle">https://github.com/lskfs/ascidian-endostyle</a>.

Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment

These are 3D live-cell imaging datasets of gastric tumor organoids in co-culture with primary human Natural Killer (NK) cells. The datasets were analyzed by a new, deep learning-based 3D image analysis software tool, SiQ-3D, which we developed and presented in the paper titled "Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment". Interested users can download the SiQ-3D software code from GitHub (https://github.com/simonlbd1/SiQ-3D) or Code Ocean (https://codeocean.com/capsule/6676007/tree/v2), analyze the 3D image datasets locally, and cross-check the results with the SiQ-3D quantified results that we provided here.

Fragment-sequencing unveils local tissue microenvironments at single-cell resolution

This dataset contains the Resolve dataset that was used in the publication. Other data set can be found in GEO with the accession number GSE216189. The code that was used during the analysis can be found on our GitHub repository: https://github.com/Moors-Code/Fragment-sequencing

Data from: Single-cell analysis identifies conserved features of immune dysfunction in simulated microgravity and spaceflight

3-dimensional super-resolution microscopy volumes of human PBMCs recorded on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope. Sample preparation and image capture: Live PBMCs were stained with 60 nM MitoTracker Red-CMX-Ros (ThermoFisher, Waltham, MA) either in 6-well plates or in the microgravity chambers for the last 2 hr of the microgravity simulation. At the end of the microgravity simulation cells were immediately fixed by 1:1 mixing the cell suspensions with 2× concentrated fixative (10% Sucrose (w/v) 120 mM KCl, 1% (w/v) glutaraldehyde, 8% (w/v) PFA pH 7.4) and incubated for 15 minutes at room temperature followed by 15 minutes on ice. Fixed cells were washed and stored in PBS until further staining for up to a week at 4 °C. 1 million fixed cells were resuspended in 1 mL of permeabilization solution (0.1% TritonX-100 in PBS) for 5 minutes. After twice washing in PBS, pellets were resuspended in 0.5 mL 1% BSA PBS containing Phalloidin-iFluor-488 (cat# ab176753, Abcam plc., Cambridge, UK) at the manufacturer’s recommended dilution, and were incubated for 90 minutes with gentle agitation. After washing in PBS, cells were stained with Hoechst 33342 (1 µg/mL in PBS) for 10 minutes. The fixed-stained cells were immobilized at 3 × 105 cells per well density in glass-bottom 96-well microplates (Greiner Bio-One, Monroe, NC), which were pre-coated with polyethyleneimine (1:15,000 (w/v)) for 16 hours in a 37 °C incubator, and washed twice with PBS. Microplates with the cell suspensions were centrifuged in a swing plate rotor centrifuge (Eppendorf 5810 R) at 400 × g and for 10 min and then fixed on the surface by adding an equal volume of 8% (w/v) PFA for 5 min. Finally, the fixative was replaced with 100 µL of antifade reagent (Vector Prolong Gold (ThermoFisher)). Samples were imaged immediately after this procedure on a Zeiss LSM980 Airyscan2 laser scanning confocal microscope (Carl Zeiss Microscopy, White Plains, NY). Single PBMCs were manually selected for recording based on low-resolution preview scans showing only nuclei. All singlet cells were selected in a small neighborhood to avoid biases. In each microscopy session, 24-40 cells were selected for recording in one well for each condition. This was performed in an interleaved manner, capturing 6-8 cells at a time, and then moving to the next well and then repeating this multiple times using the Experiment Designer module for automation. Super-resolution volumes of (358 × 358 × 70 pixels, 0.035 × 0.035 × 0.13 µm/voxel resolution) were recorded in the above-determined positions using Definite Focus autofocusing. A Plan-Apochromat 63 × 1.40 Oil lens, Airyscan2 SR (super-resolution) mode with optimal sampling and frame switching between 3 fluorescence channels to minimize spectral cross-bleed were used. MitoTracker Red, iFluor488, and Hoechs33342 were excited with 561, 488, and 405 nm solid-state lasers, respectively, using the optimal emission filter for each channel. 3D Airyscan2 processing was performed with standard filtering settings. File naming: Four zip files were deposited named as <Donor#id>.zip, where id goes from 1 to 4. Each zip file contains the following Zeiss Microscopy format image files: <Condition>_<Donor#id>_<Stain#batch>_<Cell>.czi <Condition>: <ul> <li>1G – Control culturing in 6-well plates for 25h</li> <li>uG – simulated microgravity culturing for 25h in NASA Rotating Wall Vessels</li> <li>1G+TLR – as above, with TLR 7/8 agonist (1 μM R848)</li> <li>uG+TLR– as above, with TLR 7/8 agonist (1 μM R848)</li> <li>1G+CyD– as above, with cytochalasin D</li> <li>uG+CyD– as above, with cytochalasin D</li> <li>1G+Q – as above, with quercetin 50µM</li> <li>uG+Q – as above, with quercetin 50µM</li> </ul> <Donor#id>: 1-4 indicates biological replicates <Stain#batch>: 1-2 indicates experimental replicates of phalloidin staining and imaging session <Cell>: arbitrary number to distinguish images within the same condition/donor/stain set. See image analysis pipelines used with these data at: https://github.com/gerencserlab/Superresolution-actin-and-mitochondria-analysis

Data used in paper "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON"

This directory contains the data resources of the following paper: "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON"

Single-cell and spatially resolved transcriptomic data of mouse regenerative livers under normal and fibrotic conditions

A single-cell spatial-temporal transcriptomic atlas of liver regeneration under normal and fibrotic condition, including a total of 30 mouse liver samples obtained from 15 normal and 15 fibrotic mice at timepoints Day 0, 1, 2, 3, and 7 after a partial hepatectomy (PHx) procedure with three replicates for each time point, followed by the scRNA-seq and SRT sequencing for each sample using the Stereo-seq platform.