Find research datasets worth reusing

opencc-zeroMay 2022View details →

Single-cell phenotypic characteristics of tolerance under recurring antibiotic exposure in Escherichia coli

Non-heritable drug resistance, such as tolerance and persistence towards antibiotics, is little characterized compared to genetic resistance. Tolerance and persistence allow cells to survive application of antibiotics that are bactericidal to non-tolerant cells. Non-heritable drug resistance challenges antibiotic treatment, particularly of recurrent infections, and have implications towards heritable resistance evolution. Tolerant cells have commonly been characterized as growth arrested cells prior and during antibiotic application that quickly resume growth post-application. Here, we explore characteristic of tolerant and susceptible E. coli single bacteria cells to different levels of recurrent antibiotic exposure and quantify their occurrence. In using a high throughput single-cell microfluidic device, we find that tolerant cells reduce their growth rate by about 50%, but contrary to previous findings do not go into growth arrest or near growth arrest. The growth reduction is induced by antibiotic exposure and not caused by a stochastic switch or predetermined state as previously described. Cells exhibiting constant intermediate growth survived best under antibiotic exposure and selection did not primarily act on fast growing cells, as expected for a β-lactam antibiotic. Control experiments on population cultures confirmed and challenged scaling of single cell findings to population level processes. Our findings suggest a prevalent type of tolerance that differs from previously described tolerance and persister characteristics. Our described characteristics and its high frequency of occurrence supports acclaims of an underappreciated role of tolerant cells towards resistance evolution.

opencc-zeroMay 2022View details →

A gas-only bioreactor system maintains stable culture environments and reveals that moderate pH deviations trigger transcriptome-wide responses in human cells cultured in physioxia and physiological buffers

Although pH instability is emerging as a potential driver of changes in cell physiology, pH is still poorly controlled during cell culture and in vitro experiments. Standard procedures include the use of chemicals usually not present in the primary physiological buffering system (CO2/HCO3-), such as acids and bases, to manipulate pH levels. This, however, leads to artifacts that potentially affect scientists' findings. Here, we propose a novel method for controlling pH levels by relying only on the physiological buffering system. pH was manipulated in a repurposed commercial bioreactor set-up, using a two-sided control loop of CO2 and N2 gas in NaHCO3--buffered medium. This method produces optimal and stable dO2 profiles and tightly maintains pH levels. With this procedure, we analyzed the effects of different pH levels (6.8, 7.0, 7.2, and 7.4) on the performance and transcriptome of the human GM12878 cell line over a 72-hours experiment. Our results showed that inflammation and negative cellular proliferation are among the signatures activated at low pH. This further highlights the importance of a thorough pH control during cell culture.

opencc-by-4.0Mar 2022View details →

Data for: Pericytes' Circadian Clock Affects Endothelial Cells' Synchronization and Angiogenesis in a 3D Tissue Engineered Scaffold

Raw data set and analysis files for Mastrullo et al., Frontiers in Pharmacology, 2022 DOI: 10.3389/fphar.2022.867070

openmit-licenseFeb 2022View details →

Single-cell datasets for temporal gene expression integration

Contains loom files and preprocessed adata objects to compare methods for temporal gene expression integration. Loom files can be accessed using the 'read' function in Scvelo. Preprocessed adata objects can be accessed using the 'read_h5ad' function in Scanpy. The raw single-cell RNA sequencing datasets can be found under the following accession codes. <ul> <li> Mouse embryonic cell cycle dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/nbt.3102">10.1038/nbt.3102</a>) was originally downloaded from ArrayExpress with the accession code E-MTAB-2805 </li> <li> Hematopoiesis differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/10.1182/blood-2016-05-716480</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE81682 </li> <li> NKT cell differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/ni.3437">10.1038/ni.3437</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE74596. </li> <li> Hematopoiesis differentiation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org</a>/<a href="https://doi.org/10.1038/nature19348">10.1038/nature19348</a>) was originally downloaded from the Gene Expression Omnibus with the accession codes GSE70236, GSE70240, GSE70244 </li> <li> LPS stimulation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1016/j.cels.2017.03.010">10.1016/j.cels.2017.03.010</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE94383. </li> <li> INF-gamma stimulation dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41587-020-00803-5">10.1038/s41587-020-00803-5</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE161465. </li> <li> AML chemotherapy dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41591-018-0233-1">10.1038/s41591-018-0233-1</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE116481. </li> <li> AML diagnosis/relapse dataset from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41375-021-01338-7">10.1038/s41375-021-01338-7</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE126068. </li> <li> MS case control PBMC and CSF datasets from Ref. (<a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/</a><a href="https://doi.org/10.1038/s41467-019-14118-w">10.1038/s41467-019-14118-w</a>) was originally downloaded from the Gene Expression Omnibus with the accession code GSE138266. </li> </ul>

opencc-zeroJun 2022View details →

Adipose-derived stromal cells preserve pancreatic islet function in a transplantable 3D bioprinted scaffold

Intra-portal islet transplantation is the method of choice for treatment of insulin dependent type 1 diabetes, but its outcome is hindered by limited islet survival due to the immunological and metabolic stress post transplantation. Adipose-derived stromal cells (ASCs) promise to improve significantly the islet micro-environment but an efficient long-term delivery method has not been achieved. We therefore explore the potential of generating ASC enriched islet transplant structure by 3D bioprinting. Here, we fabricate a double-layered 3D bioprinted scaffold for islets and ASCs by using alginate-nanofibrillated cellulose bioink. We demonstrate the diffusion properties of the scaffold and report that human ASCs increase the islet viability, preserve the endocrine function, and reduce pro-inflammatory cytokines secretion in vitro. Intraperitoneal implantation of the ASCs and islets in 3D bioprinted scaffold improve the long-term function of islets in diabetic mice. Our data reveals an important role for ASCs on the islet micro-environment. We suggest a novel cell therapy approach of ASCs combined with islets in a 3D structure with a potential for clinical beta cell replacement therapies at extrahepatic sites.

opencc-zeroJun 2022View details →

Data for PNASnexus article Aldehyde dehydrogenase 3A1 deficiency leads to mitochondrial dysfunction and impacts salivary gland stem cell phenotype

Adult salivary stem/progenitor cells (SSPC) have an intrinsic property to self-renew in order to maintain tissue architecture and homeostasis. Adult salivary glands have been documented to harbor SSPC, which have been shown to play a vital role in the regeneration of the glandular structures post radiation damage. We have previously demonstrated that activation of aldehyde dehydrogenase 3A1 (ALDH3A1) after radiation reduced aldehyde accumulation in SSPC, leading to less apoptosis and improved salivary function. We subsequently found that sustained pharmacological ALDH3A1 activation is critical to enhance regeneration of murine submandibular gland after radiation damage. Further investigation shows that ALDH3A1 function is crucial for SSPC self-renewal and survival even in the absence of radiation stress. Salivary glands from Aldh3a1-null mice have fewer acinar structures than wildtype mice. ALDH3A1 deletion or pharmacological inhibition in SSPC leads to a decrease in mitochondrial DNA copy number, lower expression of mitochondrial specific genes and proteins, structural abnormalities, lower membrane potential, and reduced cellular respiration. Loss or inhibition of ALDH3A1 also elevates ROS levels and accumulation of ALDH3A1 substrate 4-hydroxynonenal (4-HNE, a lipid peroxidation product), leading to decreased survival of murine SSPC that can be rescued by treatment with 4-HNE specific carbonyl scavengers. Our data indicate that ALDH3A1 activity protects mitochondrial function and is important for the regeneration activity of SSPC. This knowledge will help to guide our translational strategy of applying ALDH3A1 activators in the clinic to prevent radiation-related hyposalivation in head and neck cancer patients.

Data and processing scripts for PRISM barcode sequencing data used in "Massively parallel pooled screening reveals genomic determinants of nanoparticle-cell interactions"

Sequencing data for the PRISM barcodes generated after nano-particle treatment is presented in this repository alongside the code to process the sequencing counts to generate the binning probabilities and weighted scores.  For the details please see the original publication or the bioarxiv preprint:  https://doi.org/10.1101/2021.04.05.438521 The raw data is provided in PILOT_DATA_COUNTS.csv and EXPERIMENT_DATA_COUNTS.csv files, for the pilot and the actual experiment.  For each of these files an R script is provided to process them, along with the output of the scripts (PILOT_DATA_PROBABILITIES.csv and EXPERIMENT_DATA_PROBABILITIES.csv)

Multimodal single cell analysis of the paediatric lower airway reveals novel immune cell phenotypes in early life health and disease

RDS files of SingleCellExperiment objects containing raw single cell RNA-seq count data required to replicate the analyses presented at: <a href="https://oshlacklab.com/paed-cf-cite-seq/">https://oshlacklab.com/paed-cf-cite-seq/</a> and described in the pre-print titled: "Multimodal single cell analysis of the paediatric lower airway reveals novel immune cell phenotypes in early life health and disease". Instructions for how to incorporate the raw data into the analysis can be found at: <a href="https://oshlacklab.com/paed-cf-cite-seq/gettingStarted.html">https://oshlacklab.com/paed-cf-cite-seq/gettingStarted.html</a> and the complete analysis code and additional data files can be cloned/downloaded from: <a href="https://github.com/Oshlack/paed-cf-cite-seq">https://github.com/Oshlack/paed-cf-cite-seq</a>.

opencc-by-4.0May 2021View details →

Deep cross-omics cycle attention model for joint analysis of single-cell multi-omics data

We proposed DCCA for accurately dissecting the cellular heterogeneity on joint-profiling multi-omics data from the same individual cell by transferring representation between each other.

Cell Graph data for predicting MSI vs. MSS from histological images

Abstract: The cell graph data extracted from histological images for predicting microstatellite status. This is a patch-level (patch refers to the sub-image tessellated from the whole-slide image) binary classification task: the two datasets classify images patches to either MSS (microsatellite stable) or MSIMUT (microsatellite instable or highly mutated). The original histology patches are available from [1].  Cell Graph Contraction: In this dataset, we establish a cell graph for each patch. The cell graph can represent the cell-cell interaction and the collection of cell graphs for all patches provide a precise characterization of tumor microenvironment. With only the availability of raw image patches, we leverage the nuclei regions segmented by a well-tuned CA2.5-Net [2] to extract the node features of each single nuclei node. And then we extract a total number of 94 pre-defined pathomics features for each nuclei region as the corresponding graph node feature. As the morphological signals are believed relative to cell-cell interplay, the cell-specific features which include the nuclei coordination, optical, and representations, then characterize the cell-level morphological behavior. We then calculate the pair-wise Euclidean distance between nuclei centroids to establish edges of a cell graph Reference For more details about the construction of cell-graph, please visit our paper: https://arxiv.org/abs/2206.07599. The associated codes and usage for the datasets are available at: https://github.com/yiqings/HEGnnEnhanceCnn. [1] Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples. https://doi.org/10.5281/zenodo.2530835 [2] Huang, J., Shen, Y., Shen, D., & Ke, J. (2021, September). CA 2.5-Net Nuclei Segmentation Framework with a Microscopy Cell Benchmark Collection. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 445-454). Springer, Cham.

opencc-by-4.0Dec 2021View details →

Optogenetic activation of striatal D1R and D2R cells differentially engages downstream connected areas beyond the basal ganglia

This dataset supports the Cell Reports publication entitled 'Optogenetic activation of striatal D1/D2 medium spiny neurons differentially engages downstream connected areas beyond the basal ganglia' (https://doi.org/10.1016/j.celrep.2021.110161). Please refer to this publication for detailed information.  The dataset contains functional (GE-BOLD) and anatomical (T1-weighted) scans of a total of 34 subjects (Cre-mouse lines Drd1-Cre, Drd2-Cre, A2A-Cre, and wildtype mice) in NIFTI format, multiplied by 10. The 'StimulationProtocol' text file specifies the precise onset (in seconds) and duration (in seconds) of the optogenetic stimulation during a 480 seconds functional scan. The 'SubjectDetails' file lists the subject IDs, their gender, their genotype, the number of functional scans, and whether they have an associated anatomical scan.

Single-cell datasets for NK cell

The NK cell mass cytometry dataset total collects 20 patients with 29 features. The attribute is divided into stimulated and unstimulated data. By dealing with data pre-processing in NK cell dataset, the raw data in each feature for every patient were transformed using an arcsinh normalization, which is f(x) = arcsinh(x/5).

single cell dataset for covid

The dataset is covid with data preprocessing. The covid dataset contains 65 million cells from 49 patients, out of which there were six healthy subjects, 23 mild cases of COVID, and 20 severe conditions under ICU care. For data pre-processing in the covid dataset, we collected the data for each experimental condition and removed unnamed proteins (columns with blank titles). We selected the remaining protein features and the entire cell population in the dataset. However, some genes are expressed as negative values in raw data; hence, we set every negative number to 0. Finally, the ward cases and ICU cases total contain 43 patients with 23 features.

Single-cell datasets for preeclampsia

The dataset is preeclampsia with data preprocessing.

opencc-by-4.0Jul 2022View details →

Cell-ACDC: segmentation, tracking, annotation and quantification of microscopy imaging data (dataset)

This repository includes all the data generated or analysed during the preparation of Cell-ACDC publication, including test datasets for testing the software. Cell-ACDC is open-source software available on GitHub <a href="https://github.com/SchmollerLab/Cell_ACDC">here</a>.

opencc-by-4.0Mar 2022View details →

Investigation on anodes from anode supported cells manufactured by Solidpower and aged by IEES-BAS with electrochemical characterization.

samples are mounted in epoxy-resin and polished up to 250nm using diamond paste on specific tissue (MD-NAP, Struers). Observation by SEM (ZEISS EVO 40) using backscattered electrons as the contrast method. Chemical analysis performed using EDS after calibration with Co standard and using ZAF algorithm correction. The standard magnification is 5000. The pictures are further threated using ImageJ software for quantitative image analysis. Porosity and Nickel particles are highlighted by using Coreldraw software image enhancements.

opencc-by-4.0Jul 2022View details →

SIRV: Spatial inference of RNA velocity at the single-cell resolution

Spatial transcriptomics and scRNA-seq datasets used for integration and prediction of un/spliced expression for spatially measured genes using SIRV, used to infer the RNA velocity in the spatial context

opencc-zeroJul 2022View details →

Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus

Heterogeneity plays an important role in diversifying neural responses to support brain function. Adult neurogenesis provides the dentate gyrus with a heterogeneous population of granule cells (GCs) that were born and developed their properties at different times. Immature GCs have distinct intrinsic and synaptic properties than mature GCs and are needed for correct encoding and discrimination in spatial tasks. How immature GCs enhance the encoding of information to support these functions is not well understood. Here, we record the responses to fluctuating current injections of GCs of different ages to study how they encode stimuli. Immature GCs produce unreliable responses compared to mature GCs, exhibiting imprecise spike timings across repeated stimulation. We use a statistical model to describe the stimulus-response transformation performed by GCs of different ages. We fit this model to the data and obtain parameters that capture GCs encoding properties. Parameter values from this fit reflect the maturational differences of the population and indicate that immature GCs perform a differential encoding of stimuli. To study how this age heterogeneity influences encoding by a population, we perform stimulus decoding using populations that contain GCs of different ages. We find that, despite their individual unreliability, immature GCs enhance the fidelity of the signal encoded by the population and improve the discrimination of similar time dependent stimuli. Thus, the observed heterogeneity confers the population with enhanced encoding capabilities.

opencc-zeroJul 2022View details →

Single-cell expression and TCR data from CD19-specific CAR T cells in a phase I/II clinical trial

By leveraging single-cell transcriptome and T cell receptor (TCR) sequencing, we aimed to track the transcriptional signatures of CAR T cell clonotypes throughout the course of treatment and furthermore identify molecular patterns leading to potent CAR T cell cytotoxicity. The data presented in this study encompass blood and bone marrow samples from patients ≤ 21 years of age with relapsed or refractory B-cell acute lymphoblastic leukemia (B-ALL) participating in the SJCAR19 phase I/II clinical trial (<a href="https://clinicaltrials.gov/ct2/show/NCT03573700">NCT03573700</a>). In brief, patients enrolled in the clinical trial received either 1 x 10^6 (dose level 1) or 3 x 10^6 (dose level 2) per kilogram of body weight following successful generation of autologous CAR T cell products and lymphodepleting chemotherapy. Peripheral blood was drawn from each participant every week until week 4 post-infusion, at week 6 or 8, and month 3 or 6 if feasible. At week 4 post-infusion, blood marrow was also collected from participants. Total T cells (CD3+) were sorted from each post-infusion sample, as well as the pre-infusion CAR T cell products, and processed through 10x Genomics' single-cell gene expression and V(D)J sequencing platform using the standard protocol. We identified a unique and unexpected transcriptional signature in a subset of pre-infusion CAR T cells that shared TCRs with post-infusion cytotoxic effector CAR T cells. Functional validation of cells with even a subset of these pre-effector markers demonstrated their immediate cytotoxic potential and resistance to exhaustion.