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Mapping of nanoparticles diffusion in cells

We mapped the diffusion of particles in the cytoplasm. To do so, we electroporated QDs-SB particles in cells as shown in Debayle et al 2019. Here we were using RPE1 cells. We present the recording of three cells and the scripts that we used to generate some maps of diffusion in the cell. The scripts were made to use the output tracks from Slimfast 1.103e but can be adapted by changeing the beginning of the script. Some functions were found in Matlab Central and their corresponding license is in their respective folders.

Diffusion of nanoparticles in the cell cytoplasm

In Etoc 2018, we presented the diffusive behavior inside the cytosol of living cells for different kind of nanometric sized nanoparticles. We showed that the passivation of the particles is the main parameter that regulate the diffusion properties of particles smaller than 70nm not only in Hela cells but also in RPE1 and MSC cells. Here we open the data for the MSC cells. Experiments consists in the recording of small subregions of the cytoplasm located at the periphery of the cell. A README file is included in the archive with the recording parameters. DATA were recorded by Elie BALLOUL Scripts were written by Mathieu COPPEY, Fred ETOC and modified by Elie BALLOUL.(to use polyfit instead of ezyfit) We also make available the scripts that we used to generate the maps. Some functions were found in Matlab Central and their corresponding license is in their respective folders.

opencc-by-4.0Apr 2019View details →

Pre-processed B cell receptor repertoire sequencing data from BioProject PRJNA527941

Data Processing   Samples were demultiplexed via their Illumina indices, and processed using the Immcantation toolkit(1,2). Raw fastq files were filtered based on a quality score threshold of 20. Paired reads were joined if they had a minimum length of 10 nt, maximum error rate of 0.3 and a significance threshold of 0.0001. Reads with identical UMI were collapsed to a consensus sequence. Reads with identical full-length sequence and identical constant primer but differing UMI were further collapsed. Sequences were then submitted to IgBlast (3) for VDJ assignment and sequence annotation. Constant region sequences were mapped to germline using Stampy(4). The number and type of V gene mutations was calculated using the shazam R package.(2)   software_versions            pRESTO:0.5.3,Change-O:0.3.4,IgBlast 1.6.1, stampy1.0.21. shazam0.1.8 quality_thresholds            FilterSeq.py pRESTO Q>20 paired_reads_assembly        AssemblePairs.py pRESTO minlen 10 maxerror 0.3 alpha 0.0001 primer_match_cutoffs        MaskPrimers.py pRESTO C primer & V primer maxerror 0.2 consensus_building        BuildConsensus.py pRESTO maxerror 0.1 maxgap 0.5 collapsing_method        CollapseSeq.py pRESTO germline_database        IMGT   Format   Processed sequences are provided in a tab delimited file format, including the following annotations:   C_CALL                    Isotype subclass SEQUENCE_ID                Sequence identifier V_CALL                    V segment gene and allele D_CALL                    D segment gene and allele J_CALL                    J segment gene and allele JUNCTION_LENGTH            Junction length CONSCOUNT                Raw read count from which UMI consensus sequences were generated, summed over all UMIs for the given unique sequence. DUPCOUNT                    UMI count for the given unique sequence ISOTYPE                    Constant region primer (isotype) MU_COUNT_CDR_R            Number of replacement mutations in CDR region MU_COUNT_CDR_S            Number of silent mutations in CDR region MU_COUNT_FWR_R            Number of replacement mutations in FWR region MU_COUNT_FWR_S            Number of silent mutations in FWR region MUT_TOTAL                    Total number of mutations in V gene  SEQUENCE_INPUT            Full length sequence SEQUENCE_IMGT                Gapped IMGT sequence V_GERM_START_VDJ            position of the first nucleotide in ungapped V germline sequence alignment JUNCTION                    Junction nucleotide sequence GERMLINE_IMGT_D_MASK        IMGT-gapped germline nucleotide sequence with ns masking the NP1-D-NP2 regions Run                        ID of sequencing run Sample_type                The tissue sampled (e.g Peripheral Blood, bone marrow, ..) Sex                        Sex of the Subject Age                        Age of the subject UNIQUE_ID                    Subject identifier  SAMPLE_ID                    Sample identifier, linking back to raw data Subset                    Defined B cell subset  Repertoire                    Defined B cell repertoire (Naive, Memory IgM/IgD, IgA, IgG) R_SCDR                    R/S ratio in CDR region R_SFWR                    R/S ratio in FWR region V_FAM                    V family gene V_GENE                    V segment gene D_GENE                    D segment gene J_GENE                    J segment gene Clust_Rank                    Cluster rank Clust_REPRES                Cluster representative Clust_SIZE                    Cluster size Clust_MAXFREQ                Cluster maximum frequency Clust_SHAREDNESS            Cluster sharedness CDR3_AA_GRAVY                CDR3 hydrophobicity index CDR3_AA_CHARGE            CDR3 charge CDRH3PDB                    CDRH3 PDB (Structure) code H1Canon                    H1 Canonical class H2Canon                    H2 Canonical class H1_GERMLINE                H1 Germline Canonical class H2_GERMLINE                H2 Germline Canonical class   References 1. Vander Heiden, J. A., G. Yaari, M. Uduman, J. N. H. Stern, K. C. O’Connor, D. A. Hafler, F. Vigneault, and S. H. Kleinstein. 2014. PRESTO: A toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires. Bioinformatics30: 1930–1932. 2. Gupta, N. T., J. A. Vander Heiden, M. Uduman, D. Gadala-Maria, G. Yaari, and S. H. Kleinstein. 2015. Change-O: A toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data. Bioinformatics31: 3356–3358. 3. Ye, J., N. Ma, T. L. Madden, and J. M. Ostell. 2013. IgBLAST: an immunoglobulin variable domain sequence analysis tool. Nucleic Acids Res.41. 4. Lunter, G., and M. Goodson. 2011. Stampy: A statistical algorithm for sensitive and fast mapping of Illumina sequence reads. Genome Res.21: 936–939.

Development of a high-throughput small molecule screening assay for phenotypical characterization of lysosomal storage disorder-affected cells, with infantile cystinosis as a proof of principle

Together with the Pivot Park Screening Centre we performed a drug screen on CTNS-/- proximal tubule cells. For this we developed an assay to evaluate LC3-II positive puncta, and which may be applied for any disease in which autophagy plays an important role. The screen was optimized by the hotel for a 384 well format, making it useful for high throughput screening. The screen was performed with 1280 compounds from the Prestwick library.

Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry

Supplemented Videos used in "Evaluation of the impact of imprinted polymer particles on morphology and motility of breast cancer cells by using digital holographic cytometry"

opencc-by-4.0Mar 2018View details →

Neurogliaform cortical interneurons derive from cells in the preoptic area

Excel document containing the raw dataset used in each figures and links to morphology reconstructions

opencc-by-4.0Feb 2020View details →

Generating an expression matrix for droplet single-cell RNA-seq (dscRNA-seq) data

This tutorial is adapted from the 'Generating an expression matrix' training session at the EBI (https://www.ebi.ac.uk/training/events/2019/single-cell-rna-seq-analysis-questions-clusters).

Dataset: 2D particle-in-cell (PIC) simulation of the magnetic reconnection for the paper "Electron mixing and isotropization in the exhaust of asymmetric magnetic reconnection with a guide field"

This repository contains pubilicly available numerical data of a 2D magnetic reconnection event, which includes the field data and plasma moment data. The simulation is performed with the VPIC code. The simulated data are used for the paper "Electron mixing and isotropization in the exhaust of asymmetric magnetic reconnection with a guide field".

3D time-course iSIM dataset HeLa cells MitoTracker Red CMXRos

2020-03-17 Microscopy dataset: - Type: Fluorescence, 3D, time-course, iSIM Visitech, 100x Silicon NA 1.35 (65 nm x 65 nm x 300 nm xyz pixel sizes), 20s time interval - Cell type: HeLa, labelled with MitoTracker Red CMXRos   Romain F. Laine, r.laine@ucl.ac.uk, MRC-LMCB, UCL, London, UK

Dataset related to article "NK cell recruitment in salivary glands provides early viral control but is dispensable for tertiary lymphoid structure formation."

Salivary glands (SGs) represent a permissive site for several sialotropic viruses whose persistence is linked to the development of autoimmunity. Natural Killer (NK) cells play a key role in viral clearance but their involvement in viral infection control and in tertiary lymphoid structures (TLS) development within SGs is unknown. By using an inducible model of TLS in the SGs of wild-type C57BL/6 mice, induced by the local delivery of a replication-defective adenovirus (AdV), we demonstrated that circulating NK cells are rapidly recruited to SGs and highly enrich the early inflammatory infiltrate prior to TLS development. NK cells migrating to SGs in response to AdV infection up-regulate NKp46, undergo proliferation, acquire cytotoxic potential, produce Granzyme-B and IFN-γ, and reduce viral load in the acute phase of the infection. Nonetheless, the selective depletion of both circulating and infiltrating NK cells in AdV-infected mice neither affect the development and frequency of TLS nor the onset of autoimmunity. These data demonstrate that, upon local viral delivery of AdV, peripheral NK cells homing to SGs can exert an early control of the viral infection but are dispensable for the formation of TLS and breach of immunologic tolerance.   This research used pzf and ets file form extensions, we attach pdfs information about

Dataset related to article "CXCR3 Identifies Human Naive CD8+ T Cells with Enhanced Effector Differentiation Potential."

In mice, the ability of naive T (TN) cells to mount an effector response correlates with TCR sensitivity for self-derived Ags, which can be quantified indirectly by measuring surface expression levels of CD5. Equivalent findings have not been reported previously in humans. We identified two discrete subsets of human CD8+ TN cells, defined by the absence or presence of the chemokine receptor CXCR3. The more abundant CXCR3+ TN cell subset displayed an effector-like transcriptional profile and expressed TCRs with physicochemical characteristics indicative of enhanced interactions with peptide-HLA class I Ags. Moreover, CXCR3+ TN cells frequently produced IL-2 and TNF in response to nonspecific activation directly ex vivo and differentiated readily into Ag-specific effector cells in vitro. Comparative analyses further revealed that human CXCR3+ TN cells were transcriptionally equivalent to murine CXCR3+ TN cells, which expressed high levels of CD5. These findings provide support for the notion that effector differentiation is shaped by heterogeneity in the preimmune repertoire of human CD8+ T cells.

opencc-by-4.0Jun 2019View details →

Raw data related to: "Resiquimod-Mediated Activation of Plasmacytoid Dendritic Cells Is Amplified in Multiple Sclerosis"

Introduction Multiple sclerosis (MS) is a chronic inflammatory autoimmune disease of the central nervous system. The cause of multiple sclerosis is unknown but there are several evidences that associate the genetic basis of the disease with environmental causes. An important association between viral infection and development of MS is clearly demonstrated. Viruses have a strong impact on innate immune cells. In particular, myeloid dendritic cells (mDCs) and plasmacytoid dendritic cells (pDCs), are able to respond to viruses and to activate the adaptive immune response. Methods  In this study we mimic viral infection using synthetic single-strand RNA, Resiquimod, and we compared the response of both DC subsets derived from healthy donors and MS patients by characterizing the expression of costimulatory molecules on the DC surface. Results We found that pDCs from MS patients express higher levels of OX40-L. Moreover, we found that blood cells from MS patients and healthy donors upon Resiquimod-stimulation are enriched in a subpopulation of pDCs, characterized by a high amount of costimulatory molecules. Conclusion Overall, these results indicate that activation of pDCs is enhanced in MS, likely due to a latent viral infection, and that costimulatory molecules expressed on pDCs could mediate a protective response against the viral trigger of autoimmunity.

Multiplexed histology reveals phenotypic and spatial characteristics of human Innate Lymphoid Cells in chronic inflammation - Data tables

Data tables containing image-based, single cell information for a tonsil and a colon data-set analyzed in CellProfiler: cell numbers, MFI of all markers, annotated cell types and cell allocations (X, Y coordinates).

A Bayesian Phylogenetic Hidden Markov Model for B Cell Receptor Sequence Analysis

simulation and PC64/VRC01 input/output data files

opencc-by-4.0Feb 2020View details →

Dataset of PNT1A and PC-3 cells - efect of FITC phototoxicity, quantitative phase imaging (1/2)

Part of article Feith, M,Vičar, T., Gumulec, J.,  Raudenská, M. Wingren, AG, Masařík, M., Balvan, J. Quantitative Phase Dynamics of Cancer Cell Populations Affected by Blue Light, Appl. Sci. 2020, 10 Increased exposition to blue light may induce many changes in cell behavior and significantly affect the critical characteristics of cells. Here we show that multimodal holographic microscopy (MHM) within advanced image analysis is capable of correctly distinguishing between changes in cell motility, cell dry mass, cell density, and cell death induced by blue light. We focused on the effect of blue light with a wavelength of 485 nm on morphological and dynamical parameters of four cell lines, malignant PC-3, A2780, G361 cell lines, and the benign PNT1A cell line. We used MHM with blue light doses 24 mJ/cm2, 208 mJ/cm2 and two kinds of expositions (500 and 1000 ms) to acquire real-time quantitative phase information about cellular parameters. It has been shown that specific doses of the blue light significantly influence cell motility, cell dry mass and cell density. These changes were often specific for the malignant status of tested cells. Blue light dose 208 mJ/cm2 × 1000 ms affected malignant cell motility but did not change the motility of benign cell line PNT1A. This light dose also significantly decreased proliferation activity in all tested cell lines but was not so deleterious for benign cell line PNT1A as for malignant cells. Light dose 208 mJ/cm2 × 1000 ms oppositely affected cell mass in A2780 and PC-3 cells and induced different types of cell death in A2780 and G361 cell lines. Cells obtained the least damage on lower doses of light with shorter time of exposition. Materials and Methods  Cell Lines The PC-3, A2780, PNT1A, and G361 cell lines were purchased from HPA Culture Collections (Salisbury, UK). PC-3 prostate cancer cell line was derived from bone metastasis of a 4-grade prostatic adenocarcinoma of a 62-year-old Caucasian male  The A2780 cell line was derived from the ovarian carcinoma of a nontreated patient according to ECACC. PNT1A cell line was established from prostatic epithelial tissue of healthy 35-years old male and immortalized by plasmid transfection containing the SV40 genome with defective replication origin. The G361 cell line was established from a malignant melanoma of a 31-year-old male Caucasian. The G361 cells produce melanin for up to 50 population doublings. As the aim of this study is to compare the effect of blue light on the cell lines differing by morphology, transformation state, sensitivity to cell death, and origin, we decided to use the cell lines listed above. PC-3 cells are larger in comparison with small A2780 cells. Benign PNT1A cell line differs from malignant PC-3, and all four cell lines are derived from diverse tissues of origin. Furthermore, melanoma G361 cells expressing melanin may differ in the reaction of cells to blue light exposure. Cell Cultivation All four cell lines were cultivated in 25 cm2 flasks with 5 ml of media at 37 °C in a humidified incubator (60%) with 5% CO2 (Sanyo, Osaka City, Japan). Cell lines A2780, PNT1A and G361 were cultured in RPMI-1640 medium with phenol red indicator, L–glutamine, FBS and antibiotics penicillin/streptomycin (Sigma Aldrich Co., St. Louise, MO, USA). For the PC-3 cell line cultivation, Ham´s F-12 medium with FBS and antibiotics (Sigma Aldrich Co., St. Louis, MO, USA) was used. The same supplementation with antibiotics (penicillin 100 U/mL and streptomycin 0.1 mg/mL) and 10% FBS was used in both media. The cell medium was changed two times per week. Cell subculturing was done with 10% of trypsin solution (PAA, Pasching, Austria) with previous washing with EDTA (0.02% in PBS buffer). QPI and Holographic Microscopy and Fluorescence Setting QPI was performed by using a Q-PHASE multimodal holographic microscope (Telight, Brno, CZ). The Q-PHASE is equipped with fluorescence module using a halogen lamp as a non-coherent source of blue light. In this work, the module was used as a source of blue light for treatment of observed cell lines. The 485 nm light waves are emitted by the fluorescence light source of the attached module. Before the imaging experiment, cells were cultivated overnight in a concentration of 7000 cells/mL in flow chamber µ-Slide I Lauer Family (Ibidi, Martinsried, Germany). During the measurements, the chamber with cells was incubated in 37 °C humidified, 5% CO2 atmosphere in H201–for Mad City Labs Z100/Z500 piezo Z-stages (Okolab, Ottaviano NA, Italy). Images and holograms were captured with lens Nikon Plan 10/0.3 and CCD camera (XIMEA MR4021 MC-VELETA, Münster, Germany) respectively. The fluorescence mode used was a plasma light source (Sutter Instrument Lambda XL Novato, CA, USA). Cells were irradiated with a 485 nm light with a 25 nm bandwidth. Light doses 0 mJ/cm2, 24 mJ/cm2 and 208 mJ/cm2 were achieved by the combination of time exposition and light intensity. The images were acquired automatically from seven positions every 3 min for 24 h. Holographic images were collected by custom software and raw data were numerically reconstructed. The numerical reconstruction was performed by custom software where the established methods of the fast Fourier-transform and phase unwrapping are implemented. The output from the software is an unwrapped phase image. This image has high intrinsic contrast and can be processed by an available image processing software. The unwrapped phase image is integrated phase shift through the cell and it is proportional to integrated cell dry mass density.

Well overviews of A549 spheroids at varying cell plating density and Matrigel concentrations

Above images depict well overviews of 5-day old A549 spheroid culture at varied cell plating density (1000,2000 and 3000 cells per well) and Matrigel density (10, 20 and 25 microliters). These were imaged using Perkin Elmer Opera Phenix High Content Screening System and were stained with a nuclear marker (Hoescht 33342) and an actin marker phalloidin/Alexa568.

Well overviews of A549 spheroids at varying cell plating density and Matrigel concentrations

Dysregulation in mTOR/HIF-1 signaling identified by proteo-transcriptomics of SARS- CoV-2 infected cells - Proteomic data obtained with Huh-7 cells

Cultured human Huh-7 cells were infected with SARS-CoV-2 and harvested after 24, 48 and 72 h. The extracted proteins were processed in triplicates preparing for mass spectrometric analysis. Data acquisition was completed, including control samples of non-infected cells, following isobaric tandem mass tag (TMT) chemical labeling and on-line fractionation of the 12 combined biological replicates. The resulted vendor specific raw files (Thermo Scientific) of 12 fractions are provided. The data is further analyzed in order to identify regulated proteins upon SARS-CoV-2 infection to understand the underlying biological processes through pathway analysis. Additional details about the study is going to be completed in the manuscript already submitted for publication.