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2,678 results for “cell signalling”
Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning
<p>In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning".</p> <p>These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification. </p> <p>The datasets are as follows:</p> <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> <p>We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH</p> <p> </p>
Confocal images from: Cell density, alignment, and orientation correlate with C-signal-dependent gene expression during Myxococcus xanthus development
<p>Starving <em>Myxococcus xanthus</em> bacteria use short-range C-signaling to coordinate their movements and construct multicellular mounds, which mature into fruiting bodies as rods differentiate into spherical spores. Differentiation requires efficient C-signaling to drive the expression of developmental genes, but how the arrangement of cells within nascent fruiting bodies (NFBs) affects C-signaling is not fully understood. Here, we used confocal microscopy and cell segmentation to visualize and quantify the arrangement, morphology, and gene expression of cells near the bottom of NFBs at much higher resolution than previously achieved. We discovered that "transitioning cells" (TCs), intermediate in morphology between rods and spores, comprised 10 to 15% of the total population. Spores appeared midway between the center and the edge of NFBs early in their development and near the center as maturation progressed. The developmental pattern as well as C-signal-dependent gene expression in TCs and spores were correlated with cell density, the alignment of neighboring rods, and the tangential orientation of rods early in the development of NFBs. These dynamic radial patterns support a model in which the arrangement of cells within the NFBs affects C-signaling efficiency to regulate precisely the expression of developmental genes and cellular differentiation in space and time. Developmental patterns in other bacterial biofilms may likewise rely on short-range signaling to communicate multiple aspects of cellular arrangement, analogous to juxtacrine and paracrine signaling during animal development.</p>
miR-1285-3p targets TPI1 to regulate the glycolysis metabolism signaling pathway of Tibetan sheep Sertoli cells
<p><span>Glycolysis in sertoli cells (SCs) can provide energy substrates for the development of spermatogenic cells. Triose phosphate isomerase 1 (TPI1) is one of the key catalytic enzymes involved in glycolysis. However, the biological function of TPI1 in SCs and its role in glycolytic metabolic pathways are poorly understood. On the basis of previous research, we isolated primary SCs from Tibetan sheep and overexpressed <em>TPI1</em> gene to determine its effect on the proliferation, glycolysis, and apoptosis of SCs. Secondly, we investigated the relationship between <em>TPI1</em> and miR-1285-3p, and whether miR-1285-3p regulates the proliferation and apoptosis of SCs, and participates in glycolysis by targeting <em>TPI1</em>. Results showed that overexpression of <em>TPI1</em> increased the proliferation rate and decreased apoptosis of SCs. In addition, overexpression of <em>TPI1</em> altered glycolysis and metabolism signaling pathways and significantly increased the amount of the final product lactic acid. Further analysis showed that miR-1285-3p inhibited <em>TPI1</em> by directly targeting its 3'untranslated region. Overexpression of miR-1285-3p suppressed the proliferation of SCs, and this effect was partially reversed by restoration of <em>TPI1</em> expression. In summary, this study shows that the miR-1285-3p/TPI1 axis regulates glycolysis in SCs. These findings add to our understanding of the regulation of spermatogenesis in sheep and other mammals.</span></p>
Effect of gedunin on cell proliferation and apoptosis impact in skin melanoma cells A431 via PI3K/JNK signaling pathway
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Supplemental Data: Differential roles of kinetic on- and off-rates in T-cell receptor signal integration revealed with a modified Fab'-DNA ligand
<p>Microscopy data and associated code for analysis. For more information refer to https://doi.org/10.1101/2024.04.01.587588.</p>
Single cell RNA-seq data from: Differentiation signals induce APOBEC3A expression via GRHL3 in squamous epithelia and squamous cell carcinoma
<p>Seurat object for single cell RNA-seq of 10 head and neck squamous cell carcinoma patients, epithelial cells only. From "Differentiation signals induce APOBEC3A expression via GRHL3 in squamous epithelia and squamous cell carcinoma ". <span><span>Two APOBEC (apolipoprotein-B mRNA editing enzyme catalytic polypeptide-like) </span><span>DNA </span><span>cytosine deaminase enzymes (APOBEC3A and APOBEC3B) generate somatic mutations in cancer, driving tumour development and drug resistance. Here we used single cell RNA sequencing to study </span></span><span><span>APOBEC3A</span></span><span><span> and </span></span><span><span>AP</span><span>OB</span><span>EC3B</span></span><span><span> expression in healthy and malignant mucosal epithelia, </span><span>validating</span> <span>key</span><span> observations </span><span>with</span><span> immunohistochemistry, spatial </span><span>transcriptomics</span><span> and functional experiments. Wh</span><span>ereas</span> </span><span><span>APOBEC3B</span></span><span><span> is expressed in keratinocytes entering mitosis, we show that </span></span><span><span>APOBEC3A</span></span><span><span> expression is confined</span> <span>largely</span><span> to</span><span> terminally differentiating cells</span><span> and </span><span>requires </span><span>Grainyhead</span><span>-like transcription factor 3 (GRHL3). T</span><span>hus</span><span>, in normal tissue,</span><span> neither </span><span>deaminase</span> <span>appears to be</span><span> expressed at </span><span>high levels</span><span> during DNA replication, the c</span><span>ell cycle stage</span> <span>associated with</span><span> APOBEC-mediated mutagenesis. </span><span>In</span><span> contrast, we show that in squamous cell carcinoma, there is expansion of </span></span><span><span>GRHL3</span></span><span> <span>expression and </span><span>activity to a subset of cells undergoing DNA replication and concomitant extension of </span></span><span><span>APOBEC3A</span></span><span><span> expression to proliferating cells. </span></span><span><span>These findings </span><span>suggest</span><span> that</span> <span>APOBEC3A</span><span> may play a functional role during keratinocyte differentiation</span><span> and offer</span> </span><span><span>a mechanism for acquisition of APOBEC3A mutagenic activity in tumour</span><span>s</span><span>.</span></span><span> </span></p>
Persistent Bone Marrow Hemozoin Accumulation Confers Survival Advantage Against Bacterial Infection via Myd88-Cell Intrinsic Signaling
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Dataset for: Interplay of Cellular Nrf2/NFκB Signalling after Plasma Stimulation of Malignant vs. Non-Malignant Dermal Cells
<p>Data used in the publication "Interplay of Cellular Nrf2/NFκB Signalling after Plasma Stimulation of Malignant vs. Non-Malignant Dermal Cells<br>By: Kristina Manzhula, Alexander Rebl, Kai Budde-Sagert, Sascha Spors, Henrike Rebl".</p> <p>Abbreviations:<br>A = A431 cells<br>H = HaCaT cells</p> <p>1. The GraphPad PRISM file "paper.pzfx" contains all final data used for the analyses of vitality, ROS, the antioxidant capacity of the cells, and hydrogen peroxide in the medium. Statistical analyses, as well as graphics, are included.</p> <p>2. The file "imagedata.zip" contains the microscopy images.</p> <p>3. The file "Rscript.zip" contains a directory with the analysis results. (This data can be recreated when changing the parameter 'reanalyze_images == TRUE'.) The zip container also contains the R script for further analyzing and plotting the image analysis results.</p>
Rewiring of endogenous signaling pathways to genomic targets for therapeutic cell reprogramming
<p>Data underlying the figures in the publication “Rewiring of endogenous signaling pathways to genomic targets for therapeutic cell reprogramming”, published in <em>Nat Commun</em>, <strong>2020</strong>, 11, 608. <a href="https://www.nature.com/articles/s41467-020-14397-8">https://www.nature.com/articles/s41467-020-14397-8</a></p> <p>Table of contents:</p> <p><strong>1. Source Data</strong>; Excel file with the data for <em>Figures 2, 3b, 3d, 3c, 4a, 4b, 4c, 5</em> as well as <em>S2, S3, S4, S5, S6, S7a, S7b, S8, S9, S10, S11, S12, S13a</em> and <em>S13b</em>.</p> <p><strong>Data 1 – Transgene expression by SEAP</strong></p> <p>Data for <em>Figures 2, S2, S4, S5, S7a, S8, S9, S12</em> and <em>S13b</em>.</p> <p>SEAP (human placental secreted alkaline phosphatase) levels were profiled in cell culture supernatants using a colorimetric assay. 100 µL 2x SEAP assay buffer (20 mM homoarginine, 1 mM MgCl2, 21% diethanolamine, pH 9.8) was mixed with 80 µL heat-inactivated (30 min at 65 °C) cell culture supernatant. After the addition of 20 µL substrate solution (120 mM p-nitrophenyl phosphate; cat. no. AC128860100, Thermo Fisher Scientific, Waltham, MA, USA), the absorbance time course was recorded at 405 nm and 37 °C using a Tecan Genios PRO plate reader (cat. no. P97084; Tecan Group AG, Maennedorf, Switzerland) and the SEAP levels were determined as follows: first, absorbance change over time (slope) was calculated. According to the Beer–Lambert’s law, absorbance is proportional to the concentration of a colored compound and depends on the light path length (d) and extinction coefficient (ε) (ε for p-nitrophenyl (εpNP) = 18.600 M−1 cm−1). Enzymatic activity EA [U/L] was calculated from the equation: EA = slope × dilution factor × εpNP−1 × d−1</p> <p>Values in the file present determined SEAP levels.</p> <p><strong>Data 2 – Endogenous gene expression by qPCR</strong></p> <p>Data for <em>Figures 3b, 3d, 4a, 4b, 5, S3, S6, S10, S11</em> and <em>S13a</em>.</p> <p>Total RNA of HEK293T cells was isolated using the Quick-RNA kit (Zymo Research, Irvine, CA, USA). Reverse transcription was performed using a High-Capacity cDNA Reverse Transcription Kit (cat. no. 4368814, Thermo Fisher Scientific, Waltham, MA, USA). Quantitative PCR was performed with the SsoAdvanced Universal SYBR® Green Supermix (cat. no. 1725270, Bio-Rad, Hercules, CA, USA). The Eppendorf Realplex Mastercycler (Eppendorf GmbH) was set to the following amplification parameters: 30 s at 95 °C and 40 cycles of 15 s at 95 °C followed by 30 s at X °C (X = 59 for insulin, 64 for IL-12, 59 or 64 for GAPDH). The relative threshold cycle (Ct) was determined and normalized to the endogenous glyceraldehyde 3-phosphate dehydrogenase (GAPDH) transcript. The fold change for each transcript relative to the control was calculated using the comparative Ct method.</p> <p>Values in the file present determined mRNA levels relative to GAPDH.</p> <p><strong>Data 3 – Secreted protein level by ELISA</strong></p> <p>Data for <em>Figures 3c</em> and <em>4c</em>.</p> <p>Human Insulin was quantified with the Mercodia Insulin ELISA (cat. no. 10-1113-01, Mercodia, Uppsala, Sweden). IL-12 was quantified with the human IL-12 (p40) ELISA Kit (cat. no. KAC1561, Thermo Fisher Scientific, Waltham, MA, USA).</p> <p>Values in the file present protein levels as determined by ELISA kit.</p> <p><strong>Data 4 – Nanoluc luciferase</strong></p> <p>Data for <em>Figure S7b</em>.</p> <p>NanoLuc® luciferase was quantified in cell culture supernatants using the Nano-Glo® Luciferase Assay System (cat. no. N1110; Promega, Duebendorf, Switzerland). In brief, 7.5 µL of cell culture supernatant was added per well of a black 384-well plate and mixed with 7.5 µL substrate-containing assay buffer. Total luminescence was quantified using a Tecan Genios PRO plate reader (Tecan Group AG).</p> <p>Values in the file present luminescence as measured.</p>
Bioactive Catalytic Nanocompartments Integrated into Cell Physiology and Their Amplification of a Native Signaling Cascade
<p>Data underlying the figures in the publication “Bioactive Catalytic Nanocompartments Integrated into Cell Physiology and Their Amplification of a Native Signaling Cascade”, published in <em>ACS Nano,</em> <strong>2020</strong>, 14, 9, 12101–12112.</p> <p><a href="https://pubs.acs.org/doi/10.1021/acsnano.0c05574">https://pubs.acs.org/doi/10.1021/acsnano.0c05574</a></p> <p>Table of contents:</p> <p><strong>1. Figure 1</strong>; Zip file containing the TEM micrographs, and numerical data for <em>Figure 1</em>.</p> <p><strong>2. Figure 2</strong>; Zip file containing the numerical data for the activity studies of <em>Figure 2</em>.</p> <p><strong>3. Figure 3</strong>; Zip file containing the numerical data for the activity studies of <em>Figure 3</em>.</p> <p><strong>4. Figure 4</strong>; Zip file containing the numerical data for <em>Figure 4</em>.</p> <p><strong>5. Figure 6</strong>; Zip file containing the numerical data for <em>Figure 6</em>.</p> <p> </p>
Single-cell and spatial transcriptomics of cardiac neural crest reveal a dual role of vinculin in Tgf beta signaling and cell-extracellular matrix interaction during cardiac outflow tract development
<p>single-cell RNA-seq data analysis:</p> <p>all.cncc.combined.EMBO.mapped.Rdata: public CNCC single-cell RNA-seq data integration</p> <p>E13.5_CNCC_merged_updated.Rdata: single-cell RNA-seq data of E13.5 CNCC generated in Elly lab</p> <p>all.seurat.GFP.Rdata: scRNA-seq data with GFP detected</p> <p>visium.merge_AB.control.Rdata: R processed ST data for slice A and B</p> <p>visium.merge_CD.mutant.Rdata: R processed ST data for slice A and B</p> <p> </p>
Anti-cytokine storm activity of fraxin and quercetin, alone and in combination, and their possible molecular mechanisms via TLR4 and PPARγ signaling pathways in LPS-induced RAW 264.7 cell line article data
<p>Anti-cytokine storm activity of fraxin and quercetin, alone and in combination, and their possible molecular mechanisms via TLR4 and PPARγ signaling pathways in LPS-induced RAW 264.7 cell line article data</p>
Anti-cytokine storm activity of fraxin and quercetin, alone and in combination, and their possible molecular mechanisms via TLR4 and PPARγ signaling pathways in LPS-induced RAW 264.7 cell line article data
<p>Anti-cytokine storm activity of fraxin and quercetin, alone and in combination, and their possible molecular mechanisms via TLR4 and PPARγ signaling pathways in LPS-induced RAW 264.7 cell line article data</p>
Mechanical manipulation of cancer cell tumorigenicity via heat shock protein signaling
<p><span>Biophysical cues of rigid tumor matrix play a critical role in cancer cell malignancy. Herein, we report that stiffly confined cancer cells exhibit robust growth of spheroids in the stiff hydrogel that exerts substantial confining stress on the cells. The stressed condition activated Hsp (heat shock protein)-STAT3 signaling via the TRPV4-PI3K/Akt axis, thereby upregulating the expression of the stemness-related markers in cancer cells, whereas these signaling activities were suppressed in cancer cells cultured in softer hydrogels or stiff hydrogels with stress relief or Hsp70 knockdown/inhibition. This mechanopriming based on 3D culture enhanced cancer cell tumorigenicity and metastasis in animal models upon transplantation, and pharmaceutically inhibiting Hsp70 improved the anticancer efficacy of chemotherapy. Mechanistically, our study reveals the crucial role of Hsp70 in regulating cancer cell malignancy under mechanically stressed conditions and its impacts on cancer prognosis-related molecular pathways for cancer treatments. </span></p>
Signalling pathway crosstalk stimulated by L-proline drives mouse embryonic stem cells
<p>Contains data and R code to reproduce paper models, statistics and plots</p>
IL-3 receptor signaling suppresses chronic intestinal inflammation by controlling mechanobiology and tissue egress of regulatory T cells
<p>RNA from unchallenged mouse <em>Il3r</em><sup>-/-</sup> and <em>Il3r</em><sup>+/+</sup> thymocytes was isolated with the NucleoSpin RNA Kit for RNA isolation (Machery Nagel). RNA-Analysis and sequencing was performed by the Core unit next generation sequencing of the University Hospital Erlangen. The quality of isolated RNA was determined using an Agilent 2100 bioanalyzer with RNA 6000 Nano kit and related software (Agilent). Sequencing libraries were generated from RNA samples using the TruSeq Stranded mRNA Kit (Illumina). Libraries were sequenced on a HiSeq 2500 platform (Illumina). The reads were aligned with Star v2.5.4b against the GRCm38 reference genome. Reads were counted with featureCounts of the Rsubread package v2.8.1.</p>
Fig. 7 in Triterpenoids from Liquidambar Fructus induced cell apoptosis via a PI3KAKT related signal pathway in SMMC7721 cancer cells
Fig. 7. Effects of compounds 5, 7 and 8 on expression of apoptosis related genes in SMMC7721 cells. (A). Representative Western blot results. *P <0.05 vs control. (B) RT-qPCR results of three independent experiments. *P <0.05 vs control.
Fig. 5 in Triterpenoids from Liquidambar Fructus induced cell apoptosis via a PI3KAKT related signal pathway in SMMC7721 cancer cells
Fig. 5. Induction of apoptosis by compounds 5, 7 and 8 in SMMC7721 cancer cells at 36 h *P <0.05 vs control.
Fig. 9 in Triterpenoids from Liquidambar Fructus induced cell apoptosis via a PI3KAKT related signal pathway in SMMC7721 cancer cells
Fig. 9. Expression changes of PI3K-AKT signal pathway related genes. (A) Representative Western blot results of compounds 5, 7 and 8 inducing expression changes of PI3K-AKT signal pathway related proteins in SMMC7721 cells; (B) Statistical analysis of protein expression levels in (A). The results shown were representative of three independent experiments. *P <0.05 vs control.
Fig. 8 in Triterpenoids from Liquidambar Fructus induced cell apoptosis via a PI3KAKT related signal pathway in SMMC7721 cancer cells
Fig. 8. Effects of compounds 5, 7 and 8 on expression of PI3K-AKT pathway related genes on SMMC7721 cell. The RT-qPCR results shown were representative of three independent experiments. *P <0.05 vs control.
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.