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25,372 results for “Transcriptomics”
Transcriptomics for clinical and experimental biology research: hang on a seq
<p>Additional R script, source data files and figures associated with the paper.</p> <p>The following source files can generate the corresponding figures using the R script (Omics_review_HC_final.R).</p> <p>Figure 1: Fig1_upset_new_V2_general.type.csv</p> <p>Figure 2 & 3: SMP_ENSG_biotypes_average_data_All_genes_V2.xlsx; FUSION_study_CPM-data_fusion_annotated.xlsx</p> <p>Extra Figures (RNA_review_figures_brain_HC.pdf) uploaded here : GSE47774_SEQC_ILM_BGI_Human_Brain_Only.xlsx; GSE47774_SEQC_ILM_BGI_Human_Brain_Only.xlsx</p> <p>Additional raw counts source files:</p> <p>RNA-seq.A: summary data is available at https://www.ebi.ac.uk/birney-srv/FUSION/. Full dataset is available at dbGaP accession phs001048.v2.p1.</p> <p>RNA-seq.B: GSE164471_GESTALT_Muscle_ENSG_counts_annotated.csv</p> <p>RNA-seq.C: GSE97084_Robinson_GeneCount_raw.tsv; GSE97084_Robinson_GeneCount_raw_2.tsv</p> <p>RNA-seq.D: GSE157585_Kulkarni_Peck_et_al_MASTERS_raw_counts.txt</p> <p>RNA-seq.E: GSE151066_Rubenstein_rsem_genes_count.csv</p> <p>HTA2.0 array: SMP191_iron_output_M_GC-HTA_ENST_Grch38_25.500.25.10_log2_2_minimum.csv; ENSG_biotypes_RNA_review_SMP_data</p> <p>Figure 5 & 6 are generated from various source files above following the R script.</p>
Foraging behaviour variations, for gene expression and transcriptomic divergence in parasitic wasp populations of Venturia canescens
<p><span>Foraging behaviours encompass strategies to locate resources and to exploit them. In many taxa, these behaviours are driven by a major gene called <em>for</em>, but mechanisms vary </span><span>between species. In the parasitoid wasp <em>Venturia</em> <em>canescens</em>, sexual and asexual populations coexist in sympatry but differ in life-history trait, physiology and behaviours,</span> <span>which could impact their foraging strategies. </span><span>Here, we explored the molecular bases underpinning divergence in behaviours by testing two mutually nonexclusive hypotheses: first, </span><span>the divergence in the <em>for</em> gene correlates with difference in foraging strategies, and second, the latter rely on a divergence in whole-genome expression. Using comparative genomics, we showed that the <em>for</em> gene was conserved across insects considering both sequence and gene model complexity. Polymorphism analysis did not support the occurrence of two allelic variants diverging across the two populations, yet the asexual population exhibited less polymorphism than the sexual population. Sexual and asexual transcriptomes sharply split, with 10.9% of differentially expressed genes, but these were not enriched in behavioural-related genes. We showed that the <em>for</em> gene was more highly expressed in asexual female heads than in sexual heads and that those differences correlate with divergence in foraging behaviours in our experiment since asexuals explored the environment more and exploited more host patches. Overall, these results suggested that fine tuning of <em>for</em> gene expression between populations may have led to distinct foraging behaviours. We hypothesized that reproductive polymorphism and coexistence in sympatry of sexual and asexual populations specialized to different ecological niches via divergent optima on phenotypic traits could imply adaptation through different expression patterns of the for gene and at many other loci throughout the genome.</span></p>
Reproductive transcriptome of Nicotiana tabacum male gametophyte unveils decreasing number of new transcription factors during pollen ontogeny
<p>Plants with highly reduced male gametophytes represent a successful adaptation to sexual reproduction, which plays an important role in the colonization and radiation of terrestrial ecosystems. During pollen maturation, microsporocytes to mature pollen grain cells switch from mitosis to meiosis and ultimately form a haploid male gametophyte, a widely used model to study plant development. We performed RNASeq analysis of <em>Nicotiana tabacum</em> at six developmental stages from microspores to mature pollen grain to characterize in detail key transcription factor (TF) genes involved in pollen ontogeny. Our results provide the most complete transcriptomic data during pollen development in the important model plant <em>Nicotiana tabacum. </em>We have identified DEGs associated with six ontogenetic stages of the male gametophyte, providing insights into the molecular regulation of reproductive development by TFs that have a high potential for agronomic research investigating molecular networks associated with pollen sterility and related issues.</p>
Integrating methylome and transcriptome signatures expands the molecular classification of the pituitary tumors
<p><span><strong>Purpose</strong>:</span><span> To explore pituitary tumors by methylome and transcriptome signatures in a heterogeneous ethnic population. </span></p> <p><span><strong>Design</strong>: Retrospective cross-sectional study.</span></p> <p><span><strong>Patients and Methods</strong>: Clinicopathological features, methylome, and transcriptome were evaluated in pituitary tumors from 77 patients (61% women, age: 12-72 years)followed due to functioning (FPT: GH-secreting n=18, ACTH-secreting n=14) and non-functioning pituitary tumors (NFPT, n=45) at Ribeirao Preto Medical School, University of Sao Paulo. </span></p> <p><span><strong>Results</strong>: </span><span>U</span><span>nsupervised hierarchical clustering analysis (UHCA) of methylome </span><span>(n=77) </span><span>and transcriptome </span><span>(n=65 out of 77)</span><span> revealed three clusters each: one enriched by FPT, other by NFPT, and </span><span>another by </span><span>ACTH-secreting</span><span> and NFPT. Comparison between each omics-derived cluster identified 3,568 and 5,994 </span><span>differentially methylated and </span><span>expressed genes, respectively, </span><span>which were associated with each other, with tumor clinical presentation, and with 2017 and 2022 WHO classifications. UHCA considering 11 transcripts related to pituitary development/differentiation also supported three clusters: <em>POU1F1</em>-driven somatotroph, <em>TBX19</em>-driven </span><span>corticotroph, and</span><span> <em>NR5A1</em>-driven gonadotroph adenomas, with rare exceptions (</span><em><span>NR5A1</span></em><span> expressed in few GH-secreting and corticotroph-silent adenomas; <em>POU1F1</em> in few ACTH-secreting adenomas; and <em>TBX19</em> in few NFPTs). </span></p> <p><span><strong>Conclusions</strong>: This large heterogenic ethnic Brazilian cohort confirms that integrated methylome and transcriptome signatures classify FPT and NFPT, which are associated with clinical presentation and tumor invasiveness. Moreover, the cluster NFPT/ACTH-secreting adenomas raises interest regarding tumor heterogeneity, supporting the challenge raised by the 2017 and 2022 WHO definitions regarding the discrepancy, in rare cases, between clinical presentation and pituitary lineage markers. Finally, making our data publicly available enables further studies to validate genes/pathways involved in pituitary tumor pathogenesis and prognosis.</span></p>
Rapid changes in transcriptomic profile and mitochondrial function in human soleus muscle after three-day dry immersion
<p>Supplemental Tables for a study <strong>Rapid changes in transcriptomic profile and mitochondrial function in human soleus muscle after three-day dry immersion</strong></p>
Data for: Trinity assembled transcriptome of a Eurasian (Myriophyllum spicatum) and a hybrid (M. spicatum × M. sibiricum) genotype of watermilfoil
<p>Aquatic plant managers frequently treat Eurasian watermilfoil (<em>Myriophyllum spicatum</em> L.; EWM) and hybrid watermilfoil (<em>Myriophyllum spicatum</em> L. × <em>Myriophyllum sibiricum</em> Komarov) with 2,4-dichlorophenoxyacetic acid (2,4-D) herbicide. However, watermilfoil genotypes can differ in their response to 2,4-D. In this study, we compared facultative and constitutive gene expression differences for two watermilfoil genotypes (one Eurasian and one hybrid) that differ in their sensitivity to 2,4-D. To do this, we compared between control and 0.5mg L-1 2,4-D treated plants at four time points after treatment. We also assembled the first de novo watermilfoil transcriptome. We found that while qualitatively similar, the facultative transcriptional response of the EWM genotype to 2,4-D treatment was much stronger than the hybrid genotype, indicated by a greater number and log-fold-change of differentially expressed genes at all time points after treatment. Further, we found that the EWM and hybrid genotype differed in their 9-cis-epoxycarotenoid dioxygenase (NCED) and abscisic acid (ABA) gene response, and that there was a greater amount of photosynthesis gene downregulation (both in number and log-fold-change) in the EWM than the hybrid genotype. At the constitutive level, overall, the hybrid expressed genes at a higher level than the EWM genotype, but not the genes of the 2,4-D response pathway. These differences in gene expression match with the degree of phenotypic difference in growth observed between these genotypes when exposed to 2,4-D. The hybrid genotype used here mitigates the effects of 2,4-D treatment better than the EWM genotype at both the molecular and phenotypic level. More study is needed to understand the mechanism(s) of mitigation and whether this is a cause of hybridity, or the specific genotypic backgrounds used here.</p>
The ginseng transcriptome, ginsenoside and environmental factors dataset
<p>Ginseng is a world-renowned and precious Chinese herbal medicine. Its practical components have apparent effects on alleviating sub-health and rehabilitation. In our study, we conducted WGCNA bioinformatics analysis and verified it using transcriptome expression, saponin phenotype, and environmental factors data. We found the basic rule of typical saponins accumulation mediated by transcriptome expression profiles through the effect of some typical environmental factors and built a prediction model that makes biological sense. Using relevant data, we can further analyze the relationship between the change in environmental factors and the accumulation of effective components of ginseng, which lays a foundation for the establishment of a digital ginseng model more in line with the growth and development characteristics of ginseng.</p> <p>This data set involves the transcriptome expression of 42 ginseng samples, the content of saponins, the expression of 11 key enzyme genes associated with saponin Rb1 and typical ecological factors at the same time.</p>
The full-length Quillaja brasiliensis (Quillajaceae) reference transcriptome
<p><strong>Introduction</strong></p> <p>In this study, we used the PacBio Iso-Seq technology to uncover the full-length transcriptome of <em>Quillaja brasiliensis </em>(Quillajaceae), a tree species native to Brazil and of great importance for the extraction of bioactive saponins. Establishing a full-length transcriptome is essential for understanding the metabolic pathways leading to saponin biosynthesis in <em>Q. brasiliensis </em>and should set the stage for upcoming investigations.</p> <p> </p> <p><strong>Sample obtention and processing</strong></p> <p>We collected seeds of <em>Quillaja brasiliensis</em> (A.St.-Hil. & Tul.) Mart. (Quillajaceae) from the city of Canguçú (Rio Grande do Sul, Brazil) in March/2018. After <em>in vitro</em> germination, we explanted the seedlings and submitted them to callogenesis or transferred them to the grow room at 25°C under a 16h/day photoperiod. We detached leaves from 2.8-year-old individuals for the experiments hereby presented. After leaf cleanup, we detached approximately 15 leaves and exposed them to ultraviolet radiation (UV-C germicide lamp, ʎ maximum 254 nm) or white light (control) (Table 1). After incubation, we froze the plant material in liquid nitrogen and stored it at -80 °C until the next processing step. We obtained calluses from <em>in vitro </em>germinated explants and submitted them to cellular suspension induction. We maintained the cell cultures in MS medium supplemented with naphthaleneacetic acid (5 mg/L) and kinetin (0.1 mg/L) in the absence of light, using 250 mL culture flasks under the agitation of 120 RPM. After establishing the growth profile, we collected three flasks for each growth phase of the liquid cell cultures: lag phase (three days), log phase (seven days), and stationary phase (21 days). For the sample collection, cells were filtered using the Büchner funnel, washed with distilled water, and flash-frozen in liquid nitrogen. We stored the collected material at -80 °C until the next processing step. We extracted RNA samples from different tissues and growth conditions using the cetyltrimethylammonium bromide method (CTAB) and performed their purification using the RNeasy MinElute Cleanup Kit (QIAGEN, Hilden, Germany). We quality-controlled the samples using fluorometric and spectrophotometric methods before submitting them to sequencing by Novogene (Beijing, People's Republic of China). We made an equimolar pool of the five sample types for sequencing using the Iso-Seq strategy (PacBio, Menlo Park, US) in circular consensus sequencing mode for the obtention of long reads. The raw reads are available at the European Nucleotide Archive (ENA) under accession PRJEB58985.</p> <table> <tbody> <tr> <td> <p><strong>Sample</strong></p> </td> <td> <p><strong>Sample source</strong></p> </td> <td> <p><strong>Treatment or Growth phase</strong></p> </td> </tr> <tr> <td> <p>Leaf UV light</p> </td> <td> <p>Leaf</p> </td> <td> <p>Ultraviolet light treatment.</p> </td> </tr> <tr> <td> <p>Leaf white light</p> </td> <td> <p> </p> </td> <td> <p>White light treatment</p> </td> </tr> <tr> <td> <p>Cell suspension lag</p> </td> <td> <p>Cell suspension</p> </td> <td> <p>Lag phase (three days)</p> </td> </tr> <tr> <td> <p>Cell suspension log</p> </td> <td> <p> </p> </td> <td> <p>Log phase (seven days)</p> </td> </tr> <tr> <td> <p>Cell suspension stationary</p> </td> <td> <p> </p> </td> <td> <p>Stationary phase (21 days)</p> </td> </tr> </tbody> </table> <p><strong>Table 1</strong>. Brief description of pooled sequenced samples. </p> <p> </p> <p><strong>Zenodo repository content</strong></p> <p>This repository stores the full-length transcriptome FASTA file obtained after running the IsoSeq v3 pipeline, followed by one round of polishing by LoRDEC and transcript-collapsing by Cogent/Cupcake-ToFU (collapsed_isoforms.fa). We also performed coding sequence prediction and functional annotation using CodAn and TRAPID 2.0 (PLAZA 4.5 Dicots), respectively (transcriptome_functional_characterization.tsv). A brief description of column names for the functional characterization table is also available here.</p> <p> </p>
Transcriptome profiling of human monocytes from accute phase and convalescent patients infected with Plasmodium vivax malaria
<p> RNA sequencing raw data of monocytes obtained from five patients in acute phase of <em>Plasmodium vivax </em>malaria and 45 days after treatment. Individuals from Rondônia (Brazil) were between 18 and 60 years and attended to the exclusion criteria: chronic inflammatory or infectious diseases, pregnancy, and breastfeeding. Diagnostic of malaria by clinical symptoms and thick blood smear was also confirmed by qPCR. According to the recommended protocol of the Brazilian Ministry of Health patients were treated with chloroquine associated to primaquine. Protocols used were approved by the Ethical Committees on Human Experimentation from Instituto Rene ́ Rachou, Fiocruz, and National Ethical Council (CAAE: 59902816.7.0000.5091). </p> <p> Sequencing was performed in a NextSeq 500 system using the NextSeq 500/550 High Output Kit v2.5 for 75 cycles (both from Illumina). Before sequencing total mRNA samples were prepared using TruSeq Stranded mRNA Kit (Illumina) according to the manufacturer’s protocol. Files are in FASTQ format, just as they were downloaded from Illumina's cloud environment (BaseSpace) right after sequencing. Files are identified by patient number (1 to 5) and health condition: acute phase of the disease or convalescent (45 days after treatment).</p>
Comparative transcriptomics reveals divergence in pathogen response gene families amongst twenty forest tree species
<p>Forest trees provide critical ecosystem services for humanity that are under threat due to ongoing global change. Measuring and characterizing genetic diversity is key to understanding adaptive potential and developing strategies to mitigate negative consequences arising from climate change. <span>In the area of forest genetic diversity, genetic divergence caused by large-scale changes at the chromosomal level has been largely understudied. In this study, </span>we used the RNA-seq data of twenty co-occurring forest tree species from genera including <em>Acer, Alnus, Amelanchier, Betula, Cornus, Corylus, Dirca, Fraxinus, Ostrya, Populus, Prunus, Quercus, Ribes, Tilia, </em>and<em> Ulmus </em>sampled from Upper Peninsula of Michigan. These data were used to infer the origin and maintenance of gene family variation, species divergence time, as well as gene family expansion and contraction. We identified a signal of common whole genome duplication events shared by core eudicots. We also found rapid evolution, namely fast expansion or fast contraction of gene families, in plant-pathogen interaction genes amongst the studied diploid species. Finally, the results lay the foundation for further research on the genetic diversity and adaptive capacity of forest trees, which will inform forest management and conservation policies.</p>
Meta-analysis of diurnal transcriptomics in mouse liver reveals low repeatability of rhythm analyses
<p>The accumulation of public transcriptomic timeseries data enables robust meta-analyses that were not possible until recently. To assess the consistency of biological rhythms across studies, 57 public mouse liver tissue timeseries totaling 1096 RNA-seq samples were obtained and analyzed. Only the control groups of each study were included, to create comparable data. Technical factors in RNA-seq library preparation were the largest contributors to transcriptome-level differences, beyond biological or experiment-specific factors such as lighting conditions. Core clock genes were remarkably consistent in phase across all studies. Overlap of genes identified as rhythmic across studies was generally low, with no pair of studies having over 60% overlap. Distributions of phases of significant genes were remarkably inconsistent across studies, but the genes that consistently identified as rhythmic had acrophase clustering near ZT0 and ZT12. Despite the discrepancies between single-study analyses, cross-study analyses found substantial consistency. Running compareRhythms on each pair of studies identified a median of only 11% of the identified rhythmic genes as rhythmic in only one of the two studies. Data was integrated across studies in a JIVE analysis, which showed that the top two components of joint within-study variation are determined by time of day. A shape-invariant model with random effects was fit to the genes to identify the underlying shape of the rhythms, consistent across all studies, including identifying 72 genes with consistently multiple peaks.<br> <br> This dataset accumulates the quantified values from the 1096 samples along with the sample and study meta-data, and the results of JTK and BooteJTK methods run on each of the individual studies. It also includes the spline-fit curves results from the Shape Invarient Models (SIM).</p>
Profiling the Heterogeneity of Colorectal Cancer Consensus Molecular Subtypes using Spatial Transcriptomics: datasets
<p>You can find here the datasets used in the publication: </p> <p><em><strong>Valdeolivas, A., Amberg, B., Giroud, N. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. npj Precis. Onc. 8, 10 (2024). https://doi.org/10.1038/s41698-023-00488-4</strong> </em></p> <p>This contents the raw Spatial Transcriptomics data, spot categorization made by pathologist, the results of the deconvolution and intermediary files required to run the analysis described in our manuscript and available in Github: </p> <p><a href="https://github.com/alberto-valdeolivas/ST_CRC_CMS">https://github.com/alberto-valdeolivas/ST_CRC_CMS</a></p> <p>In particular, you will find here several zip compressed files with the following content: </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Intermediary_FileObjects.zip?versionId=989cd48d-45f6-46b9-9f90-1927af392a7e">Intermediary_FileObjects.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some later scripts. </p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/IntermediaryFiles_ST_CRC_LiverMetastasis.zip">IntermediaryFiles_ST_CRC_LiverMetastasis.zip</a>: The intermediary files generated in the scripts hosted in the github repo and required to run some of the scripts dealing with the external CRC ST dataset used in our manuscript. </p> <p>- <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/Pathology_SpotAnnotations.zip?versionId=ce657a54-9fec-4633-9d89-31f1479b93b7">Pathology_SpotAnnotations.zip</a>: The categories assigned by the pathologists to all the spots across our set ST samples to a different anatomical category (tumor, stroma, non-neoplastic mucosa...) </p> <p>-<a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep1.zip?versionId=dbfaad0f-784b-44c9-91d1-713f063d64e3">SN048_A121573_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A121573_Rep2.zip?versionId=ae997080-ca69-44c3-86aa-65bc1d5ef120">SN048_A121573_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep1.zip?versionId=e453ed45-22d8-4d60-b7f3-4daa9212cc88">SN048_A416371_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN048_A416371_Rep2.zip?versionId=be395926-eee8-4670-b355-125e72bf6281">SN048_A416371_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A551763_Rep1.zip?versionId=6b7fa01a-a0d9-43e7-8d1d-8c56cb422374">SN123_A551763_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A595688_Rep1.zip?versionId=f625a286-fbc7-48f6-a57d-7d0df67a0574">SN123_A595688_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A798015_Rep1.zip?versionId=3540f1e5-9cf4-412c-887c-b1d0cc4e03c5">SN123_A798015_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN123_A938797_Rep1_X.zip?versionId=de59c354-fea2-4843-a5f9-5e7a8d863e51">SN123_A938797_Rep1_X.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A551763_Rep2.zip?versionId=9da50bec-8ba4-41b0-a29e-4fc778cf12b7">SN124_A551763_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A595688_Rep2.zip?versionId=29c3e99e-7db2-4c02-9004-dc9d8abf3c27">SN124_A595688_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A798015_Rep2.zip?versionId=a0cf2cca-f3c9-4c45-b311-1ddc81371e35">SN124_A798015_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN124_A938797_Rep2.zip?versionId=e6e4e2bc-1593-4c0f-ac37-b00cc2fc1124">SN124_A938797_Rep2.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep1.zip?versionId=ec31a69e-d0ce-4e4c-82dc-e7f2e617631a">SN84_A120838_Rep1.zip</a>, <a href="https://zenodo.org/api/files/a1d45f27-bec7-4f74-9d49-686dda0f77bf/SN84_A120838_Rep2.zip?versionId=89c89532-7bb1-47e4-8900-b5c12a7c4ba0">SN84_A120838_Rep2.zip</a>: The output of Space Ranger, including processed count data matrices and histological images, for the ST data generated in this study</p> <p>- <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_BelgianCohort.zip">DeconvolutionResults_ST_CRC_BelgianCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_KoreanCohort.zip">DeconvolutionResults_ST_CRC_KoreanCohort.zip</a>, <a href="https://zenodo.org/api/files/7b1f17b1-5345-4d29-ab8f-2d51fad58fc4/DeconvolutionResults_ST_CRC_LiverMetastasis.zip">DeconvolutionResults_ST_CRC_LiverMetastasis.zip</a>: These files contain the main results obtained when using the Cell2Location deconvolution approach in our samples (with two different references: Korean and Belgian cohorts) and in the external set of CRC ST samples (only Korean cohort)</p> <p> </p> <p>- We have also uploaded the whole slide images (WSI). These are the files with an ndpi extension: </p> <p><br><a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V10B01-048_new%20CRC_2021_02_16.ndpi?versionId=d5c8cbd3-40de-43da-8370-329def9e4f14">Visium Frozen_SN V10B01-048_new CRC_2021_02_16.ndp ...</a> (samples A121573_Rep1, A121573_Rep2, A416371_Rep1 and A416371_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-084.ndpi?versionId=6d91b1f9-56e9-45c3-a2e6-4714975678fb">Visium Frozen_SN V19S23-084.ndpi</a> (samples A120838_Rep1 and A120838_Rep2), <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-123.ndpi?versionId=c535482c-0a3c-4ba5-a056-f96796c366b0">Visium Frozen_SN V19S23-123.ndpi</a> (samples A551763_Rep1, A595688_Rep1, A798015_Rep1, A938797_Rep1) and <a href="https://zenodo.org/api/files/76d879db-33b7-46c3-ac12-7026c0e64877/Visium%20Frozen_SN%20V19S23-124.ndpi?versionId=49ce857c-47bb-4930-bb0c-09213e4acf28">Visium Frozen_SN V19S23-124.ndpi</a> (samples A551763_Rep2, A595688_Rep2, A798015_Rep2 and A938797_Rep2)</p> <p>- We have now included the fastq and Bam files for the different samples, excluding replicate 1 of the A938797 sample whose fastq files are missing: </p> <p><strong>IMPORTANT: Fastq files are in version 1, while bam files are in version 2 of the dashboards reported below: </strong></p> <ol> <li>Sample <a href="https://doi.org/10.5281/zenodo.13991781">S1_Cec</a> (A551763)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006187">S2_Col_R </a>(A595688)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13987002">S3_Col_R </a>(A416371) </li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13990328">S4_Col_Sig </a>(A120838)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.13989699">S5_Rec </a>(A121573)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14008051">S6_Rec </a>(A938797)</li> <li>Sample <a href="https://doi.org/10.5281/zenodo.14006810">S7_Rec/Sig</a> (A798015)</li> </ol> <p> </p> <p> </p>
RNAseq transcriptome of draining lymph node (LN) and tumor of MC38 murine tumors treated with cryoablation and chitosan/IL-12
<p class="MsoNormal">Focal ablation technologies are routinely used in the clinical management of inoperable solid tumors but often result in incomplete ablations leading to high recurrence rates. Adjuvant therapies capable of safely eliminating residual tumor cells are therefore of great clinical interest. Interleukin 12 (IL-12) is a potent antitumor cytokine that can be localized intratumorally through coformulation with <span>viscous biopolymers</span> including chitosan (CS) solutions. The objective of this research was to determine if localized immunotherapy with CS/IL-12 could prevent <span>tumor recurrence after cryoablation (CA). Tumor recurrence, overall survival, and protective immunity were assessed. Systemic immunity was evaluated in spontaneously metastatic and bilateral tumor models. Temporal bulk RNA sequencing was performed on tumor and draining lymph node samples.</span> In multiple murine tumor models, the addition of CS/IL-12 to CA reduced recurrence rates by 30–55%. Altogether, this cryo-immunotherapy induced complete durable regression of large tumors in 80–100% of treated animals. <span>Mice</span> treated with CA plus adjuvant CS/IL-12 were partially or completely protected from tumor rechallenge. <span>Systemically,</span> CS/IL-12 prevented lung metastases when delivered as a neoadjuvant to CA. However, CA plus CS/IL-12 had minimal antitumor activity against established, untreated abscopal tumors. Adjuvant anti-PD-1 therapy delayed the growth of abscopal tumors. Transcriptome analyses revealed early immunological changes in <span>the dLN</span>, followed by a significant increase in gene expression associated with immune suppression and regulation. Cryo-immunotherapy with localized CS/IL-12<span> reduces recurrences and</span> enhances the elimination of large primary tumors<span>. This focal combination therapy also induces significant systemic</span> antitumor immunity <span>although further studies are necessary</span>.</p>
Transcriptomics and proteomics reveal distinct biology for lymph node metastases and tumor deposits in colorectal cancer
<p>Spatial transcriptomic data (counts_DSP_afterQC_normalisation.csv) derived using the Nanostring GeoMx digital spatial profiler platform to analyse tumor deposits and lymph node metastases from 10 patients with colorectal cancer. 264 AOIs of cancer transcriptome atlas data. Normalised using Q3 normalisation, for further information on methods see associated publication. </p>
Example Inputs for PIPEFISH Spatial Transcriptomics Pipeline Tool
<p>This repository contains example input data, including raw images, codebooks, parameters, and segmentation labels needed to run the FISH spatial transcriptomics pipeline tool <a href="https://github.com/hubmapconsortium/spatial-transcriptomics-pipeline">PIPEFISH</a>. The datasets contained are:</p> <ul> <li><em>in situ</em> sequencing (ISS) of a whole coronal slice of a mouse brain (50 genes). <a href="https://www.biorxiv.org/content/10.1101/2021.10.12.464086v1">Link to publication</a>.</li> </ul> <p>Gataric, M., Park, J.S., Li, T., Vaskivskyi, V., Svedlund, J., Strell, C., Roberts, K., Nilsson, M., Yates, L.R., Bayraktar, O. and Gerstung, M., 2021. PoSTcode: Probabilistic image-based spatial transcriptomics decoder. <em>bioRxiv</em>, pp.2021-10.</p> <ul> <li>MERFISH of human U2-OS cell cultures (130 genes). <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047202/">Link to publication</a>.</li> </ul> <p>Moffitt, J.R., Hao, J., Wang, G., Chen, K.H., Babcock, H.P. and Zhuang, X., 2016. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. <em>Proceedings of the National Academy of Sciences</em>, <em>113</em>(39), pp.11046-11051.</p> <ul> <li>seqFISH of a developing mouse embryo (351 genes). <a href="https://www.nature.com/articles/s41587-021-01006-2">Link to publication</a>.</li> </ul> <p>Lohoff, T., Ghazanfar, S., Missarova, A., Koulena, N., Pierson, N., Griffiths, J.A., Bardot, E.S., Eng, C.H., Tyser, R.C.V., Argelaguet, R. and Guibentif, C., 2022. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. <em>Nature biotechnology</em>, <em>40</em>(1), pp.74-85.</p> <p>In order to correctly format the inputs, run the prep_input.py script for the dataset you wish to run while in the <strong>same</strong> <strong>directory</strong> as the script.</p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 1
<p>This deposit contains the supporting records of images and image analysis presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Associated Zenodo repositories:</p> <table> <thead> <tr> <th scope="col">Description</th> <th scope="col">DOI</th> </tr> </thead> <tbody> <tr> <td>Main figures PART 1, Figure 1,2,3,5</td> <td>10.5281/zenodo.7653239</td> </tr> <tr> <td>Main figures PART 2, Figure 6</td> <td>10.5281/zenodo.7900973</td> </tr> <tr> <td>Supplemental 3DTC figures: S1, S4, S5, S7, S8, S9</td> <td>10.5281/zenodo.7894632</td> </tr> </tbody> </table> <p>Contents:1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figures 2, 3 and 5. Figure 6 analyses are included in a compansion repository: 10.5281/zenodo.7900973. Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figure 1 P,Q. The supplemental figure data for RNAScope. Figures S1,S4 and S5 are found in: 10.5281/zenodo.7894633.</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", Supplemental 3DTC figures
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218 found in supplemental figures.</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses by figures in the supplemental figure data for 3D tissue cytometry. The main figure data is found at: 10.5281/zenodo.7653239 and 10.5281/zenodo.7900973.</p> <p>2) a collection of zip files containing the RNAScope image files shown in: Figures S1,S4 and S5. The main RNAScope figure data is found at: 10.5281/zenodo.7653239</p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p>
Comparative transcriptomic analysis reveals coordinated mechanisms of different genotypes of common vetch in response to Al stress
<p><strong>Supplementary Table</strong></p>
Supporting data and analysis for," A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease", main figures PART 2
<p>This deposit contains the supporting records of analysis for 3D cytometry presented in, " A spatially anchored transcriptomic atlas of the human kidney papilla identifies significant immune injury in patients with stone disease". doi: https://doi.org/10.1101/2022.06.22.497218</p> <p>Contents:</p> <p>1) a collection of .zip files contains the 3D tissue cytometry files for tissue analyzed in the manuscript doi: https://doi.org/10.1101/2022.06.22.497218. This collection includes the individual analyses for figure 6 analyses.</p> <p>Contents of zip files by figure contain at a minimum the .obx and a .tif file which includes the segmented objects and associated measurements for use by VTEA (https://vtea.wiki/). Additional files may include gate files (.vtg) or max projections (.tif).</p> <p> </p> <p>Please address any concerns or questions to the authors listed in the deposit or manuscript, doi: https://doi.org/10.1101/2022.06.22.497218</p> <p> </p>
Proteomics analysis for: The platelet transcriptome and proteome in Alzheimer's disease and aging: an exploratory cross-sectional study
<p>Alzheimer’s disease (AD) and aging are associated with platelet hyperactivity. However, the mechanisms underlying abnormal platelet function in AD and aging are yet poorly understood. To explore the molecular profile of AD and aged platelets, we investigated platelet activation (i.e., CD62P expression), proteome and transcriptome in AD patients, non-demented elderly, and young individuals as controls. AD, aged and young individuals showed similar levels of platelet activation based on CD62P expression. However, AD and aged individuals had a proteomic signature suggestive of increased platelet activation compared with young controls. Transcriptomic profiling suggested the dysregulation proteolytic machinery involved in the regulation of platelet function, particularly in the ubiquitin-proteasome system in AD and autophagy in aging. The functional implication of these transcriptomic alterations remains unclear and requires further investigations. </p>
ScienceDex guides
Understand access before you commit
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.