Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
633
datasets available to search
ShareScore release 0.7.1
Dataset results
633 results for “TCR”
Longitudinal high-throughput TCR repertoire profiling reveals the dynamics of T cell memory formation after mild COVID-19 infection
<p>Processed TCRbeta and TCRalpha repertoires after mild COVID-19 (Version 2.0: day 85 timepoints added) infection, see preprint: <a href="https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3">https://www.biorxiv.org/content/10.1101/2020.05.18.100545v3</a></p> <p>and GitHub repository: <a href="https://github.com/pogorely/Minervina_COVID">https://github.com/pogorely/Minervina_COVID</a></p> <p>Two donors (M and W), two biological replicates of PBMC (F1 and F2), CD4+, CD8+, and Memory subpopulations for each post-infection time points (day 15, 30, 37, 45, 85 post-infection), and pre-infection PBMC repertoires sampled in 2019 and 2018. </p>
Control T-cell receptor (TCR) alpha and beta chain nucleotide and amino acid sequences from human and mouse
<p>A dataset of pooled T-cell receptor (TCR) sequences for TCR alpha and beta chains of human and mouse.</p> <p>Sequences are obtained from various samples of healthy individuals/mice using our conventional protocols: see for example [Britanova et al "Dynamics of individual T cell repertoires: from cord blood to centenarians" The Journal of Immunology 2016] and [Izraelson et al. "Comparative analysis of murine T‐cell receptor repertoires." Immunology 2018].</p> <p>The sequences are stored as gzipped clonotype tables in VDJtools format, see [https://vdjtools-doc.readthedocs.io/en/master/input.html#vdjtools-format].</p> <p>This control dataset can be used as a proxy for a generative VDJ rearrangement model to estimate the expected frequency distribution of TCRs and check for enrichment of rare TCR clonotypes and groups of similar TCR sequences. For the implementation of the enrichment analysis, please see CalcDegreeStats routine from VDJtools software, see [https://vdjtools-doc.readthedocs.io/en/master/annotate.html#calcdegreestats].</p> <p>Files named "human.tra.strict.txt.gz", etc are pools of random/naive TCR clonotypes containing unique V/J/CDR3 nucleotide sequence combinations observed in data. The pools.zip file is used for TCR motif inference in VDJdb database [https://github.com/antigenomics/vdjdb-motifs], it contains human.tra.aa.txt, etc files that contain random/naive TCR clonotypes grouped by CDR3 amino acid sequence with the most frequent representative V and J.</p>
Dataset of "Chronic TCR-MHC (self)-interactions limit the functional potential of TCR affinityincreased CD8 T lymphocytes"
<p><strong>Background</strong>: Affinity-optimized T cell receptor (TCR)-engineered lymphocytes targeting tumor antigens can mediate potent antitumor responses in cancer patients, but also bear substantial risks for off-target toxicities. Most preclinical studies have focused on T cell responses to antigen-specific stimulation. In contrast, little is known on the regulation of T cell responsiveness through continuous TCR triggering and consequent tonic signaling. Here, we addressed the question whether increasing the TCR affinity can lead to chronic interactions occurring directly between TCRs and MHC-(self) molecules, which may modulate the overall functional potency of tumor-redirected CD8 T cells. For this purpose, we developed two complementary human CD8 T cell models (i.e. HLA-A2 knock-in and knock-out) engineered with incremental-affinity TCRs to the HLA-A2/NY-ESO-1 tumor antigen.<br> <strong>Methods</strong>: The impact of HLA-A2 recognition, depending on TCR affinity, was assessed at the levels of the TCR/CD3 complex, regulatory receptors, and signaling, under steady-state conditions and in kinetic studies. The quality of<br> CD8 T cell responses was further evaluated by gene expression and multiplex cytokine profiling, as well as real-time quantitative cell killing, combined with co-culture assays.<br> <strong>Results</strong>: We found that HLA-A2 per se (in absence of cognate peptide) can trigger chronic activation followed by a tolerance-like state of tumor-redirected CD8 T cells with increased-affinity TCRs. HLA-A2pos but not HLA-A2neg T cells displayed an activation phenotype, associated with enhanced upregulation of c-CBL and multiple inhibitory receptors. T cell activation preceded TCR/CD3 downmodulation, impaired TCR signaling and functional<br> hyporesponsiveness. This stepwise activation-to-hyporesponsive state was dependent on TCR affinity and already detectable at the upper end of the physiological affinity range (KD ≤ 1 μM). Similar findings were made when<br> affinity-increased HLA-A2neg CD8 T cells were chronically exposed to HLA-A2pos-expressing target cells.<br> <strong>Conclusions</strong>: Our observations indicate that sustained interactions between affinity-increased TCR and self-MHC can directly adjust the functional potential of T cells, even in the absence of antigen-specific stimulation. The<br> observed tolerance-like state depends on TCR affinity and has therefore potential implications for the design of affinity-improved TCRs for adoptive T cell therapy, as several engineered TCRs currently used in clinical trials share<br> similar affinity properties.</p>
TCR-DeepInsight Reference Datasets
<p>Reference single-cell TCR immune profiling datasets for the TCR-DeepInsight analysis. We have excluded two controlled-access datasets (1) AML dataset from Abbas et al. 2021 (EGAS00001004894) and (2) Kawasaki disease dataset from Wang et al., 2021 (OEP001162).</p> <ul> <li>human_gex_reference_v2.h5ad: Processed H5AD file for transcriptome features from the single-cell TCR immune profiling datasets. <strong><em>anndata.read_h5ad<br>For raw count H5AD files, please check the additional repo here: <a href="https://zenodo.org/records/17405143">https://zenodo.org/records/17405143</a></em></strong></li> <li>human_tcr_reference_v2.h5ad: Processed H5AD file for unique TCR clonotypes single-cell TCR immune profiling dataset. <strong><em>anndata.read_h5ad</em></strong></li> <li>Yi_2023_Ankylosing_Spondylitis.h5ad: Processed H5AD file from Yi <em>et al</em>., 2023 (GSE216885). <strong><em>anndata.read_h5ad</em></strong></li> <li>GSE272993_cd8_nn_labeled_FINAL.fl_tcr.match_v2_5.transfered.h5ad: Processed H5AD file from Wang et al., 2024 (GSE272993). <strong><em>anndata.read_h5ad</em></strong><em><strong><br><br></strong></em></li> <li>human_bulk_tcr_reference.parquet: PARQUET file for bulk TCRβ sequencing. <strong><em>pandas.read_parquet</em></strong> <ul> <li>human_bulk_tcr_reference.cd4.parquet: PARQUET file for bulk TCRβ sequencing for CD4-sorted T cells<br>human_bulk_tcr_reference.mait.parquet: PARQUET file for bulk TCRβ sequencing for MAIT cells<br>human_bulk_tcr_reference.treg.parquet: PARQUET file for bulk TCRβ sequencing for Treg cells<br>human_bulk_tcr_reference.cd8.parquet: PARQUET file for bulk TCRβ sequencing for CD8-sorted T cells<br><em><br></em></li> </ul> </li> <li>Yi_2023_Ankylosing_Spondylitis.h5ad. <strong><em>anndata.read_h5ad<br></em></strong>Processed dataset from K. Yi et al. Analysis of Single‐Cell Transcriptome and Surface Protein Expression in Ankylosing Spondylitis Identifies OX40 ‐Positive and Glucocorticoid‐Induced Tumor Necrosis Factor Receptor–Positive Pathogenic Th17 Cells. <em>Arthritis & Rheumatology</em> <strong>75</strong>, 1176–1186 (2023).</li> <li>GSE272993_cd8_nn_labeled_FINAL.fl_tcr.match_v2_5.transfered.h5ad. <strong><em>anndata.read_h5ad<br></em></strong>Processed dataset from K. Wang et al. Combination anti-PD-1 and anti-CTLA-4 therapy generates waves of clonal responses that include progenitor-exhausted CD8+ T cells. <em>Cancer Cell</em>, S1535610824003064 (2024).</li> </ul> <ul> <li>human_gex_reference_v2.scatlasvae.ckpt: pretrained weight state dict from scAtlasVAE model for human_gex_reference_v2.h5ad. <strong><em>torch.load</em></strong></li> <li>human_bert_pseudosequence.tcr_v2.ckpt: pretrained weight state dict from BERT for human_tcr_reference_v2.h5ad. <em><strong>torch.load</strong><br></em></li> <li><em>human_bert_pseudosequence_pca.tcr_v2.pkl: pretrained PCA weight for </em>human_tcr_reference_v2.h5ad. <em><strong>pickle.load</strong></em></li> </ul>
Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: discovery cohort meta data and parsed TCR repertoire data
<p>Meta data corresponding the the discovery cohort for the paper, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include: </p> <p>(1) a file mapping the SNP data subject IDs to the TCR repertoire data subject IDs (gwas_id_mapping.tsv)<br> (2) a file including the PCAir PCs, self-reported ancestry, and genomic ancestry for each subject (all_pc_air.txt)<br> (3) a file including the PCAir variance explained by each PC (all_pc_air_variance.txt)<br> (3) a file including the SNP ID, chromosome, hg19 position, allele, rsid, and quality control metrics for each SNP in the SNP array (emerson_snp_rs_data.tsv)<br> (4) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (5) a file including predicted TRBD2 allele genotypes for each subject (emerson_trbd2_alleles.tsv)<br> (6) Parsed TCRB repertoire data. These raw data were first published in Emerson et. al, <em>Nature Genetics </em>2017. (emerson_parsed_tcrb.tgz)</p> <p><strong>Corresponding discovery cohort raw TCR repertoire data is available here: </strong>https: //doi.org/10.21417/B7001Z (ImmuneACCESS database)<br> <strong>Corresponding discovery cohort SNP data is available here:</strong> https: //www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001918.v1.p1 (The database of Genotypes and Phenotypes, accession number: phs001918)<br> <br> <strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
Human single-cell TCR data from irradiated sporozoite trial
<p>This is the MiAIRR-compliant single-cell AIRR-seq data of the study "Clonal evolution and TCR specificity of the human<br> TFH cell response to Plasmodium falciparum CSP" by Wahl <em>et al.,</em> <em>Sci Immunol</em> 7:eabm9644 (2022). The study defines the clonal evolution and epitope specificity of the human cTFH cell response to Plasmodium falciparum CSP.</p>
TCR-MHC Germline Interaction Scores Generated Using AIMS
<p>These data were generated using the AIMS interaction scoring function as outlined in the manuscript "A Systematic Characterization of Germline-Encoded Contacts Identifies the Source of Bias in TCR-MHC Interactions". They accompany the AIMS version 0.7 software available on GitHub: https://github.com/ctboughter/AIMS . These files are meant to be loaded into the mhc_germline_analysis.ipynb file, but are too large to be included on the GitHub page itself.</p>
Figure2. Generation of negative feedbacks gets tuned once a TCR completes stimulation beyond the threshold l. A TCell generates activation signal to BCell once it gets stimulation of its k-TCRs.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
Decoding NY-ESO-1 TCR T Cells: Transcriptomic Insights Reveal Dual Mechanisms of Tumor Targeting in a Melanoma Murine Xenograft Model
<p><span>Single-cell RNA-seq data of NY-ESO-1-specific TCR T-cells generated with the BD Rhapsody™ system.</span></p> <p><span>Biogroup information: Control (<em>n</em><span> </span>= 4), PB (murine peripheral blood, <em>n</em><span> </span>= 4).</span></p> <p><span>Cell preparation: NY-ESO-1-specific TCR T-cells were obtained via a retroviral transduction of an anti-NY-ESO-1-TCR construct, murine peripheral blood T-cells were enriched using anti-CD3 magnetic separation via MojoSortTM Human CD3 Selection Kit.</span></p> <p><span>Single-cell analysis system: BD Rhapsody™</span></p> <p><span>Library strategy: 3' mRNA sequencing</span></p> <p><span>Library preparation protocol: BD Rhapsody™ Targeted mRNA and Sample Tag Library Preparation</span></p> <p><span>mRNA panel: BD Rhapsody™ Immune Response Panel HS</span></p> <p><span>BD Pipeline version: 1.11L</span></p>
Dataset: The TCR repertoire reconstitution in multiple sclerosis: comparing one-shot and continuous immunosuppressive therapies
<p>This dataset, containing TCRbeta-chain data, is the basis for the following publication in Frontiers in Immunology: The TCR repertoire reconstitution in multiple sclerosis: comparing one-shot and continuous immunosuppressive therapies. The file key can be found in the file: file_key.xlsx. Relevant methodological details maybe found in the corresponding publication.</p>
scRNA-seq revealed the rules for CDR3 length pairing in TCR beta and alpha chains and BCR heavy and light chains
<p>The scRNAseq datasets of CDR3 length pairing in TCR beta and alpha chains which come from human cental and peripheral samples and mouse peripheral samples.</p> <p>The scRNAseq datasets of CDR3 length pairing in BCR heavy and light chainsCDR3 length pairing in TCR beta and alpha chains and BCR heavy and light chains human cental and peripheral samples and mouse cental and peripheral samples.</p> <p> </p>
Data from: DGKα/ζ inhibition lowers the TCR affinity threshold and potentiates anti-tumor immunity
<p>Checkpoint blockade immunotherapies expand neoantigen- or virus-specific T cells, and poor responsiveness to immunotherapy is associated with lower mutational burden in tumors of non-viral origin. Although mouse models demonstrate that lower affinity T cells recognizing self-antigens can contribute to tumor control if sufficiently activated, therapeutic options for enhancing T cell priming are limited. Diacylglycerol kinases (DGKs) attenuate DAG signaling by converting DAG to phosphatidic acid, thereby suppressing pathways downstream of TCR signaling. Using a novel dual DGK alpha and zeta inhibitor (DGKi), tumor-specific CD8 T cells with different affinities (TRP1high and TRP1low), and a series of altered peptide ligands, we demonstrate that inhibition of DGKα/ζ can lower the signaling threshold for T cell priming. TRP1high and TRP1low CD8 T cells produced more IL-2, IFNγ, and other effector cytokines in the presence of cognate antigen and DGKi. Effector TRP1high- and TRP1low-mediated cytolysis of tumor cells with low antigen load was MHC-restricted, mediated by IFNγ, and augmented by DGKi. Adoptive T cell transfer into mice bearing pancreatic or melanoma tumors synergized with single-agent DGKi or DGKi and αPD1, with increased expansion of low-affinity T cells and increased cytokine production observed in tumor infiltrates of treated mice. Collectively, our findings highlight DGKα/ζ as therapeutic targets for augmenting tumor-specific CD8 T cell function.</p>
Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities: validation cohort meta data and parsed TCR repertoire data
<p>Meta data corresponding the the validation cohort for the paper, "Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities" by Magdalena L Russell, Aisha Souquette, David M Levine, Stefan A Schattgen, E Kaitlynn Allen, Guillermina Kuan, Noah Simon, Angel Balmaseda, Aubree Gordon, Paul G Thomas, Frederick A Matsen IV, and Philip Bradley. These meta data include: </p> <p>(1) SNP genotypes for the two SNPs which overlap with the discovery cohort<br> - (nicaragua_snp_genotypes_ints.tsv) -- SNP genotypes as integers<br> - (nicaragua_snp_genotypes_strings.tsv) -- SNP genotypes as allele strings <br> (2) the ancestry PCs for each individual in the validation cohort (nicaragua_snp_ancestry_PCA.tsv)<br> (3) a file including IMGT genes and sequences used for parsing TCRB repertoire data (human_vj_allele_cdr3_nucseqs.tsv)<br> (4) a file including IMGT genes used for parsing TCRA repertoire data (human_vj_alleles_alpha.tsv)<br> (5) Parsed TCRA repertoire data (nicaragua_parsed_TCRA.tgz)<br> (6) Parsed TCRB repertoire data (nicaragua_parsed_TCRB.tgz) </p> <p><strong>Corresponding raw validation cohort TCR repertoire data is available here:</strong> https://www. ncbi.nlm.nih.gov/bioproject/PRJNA762269 (The BioProject database, accession number: PRJNA762269)</p> <p><strong>Software tools designed to work with these data are available here:</strong> https://github.com/phbradley/tcr-gwas</p>
Single-cell expression and TCR data from CD19-specific CAR T cells in a phase I/II clinical trial
<p><span>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.</span></p>
ESM-2 embeddings for TCR-Epitope Binding Affinity Prediction Task
<p>This is the accompanying dataset that was generated by the GitHub project: <a href="https://github.com/tonyreina/tdc-tcr-epitope-antibody-binding">https://github.com/tonyreina/tdc-tcr-epitope-antibody-binding</a>. In that repository I show how to create a machine learning models for predicting if a T-cell receptor (TCR) and protein epitope will bind to each other.</p> <p>A model that can predict how well a TCR bindings to an epitope can lead to more effective treatments that use immunotherapy. For example, in anti-cancer therapies it is important for the T-cell receptor to bind to the protein marker in the cancer cell so that the T-cell (actually the T-cell's friends in the immune system) can kill the cancer cell.</p> <div> <div>[HuggingFace](https://huggingface.co/facebook/esm2_t36_3B_UR50D) provides a "one-stop shop" to train and deploy AI models. In this case, we use Facebook's open-source [Evolutionary Scale Model (ESM-2)](https://github.com/facebookresearch/esm). These embeddings turn the protein sequences into a vector of numbers that the computer can use in a mathematical model.</div> <div> </div> To load them into Python use the Pandas library:</div> <pre><code>import pandas as pd train_data = pd.read_pickle("train_data.pkl") validation_data = pd.read_pickle("validation_data.pkl") test_data = pd.read_pickle("test_data.pkl")</code></pre> <p>The <strong>epitope_aa</strong> and the <strong>tcr_full</strong> columns are the protein (peptide) sequences for the epitope and the T-cell receptor, respectively. The letters correspond to the <a href="https://en.wikipedia.org/wiki/DNA_and_RNA_codon_tables">standard amino acid codes</a>.</p> <p>The <strong>epitope_smi</strong> column is the <a href="https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system">SMILES</a> notation for the chemical structure of the epitope. We won't use this information. Instead, the ESM-1b embedder should be sufficient for the input to our binary classification model.</p> <p>The <strong>tcr</strong> column is the CDR3 hyperloop. It's the part of the TCR that actually binds (assuming it binds) to the epitope.</p> <p>The <strong>label</strong> column is whether the two proteins bind. 0 = No. 1 = Yes.</p> <p>The <strong>tcr_vector</strong> and <strong>epitope_vector</strong> columns are the bio-embeddings of the TCR and epitope sequences generated by the Facebook ESM-1b model. These two vectors can be used to create a machine learning model that predicts whether the combination will produce a successful protein binding.</p> <p>From the TDC website:</p> <blockquote> <p>T-cells are an integral part of the adaptive immune system, whose survival, proliferation, activation and function are all governed by the interaction of their T-cell receptor (TCR) with immunogenic peptides (epitopes). A large repertoire of T-cell receptors with different specificity is needed to provide protection against a wide range of pathogens. This new task aims to predict the binding affinity given a pair of TCR sequence and epitope sequence.</p> <p>Weber et al.</p> </blockquote> <p>Dataset Description: The dataset is from Weber et al. who assemble a large and diverse data from the VDJ database and ImmuneCODE project. It uses human TCR-beta chain sequences. Since this dataset is highly imbalanced, the authors exclude epitopes with less than 15 associated TCR sequences and downsample to a limit of 400 TCRs per epitope. The dataset contains amino acid sequences either for the entire TCR or only for the hypervariable CDR3 loop. Epitopes are available as amino acid sequences. Since Weber et al. proposed to represent the peptides as SMILES strings (which reformulates the problem to protein-ligand binding prediction) the SMILES strings of the epitopes are also included. 50% negative samples were generated by shuffling the pairs, i.e. associating TCR sequences with epitopes they have not been shown to bind.</p> <blockquote> <p>Task Description: Binary classification. Given the epitope (a peptide, either represented as amino acid sequence or as SMILES) and a T-cell receptor (amino acid sequence, either of the full protein complex or only of the hypervariable CDR3 loop), predict whether the epitope binds to the TCR.</p> <p>Dataset Statistics: 47,182 TCR-Epitope pairs between 192 epitopes and 23,139 TCRs.</p> <p>References:</p> </blockquote> <ol> <li>Weber, Anna, Jannis Born, and María Rodriguez Martínez. “TITAN: T-cell receptor specificity prediction with bimodal attention networks.” Bioinformatics 37.Supplement_1 (2021): i237-i244.</li> <li>Bagaev, Dmitry V., et al. “VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium.” Nucleic Acids Research 48.D1 (2020): D1057-D1062.</li> <li>Dines, Jennifer N., et al. “The immunerace study: A prospective multicohort study of immune response action to covid-19 events with the immunecode™ open access database.” medRxiv (2020).</li> </ol> <blockquote> <p>Dataset License: CC BY 4.0.</p> <p>Contributed by: Anna Weber and Jannis Born.</p> </blockquote> <p> </p> <div>The Facebook ESM-2 model has the MIT license and was published in:</div> <div> </div> <div>* Zeming Lin et al, Evolutionary-scale prediction of atomic-level protein structure with a language model, Science (2023). DOI: 10.1126/science.ade2574 https://www.science.org/doi/10.1126/science.ade2574</div> <div> </div> <div>HuggingFace has several versions of the trained model.</div> <div> </div> <div> <table> <tbody> <tr> <td>Checkpoint name</td> <td>Number of layers</td> <td>Number of parameters</td> </tr> <tr> <td>esm2_t48_15B_UR50D</td> <td>48</td> <td>15B</td> </tr> <tr> <td>esm2_t36_3B_UR50D</td> <td>36</td> <td>3B</td> </tr> <tr> <td>esm2_t33_650M_UR50D</td> <td>33</td> <td>650M</td> </tr> <tr> <td>esm2_t30_150M_UR50D</td> <td>30</td> <td>150M</td> </tr> <tr> <td>esm2_t12_35M_UR50D</td> <td>12</td> <td>35M</td> </tr> <tr> <td>esm2_t6_8M_UR50D</td> <td>6</td> <td>8M</td> </tr> </tbody> </table> </div>
The TCR assigns naive T cells to a preferred lymph node
<p>Naive T cells recirculate between the spleen and lymph nodes where they mount immune responses when meeting dendritic cells presenting foreign antigen. As this may happen anywhere, naive T cells ought to visit all lymph nodes. Here, deep sequencing almost-complete TCR-repertoires led to a comparison of different lymph nodes within and between individual mice. We find strong evidence for a deterministic CD4/CD8 lineage choice and a consistent spatial structure. Specifically, some T cells show a preference for one or multiple lymph nodes, suggesting that their TCR interacts with locally presented (self-)peptides. These findings are mirrored in TCR-transgenic mice showing localized CD69-expression, retention, and cell division. Thus, naïve T cells intermittently sense antigenically dissimilar niches, which is expected to affect their homeostatic competition.</p>
Screen of A6 TCR against a library of HLA-A*02:01 MHC-I peptides from the human exome
<p>T2 cells expressing a library of off targets (derived from A6 and B7 binding motifs in Hausmann 1999) are co-cultured with A6, DMF5 or 1G4 expressing T cells (from a non-A2 donor) and minigenes from surviving cells are amplified.</p>
Screen of Pr20 TCR mimic antibody against a library of HLA-A*02:01 MHC-I peptides
<p>Minigene sequencing of T2 cells sorted for high and low binding to the TCR mimic antibody "Pr20."</p>
Screen of A6 and B7 TCR against a library of HLA-A*02:01 MHC-I peptides from the human exome
<p>T2 cells expressing a library of off targets (derived from A6 and B7 binding motifs in Hausmann 1999) are co-cultured with A6, B7. DMF5 or 1G4 expressing T cells (from a non-A2 donor) and minigenes from surviving cells are amplified.</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.