Mouse-T-omics
Leveraging multiomics and the power of the mouse experimental model to probe T cell differentiation in time and space
Featured Data Set
Mouse thymocyte CITEseq resource Steier et. al., 2023
A single cell data set consisting of thymocytes from both wild type, MHC mutant, and TCR transgenic mice. The resource can be explored via an interactive Vision session of full data set containing 21 annotated populations including DN thymocytes, preselection DP thymocytes, Tregs, and thymocyte undergoing negative selection. In addition to the complete transcriptome of each population, the data includes cell surface protein data for >80 markers, and can be used to identify new antibody markers for resolving thymocyte populations by flow cytometry, which may be coupled with high dimensional flow cytometry. The interactive session of positive selecting data set contains thymocytes undergoing positive selection, and includes pseudotime values.
Vision Tutorial
For instructions on how to explore the Vision sessions, please click here.
CZI CELLxGENE Full Data Set
To explore the interactive CZI CELLxGENE session of the full data set, please click here.
CZI CELLxGENE Positive Selecting Data Set
To explore the interactive CZI CELLxGENE session of the positive selecting data set, please click here.
Download Data Sets
To view and download data sets and for more information about each set, please click here.
Tools and Methods
CITE-seq
Schematic figure of (A) the CITE-seq antibody linked with the barcoded oligo (B) the CITE-seq protocol. Credit: Winston & Gregory Timp (2020), license: CC-BY 4.0CITE-Seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) is a cutting-edge method that integrates single-cell transcriptomics and proteomics. Traditionally, studying both transcriptomic and proteomic data simultaneously at the single-cell level was challenging. CITE-Seq overcomes this by using barcoded antibodies to detect a vast number of cell surface proteins and RNA in a single assay, providing a comprehensive view of cell function. Unlike traditional methods like flow cytometry, which are limited by the number of targets they can analyze and cannot provide transcriptomic data, CITE-Seq combines high-throughput sequencing with multiplexed protein detection.
10x Genomics Visium is an advanced spatial transcriptomics platform that allows researchers to analyze tissue gene expression while maintaining spatial context. This technology enables whole-transcriptome profiling with single cell-scale resolution and works with fresh frozen and formalin-fixed paraffin-embedded (FFPE) samples. By integrating gene expression data with histological images, Visium helps scientists identify novel cell types and spatial gene expression patterns, providing insights into tissue organization and function.
SCVI Tools
Gayoso, A.*, Steier, Z.*, et al., Nature Methods, 2021scvi-tools (single-cell variational inference tools) is a package for end-to-end analysis of single-cell omics data primarily developed and maintained by the Yosef Lab at UC Berkeley and the Weizmann Institute of Science.
scvi-tools has two components: Interface for easy use of a range of probabilistic models for single-cell omics (e.g., scVI, scANVI, totalVI), and tools to build new probabilistic models, which are powered by PyTorch, PyTorch Lightning, and Pyro.
Coming Soon!
Curio: Accessible, high-resolution spatial mapping solutions
Open-ST: End-to-end, open source 3D spatial transcriptomics framework
DESTVI: Deconvolution of Spatial Transcriptomics profiles using Variational Inference
Visium
Image: The Visium Spatial Gene Expression Slide (https://www.10xgenomics.com/)