Spatial Transcriptomics
An end-to-end solution to unravel spatial heterogenity, cell type signatures, and cell-cell interactions
Overview
Data infrastructure and tools to answer key biological questions about the spatial and transcriptional state of your samples.
Answer Biological Questions
What is the spatial distribution of cell types?
What are the differentially expressed genes between conditions?
What are the cellular trajectories in spatial context, and how do cells interact with one another within tissue microenvironments?
Join leading biopharma companies who analyze their spatial transcriptomics data on Latch
Scientists without deep computational expertise can now manage spatial datasets easily.”
Alecks Kutchma, Senior Bioinformatics Scientist at Takara Bio
Processing Curio Seeker data with LatchBio dramatically simplified everything... just drag, drop, and go. No complicated HPC setups or coding required.”
Scientist @ Broad Institute
I got exactly what I expected. I am going to play more with the ExCellxGene Latch Pod to dig deeper into the data. I have tried many solutions but this is the easiest to use.”
Xuhuai Ji, Genomics Director @ Stanford University
Go from FastQ to publication-quality figures in hours
Access an ecosystem of robust downstream analyses
Analysis Lifecycle
Store Terabytes of Imaging and Sequencing Data
Scalable, cloud-native storage for any data type, from TIFFs to FastQs. A central location to organize teams. Graphical interface for scientists + CLI for programmers
Mount S3 / GCP buckets, integrate with Illumina Basespace, drag and drop from laptops, deploy custom upload scripts from local servers or instruments.
High-resolution image viewers with interactive toggles to show or hide image, transcript, and cell boundary layers.
Organize Multiple Spatial Datasets
Easily organize your spatial experiments and samples. Associate drug treatments, cell lines and other metadata with raw files in structured schemas.
Graphical control and accessibility of tables by scientists.
Project management features to organize data by experiment and biological focus.
Generate Cell (or Spot)-by-Gene Matrices
Use Takara Bio's pipelines on Latch to process Curio Seeker and Trekker, or nf-core/spatialvi to process 10x Genomics Visium transcriptomics data. Produce a cell (or bead)-by-gene count matrix in H5AD or Seurat formats, and QC reports.
Scale to hundreds of samples and pay for only the computing resources you use.
Scientists upload and edit graphical sample sheets to run bioinformatics workflows.
Pull in error-validated and typed metadata to fill out workflow parameters.
Visualize UMAP and Spatial Embedding of Multiple Samples
Use an interactive H5 viewer to see multiple samples side-by-side in their native tissue context. Visualize expression levels across genes of interest. Use lasso selection to label population of cells, and color by cell categories.
Overlay Spatial Embedding with H&E or Immunofluorescently Stained Tissue Images
Combine spatial transcriptomics datasets with tissue images to label cell types based on tissue morphology.
Pick landmark points and align image layers using basic affine transformations or advanced methods like STAlign for warped or distorted tissues.
Use interactive tools to zoom, pan, rotate, and translate images for precise alignment and exploration.
Perform Cell Type Annotation
Use cell type deconvolution tools like RCTD and Cell2location. Take advantage of curated atlases and references for various species and tissue types provided by Latch.
Perform iterative, unsupervised clustering to refine and explore subpopulations of interest.
Use interactive H5 viewer, along with classical marker genes and prior knowledge, to label newly identified cell types.
Identify Differentially Expressed Genes
Run Wilcoxon, t-tests, or logistic regression for rapid cell group comparisons. Use DESeq2 for pseudo-bulk analysis to find transcriptionally distinct features across samples. Explore and customize results directly in your browser.
Use fold change measurements to interactively identify differentially-expressed genes of interest (GOI) between conditions. Flexibly select gene panels for publication-ready heat map visualizations.
Investigate Ligand-Receptor Interactions
Zoom into specific regions-of-interests in your tissue, and investigate the interactions between cell groups through ligand-receptor analysis or cell community analysis.
Lasso-select and label tissue microenvironments or regions of interest to restrict downstream analyses to specific spatial domains.
Select cell types of interest and run bulk ligand-receptor interaction analysis across all defined regions of interest.
Find Spatially Variable Genes for Domain Detection
Identify spatially variable genes across tissue regions. Perform automated segmentation of distinct spatial domains.
Looking for an easier solution?
Eliminate manual clicking and user error with an automated plotting solution for your lab.
Request a Demo
Access the leading data platform for biology.
- The best cloud platform for processing of biological data
- Save over 72% vs. AWS, GCP, and Azure
- Trusted by more than 4,000 scientists
- Built for biological data
- Start today