# Spatial Transcriptomics

An end-to-end solution to unravel spatial heterogeneity, cell type signatures, and cell-cell interactions

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## Overview

### Data infrastructure and tools to answer key biological questions about the spatial and transcriptional state of your samples.

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### 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?

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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

## 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.

### 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.

### 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.

### 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 a 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.

### 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.

### 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.

### Investigate Ligand-Receptor Interactions

Zoom into specific regions of interest 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.

### 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.