Spatial Transcriptomics: From Theory to Practice

Journal club

Nilesh Kumar (PhD)

The University of Alabama at Birmingham

UAB Biological Data Science (U-BDS) Core

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Liz Worthey, Ph.D.

Director

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Nilesh Kumar, P.hD.�Bioinformatician III

Lara Ianov, Ph.D.

Managing Director

Austyn Trull

Bioinformatician II

Bharat Mishra, Ph.D.

Bioinformatician III

Website: https://www.uab.edu/cores/ircp/bds | Twitter: @UAB_BDS

Spatial transcriptomics

Spatial transcriptomics aims to count the number of transcripts of a gene at distinct spatial locations in a tissue.

A.

Introduction

• Multicellular organisms are composed of tissues and organs, which are specialized in a single biological process and are composed of a large diversity of cells.
• The gene expression patterns of different cells can be quite distinct, due to both internal gene regulation and signals from the external tissue microenvironment.
• Bulk RNA/RNA-Seq sequencing measures the expression of all genes in a tissue sample, but it does not provide information about the spatial distribution of genes.
• Single-cell RNA sequencing (scRNA-seq) has advanced knowledge of cellular gene expression to the single-cell level, but it destroys the original tissue structure.
• Spatial transcriptomic technologies combine the advantages of bulk RNA sequencing and scRNA-seq by preserving the tissue structure and providing information about the spatial distribution of genes.

Spatial transcriptomics is a powerful tool for understanding the spatial organization of genes and transcripts in tissues.

The beginning: The Leeuwenhoek Microscope and the Beginning of Our View into the Small

Source: https://backyardbrains.com/experiments/Leeuwenhoek

Source: https://education.nationalgeographic.org/resource/history-cell-discovering-cell/

Sequencing technology

https://en.wikipedia.org/wiki/DNA_sequencing

Frederick Sanger, a pioneer of sequencing. Sanger is one of the few scientists who was awarded two Nobel prizes, one for the sequencing of proteins, and the other for the sequencing of DNA.

Spatial transcriptomes can combine microscopic imaging and sequencing technologies to obtain gene expression data while preserving the spatial location information of samples to the greatest extent.

Spatial Transcriptomics�development

Statistics of spatial transcriptomic datasets

Yue L, Liu F, Hu J, Yang P, Wang Y, Dong J, Shu W, Huang X, Wang S. A guidebook of spatial transcriptomic technologies, data resources and analysis approaches. Computational and Structural Biotechnology Journal. 2023 Jan 16.

How it works ?

How it works ?

Embed, section, and place fresh frozen or FFPE tissue onto a Capture Area of the gene expression slide.

Fixation and staining - including hematoxylin and eosin (H&E) staining.

Permeabilize tissue and construct library

NGS short-read sequencing on Illumina sequencers for massive transcriptional profiling of entire tissue sections.

Analyze and visualize your data

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Visium Slide

Visium Slide

cDNA Synthesis

Probe Synthesis

Sequencing

Xenium

Getting started: Data sources

Computational Workflow

Data structure

scRNA

orig.ident

nCount_RNA

nFeature_RNA

Assays:RNA

graph

neighbors

reductions

Images:Placeholder

scRNA

orig.ident

nCount_Spatial

nFeature_Spatial

Assays:Spatial

graph

neighbors

reductions

images

Slice

keys

assay

spot.radius

scale.factors

Image

coordinates

RNA-Seq

ST Visualizer

10 X vs. Nanostring

Questions

nileshkr@uab.edu