High Throughput Spatial Genomics
Sanger-EBI Initative for Large-Scale Spatial Genomics
Recent technological advances in spatially resolved RNA-sequencing and high-multiplexed RNA imaging have pioneered the spatial transcriptomic analysis of tissues. The promises of these exciting spatial transcriptomics technologies – 3D cellular atlasses of entire tissues/organisms and spatial biology of hundreds of patient samples – can only be realized with large-scale experimental and computational platforms.
The High-Throughput Spatial Genomics (HTSG) Initiative utilises cutting-edge technologies to generate spatial transcriptomic data at scale. We combine single cell and spatial RNA-sequencing, high-throughput microscopy and quantitative image data analysis to enable large-scale spatial genomic studies. Our activities strongly tie into the Human Cell Atlas as well as genomic technology development and cancer biology.
The Initiative employs diverse sequencing and imaging technologies for spatial transcriptomic data generation.
Visium Spatial Transcriptomics:
The Visium technology (10X Genomics) enables spatial RNA-sequencing (RNA-seq) by postionally capturing mRNAs from thin tissue sections onto an oligonucleotide array. The Visium arrays provide a spatial resolution of 55 microns – capturing multiple cells at each tissue position. To computationally extend the resolution of the Visium assay and other spatial RNA-seq methods to map individual cell types, we developed the cell2location model. Cell2location integrates single cell and spatial transcriptomic data to comprehensively map cell types across complex tissues.
Nanostring WTA profiling:
The Nanostring Whole Transcriptome Atlas (WTA) technology enables spatial transcriptomic measurements of FFPE tissue samples. The assay uses transcriptome-wide in situ hybridisation with a sequencing readout to quantify spatial gene expression at scale across tissues. The WTA assay is integrated into the GeoMx DSP workflow to allow profiling of specific tissue regions or cell types guided by tissue imaging, which allows targeted analysis of patient derived tissue samples. As with Visium, we can resolve cell types in WTA data using integration with single cell RNA-seq.
In Situ Sequencing:
We use In Situ Sequencing (CARTANA) to quantify gene expression at single cell resolution in tissues. In Situ Sequencing (ISS) provides a cyclic and combinatorial barcoded FISH approach to label up to 600 different transcripts in a multiplexed manner. We have coupled ISS with fast tissue imaging and bespoke image processing pipelines to generate a high-throughput pipeline for various human tissues.
This protocol provides highly-sensitive measurements of small gene panels in tissues. Hiplex RNAScope provides a cyclic and signal amplified FISH approach to label up to 12 transcripts in a multiplexed manner.
Perkin Elmer Opera Phenix HCS
We have adapted this High Content Screening system for automated imaging of tissue sections labeled with ISS or RNAScope FISH. To provide high-throughput imaging at high spatial resolution, this system was assembled with a Yokogawa spinning disk and 4 cameras as well as automated water immersion objectives and software autofocus.
CAIRN “Nemo” Spinning Disk
The most recent addition to the facility, “Nemo” is a custom dual-camera spinning disk confocal microscope assembled around a CrestOptics X-light V3 by Cairn Research. The completely open hardware and software the microscope runs on gives the facility more flexibility to implement automated cyclic imaging protocols for ISS and other high-multiplexed RNA imaging methods.
CAIRN “Ahab” (coming soon)
The next planned instrument for the facility will be a larger, four-camera version of “Nemo” also to be assembled and installed by Cairn Research
Image Data Infrastructure
Cyclic Microscopy Image Analysis Pipelines
Our computational pipelines and IT platforms enable processing ISS & RNAScope image datasets at scale. Our bespoke workflows enable registration of large cyclic images to sub pixel accuracy, robust barcode decoding and cellular segmentation across a variety of different organs and organoids. We use python based tools cell segmentation (e.g. cellpose) and visualisation (napari). To enable large-scale application, we deploy nextflow/jupyter based pipelines on high performance GPUs. These IT activities are supported by the Cellgen IT and Scientific IT teams at Sanger.
Image data management and visualisation via OMERO
All image data produced by the facility is stored in our OMERO databases maintained by the Cellgen IT team. Our Image Data Resource (IDR) instances allow us to visualise and browse large tissue image datasets in the “Google Maps” format and also enable us to share image datasets with external collaborators. These databases are maintained on petabyte-scale high performance storage systems.
We collaborate with the new BioImage Archive to disseminate our large-scale spatial transcriptomic image datasets to the researcher community.
If you would like to join our team and help develop our cutting-edge research, please see our current opportunities here
Dr Omer Bayraktar
Omer's research aims to explore human brain cellular diversity using large-scale approaches. His team will harness spatial transcriptomics, imaging and functional screening to study neural diversity in health and disease. Omer is fascinated by the cellular complexity of the brain. His research team is interested in using large-scale approaches to map brain cell types, to identify how glial cells shape neuronal circuits and to discover cellular pathways affected in neurodevelopmental disorders.
Previous team members
We seek to explore the vast cellular diversity in the human brain using large-scale spatial transcriptomics, imaging and functional screens.
Cellular Genetics Informatics
Our team provides efficient access to cutting-edge analysis methods, environments and pipelines for Cellular Genetics programme, which leads and is involved ...