Visualizing the neuronal transcriptional landscape with tissue context
Material type:
TextPublication details: Institute of Science and Technology Austria 2024Online resources: | Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|
Book
|
Library | Quiet Room (Browse shelf(Opens below)) | Available | AT-ISTA#003292 |
Thesis
Abstract
About the author
Acknowledgements
Agradecimientos
List of publications
Table of Contents
List of abbreviations
List of figures
List of tables
1 Introduction
2 Nanoscale visualization of targeted mRNAs
3 Design and validation of a neuron-specific MERFISH gene panel
4 Development of the CATS-MERFISH-ExM protocol
5 Visualization of MERFISH-targeted transcripts with tissue context
Outlook and future directions
Bibliography
Appendix 1
Appendix 2
Appendix 3
Appendix 4
Appendix 5
Appendix 6
Appendix 7
Appendix 8
Appendix 9
Appendix 10
Appendix 11
Spatial omics technologies are enriching our understanding of complex biological samples, by allowing us to study their molecular composition while preserving the spatial relationships between molecules in their native context. As the field continues to advance, there are technical challenges that need to be addressed in order to take full advantage of the spatial capabilities of these methods. In this work, I present two technical developments that I established for multiplexed error robust FISH (MERFISH) throughout my PhD: (1) pushing the spatial resolution limits to the nanoscale, and (2) adding rich tissue context to the mouse brain transcriptome. To achieve nanoscale resolution with MERFISH in cultured cells, I combined it with stimulated emission depletion (STED) and expansion microscopy (ExM) to achieve a spatial resolution as low as ~20 nm, and explored the compatibility of MERFISH with singlemolecule localization microscopy (SMLM) techniques. To visualize targeted mRNAs in mouse brain tissue, I applied the comprehensive analysis of tissues across scales (CATS) toolbox, which provides an unbiased morphological readout by labeling the extracellular domain. I successfully established this method, which we call CATS-MERFISH-ExM, to work with thick mouse brain slices, being able to extract transcriptomics information with 3D tissue context. CATS-MERFISH-ExM enabled us to identify cell types and further visualize the subcellular distribution of transcripts in mouse brain tissue, shedding light on the neuropil-specific transcriptome. This method provides integrated information on cellular structure and transcriptomes in situ, and could potentially be applied with other modalities, opening new avenues for scientific discovery.