Technologies for mapping the spatial architecture of complex tissues
Abstract/Contents
- Abstract
- Single-cell RNA sequencing (scRNA-seq) is generating an expanding wealth of comprehensive single cell expression profiles of tissues in health and disease, however spatial information is missing and specific cell classes may be systematically under-represented because of difficulty in viably isolating them. The next major challenge is to map how RNA expression is spatially organized, which has been termed spatial transcriptomics. Here, we report a simple strategy to efficiently and inexpensively map inferred cell types based on single-cell sequencing data back into a 3D volume of tissue, a process we call "back-mapping." Our approach begins with a machine-learning algorithm that identifies a minimal panel of moderate to highly expressed transcripts that in combination distinguish every cell type in a scRNA-seq dataset. The target tissue is then fixed and embedded in a physically durable hydrogel that is stained for proteins of interest prior to multiplex in situ hybridization for the transcript panel. These tissues are optically imaged in 3D, single cell profiles generated in an automated fashion, then scRNA-seq classes inferred and displayed in 3D. In many cases it is possible to visualize the cytoplasmic volume of cells to display cytological features based on RNA expression. As a demonstration, we "back-map" the Tabula Muris lung dataset into intact mouse lung and illustrate candidate ligand-receptor interactions between neighboring cells. Using our strategy, biologists can easily and affordably take their single-cell RNA sequencing dataset of their healthy or diseased tissue and generate a map at single-cell resolution map, merely by staining for tens of transcripts.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Chou, Peter James |
|---|---|
| Degree supervisor | Harbury, Pehr |
| Thesis advisor | Harbury, Pehr |
| Thesis advisor | Krasnow, Mark, 1956- |
| Thesis advisor | Rohatgi, Rajat |
| Degree committee member | Krasnow, Mark, 1956- |
| Degree committee member | Rohatgi, Rajat |
| Associated with | Stanford University, School of Medicine |
| Associated with | Stanford University, Department of Biochemistry |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Peter James Chou. |
|---|---|
| Note | Submitted to the Department of Biochemistry. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/sc179fc4984 |
Access conditions
- Copyright
- © 2023 by Peter James Chou
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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