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| Status |
Public on May 05, 2021 |
| Title |
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation (Spatial scRNA-Seq) |
| Organism |
Mus musculus |
| Experiment type |
Expression profiling by high throughput sequencing
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| Summary |
The function of mammalian cells is largely influenced by their tissue microenvironment and by inter- celllular interactions. Advances in spatial transcriptomics open the way for studying these important determinants of cellular function, by enabling a transcriptome wide evaluation of gene expression in-situ. A critical limitation of the current technologies, however, is that their resolution is limited to regions (spots) of sizes well beyond that of a single cell, thus providing measurements for cell-aggregates which may mask critical interactions between neighboring cells of different types. While joint analysis with single cell RNA-sequencing (scRNA-seq) can be leveraged to alleviate this problem, current analyzes are limited to a discrete view of cell type proportion inside every spot. This limitation becomes critical in the common case where, even within a cell type, there is a continuum of cell states, which can not be clearly demarcated and may reflect important differences in the cells’ function and interaction with their surrounding. To address this, we developed Deconvolution of Spatial Transcriptomics profiles using Variational Inference (DestVI), a probabilistic method for multi- resolution analysis for spatial transcriptomics, which explicitly models continuous variation within cell types. Using simulations, we demonstrate that DestVI is capable of providing higher resolution compared to the existing methods, and that it is also the first method to enable an estimate of gene expression by every cell type inside every spot. We then introduce an automated pipeline for analysis of a tissue, as well as comparison between tissues. We apply DestVI and this pipeline to study the response of lymph nodes to a pathogen and to explore the spatial organization of a mouse tumor model. In both cases, we demonstrate that DestVI can provide a reliable view of the organization of these tissues, and that it is capable of identifying important cell-type specific changes in gene expression - between different tissue regions or between conditions. DestVI is available as an open-source software in the scvi-tools codebase (https://scvi-tools.org).
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| Overall design |
Lymph nodes from mice treated with mycobacterium or PBS or MCA205 tumor were used. Auricular LNs and the MCA205 tumor were prepared according to Visium spatial protocols of tissue preparation guide (10x genomics). Firstly, freshly obtained tissue samples were snap frozen in liquid nitrogen, then embedded in chilled Optimal Cutting Temperature compound (OCT; Tissue-Tek) and frozen on dry ice, then stored at -80°C in a sealed container for later use. For Visium samples preparation, OCT-embedded tissue blocks were cut to 10 µm thick using a LEICA CM1950 machine and mounted on the Visium spatial gene expression slide.
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| Contributor(s) |
Lopez R, Keren-Shaul H, Li B, Boyeau P, Jordan MI, Kedmi M, Pilzer D, Wagner A, Addad Y, Yosef N, Amit I |
| Citation(s) |
35449415 |
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| Submission date |
May 03, 2021 |
| Last update date |
Jan 06, 2023 |
| Contact name |
Ido Amit |
| E-mail(s) |
ido.amit@weizmann.ac.il
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| Phone |
972-8-9343338
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| Organization name |
Weizmann Institute of Science
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| Department |
Immunology
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| Street address |
234 Herzl st.
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| City |
Rehovot |
| ZIP/Postal code |
760001 |
| Country |
Israel |
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| Platforms (1) |
| GPL24247 |
Illumina NovaSeq 6000 (Mus musculus) |
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| Samples (4)
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| This SubSeries is part of SuperSeries: |
| GSE173778 |
Multi-resolution deconvolution of spatial transcriptomics data reveals continuous patterns of inflammation |
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| Relations |
| BioProject |
PRJNA727008 |
| SRA |
SRP318252 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSE173776_RAW.tar |
742.6 Mb |
(http)(custom) |
TAR (of TAR, TIFF) |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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