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Probabilistic cell typing enables fine mapping of closely related cell types in situ
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).ORCID iD: 0000-0001-7509-8071
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab). University College London, UK.
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2019 (English)In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105Article in journal (Refereed) Epub ahead of print
Abstract [en]

Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages prior scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive prior work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues.

Place, publisher, year, edition, pages
2019.
Keywords [en]
cell type, spatial transcriptome, hippocampus
National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry
Identifiers
URN: urn:nbn:se:su:diva-174727DOI: 10.1038/s41592-019-0631-4OAI: oai:DiVA.org:su-174727DiVA, id: diva2:1359442
Funder
Swedish Research Council, 2016-03645Available from: 2019-10-09 Created: 2019-10-09 Last updated: 2019-12-07
In thesis
1. Towards comprehensive cellular atlases: High-throughput cell mapping by in situ sequencing
Open this publication in new window or tab >>Towards comprehensive cellular atlases: High-throughput cell mapping by in situ sequencing
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With recent technological advancements in single-cell biology, many aspects of individual cells are characterized with unprecedented resolution and details. Cell types in human and model organisms are redefined, and multiple organ-wide atlases are proposed to integrate different types of data to provide a comprehensive view of biological systems at cellular resolution. Incorporating location information of cells in such atlases is crucial to understanding the structure and functions. Several spatially resolved transcriptomics technologies may serve this purpose, and in situ sequencing (ISS) is among the most powerful ones.

ISS detects the expression of tens to hundreds of genes in situ, i.e. inside preserved cells and tissues. ISS is a targeted approach, using probes designed to identify specific transcripts. Its key advantages, as compared to other spatially resolved gene expression analysis methods, are high throughput, cellular resolution and tissue compatibility, making it a tool ideally suited for spatial cell mapping. The work included in this thesis aims to develop tools and methods for this application.

In paper I, a network analysis tool was developed to analyze ISS and other spatially resolved data. The tool enables smooth visualization of large datasets and generates networks based on colocalization. It also includes functions to test statistical significance and resolve tissue heterogeneity.

In paper II, we studied spatio-temporal patterns of immune response in tuberculosis granuloma by targeting immune markers with ISS. Using the tool developed in paper I together with other methods, we established an immune response time course at the granuloma sites and found histologically different granulomas based on transcriptional information. The paper demonstrated that ISS can robustly detect transcripts in formalin-fixed paraffin-embedded tissues across biological samples and reveal biologically relevant structures.

In paper III, we developed probabilistic cell typing by in situ sequencing (pciSeq), a method to spatially map cell types defined by single-cell RNA-sequencing. pciSeq is an integrated pipeline that includes gene selection, image analysis, barcode calling and cell type calling. We mapped closely related interneuron cell types of the mouse hippocampal CA1 region in 14 coronal sections and validated the results against ground truth.

In paper IV, we investigated the quantification bias of ISS resulting from the probe target selection. We developed a method to sequence in situ synthesized cDNA and found that the read coverage of in situ cDNA library reflected ISS counts more closely than conventional RNA sequencing, making it possible, to some extent, to predict a probe’s performance and guide the probe design.

Taken together, the developments described in this thesis comprise several tools that make ISS suitable for building cellular atlases via large-scale spatial mapping.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2019. p. 60
Keywords
Spatially resolved transcriptomics, in situ sequencing, cell type, spatial analysis
National Category
Biochemistry and Molecular Biology
Research subject
Biochemistry
Identifiers
urn:nbn:se:su:diva-174755 (URN)978-91-7797-883-1 (ISBN)978-91-7797-884-8 (ISBN)
Public defence
2019-11-25, Air & Fire, SciLifeLab, Tomtebodavägen 23 A, Solna, 09:30 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Accepted. Paper 4: Manuscript.

Available from: 2019-10-30 Created: 2019-10-10 Last updated: 2019-10-22Bibliographically approved

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