Probabilistic cell typing enables fine mapping of closely related cell types in situ

Abstract

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 previous 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 previous 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.

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Fig. 1: Detection of 99 genes in a mouse brain coronal section.
Fig. 2: Cell-type map of CA1 from an example experiment (experiment 4-3, right hemisphere).
Fig. 3: Validation of cell calling.

Data availability

Analysis files are available at https://doi.org/10.6084/m9.figshare.7150760.v1 and an interactive online viewer is at http://insitu.cortexlab.net. The raw image files are available from corresponding authors upon reasonable request. Source data for Figs. 1–3 are presented with the paper.

Code availability

Code of the ProMMT algorithm for gene selection is available at https://github.com/cortex-lab/Transcriptomics. Code for probe design is available at https://github.com/Moldia/multi_padlock_design. MATLAB code for image analysis and cell typing is available at https://github.com/kdharris101/iss. A Python version of the cell-calling algorithm, designed to work with StarFISH data standards, is available at https://github.com/acycliq/cell_call. All custom code is freely accessible.

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Acknowledgements

We thank P. Somogyi, M. Carandini, S. Linnarsson, M. Hilscher, N. Kessaris and L. Magno for valuable discussions. We thank K. Karlsson for providing scRNA-seq reads for Cxcl14 gene. This work was supported by grants from the Wellcome Trust (108726, to K.D.H., J.H.L. and M.N.), Chan–Zuckerberg Initiative (182811 to K.D.H.), the Swedish Research Council (2016-03645 to M.N.), Knut och Alice Wallenbergs Stiftelse (to M.N.) and Familjen Erling-Perssons Stiftelse (to M.N.).

Author information

X.Q. wrote the DNA probe design software, performed experiments, analyzed data, designed the in situ sequencing protocol, prepared figures and wrote the manuscript. K.D.H. conceived the study, designed and wrote analysis software and wrote the manuscript. T.H. designed the in situ sequencing protocol. D.N. designed and wrote the online web viewer, performed simulations and wrote a Python translation of the cell-calling code. A.B.M.-M. designed tissue preparation protocols and provided samples. N.S. contributed to gene panel selection. J.H.-L. conceived the study and supervised tissue sample preparation and collection. M.N. conceived the study, designed the in situ sequencing protocol, supervised experiments and wrote the manuscript.

Correspondence to Kenneth D. Harris or Mats Nilsson.

Ethics declarations

Competing interests

X.Q., T.H. and M.N. hold shares in Cartana AB, a company that commercializes in situ sequencing reagents.

Additional information

Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–19, Supplementary Methods, Supplementary Discussion and Supplementary Results.

Reporting Summary

Supplementary Table 1

Gene selection for pciSeq.

Supplementary Table 2

Probe sequences.

Supplementary Table 3

Number of reads detected in each coronal section.

Supplementary Table 4

All scRNA-seq cell types and their prior probability, and superclasses used in pciSeq.

Supplementary Table 5

Number of cells of each superclass in individual hippocampus

Source data

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Qian, X., Harris, K.D., Hauling, T. et al. Probabilistic cell typing enables fine mapping of closely related cell types in situ. Nat Methods 17, 101–106 (2020). https://doi.org/10.1038/s41592-019-0631-4

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