Tissue heterogeneity studied by in situ sequencing

Identification: Qian, Xiaoyan

Tissue heterogeneity studied by in situ sequencing

Xiaoyan Qian12, Carolina Wählby, Jens Hjerling-Leffler4, and Mats Nilsson1*

1Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Sweden; 2Institute of Neurology, University College London, UK; 3Centre for Image Analysis, Science for Life Laboratory, Uppsala University, Sweden; 4Department of Medical Biochemistry and Biophysics, Karolinska Institute, Sweden

*Corresponding author

Recent advances in single-cell technologies have greatly improved our ability to study heterogeneous cell populations in a tissue. However, most of the methods fail to preserve spatial and tissue context information, which is crucial to understanding cell-cell interactions, tissue organization and function. In order to answer these questions, we use in situ sequencing (1) technique to detect mRNA molecules directly at their original location.

We used this method to study the expression of 31 transcripts in a HER2-positive breast tumour (1) tissue and we identified multiple cell populations using tSNE based-program ACCENSE (2). Among them, two different tumour cell populations, one positive for Ki67 and the other positive for MUC1, show an intermingled pattern that does not significantly deviate from randomness. But surprisingly, MUC1-positive cells have much worse nuclear morphology than Ki67-positive cells, which indicates that they are indeed two different populations and there is potential growth advantage of having intermingled cell populations rather than pure mutually exclusive cellular clones.

The method is also applicable to other tissue types. For example, we use the method to find different cell types in mouse brain by targeting nearly 100 transcripts. In situ sequencing method is highly scalable and compatible with many other techniques because of the minimized tissue disruption. Combining it with other methods and computational approaches makes it a powerful tool to study tissue heterogeneity at single cell level.


(1) Ke et al. (2013), Nat Meth 10, 857–860.

(2) Shekhar et al. (2014), PNAS 111, 202-207.


Credits: None available.